Please use this identifier to cite or link to this item:
|Title:||Computer modeling of thermoelectric generator system||Authors:||Lee, Benjamin Ge Kai||Keywords:||DRNTU::Engineering
|Issue Date:||2016||Abstract:||Global climate change due to excessive carbon emissions is a huge problem facing many countries today. Hence, thermoelectric power generation has been increasingly seen as a viable source of energy generation for a low carbon future. A thermoelectric generator (TEG) works to produce electricity through the utilisation of the temperature gradient between the hot and cold ends of a thermoelectric (TE) material in seawater, for example. This TE material must have both high electrical conductivity and low thermal conductivity, in order to efficiently conduct electricity. The magnitude of this electricity flow, or electrical conductivity (σ), corresponds to the magnitude of the Seebeck coefficient (α). The efficiency of thermoelectric materials is related to their Figure of Merit, ZT. The bigger the ZT value, the more efficiently the thermoelectric material is able to conduct electricity. This project sought to create, verify, improvise on and utilise a 3-D computer modelling framework for thermoelectric generator systems, such that the required input parameters such as Seebeck coefficient (α), electrical conductivity (σ), hot junction (T_H) and cold junction (T_C) temperatures can be varied, and the corresponding solution outputs from the computer modeling framework can be simulated and produced, such as p-leg and n-leg current density. Further, two forms of optimisation of the simulation model were carried out: module level optimisation and imaginary system level optimisation. The effects of three varied parameters on the optimisation models were investigated, namely: the length-to-area ratio of the individual legs in the simulated TEG module, A_np or the ratio of cross-sectional areas of n-type and p-type legs, as well as different geometries of the TEG legs such as circular or diamond shapes. Both quantitative data from the simulation model and qualitative imaging from the model were utilised in this investigation.||URI:||http://hdl.handle.net/10356/67466||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
Updated on Jun 23, 2021
Updated on Jun 23, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.