Aims and scope

The International Journal of Numerical Methods for Heat & Fluid Flow (HFF) publishes peer-reviewed papers that explain how fundamental insights are gained in heat and fluid flow physics using computational methods supported by analytical and experimental research.

The Editors encourage contributions which increase the basic understanding of the interaction between heat transfer processes and fluid dynamics involved in solving engineering problems. Original and high-quality contributions in numerical methods, including deep learning methods, for solving fluid-structure interaction, micro-bio fluidics, laminar and turbulent flow, heat transfer and advection/diffusion problems are relevant and welcome. However, the application of existing numerical, and deep learning, methods to engineering problems that are not deemed to be at the forefront of research by the Editors will not be considered for review.

Topics include, but not limited to, new numerical, and deep learning, methods for solving heat and fluid flow problems in:

    Efficient energy transfer and storage processes
    Environment and Climate Change
    Cryogenics and Cryo-preservation
    Mechanical, Aerospace and Interdisciplinary Engineering 

Special Issue on Emerging Approaches in Aerodynamics
Submission Date: 2024-11-16

Introduction

This special issue focuses on emerging approaches in aerodynamics, taking advantage of massive data collected from high-fidelity numerical simulations, highly instrumented wind-tunnel testing and innovative flight tests measurements. Experimental, theoretical and numerical aspects will be addressed, ranging from fundamental research to industrial applications. They will cover both external and internal aerodynamics, with the corresponding thermal phenomena.

The research works will consider problems encountered in the aerospace and transportation domains including electric air taxis and in energy production, such as wind turbines for instance.

The emerging methods such as big data, machine learning, artificial intelligence and high-fidelity simulations, provide enhanced capabilities to major models. Data-driven turbulence modelling, disruptive geometry modelling techniques, self-adaptive meshing are among the models that benefit from these techniques. Generative modelling allows aerodynamic data fusion from multiple sources providing a more complete coverage of flight envelope. Physics-aware surrogate models combined with high-order simulation improve interdisciplinary predictions and multidisciplinary design optimization, essential for next generation of environmentally friendly products.

All contributions with numerical, theoretical and/or experimental approaches falling within the theme of this special issue are welcome. It could contain, among others, the completed versions of the most instructive contributions to the 58th 3AF International Conference on Applied Aerodynamics AERO2024 (March 27-28-29, 2024), organized in Orléans (France) by the Aerodynamics Technical Committee of the French Aeronautics and Aerospace Society (3AF). This special issue does not constitute the proceedings of this conference.

    Reviewing process: Each submitted paper is reviewed by the Guest Editor-In-Chief and Advisory Editor of the IJNMHFF journal, Prof. Abderrahmane Baïri. If it is judged suitable for publication, it will be sent to at least two independent referees for peer review with the rigorous expertise process of the IJNMHFF journal.
    Decision on article (acceptance, rejection, revision): as soon as the peer reviews have arrived
    Publication date of the special issue: March 2025
    References of the special issue: Volume 35 Issue 4 (HFF 35.4)
    Guest Editor-In-Chief: Prof. Abderrahmane Baïri (University of Paris)
    Contact: on behalf of the Guest Editor-In-Chief: Ms Aude Lurbe aude.lurbe@aaaf.asso.f

List of topic areas

The following items will be considered to address the above challenges (the list not being exhaustive):

    Data-driven aerodynamic models through data science and machine learning;
    Impact of machine learning on aerodynamic design optimization;
    Aerodynamic design assisted by reduced-order modelling and machine learning;
    Thermal modelling in aeronautics and space;
    Machine Learning for Turbulence Modelling;
    Unsteady aerodynamics, unducted propellers, wind turbines…;
    Innovative tools for numerical simulation: RANS, LES, LBM;
    Innovative mesh Generation of complex geometry, Self-adaptive mesh techniques;
    Multidisciplinary Design Optimization;
    Multiphysics interactions: heat and mass transfer, aeroacoustics, aeroelasticity;
    Data assimilation, Digital twins, Real-time flight measurements;
    Wind tunnel experiments, Innovative Post-processing of experimental measurements;
    In-flight identification of aerodynamic performance.

Guest Editors 

Professor Abderrahmane BAÏRI
Université de Paris, France
abairi@parisnanterre.fr

Dr. Nacim ALILAT
University of Paris, France
nacim.alilat@parisnanterre.fr

Dr Vincent BRION
ONERA (The French Aerospace Lab), France
vincent.brion@onera.fr

Special Issue on Digital Twinning Thermal and Energy Systems
Submission Date: 2024-12-31

Introduction

A digital twin may be defined as a platform in which a physical entity and its virtual counterpart influence each other continuously until the influence on the physical entity leads to a desired outcome, as shown in the figure below. A desired outcome may be obtained in multiple different ways. For example, a desired effect may be achieved by a better understanding of a process and using the knowledge to influence the physical system. Thus, any attempt to replicate a physical system or process using an information-driven virtual model that leads to a better outcome is within the scope of this special issue. This special issue is particularly interested in digital shadowing and twining.

Digital shadowing: A continuously evolving digital copy of a physical system using sparse data from the physical system.

Digital twin: A digital copy of the system, created using a continuous flow of data from the physical system, controlling the physical system.

What we are looking for: We are looking for data-driven modelling of thermal or energy systems as a minimum requirement. This could include digital shadowing or digital twining of a physical system. The following topics are some examples, but authors are invited to submit papers within the broader topic of digital twinning. 

    A virtual copy of a physical system or process that uses at least one set of data from the physical system.
    Digital twin demonstrators, including using surrogate (computational) physical systems.
    Dynamic (time-dependent) digital shadows.
    Health monitoring of thermal or energy systems.
    Embedding data into physics-based models.
    Enhancing physics-based models using data and machine learning and vice versa.
    Methods for combining data, AI and physics-based models.
    Any other relevant topic.

If any papers fall outside this special issue's scope but within the journal's scope, they will be considered for a standard issue. All papers will be peer-reviewed by at least two independent reviewers, and only papers with original contributions or valuable reviews will be considered for possible publication. Once accepted, papers will be published rapidly, and a virtual special issue will be compiled online with all relevant papers.

Guest Editors

Perumal Nithiarasu,
Swansea University, UK
p.nithiarasu@swansea.ac.uk

Michelle Tindall,
UK Atomic Energy Authority, UK
michelle.tindall@ukaea.uk