Differentiable Particle Swarm Optimization: A Physics-Informed Hybrid Framework for Wind Turbine Blade Design

Abstract

Optimizing wind turbine blades for maximum annual energy production is a complex real-world challenge, marked by high-dimensional search spaces and computationally expensive, non-differentiable aerodynamic simulations that hinder efficient gradient-based optimization. We present a novel, fully differentiable design framework that creates an end-to-end differentiable pipeline from Kulfan design parametrization to power efficiency (CP). Unlike prior "black-box" approaches, our pipeline composes NeuralFoil — a neural surrogate mapping Kulfan parameters to aerodynamic coefficients — with a custom differentiable implementation of Blade Element Momentum Theory (BEMT) with guaranteed convergence. This integration enables the computation of exact gradients of the whole-blade performance via automatic differentiation. To navigate the non-convex aerodynamic landscape, we propose a hybrid memetic strategy: Particle Swarm Optimization (PSO) is employed for global exploration to identify high-quality basins of attraction, followed by a gradient-based Augmented Lagrangian Optimizer (ALO) for precise local refinement. Experimental validation on the NREL 5MW reference turbine demonstrates that this physics-informed hybrid framework achieves superior design performance, reaching the Betz Limit (CP ≈ 59.3%) without losing its physical feasibility.