Prediction and analysis of energy generation from thermoelectric energy generator with operating environmental parameters

Ang, Zi Yang Adrian; Woo, Wai Lok; Ehsan, Mehdi

doi:10.1109/icgea.2017.7925459

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…(4) The number of neurons in the hidden layer: When the number of neurons is too small the neural network does not possess enough robustness, while a too large number of neurons results in long training time and also may lead to overfitting. Therefore, the range of the number of nodes in the hidden layer is decided by [31] as 7.…”

Section: Ga Based Bp Training Processmentioning

confidence: 99%

Ensemble tensor decomposition for infrared thermography cracks detection system

Song

Gao

Woo

et al. 2020

Infrared Physics & Technology

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Ferromagnetic steels are widely used in engineering structures such as rail track, oil/gas pipeline and steel hanging bridge. Cracks resulted from manufacturing processes or previous loading will seriously undermine the safety of the engineering structures and even lead to catastrophic industrial accidents. Accurate and quantitative characterization the cracks in ferromagnetic steels are therefore of vital importance. In this paper, the cracks in ferromagnetic steels are detected by the pulsed eddy current (PEC) technique. Firstly, the physical mechanism of the relative magnetic permeability of the ferromagnetic steel on the detection signal of PEC is interpreted from a microscopic level of magnetic domain wall movement. The relationship of the crack width/depth and the detection signal of PEC is then investigated and verified by numerical simulations and experimental study. Finally, the cracks are inversely characterized by using Genetic Algorithm (GA) based Back-Propagation (BP) neural network (NN) considering the nonlinearity of the crack geometric parameters with the detection signal of PEC. The prediction results indicated that the proposed algorithm can characterize the crack depth and width within the relative error of 10%. The proposed approach combining PEC and GA based BPNN has been verified to quantitatively detect cracks in ferromagnetic steel.

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Section: Ga Based Bp Training Processmentioning

confidence: 99%

Ensemble tensor decomposition for infrared thermography cracks detection system

Song

Gao

Woo

et al. 2020

Infrared Physics & Technology

View full text Add to dashboard Cite

show abstract

“…Once the ANN model has been trained, it can simulate the outcome of that process in real-time, facilitating real-time decisions [40,41]. A few attempts have been made in the literature to develop traditional ANN models for predicting TEG output [42][43][44]. Here, we provide a hybrid physico-neural network model that can predict TEG performance with a high degree of accuracy.…”

mentioning

confidence: 99%

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

2018

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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...

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Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed with verified finite element simulation data

Maduabuchi¹

2022

Applied Energy

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Prediction and analysis of energy generation from thermoelectric energy generator with operating environmental parameters

Cited by 4 publications

References 14 publications

Ensemble tensor decomposition for infrared thermography cracks detection system

Ensemble tensor decomposition for infrared thermography cracks detection system

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

Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed with verified finite element simulation data

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