2 www.intechopen.com 2 Will-be-set-by-IN-TECHthe preliminary design of a new system all its elements and the disciplines involved over the entire life-cycle are taken into account, with the objective of reducing risks and costs, and possibly optimizing the performance.When the right people all work as a team in a multi-disciplinary collaborative environment, the MBSE and the Concurrent Engineering finally converge to the definition of the system. The main concern of the engineering activities involved in system design is to predict the behavior of the physical phenomena typical of the system of interest. The development and utilization of mathematical models able to reproduce the future behavior of the system based on inputs, boundary conditions and constraints, is of paramount importance for these design activities. The basic idea is that before those decisions that are hard to undo are made, the alternatives should be carefully assessed and discussed. Despite the favorable environment created by MBSE and Concurrent Engineering for the discipline experts to work, discuss and share knowledge, a certain lack of engineering-tool interoperability and standardized design methodologies has been so far a significant inhibitor, (International Council on Systems Engineering [INCOSE], 2007). The systems mathematical models usually implemented in the collaborative environments provide exceptional engineering-data exchange between experts, but often lack in providing structured and common design approaches involving all the disciplines at the same time. In most of the cases the various stakeholders have full authority on design issues belonging to their inherent domain only. The interfaces are usually determined by the experts and manually fed to the integrated models. We believe that the enormous effort made to conceive, implement, and operate MBSE and Concurrent Engineering could be consolidated and brought to a more fundamental level, if also the more common design analytical methods and tools could be concurrently exploited. Design-space exploration and optimization, uncertainty and sensitivity analysis, and trade off analysis are certainly design activities that are common to all the disciplines, consistently implemented for design purposes at the discipline-domain level. Bringing fundamental analysis techniques from the discipline-domain level to the system-domain level, to exploit interactions and synergies and to enable an efficient trade-off management is the central topic discussed in this chapter. The methodologies presented in this chapter are designed for their implementation in collaborative environments to support the engineering team and the decision-makers in the activity of exploring the design space of complex-system, typically long-running, models. In Section 2 some basic definitions, terminology, and design settings of the class of problems of interest are discussed. In Section 3 a test case of an Earth-observation satellite mission is introduced. This satellite mission is used throughout the chapter to...
The design of complex systems has become more and more articulated during the last decade, thus forcing radical modifications on the overall methodological approach. The authors developed a design methodology, which allows the user to design a particular category of complex systems usually called System-of-systems. This paper discusses the general framework to deal with the decomposition of a system-of-systems in its elements and sub-elements, to enable a faster and more effective solution of the problem, extending the applicability of the Concurrent Engineering paradigm to design phases that go beyond the preliminary/conceptual one. A hypothetical space exploration architecture, with a rover system, a lander system and an Earth-Moon transfer mission, have been implemented and discussed, linking the elements of this particular System-of-systems with a non-hierarchical decomposition approach and a multi-disciplinary feasible formulation. Nomenclature
Uncertainties in design variables and environmental factors are common in many engineering problems, and they must be taken into account when searching for robust optimal solutions. In robust multi-objective optimization it is common practice to optimize the average performance instead of the nominal objective functions. To compute average performance, and to determine the compliance of the solutions to the constraints, sampling is needed in a neighborhood of each individual and the performance of each sample point must be evaluated. This drives the computational cost of robust optimization up. In this paper we present a repository-based approach that reduces the number of evaluations needed during robust optimization. Unlike most of the approaches available to date, we introduce methods to keep the joint probability density function of the input variables intact when pre-existing points from the repository shall be used. This allows for cheap robustoptimization also in the presence of non-uniform uncertain-variable distributions. The robust optimization of unmanned entry capsules, considering continuous shape-variation models, aerothermodynamics, flight mechanics, and thermal protection system models at the same time is a valuable test-bed for the method presented here. In this paper we discuss the results of minimizing the mass of the capsules while maximizing the internal volume and the re-usability. We demonstrate that using a double-repository archive maintenance scheme it is possible to obtain accurate results and a reduction of the computational cost that is close to 70%, if compared to classical sampling-based methods for robust optimization. The analysis of robust-optimal entry capsules demonstrates that there are design conditions for which small and fully reusable capsules for unmanned entry from low Earth orbits perform as well as capsules with ablative materials, also under uncertainties.
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