Jalili, D, Jadidi, M, Keshmiri, A, Chakraborty, B, Georgoulas, A and Mahmoudi, Y (2024) Transfer learning through physics-informed neural networks for bubble growth in superheated liquid domains. International Journal of Heat and Mass Transfer, 232. ISSN 0017-9310

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Abstract

In this paper, a physics-informed neural network (PINN) technique is developed to study the heat and mass transfer for the process of vapour bubble growth in a superheated liquid domain and tested using three working fluids including water, R-134a and FC-72. The work represents a novel step in the development of PINNs for phase change scenarios where surface tension effects dominate, and acts as a necessary validation stage before PINN techniques can be applied to complex boiling analysis. Initially, a forward analysis was performed using water and R-134a as working fluids. For each of these investigations, the PINN algorithm was trained on 50 % of the available CFD data. The proposed algorithm was able to accurately infer velocity fields, particularly in the near-interfacial region. The resultant circulatory flow was found to maintain the desired circular shape of the growing bubbles. As a result, when predicting the evolution of a water vapour bubble, the developed PINN algorithm produced a reduction in peak error by 0.87 % compared to CFD reference data, and 3.42 % reduction in peak error for prediction of the evolution of the R-134a vapour bubble. To test and optimise the transfer learning capabilities of the developed methodology, the evolution of an FC-72 vapour bubble in superheated FC-72 was predicted without supplying supporting observational data. For this scenario, the PINN algorithm produced a peak error within 1.3 % of the unobserved CFD reference data. The proposed approach confirms the robustness of PINN methodologies as a method of solving phase-change problems where surface tension plays a pivotal, promising to expedite parametric studies in practice. This study represents a pioneering effort in the development of PINNs for phase change by applying the current algorithm to investigate bubble growth within superheated liquid domains, serving as a basis for the application of PINNs for boiling problems and as a benchmark for inverse training strategy.

Item Type:	Article
Uncontrolled Keywords:	4012 Fluid Mechanics and Thermal Engineering; 40 Engineering; 01 Mathematical Sciences; 02 Physical Sciences; 09 Engineering; Mechanical Engineering & Transports; 40 Engineering; 49 Mathematical sciences; 51 Physical sciences
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	16 Apr 2025 11:37
Last Modified:	16 Apr 2025 11:45
DOI or ID number:	10.1016/j.ijheatmasstransfer.2024.125940
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26205

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