Spacecraft Thermal Control, Design and Operation

Without rigorous system verification and validation (SVV), flight systems have no assurances that they will actually accomplish their objectives (e.g., the right system was built) or that the system was built to specification (e.g., the system was built correctly). As system complexity grows, exhaustive SVV becomes time and cost prohibitive as the number of interactions explodes in an exponential or even combinatorial fashion. Consequently, JPL and others have resorted to selecting test cases by hand based on engineering judgment or stochastic methods such as Monte Carlo methods. These two approaches are at opposite ends of the search spectrum, in which one is narrow and focused and the other is broad and shallow. This paper describes a novel approach to test case selection through the use of genetic algorithms (GAs), a type of heuristic search technique based on Darwinian evolution that effectively bridges the search for test cases between broad and narrow spectrums. More specifically, this paper describes the Nemesis framework for automated test case generation, execution, and analysis using GAs. Results are presented for the Dawn Mission flight testbed.

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Spacecraft Thermal Control, Design and Operation

Abstract: Library of Congress Cataloging in Publication DataMain entry under title:Spacecraft thermal control.

Cited by 3 publications

References 4 publications

Reduction of the periodic thermoelastic deformation in truss-structures by design refinements and active loads

Reduction of the periodic thermoelastic deformation in truss-structures by design refinements and active loads

Quick Attach Docking Interface for Lunar Electric Rover

Automated Test Case Selection for Flight Systems using Genetic Algorithms

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