A decision approach for multi-stage combined design of solid rocket

The State of the Art of Information Integration in Space Applications

“…To deal with the complexity in predicting transonic buffet aerodynamics of aircrafts, Zahn et al [135] adopted a long short-term memory (LSTM) neural network in a reduced-order modeling (ROM) framework to capture time-delayed effect by unsteady aerodynamics, and the Monte-Carlo-based procedure was applied in training to estimate statistical errors. In selecting multi-stage combined schemes for solid rockets, Meng et al [136] used power generalized weighted aggregation in making a good trade-off of all critical attributes. Some special considerations of designs, reliability analysis, and manufacturing of space products as well as recent advances of reliability analysis methods have been thoroughly discussed by Yung et al [137].…”

Spacecraft Informatics

“…Through the integration with ellipse search algorithm and Modified coVariance Extended Kalman Filter (MVEKF) filter, the proposed system enhances the gridbased feature point extraction with satisfying performance and low error rate for Lunar rover's locating tasks. The last paper entitled 'A decision approach for multi-stage combined design of solid rocket' by Meng, Wang, and Wang (2020) developed a decision support system to facilitate the selection of optimal compositional scheme under a multiattributes environment with a generalised compensative weighted averaging aggregation operator. By aggregating the defined attributes in each scheme of a solid rocket, the optimal compositional scheme can be determined, which improves the effectiveness and feasibility in decision support of the spacecraft.…”