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2016•Unpublished venueRequires access

Prediction of the parameters of the thermoelectric cooling systems based on Peltier elements with neural network

Ivaylo Belovski, Sotir Sotirov, Nikolay Sotirov, A. D. Alexandrov

Open publisher page 4 citations

Abstract

Neural networks are the tools that can be used for the modelling for many systems. Thermoelectric cooling systems (TCS), generated on the basis of Peltier elements, are very widely used in the military industry and computing, which require smooth but precise thermostating of objects and volumes. The prediction of the parameters of the Thermoelectric cooling systems based on Peltier elements are very useful for the preparing the optimal conditions of the automatic control.

About this research paper

What this paper is about

Neural networks are the tools that can be used for the modelling for many systems. Thermoelectric cooling systems (TCS), generated on the basis of Peltier elements, are very widely used in the military industry and computing, which require smooth but precise thermostating of objects and volumes. The prediction of the parameters of the Thermoelectric cooling systems based on Peltier elements are very useful for the preparing the optimal conditions of the automatic control.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Limitations

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Applications

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Available abstract

Neural networks are the tools that can be used for the modelling for many systems. Thermoelectric cooling systems (TCS), generated on the basis of Peltier elements, are very widely used in the military industry and computing, which require smooth but precise thermostating of objects and volumes. The prediction of the parameters of the Thermoelectric cooling systems based on Peltier elements are very useful for the preparing the optimal conditions of the automatic control.

Key concepts: Thermoelectric effect, Thermoelectric cooling, Artificial neural network, Computer science, Thermoelectric materials, Temperature control, Mechanical engineering, Engineering

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