47th International Conference on Environmental Systems ICES-2017-151 16-20 July 2017, Charleston, South Carolina
Validation of the Virtual Spacesuit using Apollo 15 Data
Claas T. Olthoff1 Technical University of Munich, 85748 Garching, Germany
This paper presents results from the validation of a spacesuit simulation system called V-SUIT, which has been under development at the Technical University of Munich since 2012. V-SUIT combines models of spacesuit pressure garments, portable life support systems (PLSS) and a comprehensive human model, with a dynamic model of the thermal environment on an airless planetary surface. This holistic approach enables V-SUIT to depict the complex interactions between spacesuit, human and the environment in several different domains. For the purpose of validating V-SUIT, a model of the Apollo A7LB spacesuit was created and the extravehicular activities (EVAs) performed during the Apollo 15 mission were chosen as a case study. Original flight data recordings were provided by NASA’s Spacesuit Knowledge Capture program. The results of the validation show that V-SUIT is generally capable of reproducing all important aspects of the system’s dynamic behavior. Even though the simulated values do not exactly match the flight data, the trends and time dependent changes are consistent. The differences between flight and simulation data can mostly be explained by the lack of exact technical specifications for the systems that were modeled, the uncertainty associated with metabolic rate as measured during the mission and other modeling inaccuracies. As part of a global sensitivity analysis, the simulations of each of the three EVAs was performed on a model of the actual topography at the landing site, as well as on a flat surface, with identical surface properties and lighting conditions. The resulting data are also presented and discussed. With this successful correlation, V-SUIT now has a reference case that can be used in the analysis of future spacesuit systems, enabling the study of changes to the system configuration like new PLSS components or different pressure garment layups.
I. Introduction PACESUITS will be an integral part of future human space exploration missions. These missions and S extravehicular activities (EVA) will be carried out further away from the safe haven of Earth than was the case in past endeavors. There will be limited options for a fast return to Earth, limited access to spare parts and limited communication with experts on the ground due to long communication delays. Additionally, the environmental conditions that may be encountered, be it at the lunar poles, around an asteroid in deep space or on the surface of Mars, will be different and more extreme than the ones in low Earth orbit. When considering the operation of permanent bases on the Moon or Mars, the total number of EVAs that a single spacesuit has to support will significantly increase. As a result, the spacesuits of the future will have to be even more reliable and robust than those of the past. To achieve these goals, the development of new technologies and systems is needed and underway.1 Long duration missions into deep gravity wells, like the Moon and Mars, also drive the development efficient technologies that use fewer 2 consumables than the current spacesuit systems, for example the rapid cycle amine (RCA) CO2 filter or the spacesuit evaporator absorber radiator (SEAR).3 The development of new spacesuits and spacesuit technologies requires among other things a deep understanding of the dynamic behavior of and interactions between the spacesuit, its environment and the human inside. Some of this understanding can be obtained via testing, which is expensive both in financial terms as well as in time. In an effort to provide an additional source of information for spacesuit designers, a dynamic spacesuit simulation tool called the Virtual Spacesuit (V-SUIT) has been developed at the Institute of Astronautics of the Technical University of Munich (TUM). It combines a dynamic simulation model of a spacesuit portable life support system (PLSS) with a sophisticated, dynamic human model and a dynamic thermal simulation of the spacesuit’s environment. This paper
1 Ph.D. candidate, Institute of Astronautics, Boltzmannstr. 15, 85748 Garching, Germany describes the validation of this tool through a comparison of simulation results with spacesuit telemetry data from the extravehicular activities (EVA) performed during the Apollo 15 mission from July 31st to August 2nd 1971.
II. Background
A. V-SUIT – The Virtual Spacesuit Simply put, V-SUIT is the combination of two tools previously developed at TUM: the Virtual Habitat (V-HAB)4 dynamic life support system (LSS) simulation system and the Thermal Moon Simulator (TherMoS).5 V-HAB has been under development at TUM since 2006 and includes a generic simulation framework for matter, thermal and electrical flows, with a library of standard LSS components. It also includes a comprehensive model of the human physiology6,7 to dynamically provide all of the necessary inputs and outputs to the LSS, both in terms of mass flows and thermal heat flows. V-HAB was successfully validated in 2012 using a model of the International Space Station (ISS) LSS and a set of flight data.8 The main focus of the initial development of V-HAB was to demonstrate the value of, and in some cases the necessity for, dynamic mass flow simulations in contrast to purely static or steady-state LSS analysis methods, especially for systems that include bioregenerative components.9 Similarly, TherMoS was conceived to determine, if dynamic thermal analysis can improve the thermal design of moving spacecraft on the lunar surface, like rovers and astronauts. This movement can cause very rapid thermal changes, for instance by moving from sunlight into the shadow of a boulder and back again in a short period of time. The traditional thermal analysis approach focuses on determining worst cases for hot and cold environments at which the spacecraft will then have to operate at for Figure 1: Geometric long durations. TherMoS uses a ray tracing algorithm and a thermal model of lunar regolith Model of a generic to determine the solar and infrared heat flows at a given location on the lunar surface. spacesuit. These calculations are performed with a time step of one second. As Hager determined, if the solar elevation angle relative to the lunar surface is low, the dynamic thermal environment can have a significantly smaller envelope than the worst hot and cold cases would suggest.10 One of the case studies in Ref. 10 was a spacesuit model. The study, however, only considered the incoming solar and infrared heat flow and not the internal thermal dynamics of the spacesuit itself. This was the starting point for the development of V-SUIT. It combines PLSS technologies modeled in V-HAB with a dynamic thermal simulation of a spacesuit in TherMoS. Reference 11 describes this coupling process in greater detail. In summary, a thermal interface was developed between TherMoS and V-HAB that is based on the exchange of heat flow information for the individual faces of the geometric model of the spacesuit, as shown in Figure 1. Additional models of spacesuit-specific life support system technologies were developed in V-HAB and integrated with the human model into an overall model of a spacesuit.
B. Apollo 15 Apollo 15 was the fourth lunar landing performed by the United States. Astronauts Dave Scott (commander) and Jim Irwin (lunar module pilot) touched down at the Hadley Rille landing site on July 30th, 1971. As can be seen from Figure 2, this location provides a complex topography with the Rille itself and some flat terrain surrounded by mountains. Additionally, Apollo 15 was the first mission to feature three surface EVAs (plus one EVA with Dave Scott standing up and looking out of the docking hatch of the lunar module) and the first that utilized the lunar roving vehicle (LRV), or “rover”. These characteristics made it an ideal mission to use as a case study for the validation of V-SUIT. Figure 3 shows a map of the landing site and indicates the three traverses that the two astronauts took during their three excursions. It is important to note, that during EVA 1 and 2, the traverses took the astronauts partially up the base of Hadley Delta, a mountain to the south of the landing site that rises about 3.6 km above the plain.12 In Figure 3 this area is marked as “Apennine Front”. One of the analyses presented in section IVof this paper specifically looks at the thermal influence of this type of dominant topographic feature.
2 International Conference on Environmental Systems
Figure 2: Apollo 15 Landing Site at Hadley Rille. Lunar module position is indicated with blue circle. Images taken from references 13 (left) and 14 (right).
APOLLO 15
NORTH COMPLEX N
EVA 3 10 LM
9A 9 RIMA HADLEY
EVA 1 EVA 2 SOUTH CLUSTER
BRIDGE 1 DUNE ELBOW 4 2
7 SPUR 6 ST. GEORGE 6A
APENNINE FRONT 2 km
Figure 3: Apollo 15 Traverse Map. Numbers indicate science stations. Image taken from Ref. 15.
3 International Conference on Environmental Systems III. Simulation Models
A. Topography The lunar topography model is created using data from the Lunar Reconnaissance Orbiter’s (LRO) Lunar Orbit Laser Altimeter (LOLA) instrument.16 The processed of extracting and processing this data is discussed in greater detail in ref. 10. The ground resolution of the LOLA data set used to generate the model shown in Figure 4 is 102.7 m. This leads to some imperfections in the model, such as the apparent “end” of Hadley Rille in the bottom left corner of Figure 4. The Rille actually extends further, as can be seen from Figure 4, but here TherMoS interpolates between data points. The area of the topography model is 8 by 8 km, with the coordinate system origin in the center. The z-coordinate shown in Figure 4 is the local altitude relative to the center of the Moon. The triangular faces that make up the model have a surface area of 0.005 km2 each.
N 1736.5 1736
[km] 1735.5 loc z
4
2
0 4 3 2 1 -2 0 y [km] -1 loc -2 -3 x [km] -4 loc Figure 4: Topographical model of the Apollo 15 landing site. For better visibility, the view has been rotated. On the right side Hadley Delta rises in the south, while the left side shows the plain extending to the north.
B. EVA Traverses 1. Traverse Coordinates In order to move the model of the spacesuit along the individual traverse paths, it is not only necessary to know the lunar coordinates of each traverse, but also the point in time at which the astronauts where at each coordinate. In a process described in greater detail in Ref. 17, the coordinates for the three traverses were extracted from Google Earth’s “Moon” component.18 Fortunately, the astronauts frequently communicated their position to the ground in the form of readings from the rover’s navigation system. Specifically, they called out the “range” value, which indicates the linear distance between the rover and an initialization point. This initialization point was usually near the lunar module (LM). Using the “measure” feature in Google Earth and the air-to-ground transcripts19 time and lunar coordinates could be roughly matched. The range call-outs were always given when the astronauts began and ended drives with the rover, so the periods in time when they were relatively stationary could be located precisely. In between this sparse set of time-tagged coordinates, a linear interpolation was performed to provide TherMoS with a new position for each time step. Regarding the orientation of the spacesuit model during the traverses, two separate approaches were taken. During the rover drives, it was appropriate to assume, that the astronauts were facing in the direction of travel. At the science stations (as indicated by the numbers in Figure 3) the astronauts were moving around in various directions and there
4 International Conference on Environmental Systems was no way to obtain a coherent set of data from which to derive the model orientation. Instead, the spacesuit model was simply rotated around its vertical axis at a rate of one rotation every two minutes. This would create some dynamics in the simulation data, but evenly distribute the external heat flow as to not create any unrealistic peaks. 2. Metabolic Rate The V-HAB human model requires a metabolic rate as a global input parameter. It determines all of the other parameters of the model that are relevant for LSS simulations, like oxygen consumption or thermal load. Following the conclusion of the mission, the National Aeronautics and Space Administration (NASA) published a mission report20 that included the metabolic rate profiles for both crew members on all three EVAs. These profiles were generated based on heart rate measurements during the EVA and a correlation to reference activities with a known metabolic rate on Earth.21 An exemplary correlation is shown below in Figure 5. These images containing the graphic metabolic rate information were processed using the open source tool Engauge Digitizer22 to create a set of numeric values with which the simulation results could then be compared.
Figure 5: Metabolic rate and heart rate profile for the commander during EVA 1. Image taken from Ref. 20. 10-3 3. Diverter Valve Positions The water diverter valve in the Apollo PLSS was used to regulate the amount of cooling that the astronaut received via the liquid cooling garment (LCG). The valve had three positions: minimum, intermediate and maximum. Depending on the setting, a portion of the water circulating through the LCG would be routed past a sublimator to be cooled. In the maximum setting, the entire mass flow of 1.81 kg/min (4.0 lbs/min or 108.6 kg/h) was cooled, in the
intermediate setting 0.23 kg/min and in the minimum setting 0.06 kg/min. The actual switch for the diverter valve was located on the bottom right corner of the PLSS backpack and was manually actuated by the crew member, usually to personal preference. There was no telemetry item downlinked regarding the valve position, so the crew again radioed any changes to flight controllers in Houston. The air-to-ground transcripts include most, but not all of the settings changes the astronauts made during the EVAs. However, the change in LCG inlet temperature is so pronounced, that any missing mode changes could be derived from this telemetry value.
5 International Conference on Environmental Systems C. Pressure Garment 1. Thermal Model As is discussed in greater detail in Ref. 11, a 278-node model of the pressure garment was created. It is divided into 24 segments representing helmet, torso, the extremities and PLSS backpack, the latter is not shown in Figure 6. The segments provide axial discretization along the body parts. Each segment has an inside and an outside part to enable simulation of the heat flow through the suit. Each part consists of 3 to 6 nodes that provide circumferential discretization. Both inside and outside nodes have the bulk density of the spacesuit material in that section of the pressure garment. All suit nodes are connected to their neighboring nodes via conductive heat transfer models, both radially and axially. The conductivities of these connections are determined by the actual layup of the pressure garment in that section. The material data for the individual capacities and conductances were taken from NASAs Apollo Operation Handbook23 for the Extra Vehicular Activity Unit, as well as information published by the International Latex Corporation.24 Since there are less outer layer nodes in the V-HAB thermal model than there are triangular faces in the TherMoS (see Figure 1), each node is assigned more than one face in the interface Figure 6: Geometrical representation of the between the two tools, i.e. an individual node will receive the nodes in the lumped parameter model. The incoming and outgoing heat flows for several faces. left side is the front view, right side is the rear 2. Gas Flow Model view. Image taken from Ref. 11. As is shown in Figure 7, the gas flow through the pressure garment is modeled using five separate volumes of gas, referred to as “tanks” in the image. Inlet and outlet tanks represent the interfaces to the PLSS model and are of negligible size. The three main volumes represent the helmet, upper torso and lower torso. The volume of the helmet tank is equivalent to the free volume of the helmet with a human head inside, while the two torso tanks, despite their name, represent equal halves of the remaining volume, i.e. they also include the arms and legs. The gas flow enters the pressure garment model at the helmet tank and continues on to the upper torso tank. From there the flow is split between lower torso and the arms, the latter leading into the outlet tank. From the lower torso, the flow continues through the legs and also into the outlet tank. 3. Human Model Interface The interface between the human model and the pressure garment has two parts: A gas flow interface simulating the production and consumption of metabolic gases and a thermal interface that simulates the convective heat transfer 25 between the skin and ventilation gas. Figure 7 shows three tanks containing O2, CO2 and H2O, respectively. The metabolic rate dependent flow rates are set according to table 3 in Ref. 26. The interface for convective heat transfer, indicated by the blue circles labeled “HMI” in Figure 7, is implemented multiple times to account for the circumferential nodes of the pressure garment model. As shown by the red numbers in Figure 7, there are, for example, five individual flow paths with a heat transfer interface between the inlet tank and helmet, 10 between helmet and upper torso, and six flow paths for each of the extremities. The heat transfer is modeled using a simple heat exchanger model and the Effectiveness Method.27
D. Portable Life Support System 1. Component Models Figure 7 shows the overall mass flow model of the PLSS used in the Apollo A7LB spacesuit. The flow paths, as well as most of the system information, was gleaned from the Apollo EMU Operations Handbook.23 It would exceed the scope of this paper to explain the modeling principles of V-HAB and the graphic language developed to visualize the object-oriented code that is shown in Figure 7. For additional information on these topics, please refer to Ref. 28.
6 International Conference on Environmental Systems 2x6 CO2_Phase phase) (gas H2O_Phase phase) (gas H2O_Tank CO2_Tank HMI Helmet_Tank O2_Drain phase) (gas Upper_Torso_Tank Lower_Torso_Tank _ 5x 10x 10x 2x6
HMI HMI HMI HMI HAB. - Helmet_Tank Phase_1 phase) (gas Upper_Torso_Tank_Phase_1 phase) (gas Lower_Torso_Tank_Phase_1 phase) (gas InletTankPhase phase) (gas InletTank OutletTankPhase phase) (gas Outlet_Tank x 8 Model Human Interface
OutletPhase phase) (liquid InletPhase phase) (liquid Bypass OutletPlenum InletPlenum WC Sublimator F GC SuitInletPhase phase) (gas FlowPhase phase) (gas FlowPhase phase) (gas SuitVolumeIn : Schematic of the spacesuit mass flow model in V in model flow mass spacesuit the of Schematic : 7 Fan SuitOutletPhase phase) (gas SuitVolumeOut Figure FilteredPhase phase) (solid FilteredPhase phase) (liquid LiOH_Canister WaterSeparator TankPhase phase) (gas O2 Tank O2 O 2 Leak_Phase phase) (gas Leak_Tank AUX (liquid phase) AUX H AUX WaterPhase phase) (liquid SpacePhase phase) (gas O 2 Sublimator (system) Sublimator PRI (liquid phase) PRI H PRI PLSS A7LModel
7 International Conference on Environmental Systems There are many smaller models for simple components like pipes and valves included in the overall spacesuit model, that will not be discussed here to limit the scope of the paper. Instead four of the more important models will be briefly discussed, with references given to more detailed documentations of the individual models. The suit fan is dynamically modeled using a generic fan characteristic, producing a temperature, pressure and flow rate dependent volumetric gas flow for a given speed setting.29 The model also includes an increase in gas temperature due to the pressure rise, aerodynamic inefficiencies and motor operation. Since the actual characteristic of the Apollo suit fan could not be obtained, the fan speed, which was not changed during operation, was set to a value resulting in the stated flow rate of 0.16 m3/min (5.5 acfm). The lithium hydroxide (LiOH) CO2 filter model incorporates an algorithm that calculates the competitive 30 absorption of both CO2 and H2O as a function of temperature, flow rate, partial and absolute pressure. Due to a lack of test data, this model could not be validated individually. Its performance was therefore compared to a similar model of the metal oxide-based CO2 removal system found in the Space Shuttle Extravehicular Mobility Unit (EMU) and found to be within expectations. This makes the results presented in section IV.A part of the validation for this individual model. The liquid cooling garment (LCG) is modeled as a series of elements containing two submodels. Heat exchanger models using the Effectiveness Method are used for the convective heat transfer between the tubing and cooling water. Conductive heat transfer models are used between the different layers of the LCG and the tubing material. The determination of the individual heat transfer coefficients and the model validation is described in Ref. 31. The dimensions of the individual segments of the LCG are determined using the length values provided by table 1 of Ref. 32. The sublimator is also modeled as a pair of heat exchangers using the Effectiveness Method.33 The model assumes the temperature of the cool surface is held constant at the triple point temperature of 273.16 K by the sublimation of ice. The flow paths for both water and gas could be taken from Ref. 34, but not the actual dimensions. These were visually estimated from a cutaway model of the entire PLSS backpack35, whose outer width is known to be around 0.48 m (19 inches).36 The combined heat transfer from the warm gas and water flows is used directly to calculate the amount of water that is being sublimated by dividing it by a constant heat of sublimation of 2.86·106 J/kg. As was the case for the LiOH filter, no individual component test data was available, so the results presented in section IV.A are part of the validation for this individual model. 2. Human Model Interface The interface between the human model and the PLSS model, specifically the LCG, consists of a set of conductive heat transfer models between the inner layer of the LCG, the comfort garment, and the nodes representing the skin of the human model that are in contact with the LCG. As is discussed in Ref. 31, the individual heat transfer coefficients are very difficult to measure directly, which is why most studies resort to measuring or calculating an overall heat transfer coefficient for the entire body. For this reason, the conductive heat transfer coefficient between the skin and comfort garment was reverse engineered during the model validation described in Ref. 31.
E. Model Limitations As is to be expected for a simulation system the size and complexity of V-SUIT, there are many limitations as to what the models can depict and how well the results reflect the behavior of the actual system. Models are always an abstraction of reality, be it by simplification, discretization or omission. For simulation models in particular, there is always a trade-off between high-fidelity models that take a long time to execute and lower-fidelity models that produce results faster, albeit at the expense of accuracy. More detailed discussions of the limitations of the individual models can be found in the documents referenced in the preceding sections. In keeping with the scope of the paper, only the limitations that are directly relevant for the discussion of simulation results are presented here. As was mentioned in section II.A, V-HAB was conceived as a simulation system for habitat-scale life support systems. These have very large volumes, on the order of tens to hundreds of cubic meters, in comparison to a spacesuit, which only has about 0.06 m3 of free volume. As a result, the V-HAB solvers that calculate the gas flow rates throughout the system are not well suited for the very small volumes inside a spacesuit. These can lead to either the simulation time step becoming prohibitively small or the solver itself to become unstable. It was beyond the scope of the V-SUIT project to create a new flow rate solver able to handle the small volume sizes. To circumvent the solver limitation, the gas tanks in the PLSS model were increased in size until the results were stable and produced in a reasonable amount of time. As a result, the total volume of PLSS tanks is now about twice as large than the volume of the pressure garment. As will be shown in the following section, this skews some of the simulation results. The development of an improved flow rate solver has already been initiated, so this study can be revisited and smaller volumes can be used.
8 International Conference on Environmental Systems Another limitation regarding the PLSS is its lack of a detailed thermal model of the backpack and its components, like frame, piping and connectors. While the individual models, especially the sublimator model, take the temperatures of the gas and water flows through them into account, the components are not thermally connected to each other, nor is there an external thermal interface. In consequence, there is no thermal path from the environment to the PLSS components or vice versa. There is also no thermal interface between the PLSS and the back of the spacesuit model. The reason for this omission is twofold. Primarily, to create a thermal model of the internals of the PLSS backpack would have exceeded the scope of this study. Other studies37 that focus solely on the thermal domain have gone further in terms of PLSS thermal models, but the objective here was explicitly to have both thermal and mass flow models. Even if it had been attempted to create a thermal model of the PLSS internals, the second issue would have been a lack of information regarding the thermal connections between the different components. To correctly model this, detailed technical drawings and material properties would have been needed. These are not publicly available and it is doubtful that the required export clearance could be obtained. To conclude this limitation, when interpreting the results presented in the following section of this paper, it must be kept in mind, that they do not account for any heat leak into or out of the PLSS backpack, depending on the current thermal environment. By far the largest source of limitations for the presented simulation models is the previously mentioned lack of information on many details of the design of the A7LB spacesuit. While the basic operating principles for the different parts of the system could be gleaned from documents describing the generic technology or materials, values like the exact lengths and diameters of connecting pipes could only be estimated. Section III.D.1 already mentioned the procedure to determine the sublimator dimensions from images. The assumptions that were made, that have a significant influence on the simulation results will be discussed in combination in the following section. However, since this paper is describing the first round of model validations for a new simulation system, it is to be expected that some of the made assumptions turn out to be better, while others require some adjustments to better match the data from the actual system. Additionally, in the broader context of this study, the V-SUIT project is a proof of concept exercise. It is therefore self-evident, that many more refinements would have to be made, before the simulation system can be used operationally or during the design phase.
IV. Results and Discussion
A. Model Validation 1. Validation Data Source Table 1: Available Telemetry Items
Validation is concerned with representational accuracy. It is “the process of determining the degree to which the model is an accurate Telemetry Item Unit 38 representation of the” real-world system it depicts. In order to make Absolute Suit Pressure [psia] this comparison, data from this real-world system is needed. In the Battery Current [A] specific case of the presented study, this meant telemetry from the Apollo spacesuits during EVAs on the lunar surface. At the beginning of the Battery Voltage [V]
V-SUIT project, this data was not in the public domain. In cooperation CO2 Partial Pressure [mmHg] 39 with NASA’s Spacesuit Knowledge Capture Program a complete set of Feedwater Pressure [psia] PLSS telemetry from Apollo 15 was made available on the NASA Technical Reports Sever (NTRS).40 This process included creating high LCG Delta Temperature [F] resolution scans of the original charts and obtaining the proper export LCG Inlet Temperature [F] clearances through Johnson Space Center in Houston. Table 1 shows O2 Supply Pressure [psia] which PLSS telemetry items are now available. The provided images Sublimator Gas Outlet [F] were then processed using Engauge Digitizer, the same tool used to Temperature digitize the metabolic rate graphs (see section III.B.2), to create a set of numeric values for each data point. The data was manually recorded on graph paper with intervals between one and two minutes between readings. In Figure 8, a screen shot from Engauge Digitizer can be seen, showing a small blue cross for each of the manually tagged data points. Four of the items shown in Table 1 are not used for the validation. In the simulation, the absolute pressure of the pressure garment is kept constant by a pressure regulator model, so no insight can be gained by comparing the simulation data to the telemetry, which is also fairly constant as soon as the lunar module cabin was fully depressurized. The battery model is very rudimentary, simply simulating a store of electrical energy that is emptied by a constant current draw at a constant voltage. Again, no insight can be gained by this comparison. Finally, liquids are modeled as incompressible in V-SUIT, which means the feedwater pressure cannot be calculated using this simulation. The remaining 5 items are discussed in the following subsections.
9 International Conference on Environmental Systems In an effort to limit the scope of this paper, not all data sets from all three EVAs are presented. The following subsections therefore only use representative examples for each of the telemetry items. Also, due to limitations on time, a full EVA simulation took about 10 days, only simulations using the commander’s data were performed.
Figure 8: Screenshot from Engauge Digitizer with the blue markers for each data point. Background Image taken from Ref. 40.
2. CO2 Partial Pressure from EVA 1 Figure 9 shows the flight data and simulation results for the CO2 partial pressure (ppCO2) in the ventilation loop prior to entering the suit volume. This location is downstream of the LiOH canister that absorbs CO2, so low partial pressure values are expected. Per Ref. 34 the accuracy of the CO2 sensor at nominal temperatures of 291 K to 308 K (65 °-95 °F) and a CO2 partial pressure of 267 Pa (2 mmHG) is +48/-55 Pa (+0.36/-0.41 mmHg). The data shown is from the first EVA on the lunar surface. From Figure 9 it can be seen that the simulated ppCO2 is significantly lower than the flight data. In Figure 10 the relative difference between the two curves is shown. The mean difference is 80 %. As was discussed in section III.E, the total volume of the spacesuit model is about three times the actual size. The amount of CO2 that is injected into the suit, however, represents one human being, so it was to be expected that the simulated ppCO2 would be lower than the flight data. When considering the ideal gas law and assuming that the temperature and pressure remain constant, a 3x volume decrease would increase the partial pressure by the same amount. This then reduces the overall difference between the flight data and simulation results significantly, although the absorption rate in the LiOH filter would also be higher. Independent of this linear relationship, there is also a change in the dynamic behavior towards the end of the EVA. Here the difference between simulated values and flight data becomes smaller, indicating that the LiOH filter model reaches saturation earlier than should be the case. The fact, that the simulated overall CO2 level in the atmosphere is much lower than in reality actually masks this shortcoming of the model.
10 International Conference on Environmental Systems 600 150
Flight Data 400 Simulation 100
200 50 Pressure [Pa]
0 Pressure Difference [%] 0 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Time [s] 4 Time [s] 4 10 10 Figure 9: EVA 1 CO2 Partial Pressure Figure 10: EVA 1 CO2 Partial Pressure Difference
3. LCG Delta Temperature from EVA 2 Figure 11 shows the temperature difference between the inlet and outlet flows of the LCG. The data is taken from EVA 2. As can be seen, the astronaut spent most of the EVA in the “intermediate” cooling mode. The period around 5,000 seconds is a longer rover drive from the LM to science station 6, were “minimum” cooling was selected to avoid overcooling. The same is true for the short period at the end of the EVA around 21,000 seconds. The large spike at 22,500 seconds is actually several activities overlapping. First a switchover between the primary and auxiliary water tank took place, which included going to “minimum” cooling during the actual switching. This happened at the same time the astronaut was working hard, so following the tank switch, “maximum” cooling was selected, resulting in a large temperature difference across the LCG due to the cold inlet water. Per Ref. 34 the transducer used to measure this temperature difference had an error of less than 20 % at its maximum temperature differential of 27 K (15 °F). During the periods of “intermediate” cooling the simulation data is significantly lower than the flight values, while during “minimum” cooling the simulation computes slightly higher readings. In terms of relative difference, as shown in Figure 12, the difference is fairly constant at a mean value of 64 %. The large spikes at the times when the cooling mode was changed are a result of the coarse temporal resolution of the flight data, with 60 to 120 second intervals between readings. All of the mode changes were derived from the air-to-ground (AG) transcripts, except for the last switch from “minimum” to “intermediate” at around 21,500 seconds. Since the air-to-ground transcripts are time- tagged to the second, this method of extracting the times should be more exact. The mostly constant relative difference between the two data sets suggests that there is a linear parameter within the thermal transfer model between the human skin and the cooling water that must be adjusted. In this model, the heat transfer coefficients between the different layers of the LCG carry the greatest uncertainty.
20 250
15 Flight Data 200 Simulation 150 10 100 5 50
Delta Temperature [K] 0 0 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Time [s] 4 Time [s] 4 10 Delta Temperature Difference [%] 10 Figure 11: EVA 2 LCG Delta Temperature Figure 12: EVA 2 LCG Delta Temperature Difference
4. LCG Inlet Temperature from EVA 3 To fully assess the performance of the spacesuit thermal loop, including most importantly the LCG and sublimator, an absolute temperature reading was necessary. This data is provided in Figure 13 in form of the absolute inlet temperature of the LCG during EVA 3. The thermistor used for this measurement was located directly behind the diverter valve, where the two mass flows (one bypassing the sublimator, the other going through) are mixed. No information regarding the accuracy of this sensor could be found in either Ref. 23 or Ref. 34. At the beginning the third EVA, Dave Scott had the task of extracting core tubes from the lunar regolith. This was very strenuous, leading him to select “maximum” cooling around 2,800 seconds. Shortly after completing these activities, another drive using the LRV was performed and Scott switched back to “intermediate” cooling, where he remained for the duration of the EVA. Following this final switch, the relative difference between flight data and simulation results is below 1 %. (See Figure 14) This percental representation benefits from the large numbers in the Kelvin scale, but even the mean
11 International Conference on Environmental Systems absolute difference between the two data sets is less than 1 K. At first glance this is a very positive result, but during the period in “maximum” cooling, there is a large deviation and as Figure 13 shows, the actual inlet temperature is around 7 K below the simulated temperature. This indicates an error in the sublimator model, where the heat removal from the cooling water is calculated. As was mentioned in section III.D.1, the dimensions and materials of the sublimator and its components were mostly reverse engineered from images. Clearly an error was made here that leads to significantly less heat transfer. This is also confirmed by a simulated value that was not part of the telemetry stream from the spacesuit, which is the amount of feedwater used by the spacesuit. At the end of simulated EVA 2, only 2.37 kg of the initial 3.76 kg (8.3 lbs) of water in the primary water tank had been sublimated and the auxiliary water tank was still completely full at 1.32 kg (2.92 lbs). Since the AG transcripts clearly state, that a switch between the tanks took place, the actual water consumption must have been significantly higher than simulated. This is confirmed by the mission report20 where the consumed feedwater mass is stated as 4.56 kg (10.05 lbs). The difference is so pronounced, that it cannot be purely caused by various heat leaks into the system that were not included in the model (see section III.E).
310 6
300 4 290 Flight Data 2 280 Simulation Temperature [K] 270 0 0 0.5 1 1.5 2 2.5 Temperature Difference [%] 0 0.5 1 1.5 2 2.5 Time [s] 4 Time [s] 4 10 10 Figure 13: EVA 3 LCG Inlet Temperature Figure 14: EVA 3 LCG Inlet Temperature Difference
5. O2 Supply Pressure from EVA 2 Figure 15 shows the oxygen supply pressure during the second EVA. The pressure transducer accuracy is stated as ±4 % by Ref. 34. From Figure 15 it can be seen, that the simulated pressure follows the flight data very well and Figure 16 confirms this, at least until about 20,000 seconds, where the previously constant difference of around 3 % begins to increase. At the end of the EVA, the absolute pressure difference is 500 kPa, from an initial 9 MPa, where the simulated pressure is higher than the flight data. This could point to a minor error in the leak rate of the spacesuit. This value was set at a constant 0.028 kg/h (0.062 lbs/h) per Ref. 41. Unfortunately, this behavior is least pronounced for EVA 2, both EVA 1 and 3 show this effect even stronger. While it is known that the suit leak rates increased from one EVA to the next due to abrasion of the seals,42 this does not explain, why this error is larger for EVA 1 than it is for EVA 2. Another culprit may be the very simple model of the pressure regulator in combination with the increased tank volumes mentioned in section III.E. In concert, these two modeling inaccuracies could have led to less gas being expelled into the overall system than required. 106 10 Flight Data 30 Simulation 20 5
10 Pressure [Pa]
0 Pressure Difference [%] 0 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Time [s] 4 Time [s] 4 10 10 Figure 15: EVA 2 O2 Supply Pressure Figure 16: EVA 2 O2 Supply Pressure Difference
6. Sublimator Gas Outlet Temperature from EVA 3 Figure 17 shows the temperature of the gas flow as it exits the sublimator during EVA 3. Ref. 23 and Ref. 34 again did not provide any information regarding the accuracy of the thermistor used to measure this value. The spike at the end of the EVA is actually during the repressurization of the LM cabin and can thus be ignored. The flight data varies over time in response to the changing metabolic rate. In contrast the simulation data remains very stable. This is the most visible evidence of the increased gas volume in the system. All of the additional tanks act as a thermal buffer,
12 International Conference on Environmental Systems preventing the temperature changes to propagate through the system at a higher speed. Still, the absolute difference between the two data sets is on average 3 K, with a maximum deviation of 5 K, as shown in Figure 18. Given the result from section IV.A.4, that the water side of the sublimator has a significant error in the calculated heat transfer coefficient, the result for the gas side is more promising. After simulations with the correct tank volumes have been performed, this comparison will have to be repeated to determine if an adjustment is necessary.
310 10
300 Flight Data Simulation 290 5
280 Temperature [K] 270 0 0 0.5 1 1.5 2 2.5 Temperature Difference [K] 0 0.5 1 1.5 2 2.5 Time [s] 4 Time [s] 4 10 10 Figure 17: EVA 3 Sublimator Gas Outlet Temp. Figure 18: EVA 3 Sublimator Gas Outlet Temp. Diff.
B. Effects of Local Topography A great benefit of simulation models, in contrast to hardware testing, is that individual parameters can be turned on or off precisely and the effects of that individual change can be observed. In order to demonstrate this feature of V-SUIT, it was decided to remove the lunar topography at the Apollo 15 landing site to determine the effects it has on the thermal environment and performance of the spacesuit model. The previously performed model validation, despite the shortcomings that were identified, provided a good starting point for such a comparative study. The simulation scenario was only changed in this one aspect, replacing the actual topography as shown in Figure 4 with a flat surface of the same dimensions and same resolution. This model is shown in Figure 19.
1737.5 1737.4 [km] 1737.3 loc z 4 -4 3 2 -2 1 0 0 -1 2 -2 y [km] x [km] loc -3 loc 4 -4 Figure 19: Flat Topography model. The effect of this change first effects the outer layer nodes of the spacesuit. Due to computer memory limitations, not all nodes can be logged during a simulation run. For this reason, four representative nodes from different segments (see Figure 6) have been chosen that are always included in the logging setup: one node on the back of the helmet
13 International Conference on Environmental Systems segment, one node on the front of the upper right arm segment, one on the back of the lower left leg segment and one in the middle of the front upper torso segment. To maintain the scope of this paper, only one example of an outer thermal node of the spacesuit will be shown in the following paragraphs and figures. The arm and torso nodes showed very similar behavior and the lower leg node was not very interesting for this comparison, due to its proximity to the ground. Accordingly, the back helmet node was chosen for further discussion in this paper. 1. Discussion of Individual EVAs An obvious feature of the temperature plots shown in Figure 21, Figure 23 and Figure 25 are the large bands that look like noise. These are in fact the result of the spacesuit model being rotated at half a revolution per minute, as described in section III.B.1. The smoother portions of the plots are the rover drives with no rotation and the spacesuit model facing in the direction of travel. In this first part of the discussion of the simulation results, each EVA will be analyzed individually, the following section will then look at all three EVAs overall. Figure 21 shows the two temperature plots for EVA 1 with and without the topography model. The difference between the two is not very pronounced, but in general the temperatures with the influence of the topography are a bit lower. This is supported by the mean temperature difference between the two being at -1.1 K. An explanation for why the difference is not as stark is the low solar elevation angle of around 20° during this EVA. (See also Table 2). This low elevation angle means more areas of the topography are not exposed to sunlight and cannot become warmer than the spacesuit to produce a net heat gain. This is exacerbated by the fact, that the traverse of EVA 1 (see Figure 3) took the astronauts past the rim of Hadley Rille, which was at that point in time well shaded. The mean difference in incoming heat flow between the two scenarios is -0.11 W, meaning the planar topography on average provides more heat to the astronaut model than the actual topography for this specific traverse and point in time. The relative difference in temperature shown in Figure 22 is in absolute percent, hence there are no negative values. In Figure 24, showing the temperature differences for EVA 2, the two scenarios are a more distinct. Except for a short period around 14,000 seconds, where the astronauts were at the foot of Hadley Delta (station 7 in Figure 3), the temperature of the scenario with the topography is always higher than without. The overall mean temperature difference is 6.6 K, and as would be expected the mean incoming heat flow is 0.60 W higher than for the flat topography case. The temperature comparison for EVA 3, shown in Figure 25, follows a pattern similar to that of EVA 2. In the middle of the activity, between 5,500 and 10,700 seconds the astronauts spent a longer period of time exploring the rim of Hadley Rille on foot. Even though by this time the solar elevation angle had increased to almost 40°, parts of the Rille, especially the eastern wall, were still either in shadow or just beginning to warm up. This exposure to cold areas can be seen in the temperature plot as well as in Figure 26, showing the temperature difference in absolute percent. Here the difference hovers around zero during the periods near the rim. As a result, the mean overall temperature difference between the two scenarios is 5.3 K and the mean difference in heat flow is 0.63 W. 2. Discussion of Differences Between EVAs When comparing the three excursions with each other, there is an obvious difference between EVA 1 and 2. There are also some differences between EVA 2 and 3, but they are not as pronounced. As was alluded to in the previous section, the influence of the topography surrounding the spacesuit model varies with the solar illumination. When comparing EVAs 2 and 3, especially during the beginning and end of each, which were at the same location, it is apparent that the topography influence increases with increasing solar elevation angles. The comparison between EVAs 1 and 2 goes on to show, that with lower elevation angles the influence can even become negative, i.e. the spacesuit model receives less heat and becomes colder with the topography. 250 The overall incoming heat flow for With Topography the helmet node can also be plotted over 200 On a Plane time, as is shown in Figure 20. The 150 numerous spikes in the graph are an artefact of the operating principle of the 100 ray tracer that is more pronounced for the 50 case with the topographic model. This Heat Flow [W] behavior is described in greater detail in 0 Ref. 11. These spikes are large enough to -50 prohibit the use of averages. Instead, the 0 0.5 1 1.5 2 2.5 4 median heat flow values will be given Time [s] 10 here. Despite these interferences, for the Figure 20: EVA 1 Helmet Node 2 Heat Flows
14 International Conference on Environmental Systems EVA shown in Figure 20, EVA 1, the median heat flow for the helmet node is 0.77 % lower with the topography. In contrast, for EVA 2 the median is 0.24 % higher and for EVA 3 the median is 0.79 % higher. These values also support the statement, that the thermal influence of the surrounding topography increases with solar illumination.
350
300 With Topography Temperature [K] 250 On a Plane 0 0.5 1 1.5 2 2.5 Time [s] 4 10 Figure 21: EVA 1 Helmet Temperatures Figure 22: EVA 1 Temperature Difference
350
300 With Topography On a Plane Temperature [K] 250 0 0.5 1 1.5 2 2.5 Time [s] 4 10 Figure 23: EVA 2 Helmet Temperatures Figure 24: EVA 2 Temperature Difference
350
300 With Topography On a Plane Temperature [K] 250 0 0.5 1 1.5 2 2.5 Time [s] 4 10 Figure 25: EVA 3 Helmet Temperatures Figure 26: EVA 3 Temperature Difference
Table 2: Solar Angles during Apollo 15 EVAs Solar Angles Solar Angles Egress Ingress @ Egress @ Ingress MET UTC MET UTC Azimuth Elevation Azimuth Elevation EVA 1 31.07.1971 31.07.1971 119:55:00 126:05:48 98.44 19.30 100.20 22.54 13:29:00 20:39:48 EVA 2 01.08.1971 01.08.1971 142:24:57 149:18:51 104.40 29.28 106.60 32.42 11:48:48 19:01:02 EVA 3 02.08.1971 02.08.1971 163:29:06 168:00:37 111.00 38.02 113.00 40.33 08:03:06 13:34:37
15 International Conference on Environmental Systems V. Conclusions The model validation activities outlined in section IV have provided a good overview of the current status of V-SUIT, its capabilities and shortcomings. Given the number of assumptions that had to be made to determine all model parameters, the results of this validation are acceptable. It appears that with some additional information on the individual subsystems of the spacesuit, and the correction of the several errors uncovered by this study, V-SUIT could become capable of accurately reproducing the EVAs on the lunar surface. Even in its current state, V-SUIT was able to provide interesting data points not easily obtained by sensors that could be used for comparative studies like the one described in section IV.B. In particular, the study provided the somewhat unexpected result, that for low solar elevation angles the thermal influence of a complex topography surrounding an astronaut is such, that without it the overall heat flow will increase. Given this output, it can be assumed that V-SUIT is now capable of supporting comparative subsystem or parameter studies in support of spacesuit design.
VI. Future Work The highest priority will be given to correcting the error in gas volume in the spacesuit, because this modeling concession influences many other parameters of the system. It will be an ongoing process to upgrade all of the subsystem models as new information becomes available. The human model and TherMoS are under constant development at the Institute of Astronautics and the updates to these modeling tools will directly benefit V-SUIT. A project to create an upgraded and more powerful ray tracing algorithm was recently begun, with potential benefits for V-SUIT in the form of either shorter execution times or higher fidelity thermal simulations. A parallel effort is currently underway to validate V-SUIT using test data from the PLSS 1.0 prototype.26 Due to NASA’s role in this PLSS development project, a large amount of information is publicly available on which models can be based. In the near future work on the simulation of the Apollo 15 EVAs will continue until the results in all aspects of the simulation are satisfactory. The next step is then to attempt a simulation of Apollo 16 and 17 EVAs without major modifications to V-SUIT and assess the quality of the results. This will also continue the cooperation with the Spacesuit Knowledge Capture Program. Looking further ahead, design studies using V-SUIT are envisioned that create spacesuit architectures for Mars (including new models for convective heat exchange with the atmosphere) and also some more exotic locations such as Titan or Venus.
Acknowledgements The author would like to thank Cinda Chullen and Vladenka Oliva from NASA’s Spacesuit Knowledge Capture Program for their support in obtaining the PLSS Telemetry data without which this study would not have been possible. Prof. Markus Czupalla and Dr. Philipp Hager, as the creators of V-HAB and TherMoS, respectively, have literally and figuratively built the foundation for the author’s work. Jonas Schnaitmann was (and is) an indispensable source of programming support. Finally, the author would like to thank Professor Ulrich Walter for his continued support of V-HAB and the dynamic systems modeling group at the Institute of Astronautics.
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