Model Characteristics and Properties of Nanorobots in the Bloodstream Michael Makoto Zimmer

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2005 Model Characteristics and Properties of Nanorobots in the Bloodstream Michael Makoto Zimmer

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THE FLORIDA STATE UNIVERSITY

FAMU-FSU COLLEGE OF ENGINEERING

MODEL CHARACTERISTICS AND PROPERTIES OF NANOROBOTS IN THE BLOODSTREAM

MICHAEL MAKOTO ZIMMER

A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science

Degree Awarded: Spring Semester 2005

The members of the Committee approve the Thesis of Michael M. Zimmer defended on April 4, 2005.

______Yaw A. Owusu Professor Directing Thesis

______Rodney G. Roberts Outside Committee Member

______Reginald Parker Committee Member

______Chun Zhang Committee Member

Approved:

______Hsu-Pin (Ben) Wang, Chairperson Department of Industrial and Manufacturing Engineering

______Chin-Jen Chen, Dean FAMU-FSU College of Engineering

The Office of Graduate Studies has verified and approved the above named committee members.

For the advancement of technology where engineers make the future possible.

iii ACKNOWLEDGEMENTS

I want to give thanks and appreciation to Dr. Yaw A. Owusu who first gave me the chance and motivation to pursue my master’s degree. I also want to give my thanks to my undergraduate team who helped in obtaining information for my thesis and helped in setting up my experiments. Many thanks go to Dr. Hans Chapman for his technical assistance. I want to acknowledge the whole Undergraduate Research Center for Cutting Edge Technology (URCCET) for their support and continuous input in my studies. I also want to acknowledge the professors on my committee for their time and support. I must give gratitude to all the organizations and the U.S. Department of Education, Washington, DC, that funded URCCET to make this all possible. Lastly, I give all the appreciation from my heart to my family, friends, and love ones.

iv TABLE OF CONTENTS

List of Tables ...... viii List of Figures ...... ix Abstract ...... xii

Chapter 1 Introduction...... 1

1.1 Overview into Nanotechnology...... 1 1.2 Problem Description ...... 2 1.3 Project Rationale...... 3 1.4 Project Objectives ...... 3 1.5 Benefit of Research ...... 5

Chapter 2 Literature Survey...... 6

2.1 Robotics and Nanorobotics ...... 6 2.2 Ideas of Nanotechnology and Nanorobotics ...... 7 2.2.1 Positional Assemblers and Self-Replication ...... 7 2.2.2 Views for Nanorobotics ...... 10 2.3 Current Nanorobotics Research...... 12 2.4 Issues against Current Micro Robots for this Research...... 15 2.5 Miniature Model Submarine ...... 17 2.6 Physiology of the Circulatory System ...... 19 2.6.1 The Blood...... 19 2.6.2 Blood Vessels ...... 21 2.7 Transport Phenomena of the Human Bloodstream...... 22 2.7.1 Blood Flow ...... 23 2.7.2 Velocity of Blood ...... 23 2.7.3 Pressure of Blood ...... 24 2.7.4 Viscosity of Blood...... 24 2.8 Analysis of Shear Stress, Viscosity, and Vessel Dynamics ...... 25

Chapter 3 Methodology...... 29

3.1 Introduction...... 29 3.2 Design of Simulation Model ...... 29 3.2.1 Anatomy of Bloodstream...... 29

v 3.2.2 Selection of Mobile Robot for the Research ...... 30 3.2.3 Design of Apparatus and Elements for the Simulation ...... 30 3.3 Design Calculations for the Simulated Model...... 32 3.4 Trouble Shooting and Modifications...... 34 3.5 Testing with the Mobile Robot...... 34 3.6 Tests for Blood Elements...... 34 3.7 Scaling Issues...... 35

Chapter 4 Design of Experiments...... 36

4.1 Design Matrix...... 36 4.2 Particle Testing...... 37 4.2.1 Styrofoam...... 38 4.2.2 Grains and Cereal...... 38 4.3 Design Matrix Setup and Testing Procedure ...... 39 4.3.1 Test Run Steps...... 40 4.4 Issues and Modifications ...... 41

Chapter 5 Data Collection Analyses...... 44

5.1 Testing Results ...... 44

Chapter 6 Concluding Remarks and Recommendations for Further Research...... 55

6.1 Concluding Remarks ...... 55 6.1.1 Bloodstream vs. Scaled Model ...... 55 6.1.2 Comparative Study of Mobile Robot vs. Nanorobot ...... 56 6.1.3 Summary of Concluding Remarks...... 58 6.2 Recommendations for Further Research...... 59

APPENDICES ...... 61

A Experiment Charts...... 61 B Test Run Tables...... 63 C Completed Experiment Charts...... 70 D Design of Experiment Chart ...... 74 E Residuals Plots...... 76 F One Factor and Interaction Plots ...... 78 G Speed vs. Viscosity Graphs...... 83

REFERENCES ...... 85

BIOGRAPHICAL SKETCH ...... 87

vii

LIST OF TABLES

Table 2.1: Values of cellular elements in human blood...... 21

Table 2.2: Characteristics of various blood vessels ...... 22

Table 4.1: Sample of Design Chart for Mobile Robot Testing ...... 36

Table 5.1: Design of experiment partial chart...... 44

Table 5.2: ANOVA chart for response Speed ...... 47

Table 6.1: Bloodstream and Scaled Model comparison ...... 58

viii LIST OF FIGURES

Figure 1.1: Cell Repair Machine ...... 4

Figure 1.2: Seek-and-Destroy ...... 4

Figure 1.3: The Artery Cleaner ...... 4

Figure 2.1: The Stewart Platform ...... 9

Figure 2.2: The Double Tripod ...... 9

Figure 2.3: John von Neumann’s self-replicating system ...... 10

Figure 2.4: Drexler’s self-replicating system ...... 10

Figure 2.5: Model-T nanomotor ...... 13

Figure 2.6: Chiropticene Molecular Switch...... 14

Figure 2.7: Chiropticene Switch in right hand position ...... 14

Figure 2.8: Robotman ...... 16

Figure 2.9: MINI-ROBOT ...... 16

Figure 2.10: Megatech’s Ocean Explorer-1...... 18

Figure 2.11: Aquarium Micro RC Submarine ...... 18

Figure 2.12: Diagram of the Circulatory System ...... 20

Figure 2.13: Parabolic distribution of velocity of blood flow ...... 23

Figure 2.14: Velocity and pressure of blood in various vessels types ...... 24

Figure 2.15: Blood Profile (v = velocity) ...... 25

Figure 2.16: Definition of Viscosity ...... 26

Figure 2.17: Graph between Viscosity and Hematocrit ...... 27

Figure 2.18: Graph of viscosity versus vessel size ...... 28

Figure 3.1: Design of the Scaled Model of the Bloodstream ...... 31

Figure 3.2: Photo of the apparatus ...... 31

Figure 4.1: Cereal and Oatmeal at 1gal/min ...... 38

Figure 4.2: Submarine in 125 cP liquid going with flow at 1gal/min...... 40

Figure 4.3: Modified Design ...... 41

Figure 4.4: Photo of modified design ...... 42

Figure 5.1: Normal Plot of Residuals ...... 45

Figure 5.2: Residuals verses Predicted ...... 46

Figure 5.3: Residuals verses Run ...... 46

Figure 5.4: Half Normal Plot ...... 47

Figure 5.5: Speed vs. Viscosity with Flow with No Particles ...... 48

Figure 5.6: Speed vs. Viscosity Against Flow and No Particles...... 49

Figure 5.7: Speed vs. Viscosity with Flow with Particles ...... 49

Figure 5.8: Speed vs. Viscosity Against Flow with Particles...... 50

Figure 5.9: Time vs. Flow Rate with Particles at 34 cP...... 50

Figure 5.10: Time vs. Flow Rate with Particles at 125 cP...... 51

Figure 5.11: Time vs. Flow Rate with No Particles at 125 cP ...... 51

Figure 5.12: Shear rate dependence on human blood viscoelasticity...... 53

x Figure 5.13: Particles clumping together noted in Region 1 for Figure 5.8… 53

Figure 6.1: E coli bacteria with flagella, whip like tails ...... 57

ABSTRACT

Many researchers have various visions and concepts about what the nanorobot will be like and what they will do. Most people see nanorobots doing a lot of functions in the medical field, having ideas of them doing cell repair, seek-and-destroy harmful diseases, clean arteries of cholesterol buildup, and much more. There are many questions that need to be answered as to what exactly is needed for the nanorobot to perform these medical functions. This project is not interested in the design of the nanorobot, but focuses on the characteristics and parameters that should be considered for a nanorobot to function through the bloodstream of a human body, specifically. To do this, a mobile robot was being used to traverse through a scaled model of the bloodstream. The scale model consisted of clear tubing or piping enclosed in a loop filled with liquid to nearly the exact viscosity of blood. The liquid had particles to emulate the various obstacles that a nanorobot would encounter like red blood cells and other molecules. The simulation had a continuous flow at the appropriate rate and pressure expected in the bloodstream. The pipe size was calculated setting the ratio of the diameter of a particular blood vessel over the diameter face of the assumed size of a nanorobot (DBV / DNR) equaling the diameter of the pipe (unknown variable) to the diameter face of the mobile robot (DPipe / Dsub). The pipe size came to be 6.66 inches, however pipe sizes come in increments of 2 inches larger than 4 inch pipes. It was settled to use 6 inch pipes. With this variable, the Reynolds number is the diameter of pipe times the velocity of the fluid over the kinematic viscosity of the fluid (R = (DPipe * ν) / υ). Setting the Reynolds value of the bloodstream equal to the Reynolds value of the model, the velocity of the pipe could be isolated. With that the flow rate was evaluated by multiplying the velocity to the cross-sectional area of the pipe (Flow Rate was equal to 0.2021392 gallon/minute). With all conditions met for an accurate model of the bloodstream, the physical model was designed and constructed then testing with the mobile robot was done to determine how the robot functions in the simulated environment. The results of the experiment showed that the mobile robot is influenced by the environment. The speed it travels decreases as viscosity of the fluid increases. The particles in the fluid also affect the speed along with the flow of the fluid. Mobility and control of the mobile robot were hindered with the increase of viscosity and the presence of particles. When traveling against the flow of the fluid it was further hindered. Stability of the craft increased along with viscosity but was chaotic traveling with particles. The performance of the mobile robot was affected by the conditions and parameters involved in the bloodstream.

xii CHAPTER 1 INTRODUCTION

1.1 Overview into Nanotechnology

Most of the substantial things done and made in this world start from an idea. The original idea of nanotechnology can be attributed to a Physicist, Richard Feynman [1959]. Feynman’s vision of nanotechnology became known to all in his classic talk on December 29th 1959 at the annual meeting of the American Physical Society at Caltech, which was first published in the February 1960 issue of Caltech's Engineering and Science. Feynman’s talk did more than just state his idea of producing materials and equipments at the nano level. It brought forth details and concepts to consider about the whole manufacturing of nanotechnology devices. Some topics were transforming information like the encyclopedia into bits of information written on atoms. The question would be how to write information on such a small scale. So the proposal for better electron microscopes sprang forth. Currently, scientists have made fascinating discoveries with scanning probe microscopes (SPM) and atomic force microscopes (AFM). Feynman went on to propose miniaturizing computers to a nano scale. This of course, would require a different manufacturing process. He came up with miniaturization through evaporation and the applications of small moveable machines to do assembly. This idea of moveable machines has given the widely used term, ‘nanorobots.’ He suggested a way to make tiny mechanisms through the use of slave “hands,” which are controlled to perform a specific task. Yet, Feynman thinks beyond this point explaining his concept of having hundreds of tiny “hands” that manufacture copies of itself doing the same function. Though the word self-replication was not used in his talk, this was definitely the concept that scientists relate to that topic. With

1 hundreds of mechanisms, the thought of using lubrication came to mind however, Feynman said that lubrication may not be needed at all since heat dissipates rapidly from such a small device. At the end of the talk, Dr. Feynman mentioned a process that sums up the entire subject: to maneuver things atom by atom. He said that chemist could synthesize a product molecule by molecule and have it constructed to the specified arrangement. This is a concept known as position and control. Position and control and self-replication are the central ideas behind nanotechnology, which could be derived from the talk of Feynman. An important thing to note that he said is: “When we get to the very, very small world -- say circuits of seven atoms -- we have a lot of new things that would happen that represent completely new opportunities for design. Atoms on a small scale behave like nothing on a large scale, for they satisfy the laws of quantum mechanics. So, as we go down and fiddle around with the atoms down there, we are working with different laws, and we can expect to do different things. We can manufacture in different ways. We can use, not just circuits, but some system involving the quantized energy levels, or the interactions of quantized spins, etc [Feynman, 2004].”

1.2 Problem Description

There are many questions that need to be answered as to what exactly is needed for the nanorobot to perform these medical functions. This thesis work is not interested in the design of the nanorobot, but focuses on determining characteristics and parameters that should be considered for a nanorobot to function through the bloodstream of a human body. To do this, a submarine mobile robot was used to traverse through a functionally scaled model of a simulated bloodstream. The model simulation required a mobile robot that could function in a fluid environment. The environment consisted of clear tubing or piping enclosed in a loop filled with liquid to nearly the exact viscosity of blood. The liquid had particles to emulate the various obstacles a nanorobot would encounter like red blood cells and other molecules. The simulation system had a continuous flow at the appropriate rate and

2 pressure expected in the bloodstream. The size and weight of the particles in the fluid, the flow rate and pressure of the liquid stream is all dependent on the size and weight of the designed mobile robot. Some scientists assume that nanorobots will be between the sizes of one to five microns (100 – 500 nanometers). The appropriate proportions and ratios could be made if a 3-inch mobile robot is set to be 300 nanometers. If red blood cells are 5 micron in size then it will be 5 inches in diameter in the simulation. The mobile robot would be tested against these conditions so that conclusions and relations could be made if the nanorobot could function on its own propulsion or have the ability to navigate through obstacles. This experiment is entirely a primary first scratch research project since there is no basis in which to model the bloodstream. This is a first attempt to do such a project at a large scale observation.

1.3 Project Rationale

1.4 Project Objectives

The objectives of this research are three-fold: 1. To accurately model and simulate the human bloodstream for the test environment

Figure 1.1: Cell Repair Machine [Cryonics, 1998].

Figure 1.2: Seek-and-Destroy (viruses and diseases) Device [Scientific America, 1996].

Figure 1.3: The Artery Cleaner [Science Year, 1992].

4 2. To test the mobile robot under simulated conditions and collect data from the resulting behavior of the robot against the simulated system conditions. 3. Draw inferences on the mechanical properties of the mobile robot to nanorobots as to what is needed for their development for such functions.

In general, the main objective of this thesis is to study the parameters and composition of robots, particularly with mobile robots, and make comparative study of the design characteristics, application, functionality, and fabrication of nanorobots for use in the human body.

1.5 Benefit of Research

The results of the experiment can help give insight into the makeup of nanorobots for biological purposes. By knowing what happens to the submarines under the simulated conditions in the modeled bloodstream, future researchers could better proceed with experiments relating to this area. If this experiment should show the effects of mobility to be trivial then researches may seek alternative means of sight to sight transportation for nanorobots. It is hoped that this thesis work will spark questions in the plausibility of various robotic functions in such conditions. By visually understanding what happens to the mobile robot, it paints realistic ideas in what nanorobots will be about unlike the concept ideas depicted in so many illustrations such as Figure 1.2 or 1.3. In the end, it should make some small contribution to the advancement in nanotechnology and medical science.

5 CHAPTER 2 LITERATURE SURVEY

2.1 Robotics and Nanorobotics

The design of robotic systems and processes along with automation and machinery are getting smaller and smaller. Efficiency, reliability, processing power, and amount of functions a robot can perform are increasing with each new concept. Every robot comprises of sensors, controls, actuators and motors, power source, interfaces, programming and coordination, and communication [Requicha, 2004]. The four building blocks of robotics and automation are sensors and transducers, actuators, analyzers, and drivers. Whether it is a robot, a programmable mechanical manipulator, or a robot relative, a non-programmable manipulator, they are made up of these four building blocks. It is reasonably easy to design the various components to satisfy what a robot must comprise of at the macro level. However, when making this relation to the nanoscale (1- 100nm), making robots at that level becomes vastly complicated. Nanorobotics would be an entire field of study on its own as nanorobotics and nanoautomation. Nanorobots are a type of nanoelectromechanical system (NEMS) that can be composed of different nanodevices or organic substances or both. NEMS research could coincide with microelectromechanical systems (MEMS) research. Nanorobotics or NEMS is concerned with design and fabrication, programming and coordination of large numbers of nanorobots, and programmable assembly of nanometer scale components and devices by macro or micro manipulations or by self assembly [Requicha, 2004].

6 2.2 Ideas of Nanotechnology and Nanorobotics

Molecular nanotechnology or molecular manufacturing is another term used for nanotechnology. The central objective of molecular manufacturing is the design, modeling, and manufacture of systems that can inexpensively fabricate most products that can be specified in molecular detail. This would include, for example, molecular logic elements connected in complex patterns to form molecular computers, molecular robotic arms able to position individual atoms or clusters of atoms under programmatic control, and a wide range of other molecular devices [Merkle, 2004]. In the development of nanodevices, diamond is the best candidates to use for nano construction. The strength-to-weight ratio of diamond is over 50 times that of steel [Gruen, 2004]. However, diamond crystals are brittle or can easily fracture since their lattice structure makes them unable to be malleable. Making them into a fiber would be an idea to modify this problem in principle but not in practice as of now. Its strength is not the only reason that makes it attractive for this field but also its ability to resist or remain unchanged through high thermal noise. Thermal noise is distortion resulting from heating nano-environment. Diamonds also excel in electrical properties. From the point of view of electronic and semiconductor characteristics, diamonds are far superior to silicon. They have wider bandwidth to operate at higher speeds with higher electron and hole mobility [Requicha, 2004]. The hindrance in using diamond is knowing how to make devices from them. Molecular manufacturing will close the gap in the ability to synthesize and answer the problem of making devices with diamond structures [Merkle, 2004].

2.2.1 Positional Assemblers and Self-Replication

Other concepts associated with nanotechnology is positional assembly, to construct a molecular device atom by atom, and self replication, to have the nanodevice make a copy of itself without external intervention. Doing positional assembly is not against the laws and principles of physics, which makes this possible in the future. However, to achieve positional assembly would require the development of molecular manipulators or robotic arms to piece atoms together one at a time.

7 The first consideration with nano-manipulators is looking to their precision. Ralph C. Merkle states, “The need for accurate positional control (a fraction of a nm (nanometer)) in the face of thermal noise implies the positional device must be stiff. The 2 positional variance caused by thermal noise is kBT/k m , where kB is Boltzman's constant (in J/K or Joules/Kelvin), T is the temperature (in K), and k is the stiffness (spring constant) of the system in N/m (Newtons/meter). Scaling laws imply that stiffness is adversely affected by reductions in size and so stiffness is at a premium in small devices, particularly in devices that are molecular in scale yet must be able to position molecular components with an accuracy of a fraction of a nm.” A molecular robotic device at the size of 100nm is expected to achieve a stiffness of 10N/m. To accomplish highest degree of stiffness is dependent on the linkage type. Standard robotic arms today are serial linkage machines that have low stiffness to the mass. Devices made in parallel linkage give high stiffness to their given mass. The Stewart Platform, shown in Figure 2.1, and the Double Tripod, in Figure 2.2, give a few conceptual designs of parallel linkage of positional assemblers. Note that, with parallel linkage sometimes the range of motion is decreased but the Double Tripod compared to the Stewart Platform has a design that is higher in stiffness with a greater range of mobility. John von Neumann came up with the original idea about self replication, which started the basis for other proposed ideas for self replication. Self-replication for this instance is the nanodevice’s ability to duplicate itself from materials around them without intervention of other mechanical devices or manufacturing techniques. Neumann’s architecture for self replicating systems is based on two functioning parts of a nanorobot, a Universal Computer and a Universal Constructor as shown in Figure 2.3. The Universal Computer holds the program in which the Universal Constructor operates by. The Universal Constructor manufactures another Universal Computer and Universal Constructor where the new Universal Computer is programmed with a copy of the program from the original Universal Computer. From there the cycle begins in a loop.

Figure 2.1: The Stewart Platform [Drexler, 1992].

Figure 2.2: The Double Tripod [Merkle, 2004].

Figure 2.3: John von Neumann's self replicating system [Merkle, 2004].

Figure 2.4: Drexler's self-replicating system [Merkle, 2004].

K. Eric Drexler, an accomplished researcher in nanotechnology, has his own design for a self replicating system. What makes his architecture different from von Neumann’s is that it works with systems dealing with atoms. Drexler’s architecture has a Molecular Computer and a Molecular Constructor. The Molecular Constructor is then comprised of two major subsystems, a positional capability and the tip chemistry as shown in Figure 2.4. The positional capability might be provided by one or more robotic arms or any positional controller but these devices are around the size of 100nm. The tip chemistry will be a set of defined chemical reactions that take place at the tip of the robotic arms and will have defined processes to synthesize the desired device through any changes that might occur.

2.2.2 Views for Nanorobotics

Researchers or scientists/engineers have views in what nanorobots will look like, what they will do, how they can be used, and how they will be made. Some views that

10 nanorobots will just be scaled down miniaturization of the robots we see today and some believe that nanorobots will be biological in nature. Gregory Fahy and George M. Whitesides have these two contrasting views. Fahy [2004] perceives that nanorobots, especially used within the human body, will be between the sizes of 0.3 to 0.5 microns constructed from nanodevices between the sizes of 1to100 nanometers. The material composition of the nanorobot will be carbon in the form of diamond / fullerene nanocomposites because of the strength and chemical inertness of these forms. The nanorobot will have a passive diamond coating for its smoothness to cause less reaction to the human immune system. The power for the robot will be the metabolizing of local glucose and oxygen for energy or acoustic energy supplied externally. The robots will have onboard computers that can perform 1000 or few computations per second. The nanorobots will have chemotactic sensors to sense the targeted agent the robot was built for, such as cancer cells. To complete the other areas of robotics with communication and coordination, Fahy states that communication of information to the nanorobots is with broadcast-type acoustic signaling [Fahy, 2004]. Coordination with the nanorobotics is done with a navigation network installed in the body to give accurate positioning. This also allows people to keep track of the devices. Removal of the nanorobots will be done either through the human excretory channels or by active scavengers. Taking on a different perspective, George M. Whiteside’s view of the nanorobot is based on mimicking biological organism, cells, or bacteria. He states that nanoscale machines already exist in forms of living cells. The base components of the living cell, such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and proteins, make up the functioning devices like mechanic parts in current robots. DNA stores the information for fabrication and operation much like a central processing unit (CPU). A type of RNA (messenger RNA or mRNA) is the transcript of the information stored in DNA. Proteins build everything within the cell [Whitesides, 2004]. The DNA, RNA, proteins, nuclei acids, and other molecules are all molecular catalyst that cause chemical reactions within or with one another to construct sensors, structural elements, pumps, motors, the lipids that self assemble to form the flexible sheet

11 to enclose the cell, components for self replication, the mitochondrion for power source, and much more. Within the mitochondrion, a cell organ, molecules of adenosine triphosphate (ATP) are produced that move through the cell by diffusion. Certain bacteria that propel themselves by use of flagellar motors, whip like structures, use ATP through decomposition to cause changes in the shape of the molecules, the armature, to revolve the protein shaft for propulsion. Scientist now use these ATP particles for rotary motion in developing motors or power sources discussed in section 2.3 (Current Nanorobotics Research). Whitesides states that, “Considering the many constraints on the construction and operation of nanomachines, it seems that new systems for building them might ultimately look much like the ancient systems of biology.”

2.3 Current Nanorobotics Research

All research leading to the development of a nanorobot is broken down to achieve in the design and fabrication of its components. Researchers at Cornell University and at the California Molecular Electronics Corporation (CALMEC) have constructed nano size motors. Under Carlo Montemagno at the Cornell University, the Molecular Model-T was invented [Nanotechnology, 2004]. It is an engine made from organic and inorganic materials or particles. The basis of the Model-T is the enzyme ATPase. The ATPase molecular motors occur on the membranes of mitochondria. The moving part of ATPase is a central protein shaft, less than 12 nanometers in diameter, that rotates in response to electrochemical reactions with each of the molecule's three proton channels. The ATPase molecules were produced using escherichia coli bacteria. ATP (adenosine triphosphate) is the fuel for the molecular motor's motion. Energy becomes available when atomic bonds between phosphate atoms are broken during hydrolysis, converting ATP into ADP (adenosine diphosphate). During hydrolysis, the shaft rotates in a counterclockwise direction, whereas it rotates clockwise during ATP synthesis from ADP [Nanotechology, 2004].

12 To build the motor, the ATPase was attached to a nanofabricated base made from nickel by histidine peptides. Propeller-like filaments made from polymerized proteins were attached to the top of the motor shaft. When the motor was submersed in a solution of ATP the rotor shaft spun for 40 minutes at 3 to 4 revolutions per second. Figure 2.5 gives an illustrated view of the Model-T.

Figure 2.5: (Left) ATPase attached to nickel base by histidine peptides (shown as green legs); (Right) Polymerized protein on top of rotor shaft [Nanotechnology, 2004].

Researchers at the California Molecular Electronic Corporation have developed a molecular dipolar rotor comprised of a base, an axle attached perpendicular to the base, and a dipole moment attached to the axle. The base can be made from aromatic or nonaromatic ring structures of any material that can attach to surfaces by means of tentacles. The axle could be a triple bond, double bond, a metal atom, or something more complicated. The dipole moment is the rotor portion of this driver (motor). The dipole moment can be of various chemical structures that should be able rotate in alternating electric fields or other stimulus. The difference with this engine is that it is not made from any organic material where catalyst reaction is necessary for rotary motion. CALMEC has also invented the Chiropticene Molecular Switch that is hundred times smaller than the smallest semiconductor switch fundamental to all modern electronics. The chiropticene switch is a single molecule design that have left and right hands to make a natural binary pair of 1 and 0. They can create digital binary codes using optics. The switch is optically manipulated and readable using a passing light beam set at a specific frequency. Figure 2.6 shows the chiropticene switch at its base position and Figure 2.7 shows the switch at a right hand position.

Figure 2.6: Chiropticene Molecular Switch [CALMEC, 2004].

Figure 2.7: Chiropticene Switch in right hand position [CALMEC, 2004].

There are developments in nano-circuitry from the use of carbon nanotudes. Dutch researches at Delft University of Technology, headed by Cees Dekker, developed a single-electron transistor (SET) that operates at room temperature. The SET made is only 1 nanometer wide and 20 nanometers long. The size of this transistor made heat fluctuations irrelevant since it can dissipate heat rapidly on its own. With the development of a transistor at this size it makes it very possible to have molecular size circuits and processors. Other advancements were made with carbon nanotudes is the creation of the first single-molecule circuit. By using a single carbon nanotude, IBM researchers, led by Phaedon Avouris, successfully wired a working computer circuit making the carbon

14 nanotude into a voltage inverter or NOT gate. A NOT gate requires n-type transistors that switch binary codes, sent through the circuit, from zero to one or vice versa. Avouris found a way to convert p-type carbon nanotube transistors into n-type transistors by heating them in a vacuum [Nanotechnology, 2004]. There are many researches in progress for nanotechnology, however it will not fulfill the objectives of this thesis since no nanorobot has yet been constructed. Microrobotics is an alternative to investigate to accomplish the goals of this thesis.

2.4 Issues against Current Micro Robots for this Research

In the search for a suitable mobile micro robot that can operate and function in fluid environments for the purpose of this research, the micro robots seen were mostly simple in design and functionality. The robots would have mobile capabilities or not and function to perform basic or linear operations either in biological or micromechanical applications. They either operate in a vacuum or not. Some of the contributing factors that make the term ‘micro’ possible for micro robots is its use of unpackaged electronics and that the development of millimeter or micrometer size components. At this size, parts are fragile or susceptible to being damaged easily. Nearly all microrobots displayed by various institutes and industry show the robots with exposed components. Figure 2.8 and 2.9 show micro robots from the Institute of Process Control and Robotics (IPR) and Sandia National Laboratories to illustrate the point of the micro robots delicacy. There are many more micro robots with the characteristics of having no external casing to protect them from outside elements. For the purposes of this research, the micro robot needs to function in a liquid environment and sustain bombardments of

Figure 2.8: Robotman [IPR, 2004].

Figure 2.9: Mini-Robot [Sandia National Laboratories, 2004].

16 floating debris. Since the variety or extents of micro robots are limited to uses outside of water, this eliminates the alternative to use micro robots for this research. Despite the size of the micro robots, it does not mean that they are easier to manufacture and assemble. The size requires higher accuracy for assembly and fabrication. Having a company to manufacture a specified micro robot in a reasonable time may not be possible for this research. The parts must be ordered, specially designed and fabricated, and a whole new project scheme needs to be developed for its construction. The scope of this project does not require the design and make-up of a micro robot, which would be a thesis topic entirely of its own. Without the use of nano and micro size robots, the last alternative is obtaining the smallest size robot or device on the market.

2.5 Miniature Model Submarine

The size of device used for study is a determining factor for the sizes of the other elements of the apparatus to be made. The first thing that came to mind as a plausible device was a miniature model submarine. It fulfills the main criteria of being operational in a fluid environment. Two model submarines were found to be most suitable as the mobile device for the experiment. • Megatech Ocean Explorer-1 R/C Submarine –MTC 7702 • Acquarium Micro RC Submarine The Megatech Ocean Explorer 1 is remotely controlled through a radio link up to 6 feet underwater. It is 3.75 inches (in) in hull length, 1.75in in width, and 2.75in in height. The Acquarium Micro Submarine is also remotely controlled through a radio link but its dimensions are smaller at 1.5in in height, 2.5in in width, and 3in in length. Figures 2.10 and 2.11 show what each model submarine looks like. What distinguishes these submarines out of all the other model submarines found was their size, where the others ranged from 5 to 12 inches long. The smaller size is

Figure 2.10: Megatech's Ocean Explorer-1 [rcmodels, 2004]

Figure 2.11: Acquarium Micro RC Submarine [Zilard, 2004].

18 more desirable for use of the experiment. The size of the submarine and the knowledge of the physiology of the circulatory system can be put into a mathematical model to determine relevant sizes of the components needed to construct the apparatus in simulating the bloodstream.

2.6 Physiology of the Circulatory System

“The circulatory system is the transport system that supplies substances absorbed from the gastrointestinal tract and O2 to the tissues, returns CO2 to the lungs and other products of metabolism to the kidneys, functions in the regulation of body temperature, and distributes hormones and other agents that regulate cell function. The blood, the carrier of these substances, is pumped though a closed system of blood vessels by the heart, which in mammals is really 2 pumps in series with each other. From the left ventricle, blood is pumped through the arteries and arterioles to the capillaries, where the blood equilibrates with the interstitial fluid. The capillaries drain through venules into the veins and back to the right atrium. This is the major (or systemic) circulation. From the right atrium, blood flows to the right ventricle, which pumps it through the vessels of the lungs, the lesser (or pulmonary) circulation, and the left atrium to the left ventricle.

In the pulmonary capillaries, the blood equilibrates with the O2 and CO2 in the alveolar air [Ganong, 1977].” Figure 2.12 shows a diagram of the circulatory system.

2.6.1 The Blood

The blood is made of up three main cellular elements, the white blood cells, red blood cells (or erythrocytes), and platelets, which are suspended in the plasma. The plasma makes up 55% of the total blood volume that is 8% of the body weight. Table 2.1 shows the amount of each cellular element per micro liter (µl) of human blood. The size of red blood cells is about 7.5 µm in diameter and 2 µm thick. Platelets are 2 to 4 µm in diameter [Ganong, 1977].

Figure 2.12: Diagram of the Circulatory System [American Medical Association, 2004].

20 Table 2.1: Values of cellular elements in human blood [Ganong, 1977].

Cells/µl Approximate (average) Normal Range Total White Blood Cells 9000 4000-11,000 Erythrocytes Females 4.8 x 106 Males 5.4 x 106 Platelets 300,000 200,000-500,000

2.6.2 Blood Vessels

“The blood vessels are a closed system of conduits which carry blood from the heart to the tissues and back to the heart. … Blood flows through the vessels primarily because of the forward motion imparted to it by the pumping of the heart, although, in the case of the systemic circulation, diastolic recoil of the walls of the arteries, compression of the veins by skeletal muscles during exercise, and the negative pressure in the thorax during inspiration also move the blood forward [Ganong, 1977].” Table 2.2 shows the characteristics of various types of blood vessels in the human body. When making a simulated model of the bloodstream, the size factors must be proportional to each other to obtain accuracy of the experiment. Section 1.2 (Chapter 1) briefly explains the idea of the model. The size of the pipe or tubing is a critical piece of information for the construction of the model. Gathering what is known about the assumed size of nanorobots, size of the submarine, and diameter size of the various types of blood vessels, the diameter size of the piping or tubing can be calculated. Equation (2.1) is the mathematical model for determining pipe size.

DPipe / Dsub = DBV / DNR (2.1) where,

DPipe = Diameter of Pipe

DSub = Diameter of the face of the Submarine

21 DBV = Diameter of Blood Vessel

DNR = Diameter of the face of the Nanorobot

Table 2.2: Characteristics of various blood vessels [Ganong, 1977].

All Vessels of Each Type Lumen Wall Approx. Total Cross Percentage of Blood Diameter Thickness -sectional Area (sq cm) Volume Contained Aorta 2.5 cm 2 mm 4.5 2 Artery 0.4 cm 1 mm 20 8 Arteriole 30 µm 20 µm 400 1 Capillary 6 µm 1 µm 4500 5 Venule 20 µm 2 µm 4000 Vein 0.5 cm 0.5 mm 40 54 Vena cava 3 cm 1.5 mm 18

2.7 Transport Phenomena of the Human Bloodstream

Transport phenomenon encompasses fluid dynamics (momentum), heat transfer (energy), and mass transfer (mass). Since the research is largely based on the study of characteristics of the mobile robot under fluid conditions simulated as the bloodstream, this area of study becomes important to the work in initially setting up the correct calculations of the model to be geometrically similar. The Reynolds number (R) of the fluid in the model must be the same as the Reynolds number of the bloodstream. The factors involved to calculate the Reynolds number is the velocity of the fluid (ν) traveling in its space, the kinematic viscosity of the fluid (υ), and the diameter of the space the fluid travels in (D). Equation (2.2) illustrates the formula. R = (D * ν) / υ (2.2) where,

22 R = Reynolds number D = Diameter of the space the fluid travels in ν = velocity of the fluid υ = kinematic viscosity of the fluid

2.7.1 Blood Flow

The cardiac output or blood flow can be characterized by the rate of contraction or the heart rate (beats per minute) and is associated by the stroke volume, the amount of blood pumped out of each ventricle per beat. On average, the heart beats 70 beats per minute for a resting male and pumps 5.5 liters per minute. The blood flow in blood vessels is generally laminar or streamline much like fluids in a narrow, rigid tube despite the fact that blood vessels are not rigid tubes and that blood is a 2-phase system of liquid and cells. A thin layer of blood is always in contact with the vessel walls that do not move. The velocity of blood increases towards the center of the vessel tube [Ganong, 1977]. Figure 2.13 shows the parabolic distribution of the laminar flow of blood. The flow of blood becomes turbulent when the velocity reaches a critical velocity or when the Reynolds number is greater than 2000.

Velocity Profile

Blood Vessel Tube

Figure 2.13: Parabolic distribution of velocity of blood flow [Ganong, 1977].

2.7.2 Velocity of Blood

The velocity of blood varies as it travels through the various vessel types. Figure 2.14 gives a diagram of the velocity of blood through each type of vessel. The flow of blood is phasic. The velocity can reach up to 120 cm/sec during a systolic phase and

23 drop to a negative value during a backflow in a transition to diastolic phase. However the mean velocity of blood is 40 cm/sec [Ganong, 1977].

Figure 2.14: Velocity and pressure of blood in various vessel types [Ganong, 1977].

2.7.3 Pressure of Blood

The heart is an area of high pressure from which the blood flows to be pumped to an area of low pressure. Average systolic pressure (maximum pressure when excited) is between 90 to 135 mm Hg (12-18 kPa) and diastolic pressure (minimum pressure when resting) is between 50-90 mm Hg (7-12 kPa). These pressure values are not very high ranging from 1.015 to 2.61 psi. Figure 2.14 shows the pressure range for each vessel types. As the flow of blood increases, the pressure increases in a curving slope instead of linearly like it would in rigid tubes [Vilastic, 2004]

2.7.4 Viscosity of Blood

The viscosity of blood depends on the percentage of the volume of blood occupied by red blood cells known as the hematocrit (Ht). Viscosity is also affected by

24 protein concentration, pH of plasma, and temperature. If Ht is 45% then the viscosity of blood is 3.45 centi-poise (cP) at 20oC and 2.72 cP at 37oC [Vilastic, 2004].

2.8 Analysis of Shear Stress, Viscosity, and Vessel Dynamics

The blood elements such as red blood cells (RBC), white blood cells (WBC), and platelets do not behave as independent elements with independent paths in the flow of blood. In essence, blood elements only affect the fluid dynamics of blood in certain conditions but contribute very little to any values associated with the calculations of blood dynamics such as shear stress, viscosity, velocity, and area. Shear stress and viscosity describe the characteristic of blood. For both, the velocity at which blood flows is an integral measurement to calculate their values. The profile of blood shown in Figure 2.15 illustrates the increase of velocity towards the center of the blood vessel.

Figure 2.15: Blood Profile (v = velocity) [EPFL, 2004].

Viscosity is the resistance to flow in gases or fluids due to friction between lamina moving at different velocities. Viscosity is defined as the ratio of shear stress, τ, (the friction between the lamina defined as the shear force, F, divided the area, A, where τ = F/A with units Pa or Newton/m2 (N/m2)) to shear rate, γ, (the velocity gradient

25 perpendicular to the flow direction defined as v/h with units 1/s). Viscosity may be expressed as N·s/m2 (N = newton), or Pa·s (Pa = pascal, 1 Pa = 1 N/m2). Figure 2.16 gives a more in depth description of viscosity [Amersham Health, 2004]. Viscosity of blood depends on the viscosity of plasma and the combination of hematocrit, the ratio of the volume of packed red blood cells to the volume of whole blood. “The viscosity of plasma is about 0.015 Poise (1.5 cP) and the viscosity of whole blood at a physiological hematocrit of 45 is about 3.2 centipoise (cP) , or 3.2 10-3 Pa.s [EPFL, 2004].” Figure 2.17 illustrates the relation between viscosity and hematocrit. For the instance of this experiment when adding the foam particles to water to mimic the various cell elements, the viscosity will increase, however, the viscosity will not drastically increase where it will have an affect in changing the value of the Reynolds number to get a noticeable flow rate greater than 0.0003 gallons per minute as calculated in Chapter 3.

Figure 2.16: Definition of Viscosity [EPFL, 2004].

Figure 2.17: Graph between Viscosity and Hematocrit [EPFL, 2004].

At this point, since the flow rate is far to low to test how the model robot will function, the flow rate will be increased where the foam particles will stream in one direction as RBC would. Tiring to calculate the exact numbers for the experiment to get the same viscosity as blood is not a main concern unless the effects and behavior are similarly modeled. Red blood cells orient in the direction of blood flow. This lowers viscosity. RBC may gather in a mass when the shear rate is extremely low, which in turn increase viscosity to high values [EPFL, 2004]. This would be a natural behavior of any free flowing particle confined in a certain area. The area discussed would be the size of the blood vessel. Viscosity also depends on blood vessel size. The as blood vessels become smaller subject to high velocity, viscosity decreases. Figure 2.18 shows the graph of viscosity versus vessel size. It is in these smaller vessels, from 1mm in diameter and smaller, that viscosity shows irregular characteristics resulting from red blood cells [EPFL, 2004]. For some vessels like the capillaries, only one red blood cell can pass through at a time. It is in larger vessels where hemodynamics there have no irregularities. Viscosity then becomes independent of vessel size and shear rate.

Figure 2.18: Graph of Viscosity versus Vessel Size [EPFL, 2004].

The scaled model of the blood stream is based on blood vessels larger than 1mm. Viscosity will not be dependent on the size or velocity the fluid is pumped at. The experiment can be independent from these factors and the focus of the experiment will be trained on the listed objectives discussed in Chapter 1. Also to note, viscosity is virtually constant in larger vessels where shear rates are greater than 100 1/s. Temperature is the only condition that affects viscosity the most where a 1% decrease in temperature results in a 2% increase in viscosity. As long as the room remains environmentally controlled at a constant temperature then the viscosity should not fluctuate noticeably.

28 CHAPTER 3 RESEARCH METHODOLOGY

3.1 Introduction

The focus of the research centers around the topic of nanorobots, in turn, the mobile robot became the focus of the experiment. Everything that was calculated, measured, drawn, or built linked, first, to the mobile robot in use. Its size determined the specifications of the model bloodstream with the exception of the diameter size of the blood vessel.

3.2 Design of Simulation Model

The design of the scaled model environment for the mobile robot was essential to the research. There were three main areas covered for the construction of the scaled system mimicking the movement of mobile robot in the bloodstream. 1. Anatomy of the Bloodstream 2. Selection of Mobile Robot for the Research 3. Design of Apparatus and Elements for the Scaled Model System

3.2.1 Anatomy of the Bloodstream

To make the simulation of a mobile robot traversing through the bloodstream as accurately as possible, information about blood were obtained through extensive literature research. There are several parameters to consider (as discussed already in Chapter 2). • The factors for the Reynolds number of blood. The velocity, density, diameter size of the vessels, and the viscosity of blood are all the factors necessary to calculate the Reynolds number of blood.

29 • What is blood and what is in blood? Knowing what passes through blood vessels will identify the various obstacles and organisms that the mobile robot will encounter as it traverses through the bloodstream. • The flow rate of blood in the bloodstream. Blood is pumped through the body at a constant rate depending on whether the person is at a calm or excited state. Normal blood pressure is about 120/80 mmHg (millimeters of mercury). This is necessary to know in order to pump the liquid at the right level for the size of the mobile robot.

3.2.2 Selection of Mobile Robot for the Research

Since building a robot is a whole research of its own. For the purpose of reliability, time constraint, and ease of implementation, buying a robot was the better option than to build one as discussed in section 2.5 (Chapter 2). The experiment relies heavily on the purchased model robot. It is the basis for the calculations for building the parameters listed in section 3.1.1. The size of the robot determines the size of the particles and organisms in blood, the flow rate, and pressure of the liquid in order to accommodate the movement of various particles in the fluid. The robot must be as small as possible in order to function and propel itself in a fluid environment and be controlled remotely.

3.2.3 Design of Apparatus and Elements for the Simulation System

The apparatus for the simulation of mobile robot in a fluid constructed of clear PVC pipes joined in a loop to represent a portion of the arteries as show in Figure 3.1. The clear pipes allow easy observation of the mobile robot as it traverses through the fluid. The liquid was forced to flow in a specific direction using a pump. A pressure value regulated the flow. The elements that were circulated in the pipe were sized up correctly according to the size of the mobile robot. The scale to make the appropriate ratio was 1 inch was equivalent to 100 nanometers (1in : 100nm). The materials for the cellular elements were light in order to be able to flow and float in the liquid. Foam was a good candidate material (coated with some water seal or wrapped in plastic). It could also be soaked with some fluids to increase weight. Figure 3.1 provides a diagram of the

30 Insertion Point 10’5”

Water Input Water 5.5’ Outlet 3’10”

5.5’

Figure 3.1: Design of the Scaled Model of the Bloodstream.

Figure 3.2: Photo of the apparatus.

31 3.3 Design Calculations for the Simulated Model

The size of the pipes must been known to calculate the Reynolds’ number for the apparatus. Using equation (2.1):

DPipe / Dsub = DBV / DNR (2.1) where,

DPipe = Diameter of Pipe

DSub = Diameter of the face of the Submarine

DBV = Diameter of Blood Vessel

DNR = Diameter of the face of the Nanorobot and where,

DSub = 2.0 inches = 0.0508 m

DBV = 0.1cm = 0.001m -4 DNR = 3 x 10 m

DPipe = (DSub * DBV) / DNR = (0.0508 * 0.001) / 0.003 = 0.1693m thus,

DPipe = 6.66 inches

Since pipe sizes come in increments of 2 inches, it is impossible to get a 6.7 in diameter pipe. The sizes of pipes available are 4in, 6in, 8in, and 12in. For this model a 6in pipe was used. The size of blood vessels ranges from 6 micrometers to 4 centimeters, so the size of the pipes can vary a little. This would satisfy geometric similarity stated in section 2.7, Transport Phenomena of the Human Bloodstream. The flow rate of the apparatus must is a critical component of the scaled model. Here the goal is to have dynamic similarity. The blood system is dealing with low Reynolds’ number of around 500. Its flow rate is around 5.5 liters per minute. To calculate the flow rate for the model, the velocity of the fluid will need to be calculated in the pipe. Multiply the velocity of the fluid in the pipe by the cross-sectional area of the pipe to get the flow rate, thus, knowing the power of the pump needed.

32 Using equation (2.2):

R = (DPipe * ν) / υ

Setting the Reynolds value of the bloodstream equal to the Reynolds value of the model, the velocity of the pipe can be isolated.

(DPipe * νPipe) / υModel = (DBV * νBlood) / υBlood (3.1)

νPipe = [(DBV * νBlood) / υBlood] * [υModel / DPipe] where,

DBV = 0.001m

DPipe = 6in = 0.1524m

υBlood = viscosity of blood at 80oF / density of blood = η/ρ = 0.03/1060

υModel = viscosity of water at 80oF / density of water = η/ρ = 0.00862/1000

νBlood = 0.35 m/s

νPipe = [(0.001*0.35) / (0.03/1060)] * [(0.00862/1000) / 0.1524] = 0.00069948 m/s

The velocity of the water in the pipe is now multiplied by the cross-sectional area of the 6in pipe to get the flow rate.

2 Flow Rate = π * r * νPipe (3.2) = π (0.0762)2 (0.00069948) = 0.000012753 m3/s

Flow Rate = 0.2021392 gallon/minute (gal/min)

33 3.4 Trouble Shooting and Modifications

The model was tested for any errors and debugging, failures, and other unexpected problems before actual testing was conducted. The elements mimicking the red blood cells and other particles were to behave as seen in the human body. They were not to settle rest on the bottom of the piping, but freely float in midstream. Everything was to function as close to the real thing as much as possible. The submarines might have the outer frame redesigned, if possible to make it more compact or streamline. If further modifications was to be made to make it smaller without loss of functionality, then, that was to be pursued. Several submarines of the same model were purchased for the purpose of experimentation.

3.5 Testing with the Mobile Robot

Tests with the mobile robot were done by observing the robot working against and with the current flow of the liquid with and without added particles. The parameters observed included: 1. How the robot functioned in the simulated environment. 2. The parameters affecting robot performance and function in the simulated system. 3. The characteristics required for the robot to have in order to function in the simulated system.

3.6 Tests for Blood Elements

The substances that mimic the various blood elements were to have the following conditions: 1. The substances must not be completely buoyant. 2. The substances must not be completely dense to sink to the bottom of the pipes.

34 3. The substances should portray some middle ground between to the first two conditions. In other words, it should freely float or suspend in liquid water. 4. The substances should have enough dense mass where it would not deteriorate after prolong periods in liquid, even under pressure and flow of the liquid.

3.7 Scaling Issues

The model was a scaled design of the bloodstream. A blood vessel size of 1mm in diameter as a design ratio basis to get a 6 inch diameter pipe deals with many scaling issues. The water source to create the desired flow rate to fill a large volume apparatus must generate high pressure. Even when scaling down any input source must have the necessary power range or quantity involved to satisfy any conditions set forth. There must be enough of the desired resource for the experiment. Other scaling issues involved were having the appropriate equipment, resource, and supplies available for the design. Finding what is needed was important. Size differences were a concern. Getting the approximate size ranges for the various components used was a challenge. In general, scaling designs provide different challenges. A process for a particular scale range may not work the same way for another scale range. These were all in consideration.

35 CHAPTER 4 SETUP OF EXPERIMENT

4.1 Design Matrix

The experiment was categorized under two main settings where testing took place with an element freely suspended in the liquid and then with a particle filled liquid. The categories of the liquid were tested at various viscosity levels to mimic different blood type conditions. Under each category the mobile robot was tested with the flow of the liquid and then against the flow under various flow rates set a 0, 1, and 2 gallons per minute (gal/min). Table 4.1 shows a sample of the design matrix of the experiment. Appendix A provides a full chart detail.

Table 4.1: Sample of Design Chart for Mobile Robot Testing.

Category 1 Particle Free Liquid Particle Filled Liquid (Water @ 1 cP) (Water @ 34 cP) With With Flow Rate (gal/min) Flow Against Flow Flow Against Flow Speed Power 0 Mobility Environment Factor Speed Power 1 Mobility Environment Factor Speed Power 2 Mobility Environment Factor

36 The experiment tested the speed, power, mobility, and environmental factor under each category. Calculating speed and power of the mobile robot was vital to understanding the maximum potential the robot could achieve. Mobility and environmental factor are strictly dependent upon the conditions set forth by the liquid. Mobility was a given a number value according to its movement. The value ranged from 1 to 5 as follows (1 being no movement and 5 being maximum mobility): 1. No movement 2. Little forward motion 3. Little pivoting capabilities turning left and right 4. Little movement in combination of values of 2 and 3 5. Full mobility (maximum potential)

Environmental factor was described as the mobile robot’s reaction to the obstacles and particles presented in the pipes such as loss of steering due to high debris concentration. Example of an environment factor would be the mobile robot tumbling about with no control due to the bombardment of the particles in the pipe. Other factors included loss of stability in movement as the flow rate increased. In this case, it could not be given a number value because of the unpredictable outcomes only noted as seen, hence the purpose of clear piping. All together there were 28 parameters under which the submarine was tested. There were 4 viscous levels where there was an exception when adding the particles to water. The cereal and oatmeal mixed with water made the viscosity increase to 34 centipoises (cP). Each viscosity level was substantially different in value to bring a greater affect on the experiment.

4.2 Particle Testing

Finding the right substance to mimic the elements in blood was a challenging task. Finding substances that met the conditions stated in section 3.5 (Chapter 3) was not easily accomplished. There were few trials and errors done with substances readily found

37 in everyday use such as styrofoam. However, the idea of good candidates came from food products.

4.2.1 Styrofoam

Styrofoam proved to be a very poor substance failing the first condition as being completely buoyant.

4.2.2 Grains and Cereal

The idea for use of grains and cereal came from a moment of making couscous, granular semolina (durum wheat). It looks like tiny salt rocks. When added to water it had a good balance fitting condition 3 and 4. This spun other ideas using oatmeal, various types of cereal, and shredded ice that all displayed similar characteristics to couscous. Cheerios was the selected cereal of use mostly for its geometric similarity to red blood cells. After a prolong period of saturation in the liquid, the cheerios would expand in various sizes giving a more desirable quality of a better size ratio of the elements to the vessel size. Figure 4.1 is a photo showing the cereal and oatmeal in the pipes.

Figure 4.1: Cereal and Oatmeal at 1gal/min.

38 4.3 Design Matrix Setup and Testing Procedure

There were 28 parameters under which the submarine was tested. The submarine had to perform in a liquid free of particles or with particles set at various viscosity and flow rates. It was also measured either going with the flow or against the flow of liquid. The setup of each experiment had to be laid out precisely in a specific order since each category would only allow a one time use in which only a few test runs could be observed. The experiment was sectioned to run tests with no particles starting at 1 cP at each flow rate. Then tests were run with particles at each flow rate. The submarine would go at start with the flow then measured right after against flow in a continuous manner till 5 runs were completed in each direction where the submarines were swapped after 5 directional runs. The submarines only performed 5 test runs before being recharged. This took no more than a minute and a half to two minutes of use out of a 4-6 minute internal battery life. This was to make sure that the submarine operated at full potential without chancing less than maximum performance of the submarine. The system was then flushed out to setup the next category of testing. Starch was used as a substance to change the viscosity of the liquid. A 55 gallon drum was filled with a high concentration of starch that would be well above 1000 cP. The starch was poured into the system mixed with water. The solution was measured for its viscosity then test runs were performed. The tests were done starting at the highest viscosity level. This way only water had to be added to dilute the solution to get a lower viscosity. The runs were tested with no particles first at each viscosity level then the particle tests were performed where the particles were added at a moderate rate as flow occurred. Figure 4.2 is a sample photo showing the submarine traversing through 125 cP liquid with no particles and going with the flow of 1 gal/min.

Figure 4.2: Submarine in 125 cP liquid going with the flow at 1gal/min.

4.3.1 Test Run Steps

Testing was done by timing the distance it took for the submarine to go a predetermined distance except for the first set at 0 gal/min flow rate with no particles. The first set was performed that way to ensure that the submarine was working with no biased outcomes. This meaning, for example, that the submarine did not increase in speed after 5 seconds or begin to slow down after traveling more than 12 inches. To note, the maximum flow rate the submarine was tested under was 2gal/min. The piping system could only sustain the pressure inside at 2gal/min before water began to leak from any joint. That flow rate would serve more than adequately as it is already 1.8gal/min more than what the actual flow rate supposed to be at 0.20gal/min. The speed was then calculated for each run with the distance traveled divided by time (inches/seconds) and then averaged out to fill into the experiment chart seen in Appendix A. Power was averaged out after each test set and recorded likewise as speed using the formula:

Power (P) = Work / Time (4.1) (P) = [(Mass of Submarine * Acceleration) * Distance] / Time

40 where, Mass = Weight of Submarine / gravity = 0.65 lbs / 386.04 in/sec2

The overall Mobility and Environmental Factor was also recorded from the observation of each run. Appendix B gives the tables of the test runs performed at each category and Appendix C gives the completed charts of the experiment.

4.4 Issues and Modifications

During the experiment some issues arose that altered how the experiment would be performed. First, the submarine would only give maximum potential cycling through the entire system once and decrease in power rapidly on its return trip. Due to this event, Category 1 experiments for particle-free liquid, the submarine was tested only in a 6 foot section of the system. For the rest of the experiment, the tests were done in a modified design that would allow more control over the parameters involved, which can be seen in Figure 4.3 with a photo of it in Figure 4.4.

Water Outlet Insertion Point Water 3’10” Input

2’11”

Figure 4.3: Modified Design

Figure 4.4: Photo of modified design.

With this design, it could be filled with particles a third of the amount of the original design. The particles would fill the system faster and flow easier with the shorter travel distance. The system also took far less amount of starch than the original system. As water pumped into the system during flow tests, it dilutes the solution. The flow forced the heavier substance towards the outlet. The 2’11” section would have a lower viscosity than in the 3’10” section. To ensure that the viscosity would remain reasonably constant, starch was added in between test runs then measured for its viscosity again. This process continued till the viscosity was around starting level for that category. With the design being smaller in length, the time trials were measured for a shorter distance but long enough to see the effects on the submarine. For Category 3, the distance was drastically decreased for the reason that the submarine could not perform without great difficulty. When trying to do tests set at 2gal/min, the system built far greater pressure than the other categories. The reasoning being that it needed

42 substantially higher input flow rate to force the heavy substance to move at 2gal/min making it build in pressure faster than it could outlet. The viscosity levels were picked for the reason that it provided a varying range to model the performance of the submarine. Blood’s viscosity range is just below 2 cP to no more than 10 cP. If the viscosity levels were set to these values, the submarine’s performance could not be model to have a lower and upper limit. The data would show a constant performance with no effect to each setting. The submarine is what is going to be used as a comparison basis for modeling nanorobots. The full range of the submarines capabilities must be observed. Each viscosity level shows some kind of trend of its performance.

43 CHAPTER 5 DATA COLLECTION ANALYSES

5.1 Testing Results

A design of experiments was used to see what factors that influenced the system and what can be inferred from the statistical data. A sample 2 level, 4 factor design was setup from the entire test run data in Appendix B. A sample of the data would gave enough to see the characterization of the system and what it had on the submarine performance. The four factors were Viscosity, Flow, Particles, and Flow Rate. Viscosity was set at 125 cP or 517 cP. Flow was numerically quantified as either 0 (with flow) or 1 (against flow). Particles were quantified as either 0 (no particles) or 1 (with particles). Flow rate was set at 0gal/min or 1gal/min. The response was speed in seconds. Since there were 5 readings, the design is done with 5 replicates. Table 5.1 shows a partial generated design. Appendix D gives the full design.

Table 5.1: Design of experiment partial chart.

Factor 1 Factor 3 Factor 4 Response Factor 2 Std Run Block 1 A: Viscosity C: D: Flow Rate Speed B: Flow (cP) Particles (gal/min) (in/sec)

48 1 Block 1 517 0 0 1 0.666667 44 2 Block 1 125 0 0 1 3 73 3 Block 1 125 1 1 1 1.714286 1 4 Block 1 125 0 0 0 2.666667 47 5 Block 1 517 0 0 1 0.666667 71 6 Block 1 125 1 1 1 1.6 27 7 Block 1 517 0 1 0 0.421053 40 8 Block 1 517 1 1 0 0.421053 70 9 Block 1 517 0 1 1 0.533333

44 The normal plot of residuals in Figure 5.1 shows a fairly normal design. Based on the pencil test, with only a few outliers, the ‘S’ shaped curve is not great enough to have a transformation. The residuals verses the predicted, in Figure 5.2, shows a grouping pattern. This gives an indication for a transformation however the residuals verses the run, in Figure 5.3, shows a complete scatter design where no one value is influencing the design. The lack of center points could be the cause for the pattern in the residuals verses the predicted. Based on the normal plot and the residuals verses the run, the design has a constant variance. From the half normal plot in Figure 5.4, 10 effects is seen that influence the results of the experiment. Viscosity is most prevalent to affect the time trial of the submarine. Having particles or no particles in the system makes a difference and whether the submarine is going with or against flow. Also a combination effect of flow and flow rate influences the time trials. The rest of the effects are significant but do not place as high an F-value as the four mentioned. The ANOVA chart confirms the significance of the ten effects and the four major effects A, B, C, and BD in Table 5.2.

DESIGN-EXPERT Plot Normal Plot of Residuals Speed

95 90 y t il 80 b a 70 b o r 50 P % l 30 a 20 mr o N 10 5

-2 .5 0 -1 .4 3 -0 .3 6 0.70 1.77

Studentized Residuals

Figure 5.1: Normal Plot of Residuals.

45 DESIGN-EXPERT Plot Speed Residuals vs. Predicted

3.00

2 1.50 2 3 sl 3 4 a u di 3 s 4 e 4 4 43 R 0.00 22 22 2 3 d 2 4 e 3 z ti 2 2 n e 4 d ut S -1 .5 0 2 3

-3 .0 0

0.37 1.00 1.63 2.26 2.90

Predicted

Figure 5.2: Residuals verses Predicted.

DESIGN-EXPERT Plot Residuals vs. Run Speed 3.00

1.50 sl a u di s e R 0.00 d e z ti n e d ut S -1 .5 0

-3 .0 0

1 14 27 40 53 66 79

Run Number

Figure 5.3: Residuals verses Run.

46 DESIGN-EXPERT Plot Half Normal plot Speed

A: Viscosity B: Flow A C: Particles 99 D: Flow Rate C y 97 ti BD il 95 b B a AC b ABD o 90 AB pr ADD 85 CD % l 80 a mr 70 o N 60 fl a H 40

0.00 0.47 0.95 1.42 1.89

|E ffe ct|

Figure 5.4: Half Normal Plot.

Table 5.2: ANOVA chart for response Speed.

Sum of Mean F Source DF Prob > F Squares Square Value Model 79.80328 10 7.9803275 703.54102 < 0.0001 significant A 71.74502 1 71.745018 6324.9989 < 0.0001 B 1.501605 1 1.5016055 132.38066 < 0.0001 C 2.146904 1 2.1469043 189.26983 < 0.0001 D 0.521244 1 0.5212439 45.952561 < 0.0001 AB 0.584139 1 0.5841393 51.497384 < 0.0001 AC 0.625211 1 0.6252114 55.118274 < 0.0001 AD 0.391073 1 0.3910728 34.476747 < 0.0001 BD 1.501607 1 1.5016073 132.38082 < 0.0001 CD 0.202332 1 0.2023324 17.837508 < 0.0001 ABD 0.58414 1 0.5841405 51.497484 < 0.0001 Residual 0.782673 69 0.0113431 Lack of Fit 0.073602 5 0.0147203 1.3286389 0.2635 not significant Pure Error 0.709071 64 0.0110792 Cor Total 80.58595 79

47 Looking at the residuals verses each factor shown in Appendix E, it is clear that all values are either at the high or low end but are fairly scattered about the mean for a constant variance. This characterization is the same for all other categories based on the test run results indicated in Appendix B. Time has a direct relationship with speed. When looking at the one factor and interaction plots of each factor, seen in Appendix F, it shows how the high and low levels affect speed. Speed can be related to the factors showing a trend for each situation. As the flow rate increased so did the speed and power as it went with the flow and decreased going against the flow of the liquid. This pattern was similar for tests when the system was free of particles or filled with particles. Figures 5.5, 5.6, 5.7, and 5.8 are graphs illustrating this relationship. These Figures can also be found in Appendix G relating these patterns. As expected, the speed with the flow of the liquid is higher than that going against the flow. It is also higher when traveling with a particle free system compared to having particles in the system as seen from Figures 5.5 and 5.7.

With Flow with No Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min 200 Viscosity (cP) Viscosity 100 0 -100 0246810 Speed (in/sec)

Figure 5.5: Speed vs. Viscosity with Flow with No Particles.

48 Against Flow with No Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min 200 Viscosity (cP) Viscosity 100 0 -100 0123456 Speed (in/sec)

Figure 5.6: Speed vs. Viscosity Against Flow with No Particles.

With Flow with Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min

Viscosity (cP) Viscosity 200 100 0 01234 Speed (in/sec)

Figure 5.7: Speed vs. Viscosity with Flow with Particles.

49 Against Flow with Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min

Viscosity (cP) Viscosity 200 100 0 0 0.5 1 1.5 2 2.5 3 Speed (in/sec)

Figure 5.8: Speed vs. Viscosity Against Flow with Particles.

With Particles at 34 cP

16 14 12 10 With Flow 8 Against Flow 6 Time (sec) 4 2 0 -0.5 0 0.5 1 1.5 2 2.5 Flow Rate (gal/min)

Figure 5.9: Time vs. Flow Rate with Particles at 34 cP

50 With Particles at 125 cP

With Flow 10 Against Flow

Time (sec) 5

0 -0.5 0 0.5 1 1.5 2 2.5 Flow Rate (gal/min)

Figure 5.10: Time vs. Flow Rate with Particles at 125 cP

No Particles at 125 cP

18 16 14 12 10 With Flow 8 Against Flow 6 Time (sec) 4 2 0 -0.5 0 0.5 1 1.5 2 2.5 Flow Rate (gal/min)

Figure 5.11: Time vs. Flow Rate with No Particles at 125 cP

51 Depending on the rate of flow, time increased or decreased roughly in the same manner going with flow or against flow at each viscosity level. Figures 5.9 to 5.11 show the relationship between time and the flow rate. Each category showed the same characteristics no matter what the viscosity was or if there were particles or no particles in the system. The time it took the submarine to traverse the predetermine distance remained relatively constant, in a straight line manner, going with the flow rate. There was a more drastic change in time when the flow rate increased as the submarine went against the flow of the liquid. The flow rate of 1gal/min and 2gal/min was not fast enough to really make a difference in speed for the submarine going with the flow. The change in time only improved by about a second as the flow rate increased. Viscosity played an effect on the rate of speed as expected from looking at the half normal plot. As viscosity increased, speed decreased as seen in Figures 5.7 to 5.10. The One Factor Plot confirms this by showing an increase in time as viscosity increases. Viscosity also had an affect in mobility and environmental factor. When viscosity reached 517 cP, it was notably clear that the submarine had difficulty propelling itself through the viscous liquid. Reaction time was greatly reduced, making it less maneuverable then it was capable of. However, the thick starch solution allowed the submarine to settle in a more steady position without drifting. Stability was greatly increased as viscosity increased. With a more viscous liquid, particles would not freely stream by when the submarine traversed through the obstacles seen in the liquid at 34 cP. The particles in 517 cP liquid tended to work more like a web or barrier causing the propellers to seize up often when they hit the propellers. This hindered control of the submarine. Figure 5.12 shows the characteristics the particles took in relation to the behavior of red blood cells in blood. The particles tended to clump to together as seen in Region 1 for Figure 5.12 when at rest. Figure 5.13 shows the similarities of Region 1. As the flow rate increased the characteristics the particles took looked what were seen in Region 2. This is the web like barrier the submarine encountered as seen in Figure 5.1. The submarine encountered other hindrances and instability going against the flow of the liquid. The submarine had initial instability problems at the start of each run going against flow before control was established to remotely steer it appropriately. The resistance going against the flow of the liquid made it a little less maneuverable. With

52 the addition of particles as the submarine went against flow then maneuverability was greatly affected. The submarine struggled to keep on course.

Figure 5.12: Shear rate dependence on human blood viscoelasticity [Vilastic, 2005].

Figure 5.13: Particles clumping together noted in Region 1 from Figure 5.8

53 All together, the performance of the submarine was influenced by flow rate, viscosity, direction of travel, and presence of substances or obstructions. Each parameter had some kind of proportional relationship within each category and a relationship from category to category. Depending on the situation, the performance could be enhanced or hindered.

54 CHAPTER 6 CONCLUDING REMARKS AND RECOMMENDATIONS FOR FURTHER RESEARCH

6.1 CONCLUDING REMAKRS

6.1.1 Bloodstream vs. Scaled Model

The scaled model of the bloodstream built had 2 considerations whether the design was a Newtonian based or non-Newtonian based fluid system. Knowing that blood was a non-Newtonian fluid, where friction in the fluid depends on the rate at which the parts slide past each other, contained in a system of living tissue that expanded and contracted that it would be virtually impossible to recreate that kind of system. The design of the model was non-Newtonian as well, however the viscosity was dependent on the force applied. The fluid in the model would be kinematic not based on shear stress as blood is. As the experiment was in progress it was evident that the velocity profile of the modeled system did not match the description of that of the bloodstream stated in section 2.7.1. The particles in the modeled system did not orient with the flow, seen in Region 3 in Figure 5.12, toward the center of the pipe where a thin layer of water separated the substances from the wall of the pipes. The particles floated freely, never grouping together. However, the modeled system had a laminar flow. Despite the differences between the two, the scaled model had all other parameters correct such as size, flow rate, and Reynolds number. It was more than adequate to see and understand the influences involved that would act upon a mobile robot to better serve an understanding of how a nanorobot would function with the considerations mentioned about the bloodstream.

55 6.1.2 Comparative Study of Mobile Robot vs. Nanrobot

The mobile robot used is completely based on theory of what a nanorobot would do and function. There is no data supporting what nanorobots looks like, what they emcompass, and how they operate. It was vital to give the power of the submarine to show its standard and performance against the conditions it was set in as a comparison for study in the development of a nanorobot. This way there would be a base line to calibrate the nanorobot’s output to function at any settings of the bloodstream. The conditions in which the nanorobot will be put under in the bloodstream are the most critical consideration to take note of. What the nanorobot faces and is against in this environment influences on how it will function and perform. The submarine lost a lot of its maximum potential and performance handling going against the flow of the liquid as the particles collided with it continuously. The bloodstream’s velocity profile forces all the elements in the blood to cluster in the center of the vessel where the red blood cells align themselves in the direction of flow. If the nanorobot should attempt to go against the flow of blood, it would find itself in an impossible state to get past the streams of elements to make any progress that way. Depending on the size of the nanorobot, it may find a means to go against the flow of blood in the thin layer where blood separates the elements from the vessel walls. If this case was possible, it must be noted that not all vessels are the same size and will change in diameter throughout the body. This means the thickness of the layer changes too. The nanorobot could only function where vessel sizes meets its size limitations. Since no nanorobot has ever been built it is very unclear or in speculation at best if nanorobots will be built on the idea we have of submersible robots functioning in liquid environments. Will it have a motor, sensor, arms, or computer processing unit? These are questions that cannot be answered now but can be thought through. The submarine had enough power to at least propel itself through 517 centipoises liquid. The nanorobot needs to be strong enough to travel against the flow of blood at least 2 centipoises. If going against the flow of the liquid yields less than optimal performance from the submarine then it would be best that the nanorobot forgoes use of motors and allow itself to stream with the flow of blood. Also, the effects of the vessel walls expanding and contracting would be unknown on the nanorobot traveling along the

56 vessel walls. The nanorobot could at least steer itself within the channel network of the circulatory system. Many bacteria and other organisms in the human body have use of whip-like flagella or tails that allow them to maneuver themselves. George Whiteside’s idea of basing nanorobots off living organism (mentioned in section 2.2.2) suits to give the nanorobot more capabilities. Owusu [2003] has suggested that the nanorobot must have a tad-pole feature and a whip like tail for easy maneuverability much like the E coli bacteria shown in Figure 6.1. Its organic nature would give it the ability to function on chemical means such as sensing or tracking. Medicines work by chemically bonding with a specific target. A biological nanorobot with a designated chemical signature could give it a more direct functionality to do or go to a targeted point without aid of external work to direct it. The idea of having a biological nanorobot could make it compatible to the human body and avoid unwanted attention and attraction from the body’s immune system. This experiment has shown that the importance of what the system environment does upon the mobile robot and how its parameters influences what it does. The submarine performed well with the flow of the fluid. It did not take much to maneuver it in any direction. The nanorobot should be able have the same outcome functioning in this manner.

Figure 6.1: E coli bacteria with flagella, whip like tails [Nanotechnology, 2005].

57 6.1.3 Summary of Concluding Remarks

The system designed and developed for this experiment proved to give a solid basis in which to characterize the bloodstream and its properties to model how it would affect the performance of a mobile robot operating in it. It provided a range of viscous levels to mimic the types of blood conditions people have, whether they would have blooding clotting disorder or have anemia. It also provided an environment where the mobile robot would be met with obstacles such a particles mimicking the red blood cells and other elements in the bloodstream. It gave a setting where the mobile robot would have to function with the flow or against the flow of the fluid environment. Table 6.1 defines clearly the relationship between the designed system and the bloodstream.

Table 6.1: Bloodstream and Scaled Model comparison.

Bloodstream Scaled Model Non-Newtonian Shear Stress Kinematic Parabola Velocity Profile Non-Parabola Profile Laminar Flow Blood Conditions Viscosity Levels:

1cP 34cP 125cP 517cP 757cP

Reynolds Number

Flow Rate 0gal/min 1gal/min 2gal/min

Blood Elements Particles

Cereal Oatmeal

Blood Element Behavior from Figure 5.12

Region 1 Region 1 Region 2 Regi o n 2 Regi o n 3

58 Based on the results from the experiment, the full potential of a nanorobot would come when it is operating with the flow of blood. The use of motors would not be necessary. If the nanorobot should have a motor like one seen in Figure 2.5, it has a chance of becoming entangle with the blood elements like plates. The elements would interfere with the motion of the rotating blade. The nanorobots would need a way to self guide itself through the circulatory system and withstand the bombardment of the elements. Though the mobile robot faced instability and control hindrances, it had power enough to propel through the particles and navigate where directed. The nanorobot would prove less successful when being operated remotely the same way. There is no saying what kind of power output if any the nanorobot would have, especially going against the velocity profile of blood. This is the reason for the idea of a biological based nanorobot stated in section 6.1.2. There are a lot of other factors involved with the concept of nanorobots. There is questions yet of how the nanorobot will be assembled. Hopefully, this experiment gave insight of what can be done with the development of nanorobots in biological applications, especially in the bloodstream.

6.2 Recommendations for Further Research

After knowing what the nanorobot is facing in the bloodstream, future research ideas could expand into the actual design and makeup of a more realistic environment of the bloodstream. Computer simulations and statistical studies have been done on this issue but no real, functioning setups have been made to test these theories. Computer programs only do what have been designed into them. Without any real data to input into the computer simulations, the computational results would only be in theory. Research goals can also work on simple designs for nanorobots with one function of only traversing the bloodstream. Currently it would be far too difficult to make realistic concepts of nanorobots by conventional means of placing atoms together one atom at a time or through evaporation. Not all components necessary to make a robot

59 have been conceived at the nano scale. What are obtainable are micro organisms and bacteria. These biological organisms have built in processing units in the form of DNA and have signal connectors in the form of RNA. Further possible research could enhance the use of these organisms by graphing existing nanodevices or products like carbon nanotubes to them. The organism could help apply use of the properties of these nanodevices or products. All these are long term research goals that will not be accomplished in the next couple of years. However, it is hoped that this research will open the minds of other researchers to these possible ideals. Everything starts from somewhere, from some point.

60 APPENDIX A EXPERIMENT CHARTS

Category 2 Particle Free Liquid Particle Filled Liquid (Starch @ 125 cP) (Starch @ 125 cP) With With Flow Rate (gal/min) Flow Against Flow Flow Against Flow Speed Power 0 Mobility Environment Factor Speed Power 1 Mobility Environment Factor Speed Power 2 Mobility Environment Factor

Category 3 Particle Free Liquid Particle Filled Liquid (Starch @ 362 cP) (Starch @ 362 cP) With With Flow Rate (gal/min) Flow Against Flow Flow Against Flow Speed Power 0 Mobility Environment Factor Speed Power 1 Mobility Environment Factor Speed Power 2 Mobility Environment Factor

Category 4 Particle Free Liquid Particle Filled Liquid (Starch @ 757 cP) (Starch @ 757 cP) With With Flow Rate (gal/min) Flow Against Flow Flow Against Flow Speed Power 0 Mobility Environment Factor Speed Power 1 Mobility Environment Factor Speed Power 2 Mobility Environment Factor

62 APPENDIX B TEST RUN TABLES

0 gal/min with no particles at 15 seconds and 1cP Runs Distance (in) Speed (in/s) Power (Nm/s) 1 65 4.33 0.0021 2 61 4.07 0.0019 3 58 3.85 0.0017 4 62 4.13 0.0019 5 59 3.93 0.0017 Average 4.062 0.0019

0 gal/min with particles at 24 inches and 34cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 9 2.67 0.0013 2 10 2.4 0.0010 3 10 2.4 0.0010 4 9 2.67 0.0013 5 11 2.18 0.0007 Average 2.464 0.0011

1 gal/min with no particles at 72 inches with flow and 1cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 10 7.2 0.0029 2 10 7.2 0.0029 3 10 7.2 0.0029 4 10 7.2 0.0029 5 10 7.2 0.0029 Average 7.2 0.0029

1 gal/min with no particles at 72 inches against flow and 1cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 14 5.14 0.0011 2 14 5.14 0.0011 3 14 5.14 0.0011 4 14 5.14 0.0011 5 14 5.14 0.0011 Average 5.14 0.0011

2 gal/min with no particles at 72 inches with flow and 1cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 9 8 0.0040 2 8 9 0.0057 3 8 9 0.0057 4 9 8 0.0040 5 9 8 0.0040 Average 8.4 0.0047

2 gal/min with no particles at 72 inches against flow and 1cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 16 4.5 0.0007 2 17 4.24 0.0006 3 16 4.5 0.0007 4 17 4.24 0.0006 5 16 4.5 0.0007 Average 4.396 0.0007

1 gal/min with particles at 24 inches with flow and 34cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 8 3 0.0019 2 8 3 0.0019 3 9 2.67 0.0013 4 9 2.67 0.0013 5 9 2.67 0.0013 Average 2.802 0.0016

1 gal/min with particles at 24 inches against flow and 34cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 11 2.18 0.0007 2 12 2 0.0006 3 10 2.4 0.0010 4 11 2.18 0.0007 5 12 2 0.0006 Average 2.156 0.0007

2 gal/min with particles at 24 inches with flow and 34cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 8 3 0.0019 2 8 3 0.0019 3 7 3.43 0.0028 4 8 3 0.0019 5 9 2.67 0.0013 Average 3.02 0.0020

64 2 gal/min with particles at 24 inches against flow and 34cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 12 2 0.0006 2 14 1.71 0.0004 3 13 1.85 0.0004 4 14 1.71 0.0004 5 14 1.71 0.0004 Average 1.796 0.0004

0 gal/min with no particles at 24 inches and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 9 2.67 0.0013 2 8 3 0.0019 3 8 3 0.0019 4 8 3 0.0019 5 9 2.67 0.0013 Average 2.868 0.0017

0 gal/min with particles at 24 inches and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 10 2.4 0.0010 2 12 2 0.0006 3 10 2.4 0.0010 4 11 2.18 0.0007 5 11 2.18 0.0007 Average 2.232 0.0008

1 gal/min with no particles at 24 inches with flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 8 3 0.0019 2 9 2.67 0.0013 3 8 3 0.0019 4 8 3 0.0019 5 8 3 0.0019 Average 2.934 0.0018

1 gal/min with no particles at 24 inches against flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 12 2 0.0006 2 12 2 0.0006 3 13 1.85 0.0004 4 13 1.85 0.0004 5 12 2 0.0006 Average 1.94 0.0005

65 2 gal/min with no particles at 24 inches with flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 8 3 0.0019 2 8 3 0.0019 3 8 3 0.0019 4 8 3 0.0019 5 8 3 0.0019 Average 3 0.0019

2 gal/min with no particles at 24 inches against flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 14 1.71 0.0004 2 16 1.5 0.0002 3 15 1.6 0.0003 4 15 1.6 0.0003 5 16 1.5 0.0002 Average 1.582 0.0003

1 gal/min with particles at 24 inches with flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 10 2.4 0.0010 2 9 2.67 0.0013 3 10 2.4 0.0010 4 10 2.4 0.0010 5 10 2.4 0.0010 Average 2.454 0.0010

1 gal/min with particles at 24 inches against flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 14 1.71 0.0004 2 15 1.6 0.0003 3 15 1.6 0.0003 4 14 1.71 0.0004 5 14 1.71 0.0004 Average 1.67 0.0003

2 gal/min with particles at 24 inches with flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 9 2.67 0.0013 2 10 2.4 0.0010 3 9 2.67 0.0013 4 9 2.67 0.0013 5 10 2.4 0.0010 Average 2.562 0.0012

66 2 gal/min with particles at 24 inches against flow and 125cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 18 1.33 0.000166 2 17 1.41 0.000197 3 18 1.33 0.000166 4 17 1.41 0.000197 5 18 1.33 0.000166 Average 1.362 0.000178

0 gal/min with no particles at 8 inches and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 12 0.67 0.000188 2 14 0.57 0.000118 3 12 0.67 0.000188 4 13 0.62 0.000148 5 13 0.62 0.000148 Average 0.63 0.000158

0 gal/min with particles at 8 inches and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 19 0.42 0.000047 2 20 0.4 0.000040 3 19 0.42 0.000047 4 21 0.38 0.000035 5 20 0.4 0.000040 Average 0.404 0.000042

1 gal/min with no particles at 8 inches with flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 12 0.67 0.000188 2 12 0.67 0.000188 3 13 0.62 0.000148 4 12 0.67 0.000188 5 13 0.67 0.000160 Average 0.66 0.000175

1 gal/min with no particles at 8 inches against flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 18 0.44 0.000055 2 19 0.42 0.000047 3 19 0.42 0.000047 4 20 0.4 0.000040 5 19 0.42 0.000047 Average 0.42 0.000047

67 2 gal/min with no particles at 8 inches with flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 N/A N/A N/A 2 N/A N/A N/A 3 N/A N/A N/A 4 N/A N/A N/A 5 N/A N/A N/A Average N/A N/A

2 gal/min with no particles at 8 inches against flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 N/A N/A N/A 2 N/A N/A N/A 3 N/A N/A N/A 4 N/A N/A N/A 5 N/A N/A N/A Average N/A N/A

1 gal/min with particles at 8 inches with flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 15 0.53 0.000095 2 15 0.53 0.000095 3 14 0.57 0.000118 4 15 0.53 0.000095 5 14 0.57 0.000118 Average 0.546 0.000104

1 gal/min with particles at 8 inches against flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 22 0.36 0.000030 2 21 0.38 0.000035 3 22 0.36 0.000030 4 23 0.35 0.000027 5 23 0.35 0.000027 Average 0.36 0.000030

2 gal/min with particles at 8 inches with flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 N/A N/A N/A 2 N/A N/A N/A 3 N/A N/A N/A 4 N/A N/A N/A 5 N/A N/A N/A Average N/A N/A

68 2 gal/min with particles at 8 inches against flow and 517cP Runs Time (sec) Speed (in/s) Power (Nm/s) 1 N/A N/A N/A 2 N/A N/A N/A 3 N/A N/A N/A 4 N/A N/A N/A 5 N/A N/A N/A Average N/A N/A

69 APPENDIX C COMPLETED EXPERIMENT CHARTS

Category 1 Particle Free Liquid Particle Filled Liquid (Water @ 1 cP) (Water @ 34 cP) Against Against Flow Rate (gal/min) With Flow Flow With Flow Flow Speed 4.062 4.062 2.464 2.464 Power 0.179 0.179 0.066 0.066 0 Mobility 5 5 5 5 Environment Particles cause minor No influences on performance Factor control loss Speed 7.2 5.14 2.802 2.156 Power 0.562 0.287 0.0852 0.0504 Mobility 5 5 4 4 Minor Resistance 1 resistance against sub No influences Little Environment against sub with on hindrance Factor with little hindrance on performance on control initial control and instability stability Speed 8.4 4.396 3.02 1.796 Power 0.767 0.2094 0.0996 0.0352 Mobility 5 5 4 4 Minor Resistance 2 resistance against sub No influences Little Environment against sub with on hindrance Factor with little hindrance on performance on control initial control and instability stability

70 Category 2 Particle Free Liquid Particle Filled Liquid (Starch @ 125 cP) (Starch @ 125 cP) Against Against Flow Rate (gal/min) With Flow Flow With Flow Flow Speed 2.868 2.868 2.232 2.232 Power 0.0896 0.0896 0.0542 0.0542 Mobility 5 5 4 4 0 Environment Hindrance on control with No influences on performance Factor slower response

Speed 2.934 1.94 2.454 1.67 Power 0.0938 0.0406 0.065 0.0304 Mobility 5 5 4 4 Slower start No influences than in 1cP Resistance 1 Hindrance on liquid with against sub Environment on control performance, less with more Factor with slower a little more noticeable interference response stable initial from particles instability Speed 3 1.582 2.562 1.362 Power 0.098 0.0272 0.071 0.0202 Mobility 5 4 4 4

No influences Resistance 2 Hindrance on Slower against sub Environment on control performance, response to with more Factor with slower a little more turns interference response stable from particles

71 Category 3 Particle Free Liquid Particle Filled Liquid (Starch @ 517 cP) (Starch @ 517 cP) Against Against Flow Rate (gal/min) With Flow Flow With Flow Flow Speed 0.63 0.63 0.404 0.404 Power 0.0044 0.0044 0.002 0.002 Mobility 4 4 4 4 0 Still in liquid with very little Takes nearly twice as long Environment degrees of freedom. Hard to to traverse. Particles act Factor propel through the liquid. more like barriers.

Speed 0.66 0.42 0.546 0.36 Power 0.0048 0.002 0.0034 0.0012 Mobility 4 4 4 4 Hard to go against the 1 Easy to keep flow. Speed Takes nearly twice as long Environment on course but of sub to traverse. Particles act Factor loss of seemed more like barriers. forward thrust barely greater than the flow rate Speed 0 0 0 0 Power 0 0 0 0 2 Mobility 1 1 1 1 Environment Data could not be obtained. Pressure exceeding system limit Factor at this flow rate.

72 Category 4 Particle Free Liquid Particle Filled Liquid (Starch @ 757 cP) (Starch @ 757 cP) Against Against Flow Rate (gal/min) With Flow Flow With Flow Flow Speed 0 0 0 0 Power 0 0 0 0 Mobility 1 1 1 1 0 Submarine could not propel Submarine could not propel Environment through the thickness of the through the thickness of the Factor liquid liquid Speed 0 0 0 0 Power 0 0 0 0 Mobility 1 1 1 1 1 Submarine could not propel Submarine could not propel Environment through the thickness of the through the thickness of the Factor liquid liquid Speed 0 0 0 0 Power 0 0 0 0 Mobility 1 1 1 1 2 Submarine could not propel Submarine could not propel Environment through the thickness of the through the thickness of the Factor liquid liquid

73 APPENDIX D DESIGN OF EXPERIMENT CHART

Factor Factor Factor Response: A: Factor D: Flow Std Run Block C: Speed Viscosity B: Flow Rate Particles (in/sec) (cP) (gal/min) 48 1 Block 1 517 0 0 1 0.66666667 44 2 Block 1 125 0 0 1 3 73 3 Block 1 125 1 1 1 1.71428571 1 4 Block 1 125 0 0 0 2.66666667 47 5 Block 1 517 0 0 1 0.66666667 71 6 Block 1 125 1 1 1 1.6 27 7 Block 1 517 0 1 0 0.42105263 40 8 Block 1 517 1 1 0 0.42105263 70 9 Block 1 517 0 1 1 0.53333333 11 10 Block 1 125 1 0 0 2.66667 13 11 Block 1 125 1 0 0 3 7 12 Block 1 517 0 0 0 0.66666667 38 13 Block 1 517 1 1 0 0.4 77 14 Block 1 517 1 1 1 0.36363636 10 15 Block 1 517 0 0 0 0.57142857 35 16 Block 1 125 1 1 0 2.18181818 59 17 Block 1 517 1 0 1 0.44444444 8 18 Block 1 517 0 0 0 0.66666667 14 19 Block 1 125 1 0 0 3 63 20 Block 1 125 0 1 1 2.4 9 21 Block 1 517 0 0 0 0.61538462 51 22 Block 1 125 1 0 1 2 5 23 Block 1 125 0 0 0 2.66666667 79 24 Block 1 517 1 1 1 0.38095238 19 25 Block 1 517 1 0 0 0.66666667 61 26 Block 1 125 0 1 1 2.66666667 26 27 Block 1 517 0 1 0 0.4 34 28 Block 1 125 1 1 0 2.18181818 74 29 Block 1 125 1 1 1 1.6 56 30 Block 1 517 1 0 1 0.42105263 66 31 Block 1 517 0 1 1 0.53333333 36 32 Block 1 517 1 1 0 0.42105263 23 33 Block 1 125 0 1 0 2.4 67 34 Block 1 517 0 1 1 0.57142857 58 35 Block 1 517 1 0 1 0.42105263

74 30 36 Block 1 517 0 1 0 0.42105263 57 37 Block 1 517 1 0 1 0.4 39 38 Block 1 517 1 1 0 0.38095238 33 39 Block 1 125 1 1 0 2.4 31 40 Block 1 125 1 1 0 2 60 41 Block 1 517 1 0 1 0.42105263 16 42 Block 1 517 1 0 0 0.57142857 4 43 Block 1 125 0 0 0 3 32 44 Block 1 125 1 1 0 2.4 42 45 Block 1 125 0 0 1 2.66666667 41 46 Block 1 125 0 0 1 3 52 47 Block 1 125 1 0 1 1.84615385 29 48 Block 1 517 0 1 0 0.38095238 72 49 Block 1 125 1 1 1 1.71428571 46 50 Block 1 517 0 0 1 0.61538462 25 51 Block 1 125 0 1 0 2 76 52 Block 1 517 1 1 1 0.36363636 6 53 Block 1 517 0 0 0 0.61538462 53 54 Block 1 125 1 0 1 1.84615385 22 55 Block 1 125 0 1 0 2.4 17 56 Block 1 517 1 0 0 0.66666667 69 57 Block 1 517 0 1 1 0.57142857 37 58 Block 1 517 1 1 0 0.4 2 59 Block 1 125 0 0 0 3 80 60 Block 1 517 1 1 1 0.34782609 18 61 Block 1 517 1 0 0 0.61538462 43 62 Block 1 125 0 0 1 3 62 63 Block 1 125 0 1 1 2.4 50 64 Block 1 517 0 0 1 0.66666667 3 65 Block 1 125 0 0 0 3 28 66 Block 1 517 0 1 0 0.4 15 67 Block 1 125 1 0 0 3 21 68 Block 1 125 0 1 0 2.18181818 68 69 Block 1 517 0 1 1 0.53333333 54 70 Block 1 125 1 0 1 2 24 71 Block 1 125 0 1 0 2.18181818 64 72 Block 1 125 0 1 1 2.4 75 73 Block 1 125 1 1 1 1.71428571 45 74 Block 1 125 0 0 1 3 65 75 Block 1 125 0 1 1 2.4 55 76 Block 1 125 1 0 1 2 12 77 Block 1 125 1 0 0 2.66666667 20 78 Block 1 517 1 0 0 0.61538462 49 79 Block 1 517 0 0 1 0.61538462 78 80 Block 1 517 1 1 1 0.34782609

75 APPENDIX E RESIDUALS PLOTS

DESIGN-EXPERT Plot Residuals vs. Viscosity Speed 3.00

1.50 32 sl 43 a u di s 3 e 4 43 R 0.00 32 2 d 2 e 4 z 3 ti n 2 2 e 4 d ut S -1 .5 0 2 3

-3 .0 0

125.00 223.00 321.00 419.00 517.00

Viscosity

DESIGN-EXPERT Plot Residuals vs. Flow Speed 3.00

1.50 32 32 sl 4 3 a u di s 3 e 2 2 2 23 R 0.00 2 32 d 2 e 2 2 z 3 ti 2 2 n e 4 d ut S -1 .5 0 2 2

-3 .0 0

0 1

Flow

76 DESIGN-EXPERT Plot Speed Residuals vs. Particles

3.00

2 1.50 3 2 sl 4 3 a u di 3 s 4 e 4 4 3 4 R 0.00 23 22 d 4 2 e 3 z ti 2 2 n e 4 d ut S -1 .5 0 2 3

-3 .0 0

0 1

Particles

DESIGN-EXPERT Plot Speed Residuals vs. Flow Rate 3.00

1.50 32 sl 43 a u di s 3 e 4 4 3 R 0.00 2 232 d 4 2 e 3 z ti 2 n e 4 d ut S -1 .5 0 2 3

-3 .0 0

0 1

Flow Rate

77 APPENDIX F ONE FACTOR AND INTERACTION PLOTS

DESIGN-EXPERT Plot One Factor Plot

Speed 3 W arning! F ac tor inv olv ed in an interac tion.

X = A: Viscosity

Actual Factors 2.33696 B: Flow = 0.50 C: Particles = 0.50 D: Flow Rate = 0.50

d e 1.67391 pe S

1.01087

0.347826

125.00 223.00 321.00 419.00 517.00

A: Viscosity

DESIGN-EXPERT Plot One Factor Plot W arning! F ac tor inv olv ed in an interac tion. Speed 3

X = B: F low

Actual Factors 2.33696 A: Viscosity = 321.00 C: Particles = 0.50 D: Flow Rate = 0.50

d e 1.67391 pe S

1.01087

0.347826

0.00 0.25 0.50 0.75 1.00

B: Flow

78 DESIGN-EXPERT Plot One Factor Plot

Speed 3 W arning! F ac tor inv olv ed in an interac tion.

X = C: Particles

Actual Factors 2.33696 A: Viscosity = 321.00 B: Flow = 0.50 D: Flow Rate = 0.50

d e 1.67391 pe S

1.01087

0.347826

0.00 0.25 0.50 0.75 1.00

C: Particles

DESIGN-EXPERT Plot One Factor Plot W arning! F ac tor inv olv ed in an interac tion. Speed 3

X = D : F low R at e

Actual Factors 2.33696 A: Viscosity = 321.00 B: Flow = 0.50 C: Particles = 0.50

d e 1.67391 pe S

1.01087

0.347826

0.00 0.25 0.50 0.75 1.00

D: Flow Rate

79 DESIGN-EXPERT Plot Interaction Graph B: Flow Speed 3 X = A: Viscosity Y = B: Flow

B- 0.000 2.33696 B+ 1.000 Actual Factors C: Particles = 0.50 D: Flow Rate = 0.50 d e 1.67391 pe S

1.01087

0.347826

125.00 223.00 321.00 419.00 517.00

A: Viscosity

DESIGN-EXPERT Plot Interaction Graph C: Particles Speed 3

X = A: Viscosity Y = C: Particles

C- 0.000 2.33696 C+ 1.000 Actual Factors B: Flow = 0.50 D: Flow Rate = 0.50 d e 1.67391 pe S

1.01087

0.347826

125.00 223.00 321.00 419.00 517.00

A: Viscosity

80 DESIGN-EXPERT Plot Interaction Graph D: Flow Rate Speed 3 X = A: Viscosity Y = D: Flow Rate

D- 0.000 2.33696 D+ 1.000 Actual Factors B: Flow = 0.50 C: Particles = 0.50 d e 1.67391 pe S

1.01087

0.347826

125.00 223.00 321.00 419.00 517.00

A: Viscosity

DESIGN-EXPERT Plot Interaction Graph D: Flow Rate Speed 3

X = B: F low Y = D: Flow Rate

D- 0.000 2.33696 D+ 1.000 Actual Factors A: Viscosity = 321.00 C: Particles = 0.50 d e 1.67391 pe S

1.01087

0.347826

0.00 0.25 0.50 0.75 1.00

B: Flow

81 DESIGN-EXPERT Plot Interaction Graph D: Flow Rate Speed 3 X = C: Particles Y = D: Flow Rate

D- 0.000 2.33696 D+ 1.000 Actual Factors A: Viscosity = 321.00 B: Flow = 0.50 d e 1.67391 pe S

1.01087

0.347826

0.00 0.25 0.50 0.75 1.00

C: Particles

82 APPENDIX G SPEED VS. VISCOSITY GRAPHS

With Flow with No Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min 200 Viscosity (cP) Viscosity 100 0 -100 0246810 Speed (in/sec)

Against Flow with No Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min 200 Viscosity (cP) Viscosity 100 0 -100 0123456 Speed (in/sec)

With Flow with Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min

Viscosity (cP) Viscosity 200 100 0 01234 Speed (in/sec)

Against Flow with Particles

800 700 600 500 0gal/min 400 1gal/min 300 2gal/min

Viscosity (cP) Viscosity 200 100 0 0 0.5 1 1.5 2 2.5 3 Speed (in/sec)

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86 BIOGRAPHICAL SKETCH

I was born at Yokota Air Base, Japan, a military installation, October 21, 1980, the son of Russell and Fumiko Zimmer. I spent my life up until college living around the military life. I have spent years in Norfolk, Virginia, where my brother, Christopher, was born; Korea; Tampa; Florida; and Alconbury, England. I graduated at Alconbury High School in England. Through the years I have had the privilege of learning various arts outside of school such as playing the league baseball, piano, and doing martial arts. I thoroughly enjoyed playing high school sports like football, wrestling, and soccer, where I have received numerous awards. I chose to go to Florida State University for my undergraduate years in Industrial and Manufacturing Engineering, earning my degree in the fall of 2002. I stayed to continue my master’s degree in the same field, taking an interest in nanotechnology for my thesis work. I have plans to further my education for a Ph. D. I hope to get a job that will fulfill my enjoyment in the degree I have obtained and allow me the luxury of life of moving about the world as I have had the pleasure of doing as a youth.