scholarly journals A Data-Driven Space-Time-Parameter Reduced-Order Model with Manifold Learning for Coupled Problems: Application to Deformable Capsules Flowing in Microchannels

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1193
Author(s):  
Toufik Boubehziz ◽  
Carlos Quesada-Granja ◽  
Claire Dupont ◽  
Pierre Villon ◽  
Florian De Vuyst ◽  
...  
Keyword(s):  
Microfluidic Channel ◽  
Space Time ◽  
Reduction Technique ◽  
Time Parameter ◽  
Data Driven ◽  
Coupled Problems ◽  
Reduced Basis ◽  
Innovative Drug ◽  
Reduced Order ◽  
Low Order

An innovative data-driven model-order reduction technique is proposed to model dilute micrometric or nanometric suspensions of microcapsules, i.e., microdrops protected in a thin hyperelastic membrane, which are used in Healthcare as innovative drug vehicles. We consider a microcapsule flowing in a similar-size microfluidic channel and vary systematically the governing parameter, namely the capillary number, ratio of the viscous to elastic forces, and the confinement ratio, ratio of the capsule to tube size. The resulting space-time-parameter problem is solved using two global POD reduced bases, determined in the offline stage for the space and parameter variables, respectively. A suitable low-order spatial reduced basis is then computed in the online stage for any new parameter instance. The time evolution of the capsule dynamics is achieved by identifying the nonlinear low-order manifold of the reduced variables; for that, a point cloud of reduced data is computed and a diffuse approximation method is used. Numerical comparisons between the full-order fluid-structure interaction model and the reduced-order one confirm both accuracy and stability of the reduction technique over the whole admissible parameter domain. We believe that such an approach can be applied to a broad range of coupled problems especially involving quasistatic models of structural mechanics.

Data-driven reduced order modeling based on tensor decompositions and its application to air-wall heat transfer in buildings

SeMA Journal ◽  
2021 ◽  
Author(s):  
M. Azaïez ◽  
T. Chacón Rebollo ◽  
M. Gómez Mármol ◽  
E. Perracchione ◽  
A. Rincón Casado ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Wall Heat Transfer ◽  
Reduced Order ◽  
Tensor Decompositions

scholarly journals Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 266
Author(s):  
Péter German ◽  
Mauricio E. Tano ◽  
Carlo Fiorina ◽  
Jean C. Ragusa
Keyword(s):  
Heat Transfer ◽  
Porous Medium ◽  
Porous Media ◽  
Steady State ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Data Driven ◽  
Reduced Order ◽  
Medium Formulation ◽  
Flow Resistances

This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).

scholarly journals Data‐Driven Learning of Reduced‐Order Dynamics for a Parametrized Shallow Water Equation

PAMM ◽  
2021 ◽  
Vol 20 (S1) ◽  
Author(s):  
Süleyman Yıldız ◽  
Pawan Goyal ◽  
Peter Benner ◽  
Bülent Karasözen
Keyword(s):  
Shallow Water ◽  
Shallow Water Equation ◽  
Data Driven ◽  
Reduced Order

scholarly journals Space–time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems

2021 ◽  
Vol 424 ◽  
pp. 109845
Author(s):  
Youngsoo Choi ◽  
Peter Brown ◽  
William Arrighi ◽  
Robert Anderson ◽  
Kevin Huynh
Keyword(s):  
Dynamical Systems ◽  
Large Scale ◽  
Space Time ◽  
Reduced Order Model ◽  
Boltzmann Transport ◽  
Order Model ◽  
Linear Dynamical Systems ◽  
Reduced Order ◽  
Transport Problems

Data-driven temperature estimation of non-contact solids using deep-learning reduced-order models

2022 ◽  
Vol 185 ◽  
pp. 122383
Author(s):  
Genghui Jiang ◽  
Ming Kang ◽  
Zhenwei Cai ◽  
Yingzheng Liu ◽  
Weizhe Wang
Keyword(s):  
Deep Learning ◽  
Data Driven ◽  
Reduced Order Models ◽  
Temperature Estimation ◽  
Reduced Order

scholarly journals A Hybrid Analytics Paradigm Combining Physics-Based Modeling and Data-Driven Modeling to Accelerate Incompressible Flow Solvers

Fluids ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 50 ◽  
Author(s):  
Sk. Rahman ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Poisson Equation ◽  
Incompressible Flow ◽  
Data Exchange ◽  
Incompressible Flows ◽  
Stokes Equations ◽  
Time Integration ◽  
Data Driven ◽  
Integration Algorithm ◽  
Reduced Order ◽  
Advection Diffusion Equation

Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm, which constitutes the major component of the computational expense. In this study, we propose a hybrid analytics procedure combining a data-driven approach with a physics-based simulation technique to accelerate the computation of incompressible flows. In our approach, proper orthogonal basis functions are generated to be used in solving the Poisson equation in a reduced order space. Since the time integration of the advection–diffusion equation part of the physics-based model is computationally inexpensive in a typical incompressible flow solver, it is retained in the full order space to represent the dynamics more accurately. Encoder and decoder interface conditions are provided by incorporating the elliptic constraint along with the data exchange between the full order and reduced order spaces. We investigate the feasibility of the proposed method by solving the Taylor–Green vortex decaying problem, and it is found that a remarkable speed-up can be achieved while retaining a similar accuracy with respect to the full order model.

scholarly journals Reduced Order Models for Nonlinear Dynamic Analysis With Application to a Fan Blade

2019 ◽  
Author(s):  
Mikel Balmaseda ◽  
G. Jacquet-Richardet ◽  
A. Placzek ◽  
D.-M. Tran
Keyword(s):  
Normal Modes ◽  
Nonlinear Dynamic Analysis ◽  
Evaluation Procedure ◽  
Reduced Order Models ◽  
Reduced Basis ◽  
Reduced Order ◽  
Stiffness Evaluation ◽  
Fan Blade ◽  
Proper Orthogonal ◽  
Good Agreement

Abstract In the present work reduced order models (ROM) that are independent from the full order finite element models (FOM) considering geometrical non linearities are developed and applied to the dynamic study of a fan. The structure is considered to present nonlinear vibrations around the pre-stressed equilibrium induced by rotation enhancing the classical linearised approach. The reduced nonlinear forces are represented by a polynomial expansion obtained by the Stiffness Evaluation Procedure (STEP) and then corrected by means of a Proper Orthogonal Decomposition (POD) that filters the full order nonlinear forces (StepC ROM). The Linear Normal Modes (LNM) and Craig-Bampton (C-B) type reduced basis are considered here. The latter are parametrised with respect to the rotating velocity. The periodic solutions obtained with the StepC ROM are in good agreement with the solutions of the FOM and are more accurate than the linearised ROM solutions and the STEP ROM. The proposed StepC ROM provides the best compromise between accuracy and time consumption of the ROM.

scholarly journals Data-Driven Filtered Reduced Order Modeling of Fluid Flows

2018 ◽  
Vol 40 (3) ◽  
pp. B834-B857 ◽  
Author(s):  
X. Xie ◽  
M. Mohebujjaman ◽  
L. G. Rebholz ◽  
T. Iliescu
Keyword(s):  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Reduced Order
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