International Journal of Numerical Methods for Heat & Fluid Flow: Volume 34 Issue 8

Guest editorial: Data-driven methods for heat transfer and fluid flow

R.S. Ransing

Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis

Umair Khan, William Pao, Karl Ezra Salgado Pilario, Nabihah Sallih, Muhammad Rehan Khan

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime…

Prediction of multi-physics field distribution on gas turbine endwall using an optimized surrogate model with various deep learning frames

Weixin Zhang, Zhao Liu, Yu Song, Yixuan Lu, Zhenping Feng

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most…

Swirl-induced motion prediction with physics-guided machine learning utilizing spatiotemporal flow field structure

Ziming Zhou, Fengnian Zhao, David Hung

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine…

Deep learning algorithms for temperature prediction in two-phase immersion-cooled data centres

Pratheek Suresh, Balaji Chakravarthy

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a…

Permeability estimation for deformable porous media with convolutional neural network

Kunpeng Shi, Guodong Jin, Weichao Yan, Huilin Xing

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel…

Investigating embedded data distribution strategy on reconstruction accuracy of flow field around the crosswind-affected train based on physics-informed neural networks

Guang-Zhi Zeng, Zheng-Wei Chen, Yi-Qing Ni, En-Ze Rui

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of…

Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

Ryley McConkey, Nikhila Kalia, Eugene Yee, Fue-Sang Lien

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be…

A neural based modeling approach for predicting effective thermal conductivity of brewer’s spent grain

Amanda de Oliveira e Silva, Alice Leonel, Maisa Tonon Bitti Perazzini, Hugo Perazzini

Brewer's spent grain (BSG) is the main by-product of the brewing industry, holding significant potential for biomass applications. The purpose of this paper was to determine the…

Artificial intelligence-based droplet size prediction for microfluidic system

Sameer Dubey, Pradeep Vishwakarma, TVS Ramarao, Satish Kumar Dubey, Sanket Goel, Arshad Javed

This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with…

Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations

Thenysson Matos, Maisa Tonon Bitti Perazzini, Hugo Perazzini

This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for…

An artificial intelligence approach for the estimation of conduction heat transfer using deep neural networks

Mohammad Edalatifar, Jana Shafi, Majdi Khalid, Manuel Baro, Mikhail A. Sheremet, Mohammad Ghalambaz

This study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using…

Physics-informed neural networks (P INNs): application categories, trends and impact

Mohammad Ghalambaz, Mikhail A. Sheremet, Mohammed Arshad Khan, Zehba Raizah, Jana Shafi

This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from…

Data-driven wall modeling for LES involving non-equilibrium boundary layer effects

Sarath Radhakrishnan, Joan Calafell, Arnau Miró, Bernat Font, Oriol Lehmkuhl

Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall…

Physically consistent temperature fields for geophysical inversion based on the parametrized location of an isotherm

Mariano Tomás Fernandez, Sergio Zlotnik, Pedro Diez

This paper aims to provide a method for obtaining physically sound temperature fields to be used in geophysical inversions in the presence of immersed essential conditions.

Reinforcement learning for cooling rate control during quenching

Elie Hachem, Abhijeet Vishwasrao, Maxime Renault, Jonathan Viquerat, P. Meliga

The premise of this research is that the coupling of reinforcement learning algorithms and computational dynamics can be used to design efficient control strategies and to improve…

Flow control by a hybrid use of machine learning and control theory

Takeru Ishize, Hiroshi Omichi, Koji Fukagata

Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid…