51 citations · 145 across the 10 of their papers we have counts for
10 papers
Feature-based manifold model of actuated wakes
Alicia Rodríguez-Asensio, Guy Y. Cornejo Maceda, Bernd R. Noack +2
We propose a feature-based reduced-order model to predict the transient dynamics of bluff-body wakes under arbitrary time-varying actuation. Starting point is a control-oriented PO…
Divide and conquer complex flows. Part I: cluster and manifold-based local analysis
Qihong L. Li-Hu, Guy Y. Cornejo Maceda, Andrea Ianiro +1
This work is a two-part study on the description and prediction of complex fluid flows through the partitioning of the flow domain. In this first part, we propose a framework for a…
Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)
Isaac Robledo, Yiqing Li, Guy Y. Cornejo Maceda +1
The Hybrid Genetic Optimisation framework (HYGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and function…
Sensor optimization for urban wind estimation with cluster-based probabilistic framework
Yutong Liang, Chang Hou, Guy Y. Cornejo Maceda +8
We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulation…
Topologically assisted optimization for rotor design
Tianyu Wang, Yannian Yang, Xuanwu Chen +4
We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit (). This topologically assisted optimization (TAO) contain…
xMLC -- A Toolkit for Machine Learning Control
Guy Y. Cornejo Maceda, François Lusseyran, Bernd R. Noack
xMLC is the second book of this `Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, p…
Machine learning flow control with few sensor feedback and measurement noise
R. Castellanos, G. Y. Cornejo Maceda, I. de la Fuente +3
A comparative assessment of machine learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Kármán vorte…
Stabilization of a multi-frequency open cavity flow with gradient-enriched machine learning control
Guy Y. Cornejo Maceda, Eliott Varon, François Lusseyran +1
We stabilize an open cavity flow experiment to 1% of its original fluctuation level. For the first time, a multi-modal feedback control is automatically learned for this configurat…
Stabilization of the fluidic pinball with gradient-enriched machine learning control
Guy Y. Cornejo Maceda, Yiqing Li, François Lusseyran +2
We stabilize the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the ro…
Towards human-interpretable, automated learning of feedback control for the mixing layer
Hao Li, Guy Y. Cornejo Maceda, Yiqing Li +3
We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich data base of…