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Structured Chromosome Genetic Algorithms
Published in Ossama Abdelkhalik, Algorithms for Variable-Size Optimization, 2021
The SCEAs were used to investigate a minimum cost MGADSM trajectory for the Jupiter Europa Orbiter Mission (JEO) that was planned to take place in the decade 2018-2028 [40] (the mission has been canceled). The total number of design variables in this problem is 33 (7 DDVs and 26 CDVs). Wide ranges were allowed for the design variables. Table 7.6 lists the lower and upper bounds of the associated design variables. A population of 30 individuals was used and the maximum number of cost function evaluations was set to 300,000. The problem is solved in two steps, the Zero-DSM step then the MGADSM step. The resulting fittest scenario is a three gravitational assist trajectory around Venus, Earth, and Earth (EVEEJ), as shown in Figs. 7.10(a) and 7.10(b). The details of the resulting solution is listed in Table 7.7. The solution is a posigrade multi-revolution trajectory. The cost of the resulting solution is 8.93 km/sec. The resulting solution has one DSM.
Extreme Environments in NASA Planetary Exploration
Published in John D. Cressler, H. Alan Mantooth, Extreme Environment Electronics, 2017
Kolawa Elizabeth, Mojarradi Mohammad, Castillo Linda Del
Very high ionizing radiation will be seen by Jupiter Europa Orbiter [10], a mission to orbit the Jovian satellite Europa. A typical mission profile of 2 years in Jupiter orbit followed by a 90 day mission in Europa orbit would involve radiation doses to the spacecraft five times that experienced by the Galileo mission. On the other hand, future missions that include Europa lander may experience lower dose rates than the orbiter due to Europa’s self-shielding. However, lander missions are much more mass constrained than orbiters, so it is possible that the requirements on the components might be even more demanding.
Data and model-based triple V product development framework and methodology
Published in Enterprise Information Systems, 2022
Qing Li, Hailong Wei, Chao Yu, Shuangshuang Wang
MBSE or a methodology close to it has been used very early in the field of product development. Forsberg and Mooz (1991) introduced systems engineering methodology into the V framework and created a new dimension to review the product development life cycle through reference architectures and models. Oliver, Kelliher, and Keegan (1997) formalised the use of models throughout the whole lifecycle of the target product, including requirement analysis, trade-off study, system design, implementation, and VV&A stage. Tepper (2011) explored the use of MBSE in the development of system architecture of Navy ship design and acquisition, showing its robust and comprehensive manner in developing complex systems. MBSE is also widely used in major aerospace projects (Madni and Sievers (2018b); Spangelo et al. (2013)), such as the Jupiter Europa Orbiter (JEO) mission (Bayer et al. (2012)), Mars2020 for implementing MBSE to aid the design of the Flight System (Fosse et al. (2015)). The V model has undergone new changes because of the extensive use of MBSE in product development. Jason et al. (2018) presented a model-based engineering (MBE) diamond framework, as shown in Figure 2, which is consisted of a classical physical V model and a corresponding digital twin V model. The digital twin is to make full use of data generated by physical model, sensor update, operation history, etc., to build a clone that reflects the real operation situation of the physical model in the virtual space (Zhuang et al. (2017)). Clark (2009) proposed a Dual-V model, which contains a vertical System-V model and several horizontal Element-V model based on each system module. Madni and Sievers (2018a) emphasised the use of MBSE in VV&A of the classical V model and constructed a crossed double V model. Based on the review of the above-mentioned studies, it is not difficult to find that multi-dimension, double V, is a direction for the improvement of the classical V model. We propose a general double V framework with MBSE for the unified understanding, as shown in Figure 3.