Abstract
Abstract We consider the problem of selecting among different physics-based computational models of varying, and oftentimes not assessed, fidelity for evaluating the objective and constraint functions in numerical design optimization. Typically, higher-fidelity models are associated with higher computational cost. Therefore, it is desirable to employ them only when necessary. We introduce a relative adequacy framework that aims at determining whether lower-fidelity models (that are typically associated with lower computational cost) can be used in certain areas of the design space as the latter is being explored during the optimization process. We implement our approach by means of a trust-region management framework that utilizes the mesh adaptive direct search derivative-free optimization algorithm. We demonstrate the link between feasibility and fidelity and the key features of the proposed approach using two design optimization examples: a cantilever flexible beam subject to high accelerations and an airfoil in transonic flow conditions.
Highlights
Numerical engineering design optimization requires computational models to predict system behavior in large and multi-dimensional design spaces
3 Numerical Examples We demonstrate the application of our proposed methodology on constrained design optimization problems considering both computational structural dynamics (CSD) and computational fluid dynamics (CFD) disciplines
The high-cost model M0 is used as a reference to improve both lower-fidelity models M1 and M2 while keeping the computational cost as low as possible
Summary
Numerical engineering design optimization requires computational models to predict system behavior in large and multi-dimensional design spaces. How do we choose among different models during the optimization process depending on their fidelity level in different areas of the design space? The use of the term can differ substantially, which can lead to misconceptions and inappropriate methods for multi-model management. The objective of this work is to develop a framework for managing the use of physics-based models of varying fidelity regardless of their disciplinary context. The proposed methodology will support decisions related to which model(s), and at which computational cost, should be used during the design exploration of the optimization process
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