Engineering design calculations with fuzzy parameters

1992 ◽  
Vol 52 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Kristin L. Wood ◽  
Kevin N. Otto ◽  
Erik K. Antonsson
Author(s):  
K. L. Wood ◽  
E. K. Antonsson

Abstract Preliminary engineering design intrinsically consists of imprecise descriptions of the input parameters. We present new conceptual and algorithmic procedures for dealing with such imprecise descriptions. Specifically, a two-part method is outlined for performing design calculations on these “fuzzy” parameters, as well as determining a measure for the parameters’ coupling. By interpreting the input set of variables for preliminary design in terms of fuzzy sets, we demonstrate how the engineer may associate his subjective meaning with the input parameters and the output functional requirement, leading to the foundation of our approach. An example of the method highlights the primary issues and the implementation scheme as a computational tool.


Author(s):  
Marcel Escudier

In this chapter the wide array of engineering devices, from the kitchen tap (a valve) to supersonic aircraft, the basic design of which depends upon considerations of the flow of gases and liquids, is shown. Much the same is true of most natural phenomena from the atmosphere and our weather to ocean waves, and the movement of sperm and other bodily fluids. In this textbook a number of the concepts, principles, and procedures which underlie the analysis of any problem involving fluid flow or a fluid at rest are introduced. In this Introduction, examples have been selected for which, by the end of the book, the student should be in a position to make practically useful engineering-design calculations. These include a dam, a rocket motor, a supersonic aerofoil with shock and expansion waves, a turbojet engine, a turbofan engine, and the blading of a gas turbine.


Author(s):  
K. L. Wood ◽  
E. K. Antonsson ◽  
J. L. Beck

Abstract A technique to perform design calculations on imprecise representations of parameters using the calculus of fuzzy sets has been previously developed [17]. An analogous approach to representing and manipulating uncertainty in choosing among alternatives (imprecision) using probability calculus is presented and compared with the fuzzy calculus technique. We find that the fuzzy calculus is well suited to representing and manipulating the imprecision aspect of uncertainty, and probability is best used to represent stochastic uncertainty.


Author(s):  
William S. Law ◽  
Erik K. Antonsson

Abstract The Imprecise Design Tool (IDT) presented in this paper is a working computer implementation of the method of imprecision, a formal theory that represents preferences among design alternatives. An aircraft engine design example indicates how the IDT may be applied to support engineering design decisions, using the Engine Development Cost Estimator provided by General Electric Aircraft Engines, Cincinnati, Ohio.


1989 ◽  
Vol 111 (4) ◽  
pp. 616-625 ◽  
Author(s):  
K. L. Wood ◽  
E. K. Antonsson

A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy calculus. Calculations can be performed using this method, to produce (imprecise) performance parameters from imprecise (input) design parameters. The Fuzzy Weighted Average technique is used to perform these calculations. A new metric, called the γ-level measure, is introduced to determine the relative coupling between imprecise inputs and outputs. The background and theory supporting this approach are presented, along with one example.


Author(s):  
Michael T. Postek

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.


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