Solidification of Cryogenic Fluids for Energy and Transportation Applications

Aims & Objectives

The project aims to combine computational fluid dynamics (CFD), molecular dynamics (MD) and machine learning (ML) to accurately model cryogenic solidification, enhancing the predictive capabilities of cryogenic systems dynamics. Research questions: - How can accurate, efficient numerical methods be developed to model solidification at many scales?

- How can these models bring improvement to the design of energy systems? Objectives: - Derive continuum level multiphase flow equations. - Validate equation accuracy by comparison to experiments. - Integrate microscale MD and macroscale CFD solidification models. - Apply the model to real-life systems.

Context

Cryogenic solidification is difficult to study due to complex multiphase physics, heat transfer challenges, and safety concerns. Traditional methods like Computational Fluid Dynamics (CFD) and Molecular Dynamics (MD) are limited by high data requirements, making them less practical for cryogenic systems. Machine learning (ML) can address these data gaps but needs accurate training data, often sourced from CFD and MD simulations.

In applications like liquid hydrogen (LH2) aviation fuel and Cryogenic Carbon Capture (CCC), where safety (e.g., explosion risks with LH2) and efficiency (e.g., CO2 capture rates in CCC) are critical, accurate computational models for cryogenic solidification are essential. This research would help optimize safety and improve the performance of these technologies by providing deeper insights into cryogenic solidification processes

Novelty of the Research Methodology This project has a three-step plan: 1. Develop a computational framework for solidification combining CFD, MD and ML for the first time. 2. Produce new understanding of cryogenic solidification and multiphase flows. 3. Use the new tools and understanding to improve CCC and lH2 safety.

Traditional and newer modelling approaches will be combined to produce a novel computational framework. ML will be integrated with CFD using data from MD. ML may also reduce the MD dataset size. The framework will help improve understanding of cryogenic solidification models. Studies will help improve cryogenic solidification models and applications.

LH2 aviation fuel and CCC are novel ideas that will help meet emissions targets so using the model to analyse how lH2 induces air solidification and CO2 solidifies in CCC will provide innovation. Alignment with EPSRC Research Areas

This research falls within the EPSRC 'fluid dynamics and aerodynamics', 'hydrogen and alternative energy vectors', and 'carbon capture and storage' research areas. The research into multiphase flows is crucial to 'fluid dynamics and aerodynamics'. This project's analysis of the solidification effects of lH2 would aid the understanding of lH2 safety of storage of clean hydrogen as a direct fuel in transportation.

Understanding solidification effects is crucial to improving CCC, helping improve the capture of CO2. The project may also fall within the 'software engineering' research area as integrating ML with CFD and MD may provide a more open environment for cryogenic modeling. Companies & Collaborators None are involved.

References

[1] X. Zheng et al. "Numerical simulation of air solidification process in liquid hydrogen with LBM-CA coupled method," International Journal of Hydrogen Energy, vol. 48, no. 30, pp. 11 567-11 577, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0360319922020183

[2] M. Baldwin et al. "Flow boiling in liquid hydrogen, liquid methane and liquid oxygen: A review of available data and predictive tools," Cryogenics, vol. 116, p. 103298, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0011227521000564

[3] C. Font-Palma et al. "Review of cryogenic carbon capture innovations and their potential applications," C, vol. 7, no. 3, 2021. [Online]. Available: https://www.mdpi.com/2311-5629/7/3/58