Journal
ENERGIES
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/en11092216
Keywords
thermoelectric; bismuth telluride; TEG; neural network; ANN
Categories
Funding
- DARPA MATRIX program
- Army SBIR program
- ICTAS Doctoral Scholarship
Ask authors/readers for more resources
Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of +/- 0.1 W, and TEG efficiency with an accuracy of +/- 0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available