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 Digital Twinning Thermal and Energy Systems
Día de Entrega: 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