Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning
Agnese Marcato, Gianluca Boccardo, Daniele Marchisio
Chemical Engineering Journal (2021) Vol. 417, pp. 128936-128936
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Li‐Tao Zhu, Xizhong Chen, Bo Ouyang, et al.
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 28, pp. 9901-9949
Open Access | Times Cited: 133
Physics-informed deep learning for multi-species membrane separations
Danyal Rehman, John H. Lienhard
Chemical Engineering Journal (2024) Vol. 485, pp. 149806-149806
Closed Access | Times Cited: 23
Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications
Wei‐Hsin Chen, Jhih-Syun Wang, Min‐Hsing Chang, et al.
Energy Conversion and Management (2022) Vol. 254, pp. 115209-115209
Closed Access | Times Cited: 59
From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media
Agnese Marcato, Gianluca Boccardo, Daniele Marchisio
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 24, pp. 8530-8541
Open Access | Times Cited: 38
Rapid monitoring of indoor air quality for efficient HVAC systems using fully convolutional network deep learning model
Sanghun Shin, Keuntae Baek, Hongyun So
Building and Environment (2023) Vol. 234, pp. 110191-110191
Closed Access | Times Cited: 23
Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid
Xiaogang Dong, Salah Knani, Hamdi Ayed, et al.
Case Studies in Thermal Engineering (2024) Vol. 57, pp. 104309-104309
Open Access | Times Cited: 10
Swin transformer based transfer learning model for predicting porous media permeability from 2D images
Shaoyang Geng, Shuo Zhai, Chengyong Li
Computers and Geotechnics (2024) Vol. 168, pp. 106177-106177
Closed Access | Times Cited: 8
3d Fluid-Particle Interaction Dynamics and Filtration Performance of Realistic Fibrous Filters Using Deep Learning and X-Ray Computed Tomography Images
Kodai Hada, Mohammadreza Shirzadi, Tomonori Fukasawa, et al.
(2025)
Closed Access
Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency
Tomoki Yasuda, Shinichi Ookawara, Shiro Yoshikawa, et al.
Chemical Engineering Journal (2022) Vol. 453, pp. 139540-139540
Open Access | Times Cited: 31
Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies
Makoto Kambara, Satoru Kawaguchi, Hae June Lee, et al.
Japanese Journal of Applied Physics (2022) Vol. 62, Iss. SA, pp. SA0803-SA0803
Open Access | Times Cited: 26
Flow prediction of heterogeneous nanoporous media based on physical information neural network
Liang Zhou, Hai Sun, Dongyan Fan, et al.
Gas Science and Engineering (2024) Vol. 125, pp. 205307-205307
Closed Access | Times Cited: 5
Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing
Mauro Bracconi
Chemical Engineering and Processing - Process Intensification (2022) Vol. 181, pp. 109148-109148
Closed Access | Times Cited: 24
Application of deep learning neural networks for the analysis of fluid-particle dynamics in fibrous filters
Mohammadreza Shirzadi, Tomonori Fukasawa, Kunihiro Fukui, et al.
Chemical Engineering Journal (2022) Vol. 455, pp. 140775-140775
Closed Access | Times Cited: 21
Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering
Agnese Marcato, Daniele Marchisio, Gianluca Boccardo
The Canadian Journal of Chemical Engineering (2023) Vol. 101, Iss. 6, pp. 3013-3018
Closed Access | Times Cited: 11
A simulation study on NOx reduction efficiency in SCR catalysts utilizing a modern C3-CNN algorithm
Peilun Han, Xiaoqian Shen, Boxiong Shen
Fuel (2024) Vol. 363, pp. 130985-130985
Closed Access | Times Cited: 4
Speeding up turbulent reactive flow simulation via a deep artificial neural network: A methodology study
Yi Ouyang, Laurien A. Vandewalle, Lin Chen, et al.
Chemical Engineering Journal (2021) Vol. 429, pp. 132442-132442
Open Access | Times Cited: 26
Deep learning methods for predicting fluid forces in dense particle suspensions
Neil Raj Ashwin, Ze Cao, Nikhil Muralidhar, et al.
Powder Technology (2022) Vol. 401, pp. 117303-117303
Open Access | Times Cited: 20
Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network
Agnese Marcato, Javier E. Santos, Gianluca Boccardo, et al.
Chemical Engineering Journal (2022) Vol. 455, pp. 140367-140367
Open Access | Times Cited: 19
Effect of fabric structure on in-plane and through-plane hydraulic properties of nonwoven geotextiles
Ghazaleh Eskandarnia, Parham Soltani
Geotextiles and Geomembranes (2023) Vol. 51, Iss. 4, pp. 1-14
Closed Access | Times Cited: 10
Heat transfer enhancement in a regenerative cooling channel using porous media
Xingzhen Zhu, Dandan Pan, Yanyan Gao, et al.
Chemical Engineering and Processing - Process Intensification (2022) Vol. 183, pp. 109234-109234
Closed Access | Times Cited: 17
Combining computational fluid dynamics, photon fate simulation and machine learning to optimize continuous-flow photocatalytic systems
Gabriela Xavier de Oliveira, Simon Kuhn, Humberto Gracher Riella, et al.
Reaction Chemistry & Engineering (2023) Vol. 8, Iss. 9, pp. 2119-2133
Open Access | Times Cited: 9
Numerical simulation of the influence of operating parameters on the inner characteristics in a hydrogen-enriched shaft furnace
Tian Xu, Heng Zhou, Yichun Zhang, et al.
International Journal of Hydrogen Energy (2023) Vol. 55, pp. 1131-1142
Closed Access | Times Cited: 9
Optimization on the performance of fibrous filter through computational fluid dynamic simulation coupled with response surface methodology
Cun-Guang Liang, Hui Li, Bin Hao, et al.
Chemical Engineering Science (2023) Vol. 280, pp. 119070-119070
Closed Access | Times Cited: 8
A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes
Pengxiang Zhao, Yunze Hui, Yuhang Qiu, et al.
Fuel (2024) Vol. 373, pp. 132273-132273
Open Access | Times Cited: 2
Progress and future directions bridging microplastics transport from pore to continuum scale: A comprehensive review for experimental and modeling approaches
Seung Ji Lim, Kyung‐Jin Lee, Hansung Nam, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 179, pp. 117851-117851
Open Access | Times Cited: 2
Page 1 - Next Page

Scroll to top