Thermoelectric Generators on Satellites—An Approach for Waste Heat Recovery in Space

Lukowicz, M. von; Abbe, Elisabeth; Schmiel, Tino

doi:10.3390/en9070541

Cited by 49 publications

(31 citation statements)

References 14 publications

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“…Experimental studies are often expensive and time-consuming when used to optimize all of the variables. Therefore, analytical and numerical models have been developed using simplified one-dimensional models [24,25] as well as complex three-dimensional models [5,26,27].Thermoelectric generators are now being utilized for a variety of applications, such as power sources for wireless sensor nodes in satellites, space probes, and unmanned remote facilities [28,29]. TEGs are being developed for use in automobiles for generating electricity from exhaust gases in order to improve overall engine efficiency [30].…”

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confidence: 99%

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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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confidence: 99%

“…Thermoelectric generators are now being utilized for a variety of applications, such as power sources for wireless sensor nodes in satellites, space probes, and unmanned remote facilities [28,29]. TEGs are being developed for use in automobiles for generating electricity from exhaust gases in order to improve overall engine efficiency [30].…”

mentioning

confidence: 99%

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

2018

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“…The P-type leg has a positive Seebeck coefficient and an excess of holes h + . The N-type leg has a negative Seebeck coefficient and an excess of free electrons e À [22]. The two legs are linked together on one side by an electrical conductor forming a junction or interconnect, usually being a copper strip.…”

Section: Teg Structure and Modelmentioning

confidence: 99%

Thermoelectric Energy Harvesting: Basic Principles and Applications

Enescu¹

2019

Green Energy Advances

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Green energy harvesting aims to supply electricity to electric or electronic systems from one or different energy sources present in the environment without grid connection or utilisation of batteries. These energy sources are solar (photovoltaic), movements (kinetic), radio-frequencies and thermal energy (thermoelectricity). The thermoelectric energy harvesting technology exploits the Seebeck effect. This effect describes the conversion of temperature gradient into electric power at the junctions of the thermoelectric elements of a thermoelectric generator (TEG) device. This device is a robust and highly reliable energy converter, which aims to generate electricity in applications in which the heat would be otherwise dissipated. The significant request for thermoelectric energy harvesting is justified by developing new thermoelectric materials and the design of new TEG devices. Moreover, the thermoelectric energy harvesting devices are used for waste heat harvesting in microscale applications. Potential TEG applications as energy harvesting modules are used in medical devices, sensors, buildings and consumer electronics. This chapter presents an overview of the fundamental principles of thermoelectric energy harvesting and their low-power applications.

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“…Electrical generation by thermoelectricity has many fields of application [20]. TEGs were used as reliable sources of electrical energy in extreme environments [21] and in remote areas for off-grid micro generation [22]. Very recently, novel designs increased the energy efficiency of solar TEGs that include solar concentrators with flat-plate micro-channel heat pipes [23].…”

Section: Thermoelectric Generatorsmentioning

confidence: 99%

Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

Comamala

Cózar

Massaguer³

et al. 2018

Energies

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The need for more sustainable mobility promoted research into the use of waste heat to reduce emissions and fuel consumption. As such, thermoelectric generation is a promising technique thanks to its robustness and simplicity. Automotive thermoelectric generators (ATEGs) are installed in the tailpipe and convert heat directly into electricity. Previous works on ATEGs mainly focused on extracting the maximum amount of electrical power. However, the back pressure caused by the ATEG heavily influences fuel consumption. Here, an ATEG numerical model was first validated with experimental data and then applied to investigate the effects that modifying the main ATEG design parameters had on both fuel economy and output power. The cooling flow rate and the geometrical dimensions of the heat exchanger on the hot side and the cold side of the ATEG were varied. The design that produced the maximum output power differed from that which maximized fuel economy. Back pressure was the most limiting factor in attaining fuel savings. Back pressure values lower than 5 mbar led to a < 0.2% increase in fuel consumption. In the ATEG design analyzed here, the generation of electrical output power reduced fuel consumption by a maximum of 0.5%.

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Thermoelectric Generators on Satellites—An Approach for Waste Heat Recovery in Space

Cited by 49 publications

References 14 publications

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

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

Thermoelectric Energy Harvesting: Basic Principles and Applications

Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

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