Authors
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Doraghi, Q.; Żabnieńska-Góra, A.; Voto, G.; Krause, B.; Pötschke, P.; Ezpeleta, I.; Mateo-Mateo, C.; Jouhara, H.
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Title
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Experimental and computational thermoelectric generator for waste heat recovery for aeronautic application
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Date
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15.06.2024
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Number
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0
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Abstract
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This study is a comprehensive exploration of a polymer nanocomposite-based Thermoelectric Generator (TEG) developed within the European project InComEss, specifically designed for aeronautical applications. The focus lies on evaluating the TEG's performance under thermal conditions representative of various aircraft flight stages. The TEG module, consisting of four sections with 17 p-n strips each, is constructed from aerospace-grade polycarbonate, exhibiting dimensions of 50 * 1 * 0.3 mm. In the laboratory phase, the TEG's performance is systematically assessed through a series of experiments. Temperature gradients, ranging from −15 °C to 55 °C, emulate conditions experienced during ascending and descending flight stages. The results indicate promising outcomes, showcasing the potential viability of polymer-based TEGs for aeronautical applications. Specifically, temperature gradients of 40–70 °C, representative of atmospheric conditions and wing leading edge skin conditions, are applied across four test trials. The model validation demonstrates creditable agreement between computational outcomes and experimental data. Insights gained from COMSOL Multiphysics simulations includes temperature distribution, electric potential, and flow dynamics. Simulations conducted under varied temperature ranges provide valuable insights into the TEG's performance variability. Key findings include temperature distribution profiles, electric potential outputs under open and closed-circuit conditions, and a detailed flow analysis within a controlled thermal environment. The validated computational model not only enhances understanding of the TEG's behaviour, but also establishes a foundation for optimizing design parameters to enhance thermoelectric efficiency. The error analysis underscores the model's reliability, exhibiting an average error of 5.68 % between computational and experimental results, reinforcing its suitability for scientific investigations of this nature.
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Publisher
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Elsevier
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Wikidata
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Citation
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Energy 297 (2024) 131286
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DOI
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https://doi.org/10.1016/j.energy.2024.131286
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Tags
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