Abstract
Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems’ optimization process to assure the systems’ quality and reliability. This chapter focuses on a robust optimization approach for developing robust and reliable advanced systems and explains the framework for using uncertainty quantification and optimization techniques. For the uncertainty analysis, a polynomial chaos-based approach is combined with the optimization algorithms MOSA (Multi-Objective Simulated Annealing), and the process is discussed with a simplified test function. For the optimization process, gradient-free genetic algorithms are considered as the optimizer scans the whole design space, and the optimal values are not always dependent on the initial values.
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Acknowledgment
The computational part of this work was supported in part by the National Science Foundation (NSF) under Grant No.OAC-1919789.
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Verma, R., Kumar, D., Kobayashi, K., Alam, S. (2023). Reliability-Based Robust Design Optimization Method for Engineering Systems with Uncertainty Quantification. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_206-1
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DOI: https://doi.org/10.1007/978-3-030-72322-4_206-1
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