☆ 4.7 Article

A data-driven machine learning framework for modeling of turbulent mixing flows

PHYSICS OF FLUIDS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Chemical

Lagrangian stochastic modelling of liquid flow in a mechanically agitated vessel

Hamzah A. Sheikh et al.

Summary: This study introduces a computationally efficient data-driven Lagrangian stochastic approach for predicting liquid flow inside a mechanically agitated vessel. Experimental results show that first and second order models provide good predictions of local flow properties, with the first order model being slightly superior.

CHEMICAL ENGINEERING SCIENCE (2022)

添加到收藏夹

Article Engineering, Chemical

Computation of Lagrangian coherent structures from experimental fluid trajectory measurements in a mechanically agitated vessel

Kun Li et al.

Summary: In this study, the hidden Lagrangian coherent structures in mechanically agitated vessels were resolved using a numerical finite-time Lyapunov exponent approach for the first time. The highly complex flow topology, which varies significantly with azimuthal position, was revealed by computing forward and backward FTLE fields and extracting repelling and attracting FTLE ridges from experimental 3D Lagrangian trajectories.

CHEMICAL ENGINEERING SCIENCE (2022)

添加到收藏夹

Article Engineering, Chemical

Numerical and Experimental Investigations of Horizontal Turbulent Particle-Liquid Pipe Flow

ZhuangJian Yang et al.

Summary: In this study, a Eulerian-Eulerian computational fluid dynamics approach is used to investigate the turbulent flow of particle-liquid suspensions in a horizontal pipe. The approach is validated through experimental data and provides accurate predictions of particle behavior and liquid velocity profiles. The results suggest that particle behavior can be classified into three categories depending on size and density ratio.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2022)

添加到收藏夹

Article Engineering, Chemical

Multiscale wavelet analysis of 3D Lagrangian trajectories in a mechanically agitated vessel

Chiya Savari et al.

Summary: This article develops a new experimental-theoretical framework to study flow turbulence properties in mixing processes. It uses a discrete wavelet transform to decompose flow trajectories into mean and stochastic components representing different scales. By constructing maps of local flow velocity, turbulent kinetic energy, turbulence dissipation rate, and turbulent diffusion coefficients, it provides valuable detailed information for equipment design and operation.

CHEMICAL ENGINEERING SCIENCE (2022)

添加到收藏夹

Article Mechanics

Eulerian-Lagrangian Modelling of Turbulent Two-Phase Particle-Liquid Flow in a Stirred Vessel: CFD and Experiments Compared

Ananda J. Jadhav et al.

Summary: This study develops a 3D Eulerian-Lagrangian CFD model to simulate the turbulent flow and mixing of coarse dense particles in a batch vessel. The model is validated by experimental measurements and shows good agreement in phase-velocity components, particle slip velocities, and particle concentration. The model can be extended to study complex particle-liquid suspensions and provide valuable Lagrangian data for other modeling techniques.

INTERNATIONAL JOURNAL OF MULTIPHASE FLOW (2022)

添加到收藏夹

Article Mechanics

Lagrangian wavelet analysis of turbulence modulation in particle-liquid mixing flows

Chiya Savari et al.

Summary: A new experimental-theoretical framework has been developed to investigate turbulence and turbulence modulation in a two-phase multicomponent particle-liquid flow in a mechanically agitated vessel. The study reveals that particle size, impeller pumping mode, and particle size distribution mode have significant impacts on liquid turbulence, with large particles enhancing liquid turbulence.

PHYSICS OF FLUIDS (2022)

添加到收藏夹

Article Engineering, Chemical

Lagrangian Recurrence Tracking: A Novel Approach for Description of Mixing in Liquid and Particle-Liquid Flows

Chiya Savari et al.

Summary: Experimental data obtained using a Lagrangian positron emission particle tracking technique have been used to develop a new methodology for evaluating mixing in mechanically agitated vessels. This data-driven approach quantifies local and global mixing using the Shannon entropy of probability distribution of recurrence structures. Results show that different impeller designs have varying effects on global and local mixing, and that suspension mixing improves with increasing impeller speed up to a plateau.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2021)

添加到收藏夹

Article Thermodynamics

Data-driven enhancement of coherent structure-based models for predicting instantaneous wall turbulence

Rahul Deshpande et al.

Summary: This study introduces a methodology to extract the 3-D geometry of significant eddies from wall-turbulence datasets, improving predictions of wall-bounded flows. Analysis reveals geometric self-similarity in large-scale wall-coherent motions, enhancing instantaneous flow predictions in structure-based models. Additionally, a flow control system concept is proposed to alter the influence of wall coherent motions on skin-friction drag.

INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2021)

添加到收藏夹

Article Mechanics

Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

Pranshu Pant et al.

Summary: A novel deep learning framework, DL-ROM, is developed to create a neural network capable of non-linear projections to reduced order states, which efficiently predicts future time steps of a simulation using 3D Autoencoder and 3D U-Net-based architectures. The model can achieve significant computational savings without the need for ground truth supervision or iteratively solving expensive equations, while reducing computational run times of iterative solvers by nearly two orders of magnitude.

PHYSICS OF FLUIDS (2021)

添加到收藏夹

Article Engineering, Mechanical

Numerical and experimental study on turbulence statistics in a large fan-stirred combustion vessel

M. E. Morsy et al.

Summary: The study employs three different turbulence models, compares mean and fluctuating fields obtained by three-dimensional computational fluid dynamics modeling with experimental data, and calculates various turbulence statistics. A reasonably isotropic and homogeneous turbulence with large turbulence intensities is achieved under experimental conditions, with negligible influence of the third dimension velocity component on the measured characteristics.

EXPERIMENTS IN FLUIDS (2021)

添加到收藏夹

Article Mechanics

Leading-edge flow sensing for detection of vortex shedding from airfoils in unsteady flows

Aditya Saini et al.

Summary: Sensing vortex shedding in unsteady airfoil flows can improve aerodynamic performance, and the time variation of the leading-edge suction parameter can be used to deduce various events related to vortex shedding. The leading-edge flow sensing technique is adapted to deduce the variation of the leading-edge suction parameter during unsteady motion in incompressible flow. By studying the variations of the outputs and calculated parameters, guidelines are presented for detecting vortex shedding events.

PHYSICS OF FLUIDS (2021)

添加到收藏夹

Article Mechanics

Explore missing flow dynamics by physics-informed deep learning: The parameterized governing systems

Hui Xu et al.

Summary: Understanding flow dynamics is crucial in various disciplines. This study introduces a physics-informed deep learning approach to recover missing flow dynamics by treating governing equations as parameterized constraints, leading to the reconstruction of data and a richer dataset with improved spatial distribution.

PHYSICS OF FLUIDS (2021)

添加到收藏夹

Review Mechanics

Machine Learning for Fluid Mechanics

Steven L. Brunton et al.

ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)

添加到收藏夹

Article Chemistry, Analytical

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control

Lepeng Song et al.

SENSORS (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets

Muhammed Kursad Ucar et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Reliable Accuracy Estimates from k-Fold Cross Validation

Tzu-Tsung Wong et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

添加到收藏夹

Article Mechanics

Theory-based Reynolds-averaged Navier-Stokes equations with large eddy simulation capability for separated turbulent flow simulations

Stefan Heinz et al.

PHYSICS OF FLUIDS (2020)

添加到收藏夹

Article Mechanics

Direct numerical simulation of multiscale flow physics of binary droplet collision

Xiaodong Chen et al.

PHYSICS OF FLUIDS (2020)

添加到收藏夹

Article Mechanics

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

S. Pawar et al.

PHYSICS OF FLUIDS (2019)

添加到收藏夹

Article Mechanics

Optimization under turbulence model uncertainty for aerospace design

L. W. Cook et al.

PHYSICS OF FLUIDS (2019)

添加到收藏夹

Article Mathematics, Applied

Data-Driven Model Reduction and Transfer Operator Approximation

Stefan Klus et al.

JOURNAL OF NONLINEAR SCIENCE (2018)

添加到收藏夹

Article Engineering, Mechanical

Jet mixing optimization using machine learning control

Zhi Wu et al.

EXPERIMENTS IN FLUIDS (2018)

添加到收藏夹

Review Engineering, Aerospace

Modal Analysis of Fluid Flows: An Overview

Kunihiko Taira et al.

AIAA JOURNAL (2017)

添加到收藏夹

Article Mechanics

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Julia Ling et al.

JOURNAL OF FLUID MECHANICS (2016)

添加到收藏夹

Article Mechanics

A statistical learning strategy for closed-loop control of fluid flows

Florimond Gueniat et al.

THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2016)

添加到收藏夹

Review Mechanics

Closed-Loop Turbulence Control: Progress and Challenges

Steven L. Brunton et al.

APPLIED MECHANICS REVIEWS (2015)

添加到收藏夹

Article Engineering, Multidisciplinary

Dynamic data-driven reduced-order models

Benjamin Peherstorfer et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2015)

添加到收藏夹

Article Energy & Fuels

Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir

Mohammad Ali Sebtosheikh et al.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2015)

添加到收藏夹

Article Mechanics

A construction of the Reynolds-averaged turbulence transport equations in a variable-density flow, based on the concept of mass-weighted fluctuations

Akira Yoshizawa et al.

PHYSICS OF FLUIDS (2013)

添加到收藏夹

Article Engineering, Chemical

Lagrangian Tools for the Analysis of Mixing in Single-Phase and Multiphase Flow Systems

Antonio Guida et al.

AICHE JOURNAL (2012)

添加到收藏夹

Article Mechanics

Freely decaying, homogeneous turbulence generated by multi-scale grids

P-A. Krogstad et al.

JOURNAL OF FLUID MECHANICS (2011)

添加到收藏夹

Article Engineering, Chemical

Shannon entropy for local and global description of mixing by Lagrangian particle tracking

Antonio Guida et al.

CHEMICAL ENGINEERING SCIENCE (2010)

添加到收藏夹

Article Engineering, Chemical

PEPT measurements of solid-liquid flow field and spatial phase distribution in concentrated monodisperse stirred suspensions

Antonio Guida et al.

CHEMICAL ENGINEERING SCIENCE (2010)

添加到收藏夹

Article Engineering, Chemical

Positron emission particle tracking (PEPT) compared to particle image velocimetry (PIV) for studying the flow generated by a pitched-blade turbine in single phase and multi-phase systems

P. Pianko-Oprych et al.

CHEMICAL ENGINEERING SCIENCE (2009)

添加到收藏夹

Article Engineering, Chemical

Using Positron Emission Particle Tracking (PEPT) to Study Mixing in Stirred Vessels: Validation and Tackling Unsolved Problems in Opaque Systems

Mostafa Barigou et al.

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN (2009)

添加到收藏夹

Article Mechanics

Horizontal laminar flow of coarse nearly-neutrally buoyant particles in non-Newtonian conveying fluids: CFD and PEPT experiments compared

M. Eesa et al.

INTERNATIONAL JOURNAL OF MULTIPHASE FLOW (2008)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deterministic learning and rapid dynamical pattern recognition

Cong Wang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2007)

添加到收藏夹

Review Automation & Control Systems

A survey of iterative learning control

Douglas A. Bristow et al.

IEEE CONTROL SYSTEMS MAGAZINE (2006)

添加到收藏夹

Review Mechanics

Turbulent mixing

PE Dimotakis

ANNUAL REVIEW OF FLUID MECHANICS (2005)

添加到收藏夹

Article Engineering, Chemical

Particle tracking in opaque mixing systems: An overview of the capabilities of PET and PEPT

M Barigou

CHEMICAL ENGINEERING RESEARCH & DESIGN (2004)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.