194 citations · 362 across the 21 of their papers we have counts for
21 papers
A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics
Rodrigo Abadia-Heredia, Xiangrui Zou, Manuel Lopez-Martin +2
Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotempo…
Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows
Xiangrui Zou, Zhuoqun Zhao, Guillermo Barragán +1
Reliable long-horizon prediction remains a challenge for data-driven CFD surrogates, because offline-trained models accumulate autoregressive errors and lose accuracy when operatin…
AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo +8
High-fidelity CFD is essential for aerodynamic design, but repeated simulations are computationally expensive, motivating surrogate models for rapid evaluation across geometries an…
Coherent structures in Newtonian and viscoelastic turbulent planar jets
Christian Amor, Adrián Corrochano, Giovanni Soligo +2
The addition of a small amount of long-chain polymers confers viscoelastic properties to Newtonian flows. The resulting non-Newtonian solution now exhibits different dynamics, such…
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta +3
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The meth…
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
Francisco Giral, Ignacio Gómez, Soledad Le Clainche
The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks, including potential collisions, disruptions to air traffic, and security thr…
Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
Rodrigo Abadía-Heredia, Adrián Corrochano, Manuel Lopez-Martin +1
This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applicat…
Data repairing and resolution enhancement using data-driven modal decomposition and deep learning
A. Hetherington, D. Serfaty, A. Corrochano +2
This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and de…
Low-cost singular value decomposition with optimal sensor placement
Ashton Hetherington, Soledad Le Clainche
This paper presents a new method capable of reconstructing datasets with great precision and very low computational cost using a novel variant of the singular value decomposition (…
A unified view of elastic and elasto-inertial turbulence in channel flows at low and moderate Reynolds numbers
Giulio Foggi Rota, Christian Amor, Soledad Le Clainche +1
The chaotic flow of elastic fluids at low Reynolds number (Re) is typically distinguished into elasto-inertial and elastic turbulence (EIT/ET). However, the clear separation among…
ModelFLOWs-app: data-driven post-processing and reduced order modelling tools
A. Hetherington, A. Corrochano, R. Abadía-Heredia +6
This article presents an innovative open-source software named ModelFLOWs-app, written in Python, which has been created and tested to generate precise and robust hybrid reduced or…
Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics
Paula Díaz, Adrián Corrochano, Manuel López-Martín +1
Fluid Dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by thi…
Hierarchical Higher-Order Dynamic Mode Decomposition for Clustering and Feature Selection
Adrián Corrochano, Giuseppe D'Alessio, Alessandro Parente +1
In this work, a new algorithm based on the application of higher-order dynamic mode decomposition (HODMD) is proposed for feature selection and variables clustering in reacting flo…
A predictive physics-aware hybrid reduced order model for reacting flows
Adrián Corrochano, Rodolfo S. M. Freitas, Alessandro Parente +1
In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orth…
Mode selection in concentric jets. The steady-steady 1:2 resonant mode interaction with O(2) symmetry
Adrián Corrochano, Javier Sierra-Ausín, Juan Ángel Martin +2
In this article, a thorough characterization of the configuration composed by two concentric jets at a low Reynolds number is presented. The analysis comprises a layout with a wide…
Data-driven modal decomposition methods as feature detection techniques for flow problems: a critical assessment
Beka Begiashvili, Nourelhouda Groun, Jesús Garicano-Mena +2
Modal decomposition techniques are showing a fast growth in popularity for their good properties as data-driven tools. There are several modal decomposition techniques, yet Proper…
Forecasting through deep learning and modal decomposition in two-phase concentric jets
León Mata, Rodrigo Abadía-Heredia, Manuel Lopez-Martin +2
This work aims to improve fuel chamber injectors' performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of…
Improving aircraft performance using machine learning: a review
Soledad Le Clainche, Esteban Ferrer, Sam Gibson +3
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (expe…
Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder
Álvaro Martínez-Sánchez, Esteban López, Soledad Le Clainche +3
The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant t…
High-resolution large-eddy simulations of simplified urban flows
Marco Atzori, Pablo Torres, Alvaro Vidal +3
High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of 10,000 based on the free-stream velocity and the obstacle…
Data-driven assessment of arch vortices in simplified urban flows
Álvaro Martínez-Sánchez, Eneko Lazpita, Adrián Corrochano +3
Understanding flow structures in urban areas is widely recognized as a challenging concern due to its effect on urban development, air quality, and pollutant dispersion. In this st…