DataToolsandProcessingPipelinefortheMarsOxygenISRUExperiment(MOXIE)

WilliamMaynard1,MichaelHecht2,JohnSwoboda2,ForrestMeyen2UniversityofMassachusettsAmherst1,MITHaystackObservatory2

AbstractIn recent years, there has been a resurgence in interest in the field of

manned space exploration, in particular, a manned mission to Mars. Such amission requires years of planning and experimentation beforehand in order toprepare. One such experiment is the Mars Oxygen ISRU Experiment (MOXIE),which will be part of the Mars 2020 rover mission. ISRU stands for In-SituResource Utilization, which means taking advantage of the resources at themission location to support the functions of the mission itself or “living off theland”. ISRU makes longer range missions such as a manned mission to Mars muchmore feasible by reducing the mass needed to be launched from Earth, thereforesaving launch expenses. MOXIE is an experiment to determine the viability ofcreating oxygen from the carbon dioxide that makes up 96% of the Martianatmosphere. MOXIE consists of ten solid oxide electrolysis (SOXE) cells arrangedin two five cell stacks. The cells produce oxygen by using electrolysis to breakdown CO2 into CO and O2. MOXIE is a test case for technology that couldhopefully be expanded to a much larger scale in order to create propellant for aMars Ascent Vehicle for the return trip of a manned mission in the 2030’s. A datapipeline has been created to detail the steps the data takes from the raw MOXIEdata sent from the rover to the fully refined data. A suite of MATLAB tools andfunctions have been created to read, derive, and plot the data at the differentsteps of the data pipeline as well as a run control table tool to approximate thefunctioning of MOXIE for an oxygen production run, given certain run conditions.The data tools include functions to read the raw data, calculate the derived datafrom the calibrated data, and plot the data.

MotivationWith an experiment like MOXIE there will be large amounts of data that need

to be calibrated and processed before it can be used and interpreted. Therefore,software tools are needed in order to do the data processing needed to make thedata ready and usable for interpretation. Additionally, tools are needed for quickinterpretation of the data check the trends in the functioning of MOXIE duringthe oxygen production run. These tools need to be straightforward and simple touse for anyone working on MOXIE who might need to use them.

MOXIEDataPipeline

The MOXIE Data Pipeline starting from the raw binary data to the engineering data record andthen onto the two reduced data records. (EDR: Engineering Data Record, RDR: Reduced DataRecord)

MOXIEDataPipelineandDataProcessingToolsA set of MATLAB functions were created to process the data collected from MOXIE and the Mars 2020 rover at each step of the data

pipeline. The steps in the pipeline include the raw binary data, the engineering data, and two different levels of reduced data. First in theprocess is the raw binary that is sent back from MOXIE and the rover, which needs to be converted into usable data values. The next step is theengineering data record which is the raw number readings from the MOXIE sensors after they have been read from the binary data file. Afterthe engineering data, the next step is the reduced data record 1 which is after the raw numbers have been calibrated into usable datacorresponding to all the MOXIE sensors. The final step is the second reduced data record which contains all the calibrated and derived data. Thesecond reduced data record contains data values that were calculated from the sensor data, such as oxygen production.

MATLAB tools for MOXIE data processing:

Read_Moxie_Data.m:• Reads MOXIE data from a binary file into a MATLAB data structure with fields for each sensor on MOXIE• Saves data to a CSV file for the EDR (engineering data record)• Data structure format is used for all the MOXIE data tools.

Derive_Moxie_Data.m:• Calculates all the derived data values for MOXIE, such as O2 production rate for each stack, total O2 production, the CO2 utilization, and the

Area Specific Resistance (used as a measure of the effectiveness of the SOXE stack)• Outputs a CSV file with the updated data structure for the second reduced data records• Future work: Calculation of flow

Read_MOXIE_CSV.m:• Reads a stored MOXIE data structure from a stored data record CSV file back into a MATLAB data structure.

plot_MOXIE_data.m:• Plots MOXIE data from MOXIE data structure as time series• Inputted character array denotes which fields from the data structure are to be plotted.

RunControlTablesIn addition to needing tools to process the data that comes back from MOXIE and the Mars 2020 rover, it is helpful to have tools to predict

the functioning of MOXIE during an oxygen production run based on inputted run conditions. A run control table (RCT) is being developed tooutline MOXIE functioning at each step during an oxygen production run. This run control table will be an extremely useful tool in terms ofquickly being able to approximate how MOXIE will perform, given specific circumstances. Run conditions and parameters, such as the MOXIEcontrol loop set points and ambient conditions, are inputted into the run control table and an accompanying parameters table. The outputs ofthe table include the estimated power and energy consumption of MOXIE as well as the O2 production rate and total O2 produced. Run controltables need to be easily stored and easily accessed from storage in case conditions from a previous run are wanted for a new run, so MATLABfunctions were created to read and write RCT’s into data structures with field for name, version, and the RCT itself with subfields for each step.The run control table is not yet completed.

MOXIEDataPlots

Pressure, current, O2 production rate, and total O2 produced plotted as time series by the plotting function, usingmock data simulating an oxygen production run. The pressure sensors are the ambient pressure, the pressure justafter the pump, just after an aperture and before the SOXE, at the CO/CO2 outlet, and the O2 outlet respectively. Theother three variables have been plotted for each stack as well as total.

RunControlTable

Exampleruncontroltable.NotshownisaseparateparametertablethathasotherinputsusedtocalculatepowerandO2 production.

SummaryandConclusionsThe data processing tools that have been created so far are an important first step in

creating data tools for every step in the MOXIE data pipeline. Future work will expand on thisfunctionality. As of now there is no tool to calibrate the engineering data or to take it from theraw values outputted by the sensor and turn into usable readings. Additionally not all thefunctions are completely finished. The function for derived data values is not complete becausemore information about the flow through the system is needed to calculate the utilization of CO2and the Nernst potential. The function may also need to be updated as the project progresses ifthere are any changes in the requirements for the data processing tools and pipeline.

The run control table will also be a useful tool once it is completed but as of now it is only adraft. More information is needed about the system itself in order to relate the ambientconditions to the functioning of MOXIE.

ReferencesMeyen F.Systemmodeling,design,andcontrolofthemarsoxygenin-situresourceutilizationexperiment(MOXIE)andimplicationsforatmosphericISRUprocessingplants.MassachusettsInstituteofTechnology;2017.