Reliability Analysis of Experiment and Simulation Data for an Integrated Water Recovery System

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