Next-Generation Computational Thermal-Fluid Sciences: Multiphysics Modeling and Data-Driven Methods

Modern engineering demands an unprecedented understanding of highly complex transport phenomena operating across diverse spatio-temporal scales. Traditional engineering frameworks often treat fluid mechanics, heat transfer, and multiphysics interactions, such as magnetohydrodynamics, electrokinetics, and multiphase porous transport, in relative isolation. However, tackling contemporary challenges in aerospace, clean energy transition, environmental safety, and biomedical engineering requires a unified, next-generation approach. While classical Computational Fluid Dynamics (CFD) and high-performance computing (HPC) have driven significant breakthroughs, traditional numerical methods face massive computational bottlenecks when applied to real-time optimization, large-scale uncertainty quantification, and flow stability analysis. The rapid emergence of data-driven paradigms, including scientific machine learning and physics-informed neural networks (PINNs), offers a transformative path forward. By seamlessly blending governing physical laws with data-driven acceleration, these methodologies pave the way for real-time reduced-order modeling and true digital twins of complex thermal-fluid systems.This Research Topic aims to collect pioneering research that bridges the gap between classical transport physics and cutting-edge computational paradigms. We seek to establish a comprehensive forum for novel numerical frameworks, data-driven methodology, and multi-disciplinary applications that enhance both the predictive accuracy and computational efficiency of thermal-fluid systems. We welcome contributions focusing on fundamental theoretical developments, advanced algorithmic frameworks, and impactful industrial applications.Potential authors are invited to submit original research articles, review papers, and short communications. Topics of interest include, but are not limited to, the following areas:1] Advanced Computational Fluid Dynamics (CFD) and high-fidelity heat and mass transfer simulations.2] Multiphase, multiphysics, and electrokinetic flows, including magnetohydrodynamics (MHD) and microfluidics.3] Transport phenomena, flow stability, and hydrodynamics in porous and fractured media.4] Statistical inference, sensitivity and uncertainty quantification, data assimilation, reduced-order and surrogate modeling, and explainable AI for prediction, optimization, control, and physical discovery in complex transport and energy systems.5] Scientific machine learning, Physics-Informed Neural Networks (PINNs), and artificial neural networks applied to fluid loops.6] Reduced-order modeling (ROM), inverse problems, and optimization workflows for thermal systems.7] Digital twins, uncertainty quantification, and hybrid physics–machine learning models.8] Cross-disciplinary applications spanning industrial, energy, environmental, aerospace, and biomedical engineering.The primary objective of this research topic is to address the severe computational bottlenecks and accuracy tradeoffs currently limiting the analysis of complex, interconnected thermal-fluid systems. While high-fidelity simulations provide exceptional detail for multiphysics phenomena, such as electrokinetic, multiphase, and magnetohydrodynamic flows, their extreme computational cost makes them impractical for real-time optimization, flow stability analysis, and comprehensive uncertainty quantification.To achieve this goal, this collection seeks to leverage recent advances at the intersection of traditional mechanics and scientific computing. Specifically, the integration of scientific machine learning, physics-informed neural networks (PINNs), and high-performance computing (HPC) has newly enabled the embedding of governing physical principles directly into data-driven architectures. By utilizing these hybrid frameworks alongside advanced reduced-order modeling (ROM), researchers can now bypass traditional computational constraints. This research topic aims to consolidate these emerging strategies, statistical inference, and explainable AI, fostering the development of robust, real-time digital twins capable of optimizing industrial, environmental, and biomedical fluid systems with unprecedented speed and accuracy.The scope of this research topic encompasses advanced physical modeling, high-performance computing frameworks, and data-driven acceleration strategies for thermal-fluid systems. We invite contributors to submit manuscripts that push the boundaries of classical transport phenomena through algorithmic innovation or novel applications.Specific themes we encourage authors to address include:1] Innovative multi-scale and multiphysics modeling, including electrokinetics, microfluidics, and magnetohydrodynamics (MHD).2] Flow, thermal, and hydrodynamic stability analysis in complex geometries or porous media.3] Algorithmic advances in Scientific Machine Learning (SciML), particularly Physics-Informed Neural Networks (PINNs) and hybrid data-physics architectures.4] Real-time optimization, inverse problems, and reduced-order modeling (ROM) for engineering workflows.5] Statistical inference, uncertainty quantification, surrogate modeling, and explainable AI for complex transport and energy systems. 6] Deployment of predictive digital twins and robust uncertainty quantification frameworks.We welcome a variety of manuscript types to foster a comprehensive dialogue, including original research articles, comprehensive review papers, short perspectives, and method articles. Topic editors: Nitish Gupta, Kuppalapalle Vajravelu, Bhargavi D, ABHAY GUPTA Source: https://www.frontiersin.org/research-topics/85750/next-generation-computational-thermal-fluid-sciences-multiphysics-modeling-and-data-driven-methods