Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748142
Title: Prediction and analytics of operating parameters on thermoelectric generator energy generation
Author: Ang, Yang Adrian
ISNI:       0000 0004 7233 2362
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2017
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.748142  DOI: Not available
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