Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
A26_01: Artificial Intelligence in Turbulence
Time:
Monday, 16/September/2024:
3:30pm - 6:00pm

Session Chair: Maurizio Quadrio, Politecnico di Milano
Location: H07

C.A.R.L.-Central Auditorium for Research and Learning Claßenstr. 11 52072 Aachen

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Presentations
3:30pm - 3:45pm

Parameter sensitivity analysis of a direct numerical simulation with heat release model as an analogy to bushfires. (YSA)

Kevin Liu, Callum Atkinson, Julio Soria

Monash University

Liu-Parameter sensitivity analysis of a direct numerical simulation with heat release-1283.pdf


3:45pm - 4:00pm

Combining deep neural networks and a differentiable lattice Boltzmann solver for wall model prediction in large eddy simulations

Hesam Salehipour1, Benedikt Dorschner2

1Autodesk Research; 2NVIDIA Corp

Salehipour-Combining deep neural networks and a differentiable lattice Boltzmann solver-1315.pdf


4:00pm - 4:15pm

A machine-learning-based zonal approach for turbulence modeling

Marco Castelletti, Maurizio Quadrio

Politecnico di Milano

Castelletti-A machine-learning-based zonal approach for turbulence modeling-798.pdf


4:15pm - 4:30pm

Convolution-compacted vision transformers for wall heat-flux modelling in turbulent channel flow

Yuning Wang, Ricardo Vinuesa

FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden

Wang-Convolution-compacted vision transformers for wall heat-flux modelling-533.pdf


4:30pm - 4:45pm

Data-driven based scale-adaptive turbulence closure modeling

Samuel Ahizi, Matilde Fiore, Lilla Koloszar, Miguel Alfonso Mendez

von Karman Institute for fluid dynamics

Ahizi-Data-driven based scale-adaptive turbulence closure modeling-1282.pdf


4:45pm - 5:00pm

Easy-attention-based transformer for temporal predictions of turbulent flows (YSA)

Marcial Sanchis Agudo1, Yuning Wang1, Roger Arnau2, Karthik Duraisamy3, Ricardo Vinuesa1

1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden; 2nstituto Universitario de Matem´atica Pura y Aplicada, Universitat Polit`ecnica de Val`encia. Camino de Vera s/n, 46022 Val`encia, Spain; 3Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA

Sanchis Agudo-Easy-attention-based transformer for temporal predictions-1061.pdf


5:00pm - 5:15pm

Embedded learning of a wall model for separated flows

Zhideng Zhou1,2, Xin-lei Zhang1,2, Guo-wei He1,2, Xiaolei Yang1,2

1The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China; 2School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

Zhou-Embedded learning of a wall model for separated flows-224.pdf


5:15pm - 5:30pm

Machine learning and CFD can work together for surgery planning in the human nose

Maurizio Quadrio, Angelo Raimondo Favero, Andrea Schillaci

Politecnico di Milano

Quadrio-Machine learning and CFD can work together for surgery planning-889.pdf


5:30pm - 5:45pm

Mean flow data assimilation of turbulent stenotic flow fields using physics-informed neural networks on 4D-flow MRI

Alexandre Villié1, Sebastian Schmitter2, Jakob von Saldern1, Simon Demange1, Kilian Oberleithner1

1Laboratory for Flow Instability and Dynamics, Technische Universität Berlin, 10623 Berlin, Germany; 2Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, 10587 Berlin, Germany

Villié-Mean flow data assimilation of turbulent stenotic flow fields using physics-informed-746.pdf


 
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