1. Reliability Analysis of Experiment and Simulation Data for an Integrated Water Recovery System Christian Douglass General Engineering University of Illinois

2. 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.

3. 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

4. Possible Solution: Crop Reliability Can we model crop reliability after economic supply and demand? S D Reliability Indicator,

5. Crop Reliability Potato crop-system model in terms of response variable Y and predictor variables X i :

6. Crop Reliability Response Variable Y Potato Leaf Dry Weight (after 90 days)

7. Predictor Variables X2X2 X1X1 X3X3 X4X4 X5X5 CO 2 concentration Photoperiod Photosynthetic photon flux Temperature Relative humidity Crop Reliability

8. Possible Solution: Crop Reliability Can we model crop reliability after economic supply and demand? S D Reliability Indicator,

9. Possible Solution: Crop Reliability Can we model crop reliability after economic supply and demand? S D Reliability Indicator,

10. 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

11. 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

12. Goal: For each subsystem, Response variables Predictor variables Y Quantity Y Quality XiXi

13. 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

14. Potential Predictor Variables (PPS) Temperature sensors Conductivity sensors Pressure transducers Valve states Testing the Model: the iWRS

15. Different sampling times Binary sensor values iWRS Problems

16. BioSim Life Support Simulation Modeling Tool Developed by NASA XML configuration files Java controllers Testing the Model: BioSim

19. BioSim Problems VCCR module air exchange  fixed OGS stochastic performance: WaterRS Potable H 2 O Outflow RateOGS Potable H 2 O Inflow Rate

20. Predictor Probability Distributions

21. 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

22. 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