This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.
This paper presents a novel optimisation approach, called Structured-Chromosome Genetic Algorithm (SCGA), that addresses the issue of handling variable-size design space optimisation problems. This is based on variants of standard genetic operators able to handle structured search spaces. The potential of the presented methodology is shown by solving the problem of defining observation campaigns for tracking space objects from a network of tracking stations. The presented approach aims at supporting the space sector in response to the constantly increasing population size in the around-Earth environment. The test case consists in finding the observation scheduling that minimises the uncertainty in the final state estimation of a very low Earth satellite operating in a highly perturbed dynamical environment. This is evaluated by coupling the optimiser with an estimation routine based on a sequential filtering approach that estimates the satellite state distribution conditional on received indirect measurements. The solutions found by employing SCGA are finally compared to the ones achieved using more traditional approaches. Namely, the problem has been reformulated to be faced using standard Genetic Algorithm and another variable-size optimiser, the "Hidden-genes" Genetic Algorithm variant.
The purpose of this work is to investigate the cyclic plasticity and creep-cyclic plasticity behaviours of particle reinforced titanium matrix composites (PRTMCs) SiC/Ti-6242, aimed to be used in high temperature applications. The investigation has been conducted upon microstructures that have been taken from a previous study where low-fidelity model-based optimization (LFMBO) has been used to maximise the elastic behaviour of particle reinforced aluminium matrix composites. The effect of the particle spatial distribution, particle fraction volume and number of particles on the shakedown limit, limit load and creep-cyclic plasticity have been explored by direct numerical techniques based on the Linear Matching Method (LMM) framework. The micromechanical approach to modelling and fifteen multi-particle unit cells have been investigated. Under cyclic loading conditions, the structural response of PRTMCs is not trivial and becomes even more significant when high temperature is involved. Hence, the factors that affect the creep and cyclic plasticity of PRTMCs are analysed and discussed, including effects of the applied load level, dwell period and temperature on the composites' performance. The applicability and accuracy of the proposed direct method has also been verified by the step-by-step analysis.
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