用物理模型同时优化多旋翼无人机设计，精准匹配市售零件并预测性能。
Multi-Domain Physics-Based MDO of Multirotor UAVs: A Deterministic Framework for Discrete COTS Sizing
- 构建耦合结构、电化学、气动等多学科的统一求解框架，避免参数误差传递。
- 对商用无人机预测最大起飞重量误差仅7.2%，电池质量误差7.9%。
- 适合需要快速迭代设计的无人机研发团队，尤其关注真实零件采购与飞行时长。
多旋翼无人机设计受结构力学、电化学、气动及运动学等非线性方程紧密耦合制约。顺序求解时，子模型参数偏差会引发质量累积性失效——质量雪球效应。本文提出AeroEval，一种基于物理的多学科设计优化（MDO）引擎，可同时处理所有子系统耦合，并将连续尺寸最优解映射为实际可采购的商用现成（COTS）组件。该引擎通过严格校准/测试协议验证：在20个自制与经典平台组成的保留集上拟合三个结构/包装系数后冻结，再盲测19个现代商用无人机平台（总重377克至76公斤）。在商用测试集上，最大起飞重量（MTOW）预测的平均绝对百分比误差（MAPE）为7.2%，复杂度因子均值 $k \≈ 1.05 \pm 0.08$，RMSE_{MTOW} = 1.59 kg；自制校准集因搭建差异大，MAPE为26.1%。单电池平台电池质量预测误差7.9%，冗余多电池企业级平台存在系统性低估。14参数敏感性分析完成超300次仿真，量化了起飞质量与续航时间的偏导数；前进速度超过25.5 m/s时因三次方寄生功率增长而发散。针对农业与配送任务的路径依赖质量释放模型，相较静态基准可降低结构框架质量达40.6%，能量容量减少33.7%。典型求解收敛需25-50次迭代，标准桌面CPU可在50毫秒内完成。
原文摘要 · Abstract (English)
Multirotor Unmanned Aerial Vehicle (UAV) design is governed by a tightly coupled system of non-linear equations spanning structural mechanics, electrochemistry, aerodynamics, and kinematics. Solved sequentially, a miscalibrated sub-model coefficient triggers a mass-compounding cascade failure--the Mass Snowball effect. This paper presents AeroEval, a physics-based, Multidisciplinary Design Optimization (MDO) engine that simultaneously resolves all subsystem couplings and maps continuous sizing optima to physically purchasable, commercial off-the-shelf (COTS) components. The MDO engine is validated under a strict calibrate/test protocol that eliminates circularity: three structural/packaging coefficients are fitted on a held-out cohort of 20 do-it-yourself (DIY) and legacy platforms, then frozen and evaluated blind on 19 modern commercial drone platforms spanning 377 g to 76 kg. On the commercial test cohort the engine predicts Maximum Takeoff Weight (MTOW) within 7.2% Mean Absolute Percentage Error (MAPE), with a mean complexity factor $k \approx 1.05 \pm 0.08$ and $\text{RMSE}_{\text{MTOW}} = 1.59$ kg; the DIY calibration cohort, characterized by high build variability, yields 26.1% MAPE. Battery mass is predicted within 7.9% on single-pack platforms, with a disclosed systematic underprediction on redundant multi-battery enterprise platforms. A 14-parameter sensitivity suite of >300 simulations quantifies partial derivatives of takeoff mass and flight time; forward velocity diverges beyond 25.5 m/s due to cubic parasite power growth. The path-dependent mass-shedding model for agricultural and delivery roles reduces structural frame mass by up to 40.6% and energy capacity by 33.7% relative to static baselines. Typical solver convergence requires 25-50 iterations, completing in under 50 ms on a standard desktop CPU.
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