Modified nodal approach to network sensitivity calculation for one-dimensional orthogonal search technique

1980 ◽  
Vol 16 (16) ◽  
pp. 641 ◽  
Author(s):  
J.T. Ogrodzki
Keyword(s):  
Search Technique ◽  
One Dimensional ◽  
Sensitivity Calculation ◽  
Network Sensitivity ◽  
Orthogonal Search

scholarly journals Parabolic Spiral Search Plan for a Randomly Located Target in the Plane

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed Beltagy ◽  
Mohamed Abd Allah El-Hadidy
Keyword(s):  
Target Position ◽  
Expected Value ◽  
Dimensional Case ◽  
Linear Search ◽  
Search Technique ◽  
One Dimensional ◽  
Search Plan ◽  
Starting Point ◽  
Complete Revolution ◽  
The One

This paper addresses the problem of searching for a located target in the plane by using one searcher starting its motion from the point . The searcher moves along parabolic spiral curve. The position of the target has a known distribution. We show that the distance between the target position and the searcher starting point depends on the number of revolutions, where the complete revolution is done when . Furthermore, we study this technique in the one-dimensional case (i.e., when the searcher moves with linear search technique). It is desired to get the expected value of the time for detecting the target. Illustrative examples are given to demonstrate the applicability of this technique assuming circular normal distributed estimates of the target position.

A new one dimensional search technique

1970 ◽  
Vol 25 (3) ◽  
pp. 425-430 ◽  
Author(s):  
James W. Gentry
Keyword(s):  
Search Technique ◽  
One Dimensional

Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger

2005 ◽  
Author(s):  
Weihua Cai ◽  
Mihir Sen ◽  
K. T. Yang ◽  
Arturo Pacheco-Vega
Keyword(s):  
Heat Exchanger ◽  
Genetic Programming ◽  
Symbolic Regression ◽  
Functional Domain ◽  
Search Technique ◽  
Tree Structures ◽  
One Dimensional ◽  
Compact Heat Exchangers ◽  
Functional Forms ◽  
Parse Trees

We describe a symbolic regression methodology based on genetic programming to find correlations that can be used to estimate the performance of compact heat exchangers. Genetic programming is an evolutionary search technique in which functions represented as parse trees evolve as the search proceeds. An advantage of this approach is that functional forms of the correlation need not be assumed. The algorithm performs symbolic regression by seeking both the functional structure of the correlation and the coefficients therein that enable the closest fit to experimental data. This search is conducted within a functional domain constructed from sets of operators and terminals that are used to build tree-structures representing functions. A penalty function is used to prevent large correlations. The methodology is tested using first artificial data from a one-dimensional function and later a set of published heat exchanger experiments. Comparison with published results from the same data show that symbolic-regression correlations are as good or better. The effect of the penalty parameters on the “best function” is also analyzed.

A Least Weight Circular Cooling Fin

1975 ◽  
Vol 97 (4) ◽  
pp. 1190-1193 ◽  
Author(s):  
S. Gu¨c¸eri ◽  
C. J. Maday
Keyword(s):  
Minimum Principle ◽  
Pattern Search ◽  
Dimensional Representation ◽  
Search Technique ◽  
Regularization Technique ◽  
One Dimensional ◽  
Additional Degree ◽  
Fin Thickness ◽  
Fin Design ◽  
Selection Of

The design of the least weight circular cooling fin is obtained through an application of the Minimum Principle. The fin temperature, thickness, and heat flux are considered to be functions only of the radius. Solutions are obtained for the exact one-dimensional representation and also for the approximate case where the profile curvature is neglected in the convection calculation. A regularization technique is used to avoid computational difficulties, especially near the tip where the fin thickness becomes vanishingly small. In the exact case, an additional degree of freedom allows the selection of the fin root dimension. This flexibility suggests the possibility of optimization in a parameter, the root dimension; this was done by using a pattern search technique. Closed form results are given for the approximate case. For kθo/qo = 100, the fin design obtained in the exact case is about 20 percent shorter and contains about 1 percent to 2 percent less material than the fin obtained in the approximate case.

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