Integration of OTSM-TRIZ and Analytic Hierarchy Process for Choosing the Right Solution

AbstractA relevant part of TRIZ literature concerns the steps of the problem solving process, hence the analysis of the troublesome situation, the identification of the core problem and its resolution. Conversely, few efforts have been dedicated to support the last phase of the conceptual design process, which regards the selection of the most promising solutions to be further developed. The lack within TRIZ of an instrument capable to fulfill the abovementioned task led the authors to investigate the classical decision making methods and their applicability in the context of selecting the most valuable concepts downstream of problem solving phases characterized by divergent thinking. Several potential approaches have been surveyed and, among the others, the Weighted Sum Method and the Analytic Hierarchy Process seem to hold some of the characteristics requested by an ideal method to facilitate the decision making. In this paper, both of them have been tested through a real case study in order to verify their actual applicability and to reveal strengths and weaknesses with a particular focus on their capability to guide the decision process when a plurality of parties (e.g. policy makers, domain experts) are involved. The testing activity revealed that the Analytic Hierarchy Process resulted overall more appreciated by the experimenters, thanks to the systematic approach employed to select the best solution among a sample of alternatives developed through the Network of Problems

Development and Use of Engineering Standards for Computational Fluid Dynamics for Complex Aerospace Systems

Computational fluid dynamics (CFD) and other advanced modeling and simulation (M&S) methods are increasingly relied on for predictive performance, reliability and safety of engineering systems. Analysts, designers, decision makers, and project managers, who must depend on simulation, need practical techniques and methods for assessing simulation credibility. The AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations (AIAA G-077-1998 (2002)), originally published in 1998, was the first engineering standards document available to the engineering community for verification and validation (V&V) of simulations. Much progress has been made in these areas since 1998. The AIAA Committee on Standards for CFD is currently updating this Guide to incorporate in it the important developments that have taken place in V&V concepts, methods, and practices, particularly with regard to the broader context of predictive capability and uncertainty quantification (UQ) methods and approaches. This paper will provide an overview of the changes and extensions currently underway to update the AIAA Guide. Specifically, a framework for predictive capability will be described for incorporating a wide range of error and uncertainty sources identified during the modeling, verification, and validation processes, with the goal of estimating the total prediction uncertainty of the simulation. The Guide's goal is to provide a foundation for understanding and addressing major issues and concepts in predictive CFD. However, this Guide will not recommend specific approaches in these areas as the field is rapidly evolving. It is hoped that the guidelines provided in this paper, and explained in more detail in the Guide, will aid in the research, development, and use of CFD in engineering decision-making

Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications

In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the probability distribution of different uncertain parameters. Usually, we can safely linearize the dependence of the desired quantities y (e.g., stress at different structural points) on the uncertain parameters xi - thus enabling sensitivity analysis. Often, the number n of uncertain parameters is huge, so sensitivity analysis leads to a lot of computation time. To speed up the processing, we propose to use special Monte-Carlo-type simulations