Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Abstract

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.Energies 2018, 11, 2216 2 of 17 in the Thomson heat, which is proportional to the electric current and absorbed or released based on the direction of the current [4].Continuous efforts have been made toward developing high performance n-type and p-type thermoelectric (TE) materials [1,[7][8][9]. The performance of TE materials is measured in terms of a dimensionless metric called the figure of merit, which is denoted as ZT. It combines three key material properties: thermal conductivity (κ), electrical resistivity (ρ), and the Seebeck coefficient (S), along with the absolute temperature (T), and it is given as ZT = S 2 κρ T [10,11]. The magnitude of ZT for most of the commercial thermoelectric materials is close to unity; however, recent studies have reported higher values for some of the state-of-the-art TE materials, such as a quantum-dot superlattice with ZT~3.5 at 575 K [12], a superlattice structure with ZT~2.4 at 300 K and ZT~2.9 at 400 K [13], and lead antimony silver telluride with ZT~2.2 at 800 K [14]. The thermoelectric modules built using nanostructured materials have been reported to exhibit thermal-to-electrical energy conversion efficiency up to 10% at a temperature difference of 500 K [15].The performance of TEG modules depends not only on the ZT of the TE material, but also on the geometric dimensions of the thermocouples and operating conditions such as the temperature difference and electrical load [16,17]. Several researchers in the past have attempted to optimize p-n leg geometry comprising of length, a cross-sectional area, and the number of thermocouples [18][19][20][21]. A few studies have attempted to provide optimal shape factor parameters such as the area ratio for p-n legs (Ap/An), the aspect ratio (leg length/leg area), and the slenderness ratio ((Ap/Lp)/(An/Ln), where Ap and An denote base area and Lp and Ln denote length of p-and n-type legs) [22,23]. However, these parameter changes depend upon the TE material used for fabricating TEG legs, the materials used in assembling the TEG modules, and oper...

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

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Energies and Its Worldwide Research

“…• "Comparison of different solar-assisted air conditioning systems for Australian office buildings" with 11 citations [77]. • "Combinatory finite element and artificial neural network model for predicting performance of thermoelectric generator" with three citations [78].…”

“…The main drawback is that the heat-to-electricity conversion efficiency of thermoelectric generators (TEG) is up to 11% in laboratory environments; it is much lower in practice. In [78] they propose a model based on artificial neural networks that can predict the performance of an TEG on demand. The model evaluated predicts the performance of an EEG at 26.4 ms per data point compared to the 6.0 min required in traditional simulations.…”

Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks

“…Their results show that the employment of an appropriate ANN would be a time-effective operating method of a photovoltaic thermal hybrid system without any complex analysis and calculations; however, the scope of their study deviated from the application of ANNs to TEGs operating under various exhaust heat conditions. Kishore et al [14] suggested a finite element-ANN combined model for predicting the performance of a TEG with a high degree of accuracy. They investigated the accuracy of the ANN while varying the design parameters of a single TEM, including the length of the nand p-type thermoelectric legs, cross-sectional area of the legs, and external load resistance.…”