基于多领域物理的多旋翼无人机多学科设计优化：离散商用现货选型的确定性框架
Multi-Domain Physics-Based MDO of Multirotor UAVs: A Deterministic Framework for Discrete COTS Sizing
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中文总结 AI 辅助
研究多旋翼无人机设计中多领域物理耦合问题，提出AeroEval物理多学科设计优化引擎，经校准测试验证，能将连续尺寸优化映射到COTS组件，预测精度高，还给出了多参数模拟结果及典型求解器收敛情况。
中文摘要 AI 辅助
多旋翼无人机设计受结构力学、电化学、空气动力学和运动学等紧密耦合的非线性方程组支配。若顺序求解，校准错误的子模型系数会引发质量复合级联故障，即质量雪球效应。本文提出了AeroEval，这是一种基于物理的多学科设计优化（MDO）引擎，可同时解决所有子系统耦合问题，并将连续尺寸优化映射到可实际购买的商用现货（COTS）组件。该MDO引擎在严格的校准/测试协议下进行了验证，消除了循环性：在20个自制（DIY）和传统平台的保留群组上拟合了三个结构/包装系数，然后在19个跨越377克至76千克的现代商用无人机平台上进行盲评估。在商业测试群组上，该引擎预测的最大起飞重量（MTOW）平均绝对百分比误差（MAPE）在7.2%以内，平均复杂度因子$k \approx 1.05 \pm 0.08$，RMSE<sub>MTOW</sub>=1.59千克；DIY校准群组的MAPE为26.1%。单包平台上电池质量预测误差在7.9%以内，冗余多电池企业平台存在系统性预测不足。通过300多次模拟的14参数灵敏度套件量化了起飞质量和飞行时间的偏导数；由于立方寄生功率增长，前向速度在超过25.5米/秒时发散。针对农业和配送任务的路径依赖质量减轻模型相对于静态基线可将结构框架质量降低多达40.6%，能量容量降低33.7%。典型求解器收敛需要25 - 50次迭代，在标准桌面CPU上不到50毫秒即可完成。
英文摘要
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.
发表机构
- International Institute of Information Technology, Hyderabad（印度海得拉巴国际信息技术学院）
- Indian Institute of Science Education and Research, Bhopal（印度博帕尔科学教育与研究学院）
机构由 AI 辅助整理，请以论文原文为准。
