Aim and Scope

Aim: The aim of the Journal of Artificial Intelligence in Fluid Dynamics is to facilitate interdisciplinary research at the intersection of artificial intelligence (AI) and fluid dynamics, with the goal of advancing scientific understanding, computational modeling, and engineering applications of fluid flow phenomena.

Scope:

  • Development and application of AI techniques, including machine learning, deep learning, and reinforcement learning, for modeling and simulating fluid dynamics.
  • Integration of AI algorithms with traditional computational fluid dynamics (CFD) methods to enhance accuracy, efficiency, and scalability of simulations.
  • Exploration of AI-driven approaches for flow control, optimization, and predictive modeling in engineering applications, such as aerodynamics, hydrodynamics, and combustion.
  • Investigation of AI-based methods for turbulence modeling, including data-driven approaches, neural network-based turbulence closure models, and hybrid simulation techniques.
  • Analysis of AI-driven optimization algorithms for design optimization, parameter estimation, and uncertainty quantification in fluid dynamics problems.
  • Examination of AI applications in multiphysics simulations, coupling fluid dynamics with other physical phenomena such as heat transfer, chemical reactions, and fluid-structure interactions.
  • Study of AI-driven approaches for flow diagnostics and feature extraction from experimental and computational data, including image-based flow analysis and sensor data fusion.
  • Exploration of AI-enabled design tools for fluidic systems, including automated design synthesis, topology optimization, and generative design methods.
  • Assessment of AI-driven decision support systems for fluid dynamics applications, including real-time control, autonomous operation, and adaptive strategies.
  • Discussion of ethical considerations, biases, and limitations associated with AI applications in fluid dynamics research and engineering practice.
  • Examination of case studies, benchmarking exercises, and validation methodologies for assessing the performance and reliability of AI-based fluid dynamics simulations.
  • Collaboration between researchers from AI, fluid dynamics, and related disciplines to foster innovation and cross-fertilization of ideas in the field.