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Your search keyword '"DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT)"' showing total 8 results
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8 results on '"DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT)"'

1. Computational Analysis of Flow Structures in Turbulent Ventricular Blood Flow Associated With Mitral Valve Intervention

Author

Kronborg, Joel, Svelander, Frida, Eriksson-Lidbrink, Samuel, Lindström, Ludvig, Homs-Pons, Carme, Lucor, Didier, Hoffman, Johan, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAtascience, trAnsition, Fluid instability, contrOl, Turbulence (DATAFLOT), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), KTH School of Electrical Engineering, Royal Institute of Technology [Stockholm] (KTH ), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], [SPI]Engineering Sciences [physics], [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Physiology, Physiology (medical), Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Physics - Fluid Dynamics, Medical Physics (physics.med-ph), [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Physics - Medical Physics
Abstract

Cardiac disease and clinical intervention may both lead to an increased risk for thrombosis events due to modified blood flow in the heart, and thereby a change in the mechanical stimuli of blood cells passing through the chambers of the heart. Specifically, the degree of platelet activation is influenced by the level and type of mechanical stresses in the blood flow. Here we analyze the blood flow in the left ventricle of the heart through a computational model constructed from patient-specific data. The blood flow in the ventricle is modeled by the Navier-Stokes equations, and the flow through the mitral valve by a parameterized model which represents the projected opening of the valve. A finite element method is used to solve the equations, from which a simulation of the velocity and pressure of the blood flow is constructed. A triple decomposition of the velocity gradient tensor is then used to distinguish between rigid body rotational flow, irrotational straining flow, and shear flow. The triple decomposition enables the separation of three fundamentally different flow structures, each generating a distinct type of mechanical stimulus on the blood cells in the flow. We compare the results to simulations where a mitral valve clip intervention is modelled, which leads to a significant modification of the ventricular flow. It was found that the shear in the simulation cases treated with clips increased more compared to the untreated case than the rotation and strain did. A decrease in valve opening area of 64 % in one of the cases led to a 90 % increase in rotation and strain, but a 150 % increase in shear. The computational analysis suggests a process for patient-specific simulation of clinical interventions in the heart with a detailed analysis of the resulting blood flow, which could support clinical risk assessment with respect to platelet activation and thrombosis events., Comment: The following article has been submitted to Frontiers in Physiology

Published
2022

2. Extension of the Circe Methodology to Improve the Inverse Uncertainty Quantification of Several Combined Thermal-Hydraulic Models

Author

Riccardo Cocci, Guillaume Damblin, Alberto Ghione, Lucia Sargentini, Didier Lucor, Service de Thermo-hydraulique et de Mécanique des Fluides (STMF), Département de Modélisation des Systèmes et Structures (DM2S), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), and Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
Nuclear and High Energy Physics, History, Polymers and Plastics, Mechanical Engineering, Industrial and Manufacturing Engineering, [SPI.MECA.GEME]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph], [SPI]Engineering Sciences [physics], Nuclear Energy and Engineering, General Materials Science, Business and International Management, Safety, Risk, Reliability and Quality, [STAT.CO]Statistics [stat]/Computation [stat.CO], Waste Management and Disposal, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience

Published
2022

3. Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding

Author

Sophie Ricci, Nicole Goutal, Matthias De Lozzo, Didier Lucor, Siham El Garroussi, DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), and Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
Environmental Engineering, Polynomial chaos, Random field, 010504 meteorology & atmospheric sciences, Computer science, Dimensionality reduction, 0207 environmental engineering, 02 engineering and technology, Solver, 01 natural sciences, Autoencoder, Surrogate model, Metric (mathematics), [SDE]Environmental Sciences, Environmental Chemistry, Applied mathematics, [INFO]Computer Science [cs], Sensitivity (control systems), [MATH]Mathematics [math], 020701 environmental engineering, Safety, Risk, Reliability and Quality, 0105 earth and related environmental sciences, General Environmental Science, Water Science and Technology
Abstract

A surrogate model is developed to accurately approximate a two-dimensional hydrodynamics numerical solver in order to conduct a reduced-cost variance-based global sensitivity analysis of the hydraulic state. The impact of uncertainties in river bottom friction and boundary conditions on the simulated water depth is analyzed for quasi-unsteady flows. An autoencoder technique adapted to non-linear variable dimension reduction is used to reduce the multi-dimensional model output so that the formulation of the surrogate remains computationally parsimonious. In addition, following the divide-and-conquer principle, a mixture of local polynomial chaos expansions is proposed to deal with non-linearity in the hydraulic state with respect to uncertain inputs. Machine learning techniques are used to automatically partition the input space into clusters that are not affected by non-linearities and support accurate surrogates. This combined strategy is applied to a reach of the Garonne River where river and floodplains dynamics are simulated by the numerical solver Telemac-2D. The merits of this strategy are highlighted when the flood front reaches regions where the topography features a strong gradient and where, consequently, strong non-linearities occur between the water depth and friction as well as hydrologic input forcing. By applying this strategy, the $$Q_2$$ Q 2 metric improves by 90% compared to a classical polynomial chaos expansion surrogate, resulting in a much more reliable sensitivity analysis. This is particularly important in floodplain areas where human and economic activities are at stake.

Published
2021

4. Closed-loop control of complex systems using deep Reinforcement Learning

Author

Guégan, Thibaut, Bucci, Michele Alessandro, Semeraro, Onofrio, Cordier, Laurent, Mathelin, Lionel, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

5. Compound parametric metamodelling of large-eddy simulations for microscale atmospheric dispersion

Author

Nony, Bastien, Rochoux, Mélanie, Lucor, Didier, Jaravel, Thomas, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Atmospheric Dispersion, Microscale, Large-Eddy Simulation, Computational Fluid Dynamics, Parametric Uncertainty, Surrogate Models, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph]
Abstract

International audience; In pollutant dispersion problems, mapping concentrations in the first tens or hundreds of meters from the source still remains a modelling challenge. Large-eddy simulations (LES) are able to represent time and space variability of turbulent atmospheric flow, which is of prime importance to assess public short-term exposure. However, they remain far from real time and subject to uncertainties, in particular to parametric uncertainties associated with the large-scale atmospheric forcing and the emission source position. In this work, we show that an efficient and accurate metamodel of the tracer concentration information provided by LES and encapsulating their associated uncertainties can be built using appropriate statistical tools combining machine learning and principal component analysis. We present a proof-ofconcept study based on a simplified but representative flow configuration (two-dimensional flow around a surfacemounted cube) using the AVBP LES solver and testing a variety of metamodels (linear regression, Gaussian processes, random forest, gradient boosting, etc.). Results reinforce the idea that for sufficiently statistically-converged quantities of interest and for a sufficiently large LES data set, a compound surrogate model can succeed in synthesizing information from the LES in the whole computational domain (with a Q 2 predictivity coefficient above 90 %). Downstream of the obstacle, the Q 2 coefficient of all metamodels reaches excellent results over 90%. Upstream, the tracer concentration is subject to strong discontinuities; combining metamodels allows to guarantee a good predictivity coefficient over 75%.

Published
2021

6. Coherent structure identification in turbulent channel flow using latent Dirichlet allocation

Author

Mohamed Frihat, Yann Fraigneau, Lionel Mathelin, Bérengère Podvin, François Yvon, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Traitement du Langage Parlé (TLP ), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Accompagnement et Soutien aux Activités de Recherche & Développement (ASARD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL)

Subjects
Scale (ratio), Turbulence, Mechanical Engineering, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Statistical model, Physics - Fluid Dynamics, Latent variable, Condensed Matter Physics, 01 natural sciences, Latent Dirichlet allocation, 010305 fluids & plasmas, Physics::Fluid Dynamics, Set (abstract data type), [SPI]Engineering Sciences [physics], symbols.namesake, Flow (mathematics), Mechanics of Materials, 0103 physical sciences, symbols, 010306 general physics, Cluster analysis, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

Identification of coherent structures is an essential step to describe and model turbulence generation mechanisms in wall-bounded flows. To this end, we present a clustering method based on latent Dirichlet allocation (LDA), a generative probabilistic model for collection of discrete data. The method is applied to a set of snapshots featuring the Reynolds stress (. Both two- and three-dimensional analysis show that LDA provides a robust and compact flow description in terms of a combination of motifs, which are latent variables inferred from the set of snapshots. We find that the characteristics of the motifs scale with the wall distance, in agreement with the wall-attached eddy hypothesis of Townsend (Physics of Fluids, 1961, pp. 97–120). Latent Dirichlet allocation motifs can be used to reconstruct fields with an efficiency that can be compared with the proper orthogonal decomposition (POD). Moreover, the LDA model makes it possible to generate a collection of synthetic fields that captures the intermittent characteristics of the original dataset more clearly than its POD-generated counterpart. These findings highlight the potential of LDA for turbulent flow analysis, efficient reconstruction of actual fields and production of a new field with suitable statistics.

Published
2021

7. Metamodelling for micro-scale atmospheric pollutant dispersion large-eddy simulation

Author

Nony, Bastien, Rochoux, Mélanie C., Lucor, Didier, Jaravel, Thomas, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and CNRS Centre Paul Langevin

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Abstract

International audience; In atmospheric dispersion problems, mapping pollutant concentrations within the first tens or hundreds of meters from the emission point source still remains a modelling challenge. Computational fluid dynamics (CFD) approaches provide relevant insights into turbulent flow and pollutant concentration patterns in complex terrain such as urban and mountainous areas. At the forefront of CFD approaches, large-eddy simulations (LES) are a promising way to represent time and space variability of turbulent atmospheric flows and to assess public short-term exposures. LES are subject to uncertainties due to the intrinsic variability of environmental factors, among whom the large-scale meteorological forcing and the emission source characteristics.

Published
2021

8. Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100®

Author

Rojano, Rodrigo, Zhussupbekov, Mansur, Antaki, James, Lucor, Didier, MSBME (Meinig School of Biomedical Engineering, Cornell University), Cornell University [New York], Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence (DATAFLOT), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
polynomial chaos expansion, sensitivity analysis, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], uncertainty quantification, PFA-100®, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], Thrombosis, [INFO.INFO-BT]Computer Science [cs]/Biotechnology
Abstract

International audience; Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. As these models become more complex to predict thrombus formation dynamics high computational cost must be alleviated and inherent uncertainties must be assessed. Evaluating model uncertainties allows to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100®). The analysis is facilitated thanks to time-evolving polynomial chaos expansions used as a parametric surrogate for the full thrombosis model considering two quantities of interest; namely, thrombus volume and occlusion percentage. The surrogate is thoroughly validated and provides a straightforward access to a global sensitivity analysis via computation of Sobol' coefficients. Six out of fifteen parameters linked to thrombus consitution, vWF activity, and platelet adhesion dynamics were found to be most influential in the simulation variability considering only individual effects; while parameter interactions are highlighted when considering the total Sobol' indices. The influential parameters are related to thrombus constitution, vWF activity and platelet to platelet adhesion dynamics. The surrogate model allowed to predict realistic PFA-100® closure times of 300,000 virtual cases that followed the trends observed in clinical data. The current methodology could be used including common anti-platelet therapies to identify scenarios that preserve the hematological balance.

Published
2021

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