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Prediction and Analytics of Operating Parameters on Thermoelectric Generator Energy Generation

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Prediction and Analytics of Operating Parameters on Thermoelectric Generator Energy Generation PREDICTION AND ANALYTICS OF OPERATING PARAMETERS ON THERMOELECTRIC GENERATOR ENERGY GENERATION Ang Zi Yang Adrian A thesis submitted to the University of Newcastle for the degree of Doctor of Philosophy School of Electrical, Electronic and Computer Engineering Faculty of Science, Agriculture and Engineering June 2017 Abstract The efficient use of energy at all stages along the energy supply chain and the utilization of renewable energies are very important elements of a sustainable energy supply system, specially at the conversion from thermal to electrical energy. Converting the low-grade waste heat into electrical power would be useful and effective for several primary and secondary applications. One of the viable means to convert the low-grade waste heat into electrical energy is the use of thermoelectric power conversion. The performance of thermoelectric generators, subjected to thermal effects, can vary considerably depending on the operating conditions, therefore it is necessary to measure and have a better understanding of the characteristics and performance of the thermoelectric generator. It is important to understand the thermoelectric generator’s dynamic behavior and interaction with its operating environmental parameters. Based on this knowledge, it is then significant to develop an effective mathematical model that can provide the user with the most probable outcome of the output voltage. This will contribute to its reliability and calculation to increase the overall efficiency of the system. This thesis provides the transient solution to the three-dimensional heat transfer equation with internal heat generation. It goes on to describes the transfer and generation of heat across the thermoelectric generator with dynamic exchange of heat. This solution is then included in a model in which the thermal masses and the operating environmental parameters of the thermoelectric generator are factored in. The resulting model is created in MATLAB. The comparison with experimental results from a thermoelectric generator system confirms the accuracy of the artificial neural network model. This thesis also presents two practical applications, the prediction of the input parameters with a given output voltage, and sensitivity analysis designed for the model. This is to enable users to customize the thermoelectric generator for their requirements. This allows for better usage of resources eventually. i Dedicating this thesis to my mother, The late Rebecca Ang Kar Cheng ii Acknowledgements I would like to express special appreciation and thanks to my supervisors Dr Wai Lok Woo and Professor Ehsan Mesbahi, who have both been a tremendous mentor to me. I would like to thank them for their encouragements, constructive ideas and allowing me to grow as a researcher. I would like to thank my annual progression panel, Dr Khalid Abidi and Dr Logan for giving me good advice at each milestone in my studies and my examiners, Dr Eugene Wong and Dr Edmund Liew for their feedbacks on my research work. I would also like to thank Joan and Eddie who helped proof-read this thesis. A special thanks to my family. Words cannot express how grateful I am to my them for all the sacrifices that they’ve made on my behalf. Your prayers for me was what sustained me thus far. I would also like to thank all my friends who supported me in writing and motivating me to strive towards my goal. Finally, I would like to express appreciation to my beloved wife, Raphaela, who spent sleepless nights with me and has always been my support in the moments when there was no answers to my queries. To you too, Kathryn, my beloved new born daughter. iii List of Publications Conferences Z. Y. A. Ang and E.Mesbahi, “TEG Review Paper,” presented at the International Symposium on Engineering and Natural Science, Macau, 2013 Z. Y. A. Ang, W.L.Woo, and E.Mesbahi,” Influence of Environmental Parameters on the Prediction of Thermoelectric Generator Energy Generation,’ in IEEE 2016 International Conference on Power and Renewable Energy, Shanghai, 21-23 October 2016, pp.100-106. (Publication awarded “Most Excellent Paper Award”) Z. Y. A. Ang, W.L.Woo, and E.Mesbahi,” Prediction and Analysis of Energy Generation from Thermoelectric Energy Generator With Operating Environmental Parameters,” in 2017 International Conference on Green Energy and Application, Singapore, 2017, pp. 1-5, 25-27 March 2017. (Publication awarded “Best Presentation Award”) Journals Z. Y. A. Ang, W.L.Woo, and E.Mesbahi,”Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters,” Journal of Clean Energy Technologies, pp.1-5, 7-9 January 2017. Z. Y. A. Ang, W. L. Woo, and E. Mesbahi, "Prediction and Analysis of Energy Generation from Thermoelectric Energy Generator In Its Operating Environment," Renewable Energy Focus, pp. 1-5, 2017. (Pending) iv Nomenclature TEG Thermoelectric Power Generator TE Thermoelectric ZT Figure of Merit GUI Graphical User Interface SC Seebeck Coefficient EMF Electromotive Force Bi2Te3 Bismuth Telluride PbTe Lead Telluride SiGe Silicon Germanium WSN Wireless Sensor Network DC Direct Current RTG Radioisotope Thermoelectric Generator NASA National Aeronautics and Space Administration ANN Artificial Neural Network 3D Three Dimensional CFD Computational Fluid Dynamics TDMA TriDiagonal Matrix Algorithm 1D One Dimensional 2D Two Dimensional MSE Mean Squared Error v SA Sensitivity Analysis RI Relative Importance vi List of Symbols T Absolute temperature σ Electrical conductivity k Thermal conductivity V Voltage difference I Electrical current W Watts Rm Maximum resistance qPeltier Rate of peltier effect heat transfer SB Seebeck Sp Seebeck coefficient of P-Type thermocouple element Sn Seebeck coefficient of N-Type thermocouple element Thot Heat source on hot side Thsur Surface temperature on hot side Tcsur Surface temperature on cold side ∆T$%& Maximum temperature gradient I$%& Maximum input current V$%& Maximum voltage E$%& Maximum electromotive force p Scalar input w Scalar weight vii b Bias N Net input F Transfer function A Scalar neuron output Lh Thickness of ceramic substrate on hot side LC Thickness of ceramic substrate on cold side Lt Height of PN junction thermocouple Lf Length of heat sink fin kh Thermal conductivity of hot side ceramic substrate Kt Thermal conductivity of PN thermocouple Kc Thermal conductivity of cold side ceramic substrate Khs Thermal conductivity of heat sink T1 Surface temperature on the underside of hot side ceramic substrate T2 Surface temperature on the underside of the cold side ceramic substrate Tamb Ambient temperature q” Convective heat flux T* Fluid temperature H Convective heat transfer coefficient Ts Surface temperature V Computational volume n Outward-pointing unit normal of the boundary S Rate of temperature change due to heat sources viii A Area P General nodal point in a cell N North nodal point E East nodal point S South nodal point W West nodal point T Top nodal point B Bottom nodal point PN PN thermocouple w Width of thermocouple kair Thermal conductivity of air Cp,air Specific heat of air ρ%-. Density of air U0 Velocity of air Rn Reynolds number Pn Prandtl number Temperature difference between internal side of hot and cold side ∆T-12 ceramic substrate Temperature difference between external side of hot and cold side ∆T3&2 ceramic substrate Hb Height of heat sink base Df Diameter of heat sink fin Nx Quantity of fins per dimension ix Ab Area of heat sink base Uapp Wind velocity approaching heat sink fin Sl Distance between centre of two consecutive fins vair Kinematic viscosity of air hf Heat transfer coefficient for heat sink fin hb Heat transfer coefficient for heat sink base η5 Efficiency of fins Rb Thermal resistance of heat sink base Rf Thermal resistance of heat sink fin Kf Average heat sink thermal conductivity Temperature difference between internal side of hot and external side of ∆T3&267% the cold side ceramic substrate Tambh Hot Side Ambient Temperature Tambc Cold Side Ambient Temperature Vwh Hot Side Wind Velocity Vwc Cold Side Wind Velocity Wc Width of Ceramic Substrate Kcsh Thermal Conductivity of Hot Side Ceramic Substrate Kcsc Thermal Conductivity of Cold Side Ceramic Substrate Lpn Length of PN junction Kpn Thermal Conductivity of PN Junction Lfin Length of Heat Sink Fins Ateg Area of Thermoelectric Generator x Tsur Surface Temperature Tambh Hot Side Ambient Temperature nh Number of hidden layer node in each ANN network ni Number of hidden input layer nodes no Number of hidden output layer nodes np Number of training samples in the dataset used Output voltage obtained from manufacturer specification sheet using Vss temperature differential obtained from hot and cold side TEG ceramic substrate surface Output voltage obtained from manufacturer specification sheet using Vep temperature differential obtained from PN junction with environmental parameters Output voltage obtained from manufacturer specification sheet using Vephs temperature differential obtained from PN junction with environmental parameters and a heat sink H Hessian matrix J Jacobian Matrix F(ω) Bayesian regularization E9 Sum of squared network weights for Bayesian regularization E: Sum of network errors for Bayesian regularization P A|B Posterior probabilities of A conditional on B for Bayes theorem P B|A Posterior probabilities of B conditional on A for Bayes theorem D Weight distribution for Bayesian regularization method λ@ Scaling factor to approximate the Hessian matrix σ@ Scaling factor to approximate
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