Deep neural network prediction for effective thermal conductivity and spreading thermal resistance for flat heat pipe

Abstract

Purpose This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance. Design/methodology/approach A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate. Findings Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD. Originality/value The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.

Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion

“…Although the result of the least-squares method is close to the averaged field, it ignores details of local flow features, and, therefore, is not conducive to local characteristic analysis of the flow field. In addition, this method requires thousands of high-fidelity data points to reconstruct a flow field (Kim and Moon, 2022), which remains a heavy burden in engineering practice.…”

Novel Infrared Approach for the Evaluation of Thermofluidic Interactions in a Metallic Flat-Plate Pulsating Heat Pipe

“…For this reason, FPPHPs might be successfully employed in those applications requiring good thermal contact on narrow surfaces, such as in the micro-electronics field. It has to be stressed that, when compared to other passive two-phase heat transfer solutions having flat layouts, e.g., flat-heat pipes, FPPHPs do not present any porous structure for the liquid backflow to the evaporator section [8][9][10]. On one side, this represents a critical aspect for the FPPHPs performance since the fluid motion cannot be controlled by the capillary effect of the wick, resulting in generally lower reliability.…”