Multidisciplinary design optimization of long-range slender guided rockets considering aeroelasticity and subsidiary loads

摘要

Due to the insufficient rigidity feature and long-range requirement, it is crucial to consider the aeroelasticity and subsidiary loads caused by the Earth rotation and fuel consumption when designing long-range slender guided rockets (LRSGRs). As a typical multidisciplinary design optimization (MDO) problem, the design optimization of LRSGRs confronts two critical challenges, i.e., accurate multidisciplinary modeling and efficient global optimization. To address the challenges, a novel MDO framework including MDO problem definition, multidisciplinary modeling, and metamodel-based optimizer is developed for LRSGR design. The LRSGR MDO problem is formulated to minimize the total mass subject to a number of practical engineering constraints such as bending mode frequencies, miss distance, and fall angle. Several disciplinary models including structure, aerodynamics, propulsion, mass, aeroelasticity, guidance control, and trajectory are established. To enhance the analysis accuracy, structural finite element analysis (FEA), three-channel autopilot, and high-fidelity trajectory models are adopted. In the aeroelasticity model, the unsteady aerodynamic loads are calculated by slender body theory and aerodynamic derivative method. The subsidiary loads including subsidiary Coriolis force, centrifugal inertial force, Coriolis force, and subsidiary Coriolis moment are incorporated in the trajectory model of LRSGRs. Since structural finite element, aeroelasticity, and trajectory models are computationally expensive (about 1.8 hours for one trial of system analysis on a well-equipped workstation), an adaptive radial basis function metamodel-based optimizer is integrated in the framework to solve the LRSGR MDO problem with moderate computational cost. The total mass of the studied LRSGR is decreased by 88 kg (i.e., 14% of the total mass) after optimization, which demonstrates the effectiveness and practicability of the proposed MDO framework for LRSGRs. (C) 2019 Elsevier Masson SAS. All rights reserved.