Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
arXiv:2603.17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics. The framework couples a low-fidelity-informed Gaussian process regression transfer model with uncertainty-triggered sampling and a synchronized elitism rule embedded in a hybrid genetic algorithm. Low-fidelity XFOIL evaluations provide inexpensive features, while sparse RANS simulations are adaptively allocated when predictive uncertainty exceeds a thre
The continuous improvement in AI and machine learning techniques, specifically active learning and surrogate modeling, is enabling more efficient optimization for complex engineering problems. This is particularly relevant as industries seek to accelerate design cycles while managing computational costs.
This development allows for faster and more cost-effective design and optimization processes for critical components like airfoils, which have broad implications for aerospace, energy, and defense sectors. It signifies a tangible application of advanced AI in reducing the resource demands of engineering R&D.
The efficiency of complex engineering design and optimization, particularly in fields requiring high-fidelity simulations, becomes significantly improved through the intelligent integration of AI-driven surrogate models. This reduces reliance on expensive full-scale simulations early in the design cycle.
- · Aerospace design firms
- · Computational fluid dynamics (CFD) software developers
- · AI/ML researchers in engineering
- · Defense contractors
- · Traditional, purely high-fidelity CFD service providers
- · Human-centric design iteration processes
Reduced lead times and costs for new aircraft and other aerodynamic system development.
Increased design complexity and performance efficiency across various industries utilizing fluid dynamics, including automotive and wind energy.
The democratization of advanced design capabilities, potentially leading to more rapid innovation cycles and specialized niche designs across smaller engineering firms.
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Read at arXiv cs.LG