Speeding up turbulent reactive flow simulation via a deep artificial neural network: A methodology study

A computational workflow to study particle transport and filtration in porous media: coupling CFD and deep learning

(2021) Agnese Marcato et al.   CHEMICAL ENGINEERING JOURNAL

Conventional and data‐driven modeling of filtered drag, heat transfer, and reaction rate in gas–particle flows

(2021) Li‐Tao Zhu et al.   AICHE JOURNAL

Computational fluid dynamics‐based optimization of dimpled steam cracking reactors for reduced CO 2 emissions

(2020) Jens N. Dedeyne et al.   AICHE JOURNAL

Machine learning to assist filtered two‐fluid model development for dense gas–particle flows

(2020) Li‐Tao Zhu et al.   AICHE JOURNAL

Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

(2020) Yongliang Yan et al.   CHEMICAL ENGINEERING JOURNAL

Non-Intrusive Classification of Gas-Liquid Flow Regimes in an S-shaped Pipeline Riser Using a Doppler Ultrasonic Sensor and Deep Neural Networks

(2020) Somtochukwu Godfrey Nnabuife et al.   CHEMICAL ENGINEERING JOURNAL

Deep learning observables in computational fluid dynamics

(2020) Kjetil O. Lye et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A data-driven reduced-order model of nonlinear processes based on diffusion maps and artificial neural networks

(2020) E.D. Koronaki et al.   CHEMICAL ENGINEERING JOURNAL

Sparse scattered high performance computing data driven artificial neural networks for multi-dimensional optimization of buoyancy driven heat and mass transfer in porous structures

(2020) Yan Su et al.   CHEMICAL ENGINEERING JOURNAL

Tar prediction in bubbling fluidized bed gasification through artificial neural networks

(2020) Daniel Serrano et al.   CHEMICAL ENGINEERING JOURNAL

Application of generative deep learning to predict temperature, flow and species distributions using simulation data of a methane combustor

(2020) Ryno Laubscher et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction

(2019) Pieter P. Plehiers et al.   Engineering

Development and evaluation of data-driven modeling for bubble size in turbulent air-water bubbly flows using artificial multi-layer neural networks

(2019) Hokyo Jung et al.   CHEMICAL ENGINEERING SCIENCE

Numerical simulation of micro-mixing in stirred reactors using the engulfment model coupled with CFD

(2016) Xiaoxia Duan et al.   CHEMICAL ENGINEERING SCIENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Assessment of Turbulence-Chemistry Interaction Models in MILD Combustion Regime

(2014) Ashoke De et al.   FLOW TURBULENCE AND COMBUSTION

A Comprehensive Study of Effects of Mixing and Chemical Kinetic Models on Predictions of n-heptane Jet Ignitions with the PDF Method

(2013) Yuanjiang Pei et al.   FLOW TURBULENCE AND COMBUSTION

Retrospect and Perspective of Micro-mixing Studies in Stirred Tanks

(2012) Jingcai CHENG et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

Comparison of the stochastic fields method and DQMoM-IEM as turbulent reaction closures

(2010) Jethro Akroyd et al.   CHEMICAL ENGINEERING SCIENCE

Progress in probability density function methods for turbulent reacting flows

(2009) D.C. Haworth   PROGRESS IN ENERGY AND COMBUSTION SCIENCE

Comparison of Various Micromixing Approaches for Computational Fluid Dynamics Simulation of Barium Sulfate Precipitation in Tubular Reactors

(2008) Alper A. Öncül et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Comparison between Lagrangian and mesoscopic Eulerian modelling approaches for inertial particles suspended in decaying isotropic turbulence

(2008) A. Kaufmann et al.   JOURNAL OF COMPUTATIONAL PHYSICS