Edge-cloud artificial intelligence digital twin thermal modeling for rotating sintered core heat pipes

• Digital twin using Simulink for sintered-core heat pipe thermal prediction. • Simulink and real-world datasets used for transient thermal simulations. • Experimental validation achieves ±5 % accuracy under varied conditions. • Digital twin deployed in real time on edge-cloud computing architecture. • Compared prediction of gradient boosting, neural network, and Transformer models. The sintered core heat pipe is widely used in precision thermal control and aerospace systems due to its high heat transfer performance. However, conventional computational fluid dynamics approaches for predicting its thermal behavior are computationally expensive and inflexible under varying operating conditions, while experimental methods are time-consuming and costly. To address the above challenges, in this study, a digital twin-based predictive thermal modeling framework is presented for sintered core heat pipes under rotational conditions, implemented within an edge-cloud artificial intelligence architecture. A fully parameterized physical model is developed on the Simulink platform using the SIMSCAPE Fluids module, enabling dynamic simulations of phase transitions and temperature responses. Validation against experimental data shows prediction errors within ±5 %. Simulation and experimental datasets are integrated to train three models-physics-informed neural network, Transformer, and light gradient boosting machine-evaluated under steady and transient thermal conditions. The physics-informed neural network achieves the lowest mean absolute error of 0.85 °C in high thermal inertia cases, while the Transformer attains the best steady-state accuracy with a root mean square error of 0.58 °C and inference latency of 150 ms after Turing Tensor R-Engine deployment. Docker-based deployment enables real-time edge inference, with the Transformer achieving an optimal balance of accuracy, memory footprint (36 MB), and response speed. The proposed framework offers a practical and scalable approach for accurate thermal prediction in advanced thermal management applications.