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    Navigating Technological Risks: An Uncertainty Analysis of Powertrain Technology in Hybrid-Electric Commuter Aircraft

    Source: Journal of Engineering for Gas Turbines and Power:;2024:;volume( 147 ):;issue: 004::page 41004-1
    Author:
    Diamantidou, Dimitra-Eirini
    ,
    Soibam, Jerol
    ,
    Zaccaria, Valentina
    ,
    Kalfas, Anestis I.
    DOI: 10.1115/1.4066528
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This study addresses the uncertainties in hybrid-electric powertrain technology for a 19-passenger commuter aircraft, focusing on two future Entry-Into-Service timeframes: 2030 and 2040. The methodology is split into a preliminary optimization of aircraft design based on nominal technology scenarios followed by Monte Carlo simulations to investigate the impact of diverse technology projections and distribution types. Advanced surrogate modeling techniques, leveraging deep neural networks (DNN) trained on a dataset from an aircraft design framework, are employed. Key outcomes from this work reveal a marked increase in computational efficiency, with a speed-up factor of approximately 500 times when utilizing surrogate models. The results indicate that the 2040 entry-into-service (EIS) scenario could achieve larger reductions in fuel and total energy consumption—20.4% and 15.8% respectively—relative to the 2030 scenario, but with higher uncertainty. Across all scenarios examined, the hybrid-electric model showcased superior performance compared to its conventional counterpart. The battery-specific energy density is proved to be a critical parameter of the aircraft’s performance across both timeframes. The findings emphasize the importance of continuous innovation in battery and motor technologies to target toward greater system-level efficiency and reduced environmental impact.
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      Navigating Technological Risks: An Uncertainty Analysis of Powertrain Technology in Hybrid-Electric Commuter Aircraft

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306327
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    contributor authorDiamantidou, Dimitra-Eirini
    contributor authorSoibam, Jerol
    contributor authorZaccaria, Valentina
    contributor authorKalfas, Anestis I.
    date accessioned2025-04-21T10:30:11Z
    date available2025-04-21T10:30:11Z
    date copyright10/15/2024 12:00:00 AM
    date issued2024
    identifier issn0742-4795
    identifier othergtp_147_04_041004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306327
    description abstractThis study addresses the uncertainties in hybrid-electric powertrain technology for a 19-passenger commuter aircraft, focusing on two future Entry-Into-Service timeframes: 2030 and 2040. The methodology is split into a preliminary optimization of aircraft design based on nominal technology scenarios followed by Monte Carlo simulations to investigate the impact of diverse technology projections and distribution types. Advanced surrogate modeling techniques, leveraging deep neural networks (DNN) trained on a dataset from an aircraft design framework, are employed. Key outcomes from this work reveal a marked increase in computational efficiency, with a speed-up factor of approximately 500 times when utilizing surrogate models. The results indicate that the 2040 entry-into-service (EIS) scenario could achieve larger reductions in fuel and total energy consumption—20.4% and 15.8% respectively—relative to the 2030 scenario, but with higher uncertainty. Across all scenarios examined, the hybrid-electric model showcased superior performance compared to its conventional counterpart. The battery-specific energy density is proved to be a critical parameter of the aircraft’s performance across both timeframes. The findings emphasize the importance of continuous innovation in battery and motor technologies to target toward greater system-level efficiency and reduced environmental impact.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNavigating Technological Risks: An Uncertainty Analysis of Powertrain Technology in Hybrid-Electric Commuter Aircraft
    typeJournal Paper
    journal volume147
    journal issue4
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.4066528
    journal fristpage41004-1
    journal lastpage41004-11
    page11
    treeJournal of Engineering for Gas Turbines and Power:;2024:;volume( 147 ):;issue: 004
    contenttypeFulltext
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