5 papers · 1 filter
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila +1
Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved t…
Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction
Víctor Francés-Belda, Carlos Sanmiguel Vila, Rodrigo Castellanos
Accurate and efficient surrogate models for aerodynamic surface pressure fields are essential for accelerating aircraft design and analysis, yet deterministic regressors trained wi…
Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
Isaac Robledo, Alberto Vilariño, Arnau Miró +3
Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic me…
Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms
Isaac Robledo, Juan Alfaro, Víctor Duro +3
Aerodynamic drag on flat-backed vehicles like vans and trucks is dominated by a low-pressure wake, whose control is critical for reducing fuel consumption. This paper presents an e…
A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
We present a data-driven active flow control framework designed for deployment with few non-intrusive sensors. The method builds upon Artificial Intelligence driven reduced-order p…