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Reliability Analysis of Experiment and Simulation Data for an Integrated Water Recovery System
Christian DouglassGeneral EngineeringUniversity of Illinois
Overview
• Problem: Can we test the reliability of life support systems before launch? Why has it been so difficult to test reliability in the past?
• Possible Solution: Crop reliability models developed, but how robust?
• Testing the solution: Crop reliability models are applied to wastewater experiment data and simulation data.
Problem • Physical means of early reliability testing
• High costs associated with testing
• Systems need to be tested until failure
• Mathematical and simulation models for early reliability testing
• Lower costs
• Systems can be tested until failure over and over
Possible Solution: Crop ReliabilityCan we model crop reliability after economic supply and demand?
S
D
Reliability Indicator,
0 DSR
)...,( 1 kXXtfS
)...,( 1 kXXtgD
Crop Reliability
k
iiiXaaY
10
k
i
biiXbY
10
Potato crop-system model in terms of response variable Y and predictor variables Xi :
Crop Reliability
Response Variable Y
Potato Leaf Dry Weight (after 90 days)
Predictor Variables
X2
X1
X3
X4
X5
CO2 concentration
Photoperiod
Photosynthetic photon flux
Temperature
Relative humidity
Crop Reliability
Possible Solution: Crop ReliabilityCan we model crop reliability after economic supply and demand?
S
D
Reliability Indicator,
0 DSR
)...,( 1 kXXtfS
)...,( 1 kXXtgD
Possible Solution: Crop ReliabilityCan we model crop reliability after economic supply and demand?
S
D
Reliability Indicator,
0 DSR
)...,( 1 kXXtfS
)...,( 1 kXXtgD
Taken from Kortenkamp, D. and Bell, S., “Simulating Advanced Life Support Systems for Integrated Controls Research,” Proceedings International Conference on Environmental Systems, SAE paper 2003-01-2546, 2003.
Testing the Model: the iWRS
• The iWRS is composed of four major subsystems:
• Biological Water Processor (BWP)• Reverse Osmosis (RO) System• Air Evaporation Subsystem (AES)• Post Processing System (PPS)
Testing the Model: the iWRS
Testing the Model: the iWRS
Goal: For each subsystem,
• Response variables
• Predictor variables
YQuantity
YQuality
Xi
• Potential Quantity Response Variables (PPS)
• Flow-meter (fm10)
•Tank weight scale (wt07)
• Potential Quality Response Variables (PPS)
• Total organic carbon sensor (toc)
• Dissolved oxygen sensor (do02)
Testing the Model: the iWRS
• Potential Predictor Variables (PPS)
• Temperature sensors
• Conductivity sensors
• Pressure transducers
• Valve states
Testing the Model: the iWRS
Different sampling
times
Binary sensor values
iWRS Problems
BioSim Life Support Simulation Modeling Tool
• Developed by NASA
• XML configuration files
• Java controllers
Testing the Model: BioSim
Testing the Model: BioSim
BioSim Problems
• VCCR module air exchange fixed
• OGS stochastic performance:
WaterRS Potable H2O Outflow Rate OGS Potable H2O Inflow Rate
Predictor Probability Distributions
Future Work
• Continue to explore possibility of using the iWRS experiment data
• Fix stochastic performance of OGS module
• Continue to find probability distributions for BioSim predictor variables
• Begin regression analyses of BioSim log data
Acknowledgements• Advisors Haibei Jiang and Professor Luis Rodríguez• Undergraduate research assistants Izaak Neveln and David Kane• Graduate student Glen Menezes• BioSim developer Scott Bell• The Illinois Space Grant Consortium• NASA grant No. NNJ06HA03G• The Boeing Company• The Aerospace Engineering Department• The Agricultural and Biological Engineering Department