Fuzzy arithmetic in qualitative reasoning

2006 ◽  
pp. 457-467 ◽  
Author(s):  
Didier Dubois ◽  
Henri Prade
Keyword(s):  
Qualitative Reasoning ◽  
Fuzzy Arithmetic

Qualitative Reasoning: A Survey of Techniques Applications

1995 ◽  
Vol 8 (3-4) ◽  
pp. 119-192 ◽  
Author(s):  
◽  
Philippe Dague
Keyword(s):  
Qualitative Reasoning

Systems thinking, a consilience of values and logic

2005 ◽  
Vol 24 (4) ◽  
pp. 259-274
Author(s):  
Sameer Kumar ◽  
Thomas Ressler ◽  
Mark Ahrens
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Systems Thinking ◽  
Qualitative Reasoning ◽  
Colleges And Universities ◽  
Decision Makers ◽  
Modern World ◽  
Problem Solving Process ◽  
Problem Solvers

This article is an appeal to incorporate qualitative reasoning into quantitative topics and courses, especially those devoted to decision-making offered in colleges and universities. Students, many of whom join professional workforce, must become more systems thinkers and decision-makers than merely problem-solvers. This will entail discussion of systems thinking, not just reaching “the answer”. Managers will need to formally and forcefully discuss objectives and values at each stage of the problem-solving process – at the start, during the problem-solving stage, and at the interpretation of the results stage – in order to move from problem solving to decision-making. The authors suggest some methods for doing this, and provide examples of why doing so is so important for decision-makers in the modern world.

scholarly journals Vibro-Acoustical Sensitivities of Stiffened Aircraft Structures Due to Attached Mass-Spring-Dampers with Uncertain Parameters

Aerospace ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 174
Author(s):  
Johannes Seidel ◽  
Stephan Lippert ◽  
Otto von Estorff
Keyword(s):  
System Response ◽  
Fuzzy Arithmetic ◽  
Parameter Uncertainties ◽  
Excitation Spectra ◽  
Radiated Noise ◽  
Linking Elements ◽  
Manufacturing Tolerances ◽  
Suitable Framework ◽  
Mass Spring ◽  
The Impact

The slightest manufacturing tolerances and variances of material properties can indeed have a significant impact on structural modes. An unintentional shift of eigenfrequencies towards dominant excitation frequencies may lead to increased vibration amplitudes of the structure resulting in radiated noise, e.g., reducing passenger comfort inside an aircraft’s cabin. This paper focuses on so-called non-structural masses of an aircraft, also known as the secondary structure that are attached to the primary structure via clips, brackets, and shock mounts and constitute a significant part of the overall mass of an aircraft’s structure. Using the example of a simplified fuselage panel, the vibro-acoustical consequences of parameter uncertainties in linking elements are studied. Here, the fuzzy arithmetic provides a suitable framework to describe uncertainties, create combination matrices, and evaluate the simulation results regarding target quantities and the impact of each parameter on the overall system response. To assess the vibrations of the fuzzy structure and by taking into account the excitation spectra of engine noise, modal and frequency response analyses are conducted.

A Framework for Building Behavioral Models for Design-Stage Failure Identification Using Dimensional Analysis

2010 ◽  
Author(s):  
Eric Coatane´a ◽  
Tuomas Ritola ◽  
Irem Y. Tumer ◽  
David Jensen
Keyword(s):  
Dimensional Analysis ◽  
Behavioral Model ◽  
Qualitative Reasoning ◽  
Design Stage ◽  
Critical Event ◽  
Behavioral Models ◽  
New Approach ◽  
Design Variables ◽  
Qualitative Physics ◽  
Failure Identification

In this paper, a design-stage failure identification framework is proposed using a modeling and simulation approach based on Dimensional Analysis and qualitative physics. The proposed framework is intended to provide a new approach to model the behavior in the Functional-Failure Identification and Propagation (FFIP) framework, which estimates potential faults and their propagation paths under critical event scenarios. The initial FFIP framework is based on combining hierarchical system models of functionality and configuration, with behavioral simulation and qualitative reasoning. This paper proposes to develop a behavioral model derived from information available at the configuration level. Specifically, the new behavioral model uses design variables, which are associated with units and quantities (i.e., Mass, Length, Time, etc…). The proposed framework continues the work to allow the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. The main contribution in this paper consists of developing component behavioral models based on the combination of fundamental design variables used to describe components and their units or quantities, more precisely describing components’ behavior.

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