Computational Investigation of Rotary Engine Homogeneous

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COMPUTATIONAL INVESTIGATION OF ROTARY ENGINE HOMOGENEOUS

CHARGE COMPRESSION IGNITION FEASIBILITY

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Engineering

Michael Irvin Resor B.S., Wright State University, 2012

2014 Wright State University WRIGHT STATE UNIVERSITY

GRADUATE SCHOOL

December 9, 2014

I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Michael Irvin Resor ENTITLED Computational Investigation of Rotary Engine Homogeneous Charge Compression Ignition Feasibility BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in Engineering.

Committee of George Huang, Ph.D. Final Examination Thesis Director

Haibo Dong, Ph.D. George Huang, Ph.D. Co-Advisor Chair Department of Mechanical and Materials Engineering George Huang, Ph.D. College of Engineering and Computer Science

Greg Minkiewicz, Ph.D.

Scott Thomas, Ph.D.

Zifeng Yang, Ph.D.

Robert E. W. Fyffe, Ph.D. Vice President for Research and Dean of the Graduate School

Abstract Resor, Michael Irvin. M.S. Egr., Department of Mechanical and Materials Engineering, Wright State University, 2014. Computational Investigation of Rotary Engine Homogeneous Charge Compression Ignition Feasibility.

The Air Force Research Laboratory (AFRL) has been investigating the heavy fuel conversion of small scale Unmanned Aerial Vehicles (UAV). One particular platform is the Army

Shadow 200, powered by a UEL Wankel rotary engine. The rotary engine historically is a proven multi-fuel capable engine when operating on spark ignition however, little research into advanced more efficient compression concepts have been investigated. A computational fluid dynamics model has been created to investigate the feasibility of a Homogeneous Charge

Compression Ignition (HCCI) rotary engine. This research evaluates the effects, rotor radius to crankshaft eccentricity ratio, known as K factor, equivalence ratio, and engine speed and how they affect the response of horsepower, maximum temperature, and peak pressure to determine the feasibility of HCCI operation. The results show that the advanced HCCI strategy is promising to significantly improve efficiency of the rotary engine.

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TABLE OF CONTENTS

Chapter 1: Introduction ...... 1

Background ...... 1

Rotary Engine ...... 1

High Temperature Combustion (SI and CI) ...... 3

Low Temperature Combustion (HCCI/PCCI) ...... 5

Research Objective ...... 7

Methodology ...... 8

Literature Review ...... 9

Thesis Outline ...... 11

Chapter 2: Validation ...... 12

Z19DTH - Piston Engine ...... 12

Modeling and Meshing ...... 13

Solver Models and Numerical Settings ...... 15

Combustion ...... 16

Turbulence and Wall Heat Flux ...... 17

Results ...... 17

Chapter 3: Rotary Engine Model ...... 19

Modeling and Meshing ...... 19

Boundary and Initial Conditions ...... 22

Solver Settings ...... 24

Chapter 4: Parametric Study on LTC Performance ...... 26

Rotary Homogeneous Charge Compression Ignition (HCCI) ...... 26

Parameters ...... 26

Responses ...... 27

Held Constant Factors ...... 28

Results of 33 Factorial...... 28

Best Fit Case Results from 33 Factorial...... 40

Equivalence Ratio Single Factor Fitting ...... 41

Best Fit Case Equivalence Ratio Single Factor Fitting ...... 44

Chapter 5: Conclusions ...... 48

Chapter 6: Future Work ...... 50

References ...... 52

LIST OF FIGURES

Figure 1 Rolls Royce Diesel Engine [1] ...... 2

Figure 2 Rotary engines of different K factors [2] ...... 3

Figure 3 The four diesel combustion phases [4] ...... 5

Figure 4 Combustion Strategies on Φ - T Diagram [7] ...... 7

Figure 5 2D Piston Pocket Cross Section ...... 14

Figure 6 Solidworks Sector Geometry at Intake Port Close ...... 14

Figure 7 Mesh Sector Geometry at Intake Port Close ...... 14

Figure 8 Z19DTH CFD Model Wall Conditions ...... 15

Figure 9 Graphical Representation of PDF [23] ...... 16

Figure 10 Pressure Trace of Z19DTH Validation ...... 18

Figure 11 Circular arc approximation of Rotor Flank ...... 20

Figure 12 Rotary Engine Geometries and Meshes ...... 21

Figure 13 Epitrochoidal Path ...... 22

Figure 14 Rotary Engine Thermal Boundary Conditions+ ...... 22

Figure 15 Design Table ...... 29

Figure 16 Summary of fit and ANOVA table for Pressure Rise Rate ...... 30

Figure 17 Summary of fit and ANOVA table for Maximum Temperature ...... 30

Figure 18 Summary of fit and ANOVA table for Horsepower ...... 31

Figure 19 Actual by Predicted Plots ...... 31

Figure 20 Parameter Estimates ...... 32

Figure 21 Plots of Residuals of Pressure Rise Rate plotted verses factors ...... 33

Figure 22 Plots of Residuals of Max Temperature plotted verses factors ...... 34

Figure 23 Plots of Residuals of Horsepower plotted verses factors ...... 35

Figure 24 2000 RPM Profiler Plot with Desirability ...... 36

Figure 25 4000 RPM Profiler Plot with Desirability ...... 37

Figure 26 6000 RPM Profiler Plot with Desirability ...... 37

Figure 27 2000 RPM Profiler Plot ...... 38

Figure 28 4000 RPM Profiler Plot ...... 39

Figure 29 6000 RPM Profiler Plot ...... 39

Figure 30 2000 RPM Data Fit ...... 42

Figure 31 4000 RPM Data Fit ...... 43

Figure 32 6000 RPM Data Fit ...... 44

Figure 33 2000 RPM Output Plots of Temperature, Pressure and PV ...... 45

Figure 34 4000 RPM Output Plots of Temperature, Pressure and PV ...... 46

Figure 35 6000 RPM Output Plots of Temperature, Pressure and PV ...... 46

Figure 36 Temperature vs Crank Angle for K Factor 9.5 2000 RPM ...... 58

Figure 37 Pressure vs Crank Angle for K Factor 9.5 2000 RPM ...... 58

Figure 38 Pressure vs Volume for K Factor 9.5 2000 RPM...... 59

Figure 39 Temperature vs Crank Angle for K Factor 9.5 4000 RPM ...... 59

Figure 40 Pressure vs Crank Angle for K Factor 9.5 4000 RPM ...... 60

Figure 41 Pressure vs Volume for K Factor 9.5 4000 RPM...... 60

Figure 42 Temperature vs Crank Angle for K Factor 9.5 6000 RPM ...... 61

Figure 43 Pressure vs Crank Angle for K Factor 9.5 6000 RPM ...... 61

Figure 44 Pressure vs Volume for K Factor 9.5 6000 RPM...... 62

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Figure 45 Temperature vs Crank Angle for K Factor 10.5 2000 RPM ...... 62

Figure 46 Pressure vs Crank Angle for K Factor 10.5 2000 RPM ...... 63

Figure 47 Pressure vs Volume for K Factor 10.5 2000 RPM...... 63

Figure 48 Temperature vs Crank Angle for K Factor 10.5 4000 RPM ...... 64

Figure 49 Pressure vs Crank Angle for K Factor 10.5 4000 RPM ...... 64

Figure 50 Pressure vs Volume for K Factor 10.5 4000 RPM...... 65

Figure 51 Temperature vs Crank Angle for K Factor 10.5 6000 RPM ...... 65

Figure 52 Pressure vs Crank Angle for K Factor 10.5 6000 RPM ...... 66

Figure 53 Pressure vs Volume for K Factor 10.5 6000 RPM...... 66

Figure 54 Temperature vs Crank Angle for K Factor 11.5 2000 RPM ...... 67

Figure 55 Pressure vs Crank Angle for K Factor 11.5 2000 RPM ...... 67

Figure 56 Pressure vs Volume for K Factor 11.5 2000 RPM...... 68

Figure 57 Temperature vs Crank Angle for K Factor 11.5 4000 RPM ...... 68

Figure 58 Pressure vs Crank Angle for K Factor 11.5 4000 RPM ...... 69

Figure 59 Pressure vs Volume for K Factor 11.5 4000 RPM...... 69

Figure 60 Temperature vs Crank Angle for K Factor 11.5 6000 RPM ...... 70

Figure 61 Pressure vs Crank Angle for K Factor 11.5 6000 RPM ...... 70

Figure 62 Pressure vs Volume for K Factor 11.5 6000 RPM...... 71

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LIST OF TABLES

Table 1 Z19DTH Engine Parameters [13] ...... 12

Table 2 Validation Models ...... 15

Table 3 Chamber volume at intake port closed ...... 23

Table 4 Common Initial Conditions ...... 23

Table 5 Equivalence ratio and mixture fraction ...... 24

Table 6 Numerical Settings ...... 25

Table 7 JMP Selected Parameters for Best Fit Cases ...... 39

Table 8 CFD Best Fit Cases Results ...... 40

Table 9 Percent Error of JMP to CFD ...... 41

Table 10 Selected Parameters for Best Fit Cases and predicted results...... 44

Table 11 CFD Best Fit Cases Results ...... 46

Table 12 Percent Error of Best Fit Cases to CFD ...... 46

Nomenclature

ANOVA – Analysis of Variance

ATDC – After Top Dead Center

BTDC – Before Top Dead Center

CI – Compression Ignition

CR – Compression Ratio

DUFL – Diesel Unsteady Flamelet

EGR – Exhaust Gas Recirculation

HCCI – Homogeneous Charge Compression Ignition

ICE – Internal combustion Engine

K factor – Rotor Radius to Crankshaft Eccentricity

LTC – Low Temperature Combustion

MPCI – Multiple Premixed Compression Ignition

PCCI – Premixed Charge Compression Ignition

PDF – Probability Density Function

RPM – Revolutions per Minute

SI – Spark Ignition

SOI – Start of Injection

TDC – Top Dead Center

UDF – User Defined Functions

Acknowledgements

I would first like to thank my advisors Dr. Haibo Dong and Dr. George Huang for their guidance and commitment to see me through this project. Thank you both for the long distance collaboration, without it I wouldn’t have been able to complete my thesis. Thanks to the flow simulation research group I was fortunate to be funded to work on engine related research projects in both my undergraduate and graduate years at Wright State University.

I would also like to thank Greg Minkiewicz of Wright Patterson Air Force Base. His determination and vision to improve all aspects of Air Force propulsion made way for this heavy fuel related project. This will be the 4th thesis funded by his heavy fuel Rotary engine project. With every thesis that he has funded different combustion technologies were studied to improve performance and efficiency of the Rotary engine.

My best friend, Alex, throughout college we both focused on thermal/fluid dynamics. Although we didn’t always see eye to eye, I couldn’t have asked for a better friend with whom to study engineering.

Last but not least I would like to thank my wife Mackenzie and our two children Mika and Marek

Thank you for your continued support and understanding. I promise I’ll have more free time to spend with all of you soon.

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Chapter 1: Introduction

Background

Rotary Engine

Invented by Dr. Felix Wankel, the Wankel rotary engine has been the only long term competitor to the reciprocating piston engine. Dr. Wankel later partnered with NSU to develop the engine into what we know today as the modern rotary engine. A rotary engine uses three main components to complete the four strokes of the Otto Cycle. These parts include a triangular rotor, eccentric shaft, and epitrochoidal housing. Opposed to the conventional reciprocating engine the rotary engine does not use valves to allow air in and out of the chamber; instead the rotor oscillates within the epitrochoidal housing in a planetary motion, classifying this engine as a planetary rotating engine. Although many companies began research and development (R&D) on the rotary engine, the following companies have made the largest contributions to the rotary engine development, Toyo Kogyo (Mazda), Curtis Wright

Corporation, and John Deere [1]. The Japanese developments at Mazda under the leadership of

Kenichi Yamamoto focused the automotive market, while US development by Curtis Wright and

John Deere focused on aircraft application. During the R&D period through the 1980’s, much of the fundamentals that are known about the rotary engine were discovered. Downstream flame propagation, operation of heavy fuels, charge motion, and rotor radius to crankshaft eccentricity ratio (K factor) effects.

To date only Rolls Royce is the only company that has built a Diesel rotary engine. This diesel engine has never made it out of the R&D phase. The engine featured a large rotor feeding air into a smaller rotor, Figure 1 [1], [2]. A single rotor CI Wankel engine is impractical, the K factor need to produce the compression ratio need for auto-ignition results in a very long and thin combustion chamber at TDC. A long and thin combustion chamber experiences high heat losses, quenching of the flame, and excessive fuel wall wetting. Traditionally engineering factors such as engine size, combustion chamber, and apex seal leaning angle has kept K factors in range of 6 to 10 [2]. Furthermore, no research efforts have attempted Homogeneous Charge

Compression Ignition (HCCI) mode on the Wankel engine.

Figure 1 Rolls Royce Diesel Engine [1]

Figure 2 Rotary engines of different K factors [2]

High Temperature Combustion (SI and CI)

Traditionally the internal combustion engine operates on one of two cycles, the Otto or

Diesel Cycle. Each of these cycles has a unique combustion process known as spark ignition and compression ignition respectfully. The spark ignited engine draws in an air-fuel mixture that is compressed, when the piston approaches TDC, an electrical current is sent to the spark plug to initiate combustion. The developed plasma spark kernel grows until a turbulent flame is generated. This flame propagates in a uniform manner throughout the combustion chamber, consuming the air-fuel mixture until it reaches a wall and is extinguished. Typically, a spark ignited engine has a lower compression than that of a diesel cycle. By selecting the proper compression ratio it is possible for the flame to propagate thought the combustion chamber without encountering abnormal combustion, known as knock. Knock is defined as the rapid auto

3 ignition of the unburnt air-fuel mixture ahead of the flame front. The mixture ahead of the flame front is known as the end gas. However, the diesel cycle will never encounter knock [3].

From the stand point of a gasoline based engine there is a clear separation between injection and combustion events both events can be discussed separately, but this is not the case for diesel engines. Since we rely on the energy in the compression charge to initiate a chemical reaction, the combustion and injection events overlap. Unlike the spark ignition engine, turbulence has little effect on the burning velocity; fuel mixture determines the combustion rate. Diesel is more efficient than the spark ignition cycle, and operates with a leaner fuel mixture than the spark ignition engine, to reduce soot particulates [3]. The diesel combustion process has four combustion phases of compression ignition, shown in Figure 3. Stage one is known as the ignition delay phase. This phase is characterized as the time between Start Of

Injection (SOI) and the first increase in the pressure rise rate. Stage two is sudden pressure increase, ranging from the start of combustion to the peak pressure. Stage three is the main combustion phase covering the range from peak pressure to peak temperature. Stage four is the delayed post-combustion starting at peak pressure, there is no defined limit to the end of this phase. However, one safe suggestion is to use the end of heat release to define the end of combustion to end the fourth stage [4].

Figure 3 The four diesel combustion phases [4]

Both of the combustion strategies, SI and CI, release the fuel’s chemical energy through a flame, turbulent and diffused respectfully. The flame is the hottest part of the combustion process, allowing for NOx and soot chemical reactions to occur at a higher rate, increasing emissions. Internal cylinder temperatures are higher than if the flame could be removed.

Low Temperature Combustion (HCCI/PCCI)

Low temperature combustion was first discovered by Shigera Ohnishi at Nippon Clean

Engine Research Institute Co., Ltd in the late 1970’s. The combustion process known as Active

Thermo-Atmosphere Combustion (ATAC) is process where a lean premixed charge is consumed by auto-ignition rather than deflagration flame front. The rapid auto-ignition of the fuel without a flame results in lower cylinder temperatures. Ohnishi found this new combustion offered benefits to fuel consumption and engine emissions [5]. Figure 4, shows the work of Kamimoto which was later updated by Sun. The diagram shows the soot and NOx production ranges on the

Φ - T diagram [6], [7]. Conventional combustion is in the range where soot and NOx are

5 produced, and Low Temperature Combustion (LTC) strategies operate below the threshold of these emissions [7]. The ATAC process is known today as HCCI and has been the study of numerous papers to reduce engine emissions. The HCCI process also offers engine efficiency similar to the diesel engine [8]. The HCCI method does have negative effects. Uncontrolled start of ignition, the combustion phasing can vary cycle to cycle, the rapid rate of heat release can cause high peak pressures, and oscillating pressure waves, similar to knock, can limit the maximum load achievable to an HCCI engine [9].

The most common way to overcome HCCI problems is to mix the fresh air charge with

Exhaust Gas Recirculation (EGR). The premixed hot EGR gases have been found to have two major effects on HCCI combustion, first ignition timing will be advanced and the species found in

EGR affect the heat release rate and lower combustion temperatures [10].

Another approach developed by Yang called Multiple Premixed Compression Ignition

(MPCI) raises the low load limit of HCCI. This method has a lean mixture into the chamber, combusts, injects, and mixes a second spray of fuel and combusts again. The two combustion events are completely separate; two smaller heat releases can be controlled to reduce the maximum pressure [11].

Figure 4 Combustion Strategies on Φ - T Diagram [7]

Research Objective

A traditional rotary engine cannot produce sufficient compression for auto-ignition due to geometric constraints. The combustion chamber near TDC is not well suited for flame propagation due to flame quenching. However, the recent work of Sher, studied the scalability limits of an engine operating in HCCI mode. Sher found that an engine as small as 0.3cc displacement with 20:1 compression ratio is possible [12]. To understand what the clearance height between the piston and chamber, we can make a few assumptions. First, assume this engine is square, where the bore and stroke are equal. Then, using Equation 1 for calculating

Compression Ratio (CR), we can find the clearance height would be approximately 0.38mm.

Equation 1

By understanding that HCCI can work in chambers where a flame cannot, SI and CI, design parameters for the rotary engine can now be explored, even though they were once thought to

7 be impractical. My research aims to study the feasibility of operating the rotary engine in HCCI mode and to find the optimal K factor for full Revolution per Minute (RPM) range operation.

Methodology

In order to save time and money Computational Fluid Dynamics (CFD) is often used to evaluate and explore a design space before turning to traditional experimental methods. The design exploration can be used to select parameter ranges to study experimentally, or can be used to derive an optimized geometric shape, or used test conditions for Internal Combustion Engine

(ICE) development. In this work a 3D CFD model of a rotary engine is used to evaluate the feasibility of HCCI operation. Due to the complex flow and combustion phenomena of an ICE, a

3D CFD model is need to accurately represent and study these processes. To date, no HCCI rotary engine exists, therefore, model and solver settings need to be validated from experimental data to that of a piston type engine. This method is not ideal, but it’s sufficient because both are positive displacement engines and operate on the HCCI/PCCI cycle. Validation data is used from the work of Lee’s [13] Ph.D. dissertation from the University of Wisconsin

Madison’s Engine Research Center. The work presented here examines the full factorial effects that three engine speeds (RPM), three equivalence ratios, and three geometric K factors have on

HCCI performance, which is a 27 case design study. Three response factors are used to evaluate rotary engine HCCI engine performance; these factors are peak pressure, pressure rise rate, and indicated power. For the K factor geometries selected, the intake port closing position is the same for all engines presented in this research and the engine displacement is the same. By doing so, uncontrolled and negligible factors have been eliminated from the study yield better parameter correlations. Furthermore, the mass of air and fuel for a given equivalence ratio for the different RPM and K factors will be the same.

Literature Review

The work of Ohnishi [5], discovered the HCCI combustion process at Nippon Clean Engine

Research Institute C. Ltd in the mid to late 1970’s and they called it Active Thermo-Atmosphere

Combustion (ATAC). Ohnishi noted a lower peak pressure than SI, on the two stroke engine operation. His work mentioned that this worked if the scavenged residuals are setup correctly; that EGR plays a significant role in HCCI combustion. Ohnishi went on to discuss the ideal engine has large displacement with low surface area to volume ratio, and that a high RPM operation is necessary to decrease thermal losses and to have high thermal efficiency.

In Aceves’ paper, HCCI Combustion: Analysis and Experiments highlights two methods of analyzing HCCI combustion using single and multi zone models. The single zone model can accurately predict the start of ignition and is a good indicator of peak pressure. However, the single zone model cannot predict Hydrocarbon and Carbon Monoxide emissions, but a multi- zone model can be used to predict these emissions. Scalability of HCCI engines is mentioned as well ranging from “small motorcycle to large ship engines”. Fundamentals of the combustion process were explained when HCCI is dominated by localized reactions in the absences of a propagating flame. Aceves discussed that if HCCI is truly Homogeneous, then turbulences will have little effect on combustion, and would affect temperature gradients more than anything.

Finally, key conditions that affect HCCI operation are considered; these include equivalence ratio, percent EGR, and intake conditions.

In Chen’s paper, “A computational study into the effect of EGR on HCCI combustion in IC engine fuelled with Methane. This paper points out that two effects occur by adding EGR, ignition delay and peak temperature reduction. This is caused by thermal and chemical effects. Another important aspect of this paper is the calculation of species mass fraction and premixed

9 temperature with a given percent EGR recirculation. This work also notes that compression ratios as high as 21:1 with gasoline fuel have been achieved. With EGR recirculation it is believed that the HCCI range of operation can be extended [10].

In the paper Fuel octane effects on gasoline multiple premixed compression ignition (MPCI) mode by Yang. This paper was more applicable to gasoline fuel, but showed a mode that was able to inject-combust, then inject-combust a second time to extend the load range of HCCI.

Traditionally, diesel fuel would not be able to run on this type of mode due to the short ignition delay time. [11]

Miniaturization limits of HCCI internal combustion engines by Sher discusses that HCCI will allow for small scale engine operation, to engine size of 0.3 cubic centimeters. Since there is a lack of a flame front, HCCI will allow complete combustion in regions of high quenching. This paper showed that engines with high heat transfer can also support HCCI [12].

In the paper by Olsson, Boosting for High Load HCCI, he discusses the benefits of fuel economy and low emissions of the HCCI mode. However, to achieve low emissions the cylinder charge needs to be very dilute, boosting was studied to overcome the high load limit. It was found that high load HCCI is possible with reduced brake thermal efficiency and that peak pressure will be the limiting factor to the engine load [14].

From the paper Understanding HCCI Characteristics in Mini HCCI Engines by Collair, the small engines studied showed that the pressure rise rate and peak pressures were affected by the thermal stratification. The strong thermal gradient within the CFD model showed that the combustion would “cascade” within the chamber, allowing for a gradual combustion instead of a rapid combustion [15].

The work of Muroki at Mazda published a paper Mazda Rotary Engine Technology. In this paper, it was found that in the spark ignition engine the combustion would not propagate below the narrow of the epitrochoidal housing. With proper care two separate stable combustion zones could occur at the same time within the chamber. This combustion is a fundamental aspect of the rotary engine sweeping volume combustion chamber that is commonly overlooked [16].

Andreae’s work entitled On HCCI Engine Knock, studied knock in the HCCI mode. His findings were audible knock transmission was caused by the radiation of structural vibrations. Also, cylinder pressure would oscillate even in the absence of knock, due to the rapid heat release rate. Andreae found that 5 MPa/ms is suitable limit for pressure rise rate for the engine studied

[17].

The work performed by Dempsey at Wisconsin Madison’s Engine Research Center titled

Computational Optimization of a Heavy-Duty Compression Ignition Engine Fueled with

Conventional Gasoline, found that HCCI combustion leads to high pressure oscillations within the chamber limiting load in a reciprocating engine. He found that Partial Premixed Compression

Ignition (PPCI) allows for low NOx with 50% gross indicated thermal efficiency at high load, but due to the large variations of equivalence ratio soot particulates were increased [18].

Thesis Outline

Chapter two covers model creation and numerical settings for the validation study, with the GM

Z19DTH diesel engine. Chapter three covers rotary engine model creation and a case study for selecting the optimal K factor, using pressure rise rate, peak pressures, and indicated power as selection criteria. Chapters four and five cover the conclusions and future work respectfully.

Finally, chapter six is a list of references.

Chapter 2: Validation

Z19DTH - Piston Engine

Lee [13] investigated PCCI effects on a Z19DTH Diesel Engine at Engine Research Center (ERC) of

Wisconsin Madison operating on N-Heptane research fuel. Using his data from the Nov4_016 test a CFD model has been created to validate model and solver settings for this thesis’ rotary engine study. Validation engine operating conditions are given in the Table 1.

Table 1 Z19DTH Engine Parameters [13]

Displacement 0.4771 L

Bore 82 mm

Stroke 90.4 mm

Compression Ratio 16:1

Swirl Ratio 1.83

Engine Speed 2000 RPM

Injector Orifice Diameter 133 micron

Number of Orifice 8

Injection Pressure 150 MPa

Included Angle 120o

SOI 35o BTDC

Fuel Temperature 55 oC

Fuel Flow Rate 17.62 mg/cycle

Intake Pressure 151 kPa

Intake Temperature 360 K

EGR Rate 55 %

Modeling and Meshing

Modeling the Z19DTH engine was complicated as this engine is not available in the United

States. However from the Lee’s dissertation, ruled images of the 2D piston geometry were available, and are shown in Figure 5. The geometry data was digitized and together with the engine parameters from Table 1 the cylinder volume was created in Solidworks. Due to the periodic nature of this piston engine, fuel distribution and combustion method, the model was reduced to a 60 degree sector to save computational time, Figure 6. No inlet or outlets are used in this model. This is only possible because the swirl ratio is know from the experimental data, see Table 1, and that only the reacting part of the cycle is of interest for this research. The swirl ratio is the measure of the rotation of the cylinder charge about the cylinders axis. The mesh was created with Ansys meshing software resulting in 66200 prism layers with a max skewness of 0.84, shown in Figure 7. Wall Identification can be seen in Figure 8, notice all the faces are walls except for two, these are used for the periodic boundary conditions.

Figure 5 2D Piston Pocket Cross Section

Figure 6 Solidworks Sector Geometry at Intake Port Figure 7 Mesh Sector Geometry at Intake Port Close

Piston Wall Cylinder Wall Cylinder Head Wall Periodic

Figure 8 Z19DTH CFD Model Wall Conditions

Solver Models and Numerical Settings

This section reviews the models and settings used for HCCI simulations. An overview of the major models used are outlined in Table 2, more information on most of the models are covered in the following sub sections, except for the particle break-up model, for more information please see reference [19].

Table 2 Validation Models

Chemical Reactions n-heptane mechanism [20]

Combustion Model DUFL

Particle Break-up Model Wave [19]

Turbulence Model k-ε RNG [21]

Wall Heat Transfer Han-Reitz [22]

Combustion

The engine model is setup using the Diesel Unsteady Flamelet (DUFL) model to handle the chemical reaction and turbulence-chemistry interaction. The chemical reaction can be solved using a flamelet approach, with reaction kinetics. The Reaction and thermal database is provided by [20] and is available from the ERC website. The kinetics file consists of 76 species and 349 reactions, and is suitable for n-heptane auto ignition and soot precursor simulation. The generated flamelet is dependent on two variables, mixture fraction and strain rate . The flamelet’s information is updated and stored in a tabulated lookup table which significantly reduces computational cost.

Flamelet information is shared with the turbulence chemistry interaction, which aims to predict the fluctuation in the flow variable’s scalar quantities. These fluctuations are found by using

Probability Density Function (PDF) approach for model closure, a PDF “can be thought of as the fraction of time that the fluid spends in the vicinity of the state ”. See Figure 9 for graphical representation a PDF [23].

Figure 9 Graphical Representation of PDF [23]

Turbulence and Wall Heat Flux

Many turbulence models are available in Ansys Fluent, selecting the best model to represent a project is very important. Of all the models available the k-ε model was selected for its robust ability and computational time. Specifically, the renormalized group (RNG) variation was selected; this model is derived from the instantaneous Navier-Stokes equations [23]. Internal combustion engines undergo a large change in density due to compression and combustion. The work of Han and Reitz suggests to better model engine flows the RNG model needs to be extended to include compressibility effects [21]. However, the version of the model in fluent does account for density variations just like the Han and Reitz model and it is used in the work presented in this paper and, no modifications appear to be needed.

Han and Reitz have also suggested that a standard heat flux function is insufficient in calculating wall heat transfer. This heat flux function needs to be considered during a varying density flows for ICE simulations [22]. The proposed heat flux function is shown in Equation 2. Good agreement was found in the results to experimental engines and many researchers use this method for calculating heat flux [13], [24] , [25]. The heat flux function was implemented into fluent via a User Defined Function (UDF) and is attached in the Appendix.

Equation 2 , [22]

Results

The results from the validation study are compared against the experimental data and another published set of CFD data. In this study the model validation is performed by comparing pressure histories from in-cylinder measurements to computational data. The data compared in

Figure 10, shows that there is good agreement, between this CFD Validation and the Experiment and Reference CFD data and can conclude that the numerical models and settings have been validated.

Figure 10 Pressure Trace of Z19DTH Validation

Chapter 3: Rotary Engine Model

Modeling and Meshing

Rotary engine fluid volume geometries were created 360 degrees Before Top Dead Center

(BTDC). These geometries were created using Solidworks and were meshed using Ansys

Meshing. The model was simplified to represent the apex seals as a simple planar surface. Many methods can be used to generate the rotor’s curved arc. This study used a modified method outline in Ansdale’s book [26] of using a circular arc approximation. The method is shown in

Figure 11. The modification made to this method allows for a clearance distance of 0.442mm between the epitrochoidal surface and the rotor flank at point (H). The pervious works of [27],

[28], [19] used three chambers in order to simulate combustion on all flanks of the rotor to account for chamber interaction. This work follows Abraham’s work that only one chamber is needed to access cycle performance [29]. Performance is done by evaluating power by calculating the work per cycle by PdV instead of rotational torque. For this to be valid we assume that all chambers produce the same power, while ignoring any chamber interactions.

Figure 11 Circular arc approximation of Rotor Flank

K=9.5 K=10.5 K=11.5

Rotor Radius (mm) 110.2 121.8 133.4

Eccentricity (mm) 11.6 11.6 11.6

Compression Ratio 15.08 15.82 15.55

Rotor Width (mm) 31.3145 28.3324 25.8684

K=9.5 K=10.5 K=11.5

Figure 12 Rotary Engine Geometries and Meshes

K=9.5 K=10.5 K=11.5

Number of Elements 56287 55937 56276

Max Skewness 0.893 0.846 0.849

Min Orthogonality 0.120 0.271 0.248

Due to combustion modeling constraints each chamber in Fluent is defined as a separate fluid zone. This means that it is not sufficient to move just the rotor which is the case in the physical engine. Therefore, the positions of both the rotor and housing need to updated together to maintain the closed volume. This complex update is performed in Ansys Fluent via a

User Defined Function (UDF), where movement of the nodes on the rotor and housing surfaces are controlled by the parametric equations that describe the motion of the apex seals around the epitrochoidal housing. The equations shown below are simply a super position of two rotations; a geometric explanation can be seen in Figure 13. Due to the large expansion and contraction of the fluid zone the side wall and fluid interior is allowed to deform and remesh, while placing control limits on element size and skewness. Mesh motion is setup using the built in-cylinder options in Ansys Fluent, where engine speed and time step size are setup.

Figure 13 Epitrochoidal Path

Boundary and Initial Conditions

Simplifications to this rotary engine model have reduced the boundary conditions to only thermal conditions, and the wall thermal conditions are shown in Figure 14.

Side Housing , T=560K Rotor , T=580K Housing , T=560K

Figure 14 Rotary Engine Thermal Boundary Conditions+

As previously mentioned, turbulence has little to no effect on HCCI combustion, if the mixture is truly Homogeneous. For this reason, the computational mesh was advanced to intake port closed position. The intake port position can be defined with respect to the housing or to crankshaft position; for the work presented here intake port closed position will be defined with

22 respect to the crankshaft. By starting simulations from this point, it saves computation time and ensures that the same mass of air and fuel is present for each K factor studied. Tabulated values of chamber volume at intake port closed position are shown in Table 3; the maximum difference of volume between the K factors is 0.743 cc or 0.36 percent.

Table 3 Chamber volume at intake port closed

K factor 9.5 10.5 11.5

Chamber Volume @ intake port closed 204.988 204.245 204.512

(cc)

The common initial conditions for each case can be seen in the Table 4. The only case specific initial condition is the equivalence ratio of 0.1, 0.25, and 0.50. However, fluent needs this condition to be given in terms of mixture fraction. Mixture fraction is calculated by Equation 3, where φ is the desired equivalence ratio and r is the stoichiometric equivalence for the given fuel. The stoichiometric equation for n-heptane is given by Equation 4, which results in an air fuel ratio of 15.1008:1. Tabulated results of the calculated mixture fractions are shown in Table

Table 4 Common Initial Conditions

Gauge Pressure (Pa) 0

X Velocity (m/s) 0

Y Velocity (m/s) 0

Z Velocity (m/s) 0

Turbulent Kinetic Energy (m2/s2) 1e-05

Turbulent Dissipation Rate (m2/s3) 1e-05

Temperature (K) 300

Mixture Fraction Variance 0

Equation 3 [23]

1C7H16 +11 (O2 + 3.76N2) -> 7CO2 + 8H2O + 41.36 Equation 4 N2

Table 5 Equivalence ratio and mixture fraction

Equivalence Ratio Mixture Fraction

0.10 0.00657860

0.25 0.01628579

0.50 0.03204963

Solver Settings

The same models and model settings are used in the Rotary model as were used for the validation model. The results of the numerical settings are shown in Table 6. With the importance of the finite rate chemistry to model the chamber conditions, a Least Squares Cell

Base Gradient was selected even though it is computationally Gradient Scheme. The equation discretization of at least a second order upwind was selected for all equations except for the turbulence equations. As previously mentioned turbulence is not as important to a perfectly

Homogeneous HCCI combustion.

Table 6 Numerical Settings

Gradient Least Squares Cell Based

Pressure Standard

Density Second Order Upwind

Momentum Second Order Upwind

Turbulent Kinetic Energy First Order Upwind

Turbulent Dissipation Rate First Order Upwind

Energy Second Order Upwind

Mean mixture Fraction Second Order Upwind

Mixture Fraction Variance Second Order Upwind

Chapter 4: Parametric Study on LTC Performance

Rotary Homogeneous Charge Compression Ignition (HCCI)

In order to identify the feasibility of a rotary engine operating in the HCCI mode, a parametric study is needed to understand how engine parameters affect engine performance. Parameters selected are essential to HCCI and performance measures to determine their importance to successful operation.

Parameters

Identification of key parameters is needed to narrow the countless parameters available to internal combustion engines. Much research in this field has been conducted on HCCI for piston engines and the lessons learned from publications have narrowed the search down. The work of

Ryan and Callahan found that compression ratio, equivalence ratio, and EGR rate are significant parameters to control a HCCI engine [30]. Traditionally, the rotary engine operates on the SI mode, but does not have a sufficient compression ratio for auto ignition. Therefore, the first parameter identified for this study is the compression ratio. Unlike reciprocating engines, significant changes to a rotary engine’s compression ratio are not as simple. The most effective way to alter the rotary engine’s compression ratio is to change the K factor. K factor changes will result in redesigning the entire engine and is a costly parameter and important to study.

In order to optimize the K factor the acceptable range must be determined by means of a design exploration. As previously mentioned in Chapter 1 K factors typically range from 6 to 10,

26 compression ratios were calculated over this range and beyond to a K factor of 11.5. The next step in the process of narrowing the K factor’s range included running simple CFD cases to check for auto-ignition. By starting with a K factor of 11.5 and decreasing by 1.0, when K factor of 8.5 was reached auto-ignition was no longer present. The results of this design exploration established the K factors selected for this study to be 9.5, 10.5, and 11.5. To eliminate erroneous factors from affecting the results, each engine’s displacement has been kept the same, by changing the rotors width. The engine geometry used in this study is based on an UEL 741 rotary engine. The parameters used from this engine include engine displacement, crankshaft eccentricity, and port opening positions. As previously stated HCCI combusts in the absence of a flame, therefore a rotor pocket is no longer necessary and is removed from this study.

The equivalence ratio is important in controlling HCCI operation and is the second parameter used in this study. Traditionally, equivalence ratio used for reciprocating HCCI engines ranges from 0.1 to 0.4 [30]. Since no information can be found on rotary engine HCCI, this study is evaluated three equivalence ratios of 0.10, 0.25, and 0.5. This range covers typical equivalence ratios and will better evaluate the equivalence ratio that will result in a maximum chamber temperature of 1800K and will account for any response curvature.

To further evaluate the engines feasibility of HCCI operation, it is important to understand how the engine will perform at different engine speeds. Typical engine speeds of 2000, 4000, and

6000 revolutions per minute have been selected for this study as the final evaluation parameter.

Responses

The engine’s response to each parameter requires quantitative measures to evaluate their effects on performance. Theses response measures are used to evaluate optimal settings in this feasibility study.

The first parameter to evaluate the response is gross indicated work, because of the simplifications of removing the inlet and outlet ports, net indicated work cannot be calculated.

The gross indicated work is used to calculate gross power for the engine assuming that each chamber has equal performance. Engine power is an important measure, it allows manufacturers of automobiles and aircraft to select the engine that will meet their desired project goals.

An important limiting factor for the operating range of HCCI in reciprocating engines is the pressure rise rate; this rate is like the shock loading of the engine. If the combustion process causes a pressure rise rate too large, it will result in an engine that will become damaged.

Research has shown that typical reciprocating engines range between 1.5 to 8 bar/degree, this is used as a guide for this rotary engine study and will try to be minimized [31].

A fundamental characteristic of HCCI is low temperature combustion, specifically temperature bellow 1800 K [31]. To insure that the engine meets this requirement, it is included as the final response to quantify engine performance. This condition will also serve as a second constraint to limit power, this limited power will aid in evaluating the feasibility of operating a rotary engine on the HCCI mode.

Held Constant Factors

Factors held constant in this study include, engine displacement, intake and exhaust opening and closing positions. These factors are held constant to allow for a more direct comparison of the different K factors across the engine operation range.

Results of 33 Factorial

CFD cases were selected to yield a full factorial design study. All completed case data is post- processed to calculate power, maximum temperature, and the maximum pressure rise rate, and

28 input to the JMP design table. The completed design table is shown in Figure 15 and plots of temperature vs crank angle, pressure vs crank angle, and pressure vs volume. Plots for this data are located in the appendix.

Figure 15 Design Table

This study aims is to evaluate the feasibility of the rotary engine operating in HCCI cycle.

Statistically analyzing only horsepower is the narrow of a focus, the analysis known limitations of HCCI operation is included to weigh in on the feasibility. Three effects analysis are presented and the prediction profiles are used to evaluate optimal settings.

The results of the Summary of Fit and ANOVA table analysis of all three responses, Pressure Rise

Rate, Maximum Temperature, and Horsepower are shown in Figure 16, Figure 17, and Figure 18 respectfully. The reported R Square values show the degree of fit for the predicted model is the response data. This analysis reported values of 0.71264, 0.662018, and 0.7874 for Pressure Rise

Rate, Maximum Temperature, and Horsepower respectfully. Values closer to one of these data

29 points are ideal for a good model prediction. However the large error values from the ANOVA analysis shows that there may be some uncontrolled factor causing low model correlation. The implication of the low fit is a weak confidence in the models response predicting power those optimal cases need to be run to confirm performance. The ANOVA analysis shows us that a factor or interaction of factors are statistically important in each analysis by the values of Prob

>F being smaller than 0.001. Due to the statistical importance of each analysis it is important to look at the parameter estimates from all three analyses to understand which parameters or parameters interactions are relevant to this study.

Figure 16 Summary of fit and ANOVA table for Pressure Rise Rate

Figure 17 Summary of fit and ANOVA table for Maximum Temperature

Figure 18 Summary of fit and ANOVA table for Horsepower

Examination of the Actual by Predicted Plots, shown in Figure 19, shows the model’s low correlation. These plots will aid in the understanding of any prediction trends that exist.

Figure 19 Actual by Predicted Plots

The Parameter Estimate Plots show which parameters are statistically significant to the response of each analysis. Statistical significance is determined by any parameter that extends beyond the parallel blue lines in the results from all three responses shown in Figure 20.

Equivalence ratio is shown to be the most significant first order the response of pressure rise rate, maximum temperature, and horsepower. RPM is another first order parameter that significantly affects horsepower; horsepower is also affected the higher order interaction of equivalence ratio and RPM. It was initially expected that compression ratio would have a more significant effect on the responses; however the effect may be less significant within the range

31 tested. If the k factor range is extended below the threshold of auto-ignition, one can expect the significance of this parameter to increase.

Figure 20 Parameter Estimates

Response residuals are analyzed by plotting the residual error of each response by each factor.

The plots allow for visual inspection of equal variance and any grouping trends. The data shown in Figure 21, Figure 22, and Figure 23, shows similar trends of variance and grouping for residuals plotted against the same factors. However, the residuals of equivalence ratio exhibit the largest range of unequal variance and grouping. The equivalence ratio has the most irregular residuals, and has fewer accurate predictions about the equivalence factor.

Figure 21 Plots of Residuals of Pressure Rise Rate plotted verses factors

Figure 22 Plots of Residuals of Max Temperature plotted verses factors

Figure 23 Plots of Residuals of Horsepower plotted verses factors

Further investigation of the feasibility of HCCI operation requires selection of the optimal parameters within the constrained problem and the evaluation of the response. Using the prediction profilers within JMP software, the range of the parameters is evaluated to select a K factor, and the equivalence ratio over the entire engine speed range (RPM). Keeping in mind that the optimal solution may not be within the range presented, the response range previously discussed in this chapter is important to know where the engine will fail to operate beyond these ranges. Since the K factor must be fixed for all engine speeds selection, the K factor was

35 the first to be determined. Evaluation K factors across the RPM and equivalence range, as the model predicted K factors effect on the response is very low. However, a trend of that the higher compression engine has better response at the low RPM, and the low compression engine has a better response at the high RPM. Evaluation of the response is weighted first by pressure rise rate and secondly by horsepower. This trend from the prediction profiler shows that the K factor of 10.5 has a medium effect on the desirable parameter plots for the entire engine speed range. The Profiler Plots with Desirability plots used to select the K factor are shown in Figure 24, Figure 25, and Figure 26. Further tailoring of the K factor is possible and should be revaluated to match the intended operating speed range of the engine.

Figure 24 2000 RPM Profiler Plot with Desirability

Figure 25 4000 RPM Profiler Plot with Desirability

Figure 26 6000 RPM Profiler Plot with Desirability

Simply using the equivalence values from the desirability plots is not possible, since the result in engine pressure rise rates that either do not support combustion or destroy the engine. By turning off the desirability plots and using the selected K factor of 10.5, the best equivalence

37 ratio is selected for the 2000, 4000, and 6000 RPM as shown in Figure 27, Figure 28, and Figure

29 respectfully. The equivalence ratio selected for each speed range predicts that the pressure rise rate is below 6.5 bar/degree. The variance of the prediction ranges is as high as ± 10 bar/degree. The large variance is expected for the low model fit discussed earlier. Final parameter selection and response is shown in Table 7. With the low model fit it is good practice to run the best case simulations and compare the results to the JMP analyses.

Figure 27 2000 RPM Profiler Plot

Figure 28 4000 RPM Profiler Plot

Figure 29 6000 RPM Profiler Plot

Table 7 JMP Selected Parameters for Best Fit Cases

Case K RPM Equivalence Pressure Rise Max Power

# Factor Ratio Rate (bar/deg) Temperature (K) (HP)

1 10.5 2000 0.165 6.49 1063.99 8.29

2 10.5 4000 0.18 6.65 1063.10 16.37

3 10.5 6000 0.2 6.04 1070.65 30.41

Best Fit Case Results from 33 Factorial

Reusing the same solver settings and inputting new equivalence ratios for each of the three selected engine speeds. Results from the final cases are shown in Table 8. The case results produced less power and lower pressure rise rates than the JMP model prediction. The pressure rise rates are so low in cases 2 and 3 that it is possible to increase the equivalence ratio to gain more power without damaging the engine. A complete listing of the percent error between each predicted value and the CFD results are shown in Table 9. The error results are higher than preferred, and once again is the result of the low statistical fit. Interesting results of maximum temperature is less than 10.5 percent error for all cases, and are approximately 0.25 percent lower than any other error. Another interesting result is the high RPM pressure rise rate, reciprocating HCCI engines have a high load limit imposed by pressure rise rates [9]. While these results are well within the acceptable range, further data reduction may improve the results to locate the best fit case.

Table 8 CFD Best Fit Cases Results

Case K RPM Equivalence Pressure Rise Max Power

# Factor Ratio Rate (bar/deg) Temperature (K) (HP)

1 10.5 2000 0.165 3.56 1090.00 4.42

2 10.5 4000 0.18 2.88 951.82 11.32

3 10.5 6000 0.2 2.59 1053.30 20.48

Table 9 Percent Error of JMP to CFD

Case K RPM Equivalence Pressure Rise Max Temperature Power

# Factor Ratio Rate Percent Error Percent Error

Percent Error

1 10.5 2000 0.165 45.15 -2.44 46.62

2 10.5 4000 0.18 56.63 10.46 30.83

3 10.5 6000 0.2 56.99 1.62 32.63

Equivalence Ratio Single Factor Fitting

The 33 full factorial is useful in K factor selection. The model’s predictive power is limited by the low model fit, and the data is reexamined for K factor of 10.5, where each RPM range is evaluated separately to find the optimal equivalence ratio. This revaluation is possible because two of the original three factors align with the design space studied. The effects of pressure rise rate vs equivalence ratio are the primary evaluation criteria, as it is the limiting factor for HCCI operation, targeting a conservative 3.5 bar/degree pressure rise rate. The prediction plots of horsepower and maximum temperature are included for predictive purposes. The complete summary of curve fitting prediction can be seen in Table 10.

Figure 30 shows the effects that equivalence ratio has on the 2000 RPM performance. The predicted equivalence ratio for this speed range matches that which the JMP study provided.

However, the predicted gross power is 7.80 HP which is slightly lower than the JMP study. The

4000 RPM prediction plots in Figure 31 show that the simple curved fit prediction does not agree with the results from JMP study. A slightly greater equivalence ratio of 0.2 has been selected for CFD evaluation. The predicted gross power output is 19.46 HP for this speed range,

41 which is slightly better than the results from the JMP study. The final speed range of 6000 RPM, shown in Figure 32, predicts a greater equivalence ratio than the previous JMP study. The simple curve fit results in an equivalence ratio of 0.26, and predict that gross power output is much greater than the JMP study of 41.5 HP.

100 25 2 80 y = 4.54628000E+02x - 20 9.23878000E+01x + 60 6.55790000E+00 15

40 10 y = -1.59115000E+02x2 + (bar/deg) 20 Horsepower 5 1.38734250E+02x -

Pressure Rise Rate 1.07503750E+01 0 0 0.00 0.20 0.40 0.00 0.20 0.40 Equivalence Ratio Equivalence Ratio

3000 y = 6.01007000E+03x2 - 2500 5.94352500E+02x + 2000 9.92658750E+02 1500 1000 500

Max TemperatureMax (K) 0 0.00 0.20 0.40 Equivalence Ratio

Figure 30 2000 RPM Data Fit

100 100 2 2 80 y = 4.48540000E+02x - 80 y = 2.07944000E+02x + 1.16517000E+02x + 8.06296000E+01x - 60 60 8.83740000E+00 4.98150000E+00

40 40 (bar/deg)

20 Horsepower 20 Pressure Rise Rate 0 0 0.00 0.20 0.40 0.00 0.20 0.40 Equivalence Ratio Equivalence Ratio

3000 2 2500 y = 7.68892167E+03x - 1.58389125E+03x + 2000 1.00991521E+03 1500 1000 500

Max TemperatureMax (K) 0 0.00 0.20 0.40 Equivalence Ratio

Figure 31 4000 RPM Data Fit

100 200 2 2 80 y = 4.33371000E+02x - y = 8.27883333E+02x - 1.52683850E+02x + 150 1.07610500E+02x + 60 1.38106750E+01 1.35311167E+01 100

40 (bar/deg)

20 Horsepower 50 Pressure Rise Rate 0 0 0.00 0.20 0.40 0.00 0.20 0.40 Equivalence Ratio Equivalence Ratio

3000 2500 y = 4.99852167E+03x2 - 3.87291250E+02x + 2000 9.40215208E+02 1500 1000 500

Max TemperatureMax (K) 0 0.00 0.20 0.40 Equivalence Ratio

Figure 32 6000 RPM Data Fit

Table 10 Selected Parameters for Best Fit Cases and predicted results

Case K RPM Equivalence Pressure Rise Max Power

# Factor Ratio Rate (bar/deg) Temperature (K) (HP)

1 10.5 2000 0.165 3.6911 1058.2 7.8088

2 10.5 4000 0.2 3.4756 1000.6 19.462

3 10.5 6000 0.26 3.4087 1177.4 41.517

Best Fit Case Equivalence Ratio Single Factor Fitting

Reusing the same solver settings and inputting new equivalence ratios for the three selected engine speeds, the results from these final cases are shown in Table 11. The 4000 and 6000

RPM speed case resulted in producing more power and slightly higher pressure rise rates than the curve fit model. A complete listing of the percent error between each predicted value and the CFD results are shown in Table 12. The error results are higher than preferred, but the pressure rise rate prediction is much better than before JMP optimal study. A conservative value of 3.5 bar per degree has been selected as the limit for pressure rise rate, the values reported in

Table 12 and this range is not a reason for concern if still within the upper limit of the acceptable range and will be used to discuss the feasibility of HCCI mode operation [31]. Plots of engine performance for the optimal cases are shown in Figure 33, Figure 34, and Figure 35. The heat release rates are very fast for HCCI mode, indicated by the steep pressure temperature rise rates. PV data plots show that an engine operating on the HCCI mode approaches the theoretical constant volume combustion of the Otto Cycle. The pressure plots allow for the visualization of the timing of the rapid heat release from HCCI combustion. The rapid pressure rise rate occurs slightly After Top Dead Center (ATDC), and this ignition timing greatly affects pressure rise rate, peak pressure, and efficiency. Ignition occurring ATDC reduces the peak pressure and rise rate, but a few draw backs occur if the ignition occurs too far ATDC, which causes decreased efficiency and an increase in engine misfires [31]. Ignition timing for all three

RPM’s studied is fairly consistent because the ignition of the fuel is controlled by reaction kinetics. If changing the ignition timing is required, changes to wall heat transfer, inlet temperature, inlet pressure, and EGR rate are also necessary.

Figure 33 2000 RPM Output Plots of Temperature, Pressure and PV

Figure 34 4000 RPM Output Plots of Temperature, Pressure and PV

Figure 35 6000 RPM Output Plots of Temperature, Pressure and PV

Table 11 CFD Best Fit Cases Results

Case K RPM Equivalence Pressure Rise Max Power

# Factor Ratio Rate (bar/deg) Temperature(K) (HP)

1 10.5 2000 0.165 3.5597 1090 4.4245

2 10.5 4000 0.2 4.1675 1047.8 29.2356

3 10.5 6000 0.26 4.1675 1047.8 43.1168

Table 12 Percent Error of Best Fit Cases to CFD

Case K RPM Equivalence Pressure Rise Max Temperature Power

# Factor Ratio Rate Percent Error Percent Error

Percent Error

1 10.5 2000 0.165 3.56 -3.00 43.33

2 10.5 4000 0.2 -19.90 -4.70 -50.21

3 10.5 6000 0.26 -22.25 11.00 -3.85

Chapter 5: Conclusions

The motivation for this work is to improve the rotary engine efficiency for Unmanned Air

Vehicles. Favorable traits such as compact size, fewer moving parts, and low vibrations, make the rotary engine an ideal aircraft engine. The combustion sealing and lower engine efficiency compared to recuperating engines deter most designers from utilizing the rotary engine for aircraft application. The work focused on improving the efficiency of the engine through changing the combustion strategy. This improvements are possible with the HCCI mode that yields near diesel efficiency with a side benefit of reducing engine emissions, which are other factor plaguing the rotary engine’s limited use.

A computational method selected for evaluating the feasibility of an HCCI rotary engine and experimental feasibility study would have been too costly without analytical justification beforehand. The computational modeling in this study used Ansys Fluent 14.5. No rotary engine like this exists; computational modeling schemes and settings were validated using experimental results from a piston engine. The piston engine data selected for validation comes from the

Wisconsin Madison Engine Research Center (ERC) conducted on a Z19DTH engine. A sector CFD model of the Z19DTH has been created for this research.

A simplified 3D rotary engine CFD model was successfully created to study HCCI feasibility. A full factorial parametric study, where twenty seven cases were run to evaluate the effects, geometric K factor, equivalence ratio, and engine speed and how they affect the response of horsepower, maximum temperature, and peak pressure to determine the feasibility of HCCI

48 operation. From the parametric study the following conclusions can be drawn in regard to HCCI rotary engines.

1. The fully premixed model showed that over the entire speed range a k factor of 10.5 is

the most favorable, these results are the least biased over the entire RPM range for the

range of k factors studied.

2. Pressure and temperature rise rates are significantly affected by the amount of

premixed fuel within the chamber.

3. Horsepower is significantly affected by engine speed and the amount of fuel premixed

within the chamber.

4. Ignition timing for the HCCI rotary engine is fairly consistent over the engine speed

range due to the auto-ignition of n-heptane fuel air mixture is governed by reaction

kinetics.

5. The ignition timing is slightly ATDC; as the engine begins the expansion stroke, the

pressure rise rates and peak pressures are reduced to extend the load range of the

engine.

6. Within the limits of HCCI engine design space, a rotary engine is able to operate with

sufficient power output, without destroying the engine, while increasing fuel efficiency.

In general, the concept of a HCCI rotary engine shows the potential to increase fuel efficiency and reduce emissions. This preliminary work finds that a HCCI rotary engine is feasible and warrant further computational and experimental study to become reality.

Chapter 6: Future Work

The preliminary findings warrant further computational and experimental research. From the literature review, it is clear that there is little research on the HCCI rotary engine. This literature can help to shape the direction of future research. This thesis simplified the problem to a perfectly premixed case with no injector related activities were considered.

An improvement can be made to study this engine using partially premixed compression ignition approach, where the injection parameters studied and optimized to produce the most

Homogeneous or stratified charge for auto-ignition.

Another parametric rotary engine HCCI study to be investigated is the effects of turbocharging, to extend high load engine operation. This study will be valuable to understanding the engines fundamentals, since the rotary engine naturally has port overlap introducing EGR into the next cycle. This mixture dilution is overcome by forcing in more air in to the chamber [14]. By forcing more air into the chamber to advance auto-ignition timing, the decrease in ignition delay can be counteracted by the increased heat capacity of the Nitrogen within the EGR [10]. This study could find the optimal amount of EGR for a given boost condition over the engine speed range.

Parameters to be studied may include EGR rate, inlet pressure, RPM, inlet temperature, EGR temperature.

A recent development in HCCI technology is MPCI process, which allows for multiple premixed charges to undergo compression ignition and auto ignite at separate time intervals. Due to the nature of the fuel and the lower heat transfer from reciprocating engines, gasoline has been the

50 only fuel that can work in this combustion mode. However, it is known that heavy fuel traditionally does not work in reciprocating spark ignited engines, the work of Curtiss Wright found that the rotary engine’s unique heat transfer characteristics allow for heavy fuel spark ignition. For this reason MPCI may work with heavy fuel in the rotary engine. Another unique aspect of the rotary engine is the planetary motion of the rotary engine which causes a sweeping motion of the control volume, which may make it easier to spatially separate the premixed charges. These characteristics of the rotary engine may allow easier implication of

MPCI and with heavy fuel. This example is another perfect parametric study of injection timing, injector location, injector orientation, and fueling rate.

One particular factor that has hindered HCCI adaption to the engine market is cycle to cycle variability. Many sensors are needed in reciprocating engines to adjust injection amount and injection timing to reduce cycle variability. In the work of Hellström it was shown that one source of cycle variability was caused by the changing of wall temperatures [32]. Understanding the transient nature of wall temperatures would be an excellent experimental study of the near wall heat transfer to quantify and compare cycle to cycle heat transfer variations for a Rotary engine to that of a reciprocating engine. If the rotary engine shows to have more stable wall temperatures than a reciprocating engine it would infer that the rotary engine would have less cycle variability.

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Appendix

Heat Flux Function UDF

#define USE_UDM 0

#include "udf.h"

#include "prox.h"

void cpySVartoUDM(Domain *domain, Svar sv, int udm)

{ size_t realsize = sizeof(real); real *svpointr = NULL; real *udmpoint = NULL;

Thread *thread = NULL;

Domain *supdom = DOMAIN_SUPER_DOMAIN(domain); if (NULLP(supdom))

{ supdom = domain;

}

if (sg_udm <= udm)

{

Error("cpySVartoUDM(): too few User Defined Memory Locations.\n"

"Location %d was requested, but there are only %d allocated.\n",

55 udm, sg_udm);

}

thread_loop_c(thread, domain)

{

Thread *supthr = THREAD_SUPER_THREAD(thread); if (NULLP(supthr))

{ supthr = thread;

}

if (NNULLP(svpointr = THREAD_STORAGE(thread, sv)) &&

(NNULLP(THREAD_STORAGE(supthr, SV_UDM_I)) ?

NNULLP(udmpoint = T_STORAGE_R_XV(supthr, SV_UDM_I, udm)) : FALSE))

{ int numbytes = realsize * thread->nelements; memcpy(udmpoint, svpointr, numbytes);

}

DEFINE_ADJUST(wall_dist,d)

{

Domain *dom = Get_Domain(1);

Thread *thr = NULL;

Alloc_Storage_Vars(dom, SV_RTMP_0, SV_NULL);

Calc_Cell_Wall_Distance_New(dom, SV_RTMP_0); cpySVartoUDM(dom, SV_RTMP_0, USE_UDM);

Free_Storage_Vars(dom, SV_RTMP_0, SV_NULL);

}

DEFINE_Profile(heat_flux)

{ face_t f; cell_t c0;

Thread *t0=t->t0; begin_f_loop(f,t)

{ c0=F_C0(f,t)

F_PROFILE(f,t,position)=(C_R(c0,t0)*C_CP(c0,t0)*C_T(c0,t0)*log((C_T(c0,t0)/F_T(f,t

)))*pow((C_K(c0,t0)*pow(0.0845,0.5)),0.5))/(2.1*log(((C_UDMI(c0,t0,0)*pow((C_K(c0, t0)*pow(0.0845,0.5)),0.5))/C_MU_L(c0,t0)))+2.5);

} end_f_loop(f,t)

}

Figure 36 Temperature vs Crank Angle for K Factor 9.5 2000 RPM

Figure 37 Pressure vs Crank Angle for K Factor 9.5 2000 RPM

Figure 38 Pressure vs Volume for K Factor 9.5 2000 RPM

Figure 39 Temperature vs Crank Angle for K Factor 9.5 4000 RPM

Figure 40 Pressure vs Crank Angle for K Factor 9.5 4000 RPM

Figure 41 Pressure vs Volume for K Factor 9.5 4000 RPM

Figure 42 Temperature vs Crank Angle for K Factor 9.5 6000 RPM

Figure 43 Pressure vs Crank Angle for K Factor 9.5 6000 RPM

Figure 44 Pressure vs Volume for K Factor 9.5 6000 RPM

Figure 45 Temperature vs Crank Angle for K Factor 10.5 2000 RPM

Figure 46 Pressure vs Crank Angle for K Factor 10.5 2000 RPM

Figure 47 Pressure vs Volume for K Factor 10.5 2000 RPM

Figure 48 Temperature vs Crank Angle for K Factor 10.5 4000 RPM

Figure 49 Pressure vs Crank Angle for K Factor 10.5 4000 RPM

Figure 50 Pressure vs Volume for K Factor 10.5 4000 RPM

Figure 51 Temperature vs Crank Angle for K Factor 10.5 6000 RPM

Figure 52 Pressure vs Crank Angle for K Factor 10.5 6000 RPM

Figure 53 Pressure vs Volume for K Factor 10.5 6000 RPM

Figure 54 Temperature vs Crank Angle for K Factor 11.5 2000 RPM

Figure 55 Pressure vs Crank Angle for K Factor 11.5 2000 RPM

Figure 56 Pressure vs Volume for K Factor 11.5 2000 RPM

Figure 57 Temperature vs Crank Angle for K Factor 11.5 4000 RPM

Figure 58 Pressure vs Crank Angle for K Factor 11.5 4000 RPM

Figure 59 Pressure vs Volume for K Factor 11.5 4000 RPM

Figure 60 Temperature vs Crank Angle for K Factor 11.5 6000 RPM

Figure 61 Pressure vs Crank Angle for K Factor 11.5 6000 RPM

Figure 62 Pressure vs Volume for K Factor 11.5 6000 RPM