Optimizing Spacecraft Design -Optimization Engine Developments Progress and Plans

摘要

At JPL and NASA, a process has been developed to perform life cycle risk management. This process requires users to identify: goals and objectives to be achieved (and their relative priorities), the various risks to achieving those goals and objectives, and options for risk mitigation (prevention, detection ahead of time, and alleviation). Risks are broadly defined to include the risk of failing to design a system with adequate performance, compatibility and robustness in addition to more traditional implementation and operational risks. The options for mitigating these different kinds of risks can include architectural and design choices, technology plans and technology back-up options, test-bed and simulation options, engineering models and hardware/software development techniques and other more traditional risk reduction techniques. Each of these risk mitigations has resource costs associated with them. The sum of all these mitigations is almost always unaffordable. Furthermore, there may be a variety of other constraints (mass, power, funding profile, leveraged programs, etc.) that further constrain acceptable selections. The challenge is therefore to emerge with an "optimal" selection of mitigations that makes best use of available resources to reduce risk to the fullest extent possible. For non-trivial design spaces, the search space of possible selections is huge. This precludes exhaustive search for the optimum, and therefore necessitates the adoption of heuristic search techniques. At JPL, we have explored application of several heuristic techniques for searching for, and refining, collections of risk mitigations, notably: genetic algorithms, simulated annealing, and machine learning. The results of research and pilot applications of these techniques for finding best combinations of life cycle risk management solutions are discussed.