47 citations · 78 across the 14 of their papers we have counts for
14 papers
Classically studied coherent structures only paint a partial picture of wall-bounded turbulence
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing th…
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, a…
Higher DNS-resolution requirements for expanded overlap region and confirmation of a convergence criterion
Sergio Hoyas, Ricardo Vinuesa, Peter Schmid +1
Direct numerical simulations (DNS) stand out as formidable tools in studying turbulent flows. Despite the fact that the achievable Reynolds number remains lower than those availabl…
Utilizing indicator functions with computational data to confirm nature of overlap in normal turbulent stresses: logarithmic or quarter-power
Hassan Nagib, Ricardo Vinuesa, Sergio Hoyas
Indicator functions of the streamwise normal-stress profiles (NSP), based on careful differentiation of some of the best direct numerical simulations (DNS) data from channel and pi…
Validation of symmetry-induced high moment velocity and temperature scaling laws in a turbulent channel flow
Francisco Alcántara-Ávila, Luis Miguel García-Raffi, Sergio Hoyas +1
The symmetry-based turbulence theory has been used to derive new scaling laws for the streamwise velocity and temperature moments of arbitrary order. For this, it has been applied…
Sensitivity study of resolution and convergence requirements for extended overlap region in wall-bounded turbulence
Sergio Hoyas, Ricardo Vinuesa, Peter Schmid +1
Direct Numerical Simulations (DNSs) are one of the most powerful tools for studying turbulent flows. Even if achievable Reynolds numbers are lower than those obtained with experime…
Identifying regions of importance in wall-bounded turbulence through explainable deep learning
Andres Cremades, Sergio Hoyas, Rahul Deshpande +8
Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One o…
A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Mustafa Z. Yousif, Linqi Yu, Sergio Hoyas +2
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance…
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…
On the generation and destruction mechanisms of arch vortices in urban fluid flows
Eneko Lazpita, Álvaro Martínez-Sánchez, Adrián Corrochano +3
Studying and interpreting the different flow patterns present in urban areas is becoming essential since they help develop new approaches to fight climate change through an improve…
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…
Aim in Climate Change and City Pollution
Pablo Torres, Beril Sirmacek, Sergio Hoyas +1
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citi…
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas +1
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful f…
Thermal convection in a cylindrical annulus heated laterally
S. Hoyas, H. Herrero, A. M. Mancho
In this paper we study thermoconvective instabilities appearing in a fluid within a cylindrical annulus heated laterally. As soon as a horizontal temperature gradient is applied a…