A novel metamodel management strategy for robust trajectory design of an expendable launch vehicle

Abstract

Uncertainty-based design optimization has been widely acknowledged as an advanced methodology to address competing objectives of aerospace vehicle design, such as reliability and robustness. Despite the usefulness of uncertainty-based design optimization, the computational burden associated with uncertainty propagation and analysis process still remains a major challenge of this field of study. The metamodeling is known as the most promising methodology for significantly reducing the computational cost of the uncertainty propagation process. On the other hand, the nonlinearity of the uncertainty-based design optimization problem's design space with multiple local optima reduces the accuracy and efficiency of the metamodels prediction. In this article, a novel metamodel management strategy, which controls the evolution during the optimization process, is proposed to alleviate these difficulties. For this purpose, a combination of improved Latin hypercube sampling and artificial neural networks are involved. The proposed strategy assesses the created metamodel accuracy and decides when a metamodel needs to be replaced with the real model. The metamodeling and metamodel management strategy are conspired to propose an augmented strategy for robust design optimization problems. The proposed strategy is applied to the multiobjective robust design optimization of an expendable launch vehicle. Finally, based on non-dominated sorting genetic algorithm-II, a compromise between optimality and robustness is illustrated through the Pareto frontier. Results illustrate that the proposed strategy could improve the computational efficiency, accuracy, and globality of optimizer convergence in uncertainty-based design optimization problems.

On modeling and dynamics of a multiple launch rocket system

“…We can contrast the calculation speed of the Monte Carlo method and other modified sampling strategies. 41,42 The launch tube is essentially flexible in actual MLRS; therefore, some comparative studies will be conducted for the rigid and flexible models of the launch tube. In addition, the dynamics control of the MLRS based on this comprehensive model can be designed to improve the rocket dispersion.…”

Multi-stage Multi-fidelity Information Correction for Artificial Neural Network Based Meta-modelling

“…A suitably trained ANN may perform tasks such as pattern recognition, identification, classification, system control and function approximation (non-linear regression) [29]. In aerospace, ANNs have been used for the estimation of aerodynamic coefficients [30], space vehicle design and trajectory optimisation [31], [32], turbo-machinery blade optimisation [33], wing design [34], flow control, aeroelasticity, and interpolation of wind tunnel data [18].…”

Ascent Trajectory Optimization for Hypersonic Vehicle Based on Improved Chicken Swarm Optimization

“…Li [18] came up with a stochastic gradient PSO for the hypersonic vehicle reentry phase, which used the history optimal solutions to accelerate the search. Bataleblu and Roshanian [19]- [21] put forward a new method, which used the neural network and the sampling method to approximate the original problem. The approximate model is used to calculate the global optimal combined with the optimization algorithms.…”