Multi-Domain Physics-Based MDO of Multirotor UAVs: A Deterministic Framework for Discrete COTS Sizing

Abstract: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.

Computational Engineering, Finance, and Science (cs.CE); Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2607.22768 [cs.CE]

(or arXiv:2607.22768v1 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2607.22768 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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