Complex-Systems Design Methodology for Systems-Engineering Collaborative Environment

Abstract

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...

A Scientific Foundation of Simulation Games for the Analysis and Design of Complex Systems

“…Research on complex systems is diverse; complexity theory is not a unified body of theory yet ( Ridolfi, Mooij, & Corpino, 2012 ; Thrift, 1999 ; Walby, 2007 ). Complexity notions have developed across a range of disciplines, from natural and social sciences, to ecology and mathematics ( Capra, 1997 ; Maturana & Varela 1980 ; Waldrop, 1992 ).…”

“…Actors shape social systems with their horizontal and vertical interactions and communications ( Klabbers, 2018 ). A complex system can very broadly be defined as “one in which there are multiple interactions between many different elements of the system” ( Ridolfi, Mooij, & Corpino, 2012 , p. 39). Due to the characteristics as emergence, self-organization and adaptation ( Holland, 1995 ), complex systems are difficult both to analyze and to design.…”

Robust Multi-Disciplinary Optimization of Unmanned Entry Capsules

“…12 The main purpose of the USM is to allow for taking all types of uncertainty distributions into account, also epistemic (i.e., where the designer only estimates the probability intervals), exploiting the properties of a low-discrepancy sampling technique like the one developed by Sobol'. 13,14 The basic idea is to uniformly sample in the probability space (between 0 and 1) and then to transform the sample to the actual design-space using the Cumulative Distribution Function (CDF).…”

Understanding Complex Systems Through Mental Models and Shared Experiences

“…Such systems are inherently bound to concepts such as those discussed by sociologist Niklas Luhmann, referring to the term social system , in terms of self-organization, adaptation, feedback mechanisms and emergence (Luhmann, 1995). A complex system can very broadly be defined as “one in which there are multiple interactions between many different elements of the system […]” (Ridolfi, Mooij, & Corpino, 2012, p. 39). Another perspective of complex systems is that they consist of individual agents that act on and react to the input from their environment.…”