AI Thermal Fluids

AI-Thermal/Fluids (AITF) is dedicated to exploratory research and advancements at the intersection of artificial intelligence (AI) and thermal-fluid science and engineering. The journal publishes full length Research Articles, Rapid Communications, Reviews, Letters to the Editor, and Vision Articles. It also publishes Original Software articles that describe and provide access to open-source software and Data Articles that describe and provide access to research data.

AITF welcomes research that leverages AI techniques, such as machine learning, neural networks and data analytics, to promote fundamental understanding, improve prediction capabilities, solve problems and design or optimize thermal-fluid systems, with a view to stimulating innovation across a broad range of science and engineering applications. Contributions may include AI-driven modelling, simulation, data enhancement, AI-assisted experimentation, and control methodologies, among others.

The list of focus areas includes (but is not limited to):

Experimental Methodologies and Measurement Techniques: AITF showcases efforts to develop AI-based experimental methodologies, measurement techniques, and instrumentation for characterizing the thermal, mass transfer, reaction and/or fluid-flow dynamics aspects of a wide range of transport phenomena and processes.

Modelling and Simulation: AITF publishes studies on the development of AI-based methodologies to design, model or predict material properties, transport phenomena and processes, enhance the capabilities of physics-based modelling and simulation tools, and analyse the data they generate, either in support of or independently from experimental measurements.

Fluid Dynamics and Thermal Transport Phenomena: AITF seeks contributions aiming to elucidate transport phenomena. Examples are fluid flow behaviour, including turbulence, and transport processes within diverse flows, including single phase and multiphase flows, both isothermal and diabatic, as well as flows in the presence of phase change and reactive flows. AI-based approaches to process and analyse high throughput data, characterize flow dynamics, phase distribution, heat and mass transfer, reactions, and fluid-solid interactions are encouraged.

Process Enhancement: AITF encourages the submission of articles focusing on innovative approaches to enhance heat and mass transfer or chemical processes in a wide range of devices, systems and applications. This encompasses design and operational optimization of heat exchangers, absorbers, separators, or reactors, the development of novel management approaches, surface modification techniques, and other passive and active enhancement strategies.

Materials Properties: AITF welcomes research focused on the evaluation of thermophysical, thermochemical or thermodynamic materials properties relevant to the phenomena, problems, and applications of interest to the journal. This includes AI-based methods for predicting properties such as thermal conductivity, viscosity, density, phase behaviour, and other crucial attributes for accurate modelling, simulation, and optimization.

Energy Exchange and Conversion: AITF features research addressing energy exchange and transformation mechanisms and their implications in engineering applications. This includes AI-driven optimization of energy harvesting techniques, energy conversion processes, energy storage, and sustainable energy technologies.

Environmental and Sustainable Applications: AITF promotes efforts to address the economic and environmental implications of relevant processes, including renewable energy technologies, energy recover and management and energy efficiency in industry, buildings and the built environment, pollution mitigation, carbon capture and storage, and a wide range of other sustainable energy production, storage, or utilization technologies.