AI Insight
This paper introduces an adaptive multi-fidelity optimization method for designing structures subject to fluid-structure interaction (FSI) that significantly reduces computational costs. The approach combines high-fidelity FSI analysis with a lightweight surrogate model for fluid loads, using Gaussian process regression to adaptively decide when full coupled analysis is needed versus when the surrogate is sufficient. Testing on benchmark problems demonstrated an 80% reduction in computational cost while maintaining results within 2.3% of full high-fidelity optimization.
Why it matters
This method could accelerate the design process for aerospace vehicles, marine structures, and other engineering systems where fluid-structure interaction is critical but computationally expensive to model. The framework requires no offline training and ensures final designs are verified with high-fidelity analysis, making it practical for real-world engineering applications.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: The design of structures and vehicles subject to fluid-structure interaction (FSI) often requires high-fidelity coupled analysis. While the design variables pertain to the structure, the computational cost is dominated by the fluid solver, making iterative optimization prohibitively expensive. This paper presents an adaptive multi-fidelity optimization method combining high-fidelity FSI analysis with a lightweight surrogate for fluid-induced loads and a decision model that selects between surrogate and high-fidelity fluid evaluations. During optimization, completed FSI analyses incrementally update a non-intrusive surrogate model based on nearest-neighbor search and radial interpolation. A hybrid Lagrangian-Eulerian mapping function is developed to transfer fluid loads between structural designs. The evolution of surface orientation is handled by decomposing the traction vectors into local orthonormal bases. An adaptive Gaussian process regression model is employed to predict surrogate error and quantify uncertainty, allowing risk-aware selection of when coupled analysis is required. As design evaluations cluster near the optimum, the accuracy of the surrogate model naturally improves, thereby reducing the reliance on the fluid solver. It requires no offline training, preserves the high-fidelity structural model in all design evaluations, and ensures that the final design is evaluated by high-fidelity FSI analysis. The fundamental idea is justified theoretically using a simplified model problem, which shows that the leading-order error is a monotonically increasing, concave, and bounded function of the fluid added mass. The framework is demonstrated on two benchmark problems. For shape optimization of a flexible panel under shock loading, results show an $80%$ reduction in computational cost while maintaining accuracy within $2.3%$ of fully high-fidelity FSI optimization.
Source: Adaptive Multi-Fidelity Structural Optimization under Fluid-Structure Interaction