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