Low-grade Thermal Energy Harvesting and Waste Heat Recovery

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Date

2018-12-14

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Volume Title

Publisher

Virginia Tech

Abstract

Low-grade heat, either in the form of waste heat or natural heat, represents an extremely promising source of renewable energy. A cost-effective method for recovering the low-grade heat will have a transformative impact on the overall energy scenario. Efficiency of heat engines deteriorates with decrease in hot-side temperature, making low-grade heat recovery complex and economically unviable using the current state-of-the-art technologies, such as Organic Rankine cycle, Kalina cycle and Stirling engine. In this thesis, a fundamental breakthrough is achieved in low-grade thermal energy harvesting using thermomagnetic and thermoelectric effects. This thesis systematically investigates two different mechanisms: thermomagnetic effect and thermoelectric effect to generate electricity from the low-grade heat sources available near ambient temperature to 200°C. Using thermomagnetic effect, we demonstrate a novel ultra-low thermal gradient energy recovery mechanism, termed as PoWER (Power from Waste Energy Recovery), with ambient acting as the heat sink. PoWER devices do not require an external heat sink, bulky fins or thermal fluid circulation and generate electricity on the order of 100s μW/cm3 from heat sources at temperatures as low as 24°C (i.e. just 2°C above the ambient) to 50°C. For the high temperature range of 50-200°C, we developed the unique low fill fraction thermoelectric generators that exhibit a much better performance than the commercial modules when operated under realistic conditions such as constant heat flux boundary condition and high thermally resistive environment. These advancements in thermal energy harvesting and waste heat recovery technology will have a transformative impact on renewable energy generation and in reducing global warming.

Description

Keywords

Energy harvesting, Heat recovery, Thermal energy, Power, Efficiency, Thermomagnetic, Thermoelectric, Optimization, Taguchi, Neural network

Citation