The design of computer experiments (DoCE) is a key technique in the field of metamodel-based design optimization. Space-filling and projective properties are desired features in DoCE. In this article, a novel algorithm of maximin Latin hypercube design (LHD) using successive local enumeration (SLE) is proposed for generating arbitrary m points in n-dimensional space. Testing results compared with lhsdesign function, binary encoded genetic algorithm (BinGA), permutation encoded genetic algorithm (PermGA) and translational propagation algorithm (TPLHD) indicate that SLE is effective to generate sampling points with good space-filling and projective properties. The accuracies of metamodels built with the sampling points produced by lhsdesign function and SLE are compared to illustrate the preferable performance of SLE. Through the comparative study on efficiency with BinGA, PermGA, and TPLHD, as a novel algorithm of LHD sampling techniques, SLE has good space-filling property and acceptable efficiency.

Energy management strategies are the key technology for hybrid electric UAVs. This paper proposes a fuzzy state machine (FSM) energy management strategy with an online potential to control the power flow for the hybrid electric UAV which includes the photovoltaic, fuel cell, and battery power sources. The FSM strategy couples the fuzzy logical strategy with a state machine strategy where the fuzzy logical strategy controls the power split between a fuel cell and a battery and the state machine deals with the power flow of photovoltaics and battery. To evaluate the FSM strategy, a simulation platform integrating the hybrid power system model and UAV model is developed with a Matlab/Simulink tool. An existed online thermostat control strategy for the same type of UAV is employed to compare with the proposed strategy based on the developed platform. The energy management process and the state of each power source are analyzed under a given mission scenario. The comparison of the two strategies about the power and energy contribution rates of each power source, the battery state of charge, and the hydrogen consumption is presented. The results indicate that the FSM strategy can satisfy the demand power effectively during the mission and performs better than the thermostat control strategy on power distribution and fuel consumption.

All-electric geostationary orbit (GEO) satellite systems design is a challenging multidisciplinary design optimization (MDO) problem, which is computation-intensive due to the employment of expensive simulations. In this paper, the all-electric GEO satellite MDO problem with multi-fidelity models is investigated. The MDO problem involving six inter-coupled disciplines is formulated to minimize the total mass of the satellite system subject to a number of engineering constraints. To reduce the computational cost of the multidisciplinary analysis (MDA) process, multi-fidelity transfer dynamics models and finite element analysis (FEA) models are developed for the geosynchronous transfer orbit (GTO) and structure disciplines, respectively. To effectively solve the all-electric GEO satellite MDO problem using multi-fidelity models, an adaptive Co-Kriging-based optimization framework is proposed. In this framework, the samples from a high-fidelity MDA process are integrated with those from a low-fidelity MDA process to create a Co-Kriging metamodel with a moderate computational cost for optimization. Besides, for refining the Co-Kriging metamodels, a multi-objective adaptive infill sampling approach is developed to produce the infill sample points in terms of the expected improvement (EI) and the probability of feasibility (PF) functions. Optimization results show that the proposed optimization framework can significantly reduce the total mass of satellite system with a limited computational budget, which demonstrates the effectiveness and practicality of the multi-fidelity modeling and adaptive Co-Kriging-based optimization framework for all-electric GEO satellite systems design.

Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world engineering applications.