On-orbit refueling plays a key role in extending the lifespan of space assets. Geosynchronous Earth orbit (GEO) satellites are of high strategic and commercial value. The optimal scheduling for geosynchronous Earth or...
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On-orbit refueling plays a key role in extending the lifespan of space assets. Geosynchronous Earth orbit (GEO) satellites are of high strategic and commercial value. The optimal scheduling for geosynchronous Earth orbit on-orbit refueling mission is studied in this paper, considering bi-objective function. Assuming that all space assets are originally located on the geosynchronous Earth orbit belt and noncoplanar, this paper aims at allocating the targets to the servicing spacecrafts, optimizing the rendezvous sequences and transfer trajectories of the servicing spacecrafts as well as their initial orbital location, dry mass, and fuel mass. Taking orbital transfer fuel cost and transfer time as the objective functions, a mathematical optimization model is proposed, as well as the solution method. Numerical simulations of various configurations are carried out to verify the effectiveness of the model and solution method. It can be concluded from the simulations that the optimal initial position of the servicing spacecraft prefers the area where the targets are concentrated;the servicing spacecraft initial orbital parameters have significant influences on the mission planning results;and it is fuel-saving and time-saving to employ more servicing spacecraft for on-orbit refueling mission and to optimize the initial location of the servicing spacecrafts.
The use of robust multiresponse constrained optimization techniques in which multiple-objective responses are involved is becoming a crucial part in additive manufacturing (AM) processes. Common and popular techniques...
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The use of robust multiresponse constrained optimization techniques in which multiple-objective responses are involved is becoming a crucial part in additive manufacturing (AM) processes. Common and popular techniques, in most cases, rely on the assumption of independent responses. In practice, however, many of the desired quality characteristics can be correlated. In this work, we propose a technique based on three ingredients: hybrid self-organizing (HSO) method, desirability function (DF), and evolutionary algorithms to analyze, model, and optimize the multiple correlated responses for the fused deposition modeling (FDM) process, one of the most popular AM technologies. The multiobjective functions are formulated by employing the HSO method and DF, where structural integrity and process efficiency metrics are considered for the data-driven correlated multiresponse models. Subsequently, layer thickness, nozzle temperature, printing speed, and raster angles are taken as process parameters (decision variables). The operational settings and capabilities for the FDM machine are defined as boundary constraints. Different EA algorithms, the nondominated sorting genetic algorithm, and the multiobjectiveparticleswarmoptimization method, are then deployed to model the AM criteria accordingly to extract the Pareto-front curve for the correlated multiresponse functions. FDM experimental design and data collection for the proposed method are provided and used to validate our approach. This study sheds light on formulating robust and efficient data-driven modeling and optimizations for AM processes.
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