Search arXiv⌕ Search

arXiv · 2611.06183

Improving Hydrofoil Efficiency Through Simulation-Driven Design Optimization

Abstract

Foil windsurfing is a sport in which competitors fine-tune their setup to optimize performance under varying environmental conditions. However, such adjustments are typically guided by intuition. This paper presents a methodology for improving hydrofoil design using computational fluid dynamics. First, structured light scanning was used to obtain the geometry of a conventional hydrofoil. Subsequently, five derived designs have been explored, inspired by advancements in airplane wing design. Following this, a preliminary design was selected, and a design of experiments approach was implemented. Generated simulation data were used to train a machine learning surrogate, which is ultimately used in the multi-objective design optimization. While the surrogate-based approach yielded promising results, it was limited to exploring a restricted set of design parameters. To overcome this constraint, additional improvements were attained through adjoint optimization, demonstrating that comparable performance can be achieved with a distinctly different hydrofoil design. Finally, the conventional design was refined through adjoint optimization. Notably, adjoint optimization yielded a 15% increase in efficiency. The results suggest that both proposed approaches can produce novel hydrofoil designs capable of outperforming the original configuration under specific conditions; however, due to its inherent flexibility, the adjoint approach with realistic constraints offers more freedom in the design exploration process.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ante Sikirica, Ivana Lučin, Marta Alvir, Tin Matulja. 2026-11-04. Improving Hydrofoil Efficiency Through Simulation-Driven Design Optimization. https://doi.org/10.1016/j.oceaneng.2025.123943

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Distributionally Robust Airport Ground Holding Problem under Wasserstein Ambiguity Sets

We develop a distributionally robust formulation of the single-airport ground holding problem (dr-SAGHP) using a $1$-Wasserstein ambiguity set with an $\ell_1$ transportation metric over the full physical capacity support. We exploit the problem's structural properties to develop an exact dynamic programming separation algorithm within delayed constraint generation (DCG-DP). We also develop K-IL, which combines Kelley's cutting-plane method with an integer L-shaped framework to solve a a restricted-support approximation, rs-dr-SAGHP. Delayed constraint generation with MILP separation (DCG-MILP) serves as the computational baseline. Computational experiments demonstrate substantial runtime reductions for DCG-DP and K-IL relative to this baseline. Airport capacity distributions are constructed using Gaussian process regression based on historical weather and operational data at Denver International Airport from 2021 to 2023. We develop a chronological expanding-window calibration procedure to select the Wasserstein radius and the results show that the calibrated dr-SAGHP approaches reduce mean operational cost by approximately $2.6\%$ and the Conditional Value at Risk $\mathrm{CVaR}_{90}$ by approximately $8\%$ relative to the stochastic SAGHP benchmark, while avoiding the excessive conservatism observed in a classical robust optimization benchmark.

math.OC↗

Q-Learning under Finite Model Uncertainty

We propose a robust Q-learning algorithm for Markov decision processes under model uncertainty when each state--action pair is associated with a finite ambiguity set of candidate transition kernels. This finite-measure framework enables user-designed uncertainty models, including competing fitted models and stress scenarios. {The learner observes successor states and their deterministic rewards from every candidate simulator, without requiring explicit knowledge of the transition probabilities or the reward function.} We establish almost sure convergence of the learned Q-function to the robust optimum, and derive a time-uniform high-probability error certificate that separates an observable empirical Bellman residual from transition-kernel estimation error. We also give quantitative finite-ambiguity approximation results for Wasserstein and parametric uncertainty models on finite state spaces. Numerical examples illustrate the effect of scenario selection and compare the tabular algorithm with robust value iteration.

math.OC↗

SAGE: A Set-based Adaptive Gradient Estimator

We introduce a set membership perspective on gradient estimation for black-box scalar functions, in which the gradient is not approximated as a single point estimate but characterised as a member of a set of admissible gradients consistent with available function samples and regularity assumptions. Bounds on gradient uncertainty, obtained from standard multivariate Taylor series results, are used to represent this admissible set through linear inequalities, making gradient uncertainty explicit and directly exploitable. Building on this formulation, we propose an adaptive strategy to refine the admissible gradient set to a desired precision by selectively acquiring new samples only when necessary. The resulting framework naturally accommodates function evaluations affected by noise with finite but a priori unknown bounds, yields the theoretical best attainable precision of the gradient set, and provides the optimal sampling distance from the point of interest to maximise information gain during refinement. Within this framework, we introduce the Set-Based Adaptive Gradient Estimator (SAGE), which extracts an estimate of the true gradient from the admissible set whilst preserving high sample efficiency and robustness to noise. Numerical experiments on large collections of random gradient-query points, as well as in the context of a first-order optimisation method, compared the performance of SAGE against commonly used gradient estimators from the literature. Our results show that SAGE achieved competitive gradient accuracy across noise levels without requiring any a priori noise characterisation. In a first-order optimisation setting, noise-informed competitors can outperform SAGE when such characterisation is available. Nevertheless, SAGE remained broadly competitive, making it particularly attractive in settings where noise characterisation is impractical to obtain.

math.OC↗