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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.

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.

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.

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.

The basic structure of Korea's formal education system is 6-3-3-4. This school system, which was established soon after its independence from Japan after World War II, has not been changed very much until recently. Primary education covers grades 1–6. Kindergarten has not been a part of the official school system until now, although making it a part of the pubic school system has been under discussion for some years. In the secondary education sector, there are two levels of schools: middle schools covering grades 7–9, and high schools covering grades 10–12. After 12 years of formal education, students advance to higher education. Typically, undergraduate degree (B.A. or B.S.) takes four years.