Numerical Methods of Computational Fluid Dynamics in the Energy Field

Topic Information

Dear Colleagues,

The global transition towards sustainable and efficient energy systems is one of the most critical challenges of our time. Computational Fluid Dynamics (CFD) stands as an indispensable tool in this endeavor, providing unparalleled insights into the complex physical phenomena that underpin energy technologies. From optimizing the aerodynamic performance of wind turbine blades and enhancing heat transfer in concentrated solar power plants to managing complex combustion dynamics in next-generation engines and securing the safety of hydrogen storage systems, CFD is at the forefront of innovation. This Topic  is dedicated to showcasing the latest breakthroughs in numerical methodologies that push the boundaries of what is possible in simulating, designing, and optimizing energy systems.

We seek original research and review articles that present significant advancements in numerical algorithms and their application to energy-related fluid flow problems. The scope includes, but is not limited to, the following detailed areas:

High-Fidelity Simulations and Advanced Turbulence Modeling:

• Applications of Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) to energy systems.
• Data-driven and machine-learning augmented turbulence models (e.g., RANS closure, sub-grid scale modeling). Hybrid RANS-LES methods for industrially relevant problems.

Multiphysics Coupling and Multiphase Flows:

• Tightly coupled simulations of fluid–structure interaction (FSI) in turbomachinery and flexible structures.
• Conjugate heat transfer (CHT) for thermal management and heat exchanger design.
• Modeling of reactive flows, combustion, and emissions formation.
• Simulations of cavitation, particle-laden flows, and electrochemical processes in battery and fuel cell systems.

Artificial Intelligence and Data-Driven Methodologies:

• AI-based surrogate modeling and model order reduction for rapid design optimization and uncertainty quantification.
• Physics-Informed Neural Networks (PINNs) for solving governing equations and enhancing experimental data.
• Reinforcement learning for active flow control and system operation optimization.

High-Performance Computing and Algorithmic Efficiency:

• Novel discretization schemes (high-order methods, lattice Boltzmann methods) tailored for exascale computing architectures.
• Algorithmic strategies for reducing computational cost while maintaining accuracy (adaptive mesh refinement, multi-fidelity approaches).
• Scalable parallel computing techniques for large-scale industrial simulations.

We invite researchers from academia, national laboratories, and industry to submit their original work. Contributions can range from fundamental theoretical and algorithmic developments to applied industrial case studies that clearly articulate the practical significance and validation of the proposed methods. The ultimate goal of this Topic is to create a collaborative platform that bridges the gap between pioneering numerical research and the pressing needs of the global energy sector, thereby contributing to a more sustainable and efficient future. 

Dr. Ming Zhao
Dr. Xiaojian Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.5	4.8	2014	18.5 Days	CHF 2400	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Computation computation	2.6	5.2	2013	13.6 Days	CHF 1800	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Fluids fluids	2.1	4.1	2016	17 Days	CHF 1800	Submit
Modelling modelling	1.8	2.4	2020	22.7 Days	CHF 1200	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Thermo thermo	3.9	4.4	2021	24.5 Days	CHF 1200	Submit

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