Reliability Analysis of Water Recovery System Data

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? Reliability Indicator, S D

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

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

7. Crop Reliability X1 CO2 concentration X2 Photoperiod Predictor Variables X3 Photosynthetic photon flux X4 Temperature X5 Relative humidity

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

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

10. Testing the Model: the iWRS 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.

11. 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)

12. Testing the Model: the iWRS • Goal: • For each subsystem, • Response variables • Predictor variables YQuantity YQuality Xi

13. Testing the Model: the iWRS • 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)

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

15. iWRS Problems Different sampling times Binary sensor values

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

18. Testing the Model: BioSim

19. BioSim Problems • VCCR module air exchange  fixed • OGS stochastic performance: WaterRS Potable H2O Outflow Rate OGS Potable H2O 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