AI-Assisted Multidisciplinary Design Optimization of Hydrogen-Electric Aircraft Integrating Aerodynamics, Propulsion, Thermal Management, and Structural Mass
空力・推進・熱管理・構造質量を統合したAI支援型水素電動航空機の多分野設計最適化 (AI 翻訳)
ABIR MAH, Paul B
🤖 gxceed AI 要約
日本語
水素電動航空機の概念設計において、空力・推進・熱管理・構造の4分野を統合したAI加速型MDOフレームワークを提案。物理情報ニューラルネットワーク（PINN）サロゲートをNSGA-II最適化ループ内で用い、MTOW・航続距離・熱管理抵抗のパレートトレードオフを高速探索する。既存デモ機（HY4、Do228、ZEROe）の公開データでベンチマークを構築し、再現性を重視したオープンソース設計を提示している。
English
This paper proposes an AI-accelerated MDO framework for hydrogen-electric aircraft that integrates aerodynamics, propulsion, thermal management, and structural mass. Physics-informed neural network surrogates replace high-fidelity solvers within an NSGA-II loop, enabling rapid Pareto exploration of MTOW, range, and thermal drag. The framework is benchmarked against existing demonstrators (HY4, Do228, ZEROe) and emphasizes reproducibility with open-source tools.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の航空機産業（JAXA、川崎重工等）は水素航空機の研究開発を進めており、本フレームワークは設計初期段階での脱炭素性能評価に貢献し得る。また、SSBJや有報での気候関連開示が進む中、航空分野の技術ロードマップ策定や投資家向け情報提供にも示唆を与える。
In the global GX context
Globally, hydrogen-electric aircraft are a key pathway to zero-carbon aviation, aligning with ICAO's net-zero goals and the growing emphasis on transition finance for sustainable aviation. This framework offers a reproducible, AI-driven design tool that could accelerate the development of cleaner aircraft, supporting climate disclosure and transition planning in the aerospace sector.
👥 読者別の含意
🔬研究者:Provides an integrated MDO framework with AI surrogates for hydrogen-electric aircraft design, offering a benchmark dataset and reproducibility guidelines.
🏢実務担当者:Offers a design optimization tool that can help aerospace firms evaluate trade-offs in hydrogen-electric aircraft concepts, informing R&D investment decisions.
🏛政策担当者:Highlights the technical feasibility and design considerations for hydrogen aviation, useful for shaping R&D funding and regulatory support for zero-carbon aviation.
📄 Abstract（原文）
<title>Abstract</title> <p>Hydrogen-electric aircraft represent a promising pathway to zero-carbon aviation, but their conceptual design is governed by strong interdisciplinary couplings among aerodynamics, electric propulsion, fuel-cell thermal management, and cryogenic structural mass estimation. Existing multidisciplinary design optimization (MDO) studies address subsets of these disciplines, typically aero-structural or thermal-propulsion coupling, without an integrated, AI-accelerated framework that simultaneously captures all four. This paper proposes a novel MDO architecture in which physics-informed neural network (PINN) surrogates replace expensive high-fidelity disciplinary solvers inside an NSGA-II multi-objective optimization loop, enabling rapid exploration of the Pareto trade space between maximum take-off weight (MTOW), mission range, and thermal management system (TMS) drag penalty. The aerodynamic module is based on Reynolds-Averaged Navier–Stokes (RANS) solutions computed with the open-source SU2 solver; the propulsion module couples a PEM fuel-cell electrochemical-thermal model with electric motor efficiency maps; the thermal module integrates ram-air heat-exchanger sizing; and the structural module employs semi-analytical wing-box mass estimation (validated against three aircraft with < 5% error) combined with ASME Boiler and Pressure Vessel Code cryogenic tank sizing. A benchmark reference dataset is assembled from published specifications of existing hydrogen-electric demonstrators (DLR/H2FLY HY4, ZeroAvia Do228, and Airbus ZEROe) to anchor validation. The methodology includes mesh-independence studies, turbulence-model selection, convergence-criteria documentation, sensitivity analysis, and end-to-end uncertainty quantification. The results framework specifies the required comparison variables, contour plots, statistical tests, and baseline cases without fabricating numerical outcomes. A critical peer review identifies likely reviewer concerns about surrogate generalization, validation fidelity, and data-leakage risk, and a revised strategy addresses each. The framework is designed for reproducibility using open-source tools (SU2, OpenMDAO, OpenVSP, Python/PyTorch) and is targeted at Q1 journals in aerospace engineering and applied energy.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10576314/v1first seen 2026-08-06 04:33:38
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