Collection

Special Issue: Recent Advances in Numerical Modeling and Simulation of Fluid Flow and Heat Transfer in Porous Media

Submission status
Closed

Fluid flow and heat transfer in porous media are common phenomena in various scientific and industrial applications, including subsurface carbon sequestration, energy storage and hydrogen storage, geothermal reservoirs, etc. Numerical modeling and simulation of fluid flow and heat transfer problems have been recognized as an effective approach to benefit such applications.

Despite a number of scientific and technological advances have been witnessed in numerical modeling and simulation methods for fluid flow and heat transfer in porous media, uncertainties and knowledge gaps still exist. For example, the accurate modeling of fluid flow and heat transfer in heterogeneous porous media, the efficient simulation of multiphase/multiphysics fluid flow and heat transfer, coupling of fluid flow and heat transfer in multiple-scales, error estimation, cross-scale analysis, and uncertainty quantification of fluid flow and heat transfer in porous media, fluid flow and heat transfer combined with other physical, chemical, and biological interactions, etc. The emerging popularity on artificial intelligence enabled us to accelerate conventional simulations, but there remains a number of scientific questions regarding the theory and algorithm of machine learning and deep learning. For example, how can we apply ideas from machine learning to existing problems in numerical analysis, such as Bayesian inference, operator estimation, solution of PDE, density estimation, sampling methodology, and uncertainty quantification? Our developing understanding of the physics controlling the fluid flow and heat transfer in a number of engineering applications are also requiring advanced physical models explaining the underlying mechanisms. For example, the phase change and splitting may help the hydrogen on-site production and purification in subsurface porous media, but the phase stability is concerned in the gathering and transportation. Thermodynamic analysis should also be focused in the development of geothermal energies, and a unified model is expected to govern the whole process from the reservoir to the well, where the environmental conditions may change drastically.

This special collection seeks to include research by engineers and scientists in the aforementioned fields or those who are interdisciplinary, to highlight the current developments of fluid flow and heat transfer in porous media both in physical models and numerical methods, to exchange the latest research ideas, and to promote further collaborations in the community. We invite investigators to make both practical and theoretical contributions to the literature in this special collection. The leading guest editors will chair the 2022 KAUST Research Conference on Scientific Computing and Machine Learning and a number of symposiums in The 29th International Conference on Computational & Experimental Engineering and Sciences (ICCES2023), which can provide us a lot of high-standard contributions all around the world.

- Specific topics of interest for this special collection include, but are not limited to:

- Advanced physical models of fluid flow and heat transfer

- Novel numerical methods for fluid flow and heat transfer simulation

- Machine Learning and Deep Learning algorithms in fluid flow and heat transfer

- Muiltiphase and multiphysics modeling and simulation

- Physics-Informed Neural Network

- Error estimation, cross-scale analysis, and uncertainty quantification

- Enhanced geothermal system

- CO2 geological sequestration

- Energy storage in porous media

- Multi-applications of fluid flow and heat transfer coupled with other interactions

Editors

  • Shuyu Sun Shuyu Sun , 

    Shuyu Sun

    King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia E-mail: shuyu.sun@kaust.edu.sa

  • Bo Yu Bo Yu , 

    Bo Yu

    Beijing Institute of Petrochemical Technology 19 Qingyuan N Rd Daxing District, Beijing China, 102699

  • Florian Frank Florian Frank , 

    Florian Frank

    Friedrich-Alexander-Universitaet Erlangen-Nuernberg Schloßplatz 4 91054 Erlangen Germany

  • Tao Zhang Tao Zhang  &

    Tao Zhang

    King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia

  • Jingfa Li Jingfa Li

    Jingfa Li

    Beijing Institute of Petrochemical Technology 19 Qingyuan N Rd Daxing District, Beijing China, 102699

Articles (9 in this collection)