Minimization of Total Refrigeration Power of Liquid Neon and Nitrogen Cooled Intercepts for SMES Magnets

Li, Q.; Li, X.; McIntosh, G. E.; Boom, R.

doi:10.1007/978-1-4613-0639-9_100

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…In considering systems with more general types of insulation, Chato and Khodadidi [14] sought to minimize entropy, similar to the formulation given by Bejan [10]. Other related attempts to optimize the design of these systems are found in [30], [35], and [41]. An actual implementation of these types of systems for the Large Hadron Collider (LHC) project is discussed in three technical reports [18,24,31].…”

Section: Introductionmentioning

confidence: 99%

Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm

Abramson

2004

Optimization and Engineering

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This paper describes the optimization of a load-bearing thermal insulation system characterized by hot and cold surfaces with a series of heat intercepts and insulators between them. The optimization problem is represented as a mixed variable programming (MVP) problem with nonlinear constraints, in which the objective is to minimize the power required to maintain the heat intercepts at fixed temperatures so that one surface is kept sufficiently cold. MVP problems are more general than mixed integer nonlinear programming (MINLP) problems in that the discrete variables are categorical; i.e., they must always take on values from a predefined enumerable set or list. Thus, traditional approaches that use branch and bound techniques cannot be applied.In a previous paper, a linearly constrained version of this problem was solved numerically using the Audet-Dennis generalized pattern search (GPS) method for MVP problems. However, this algorithm may not work for problems with general nonlinear constraints. A new algorithm that extends that of Audet and Dennis by incorporating a filter to handle nonlinear constraints makes it possible to solve the more general problem. Additional nonlinear constraints on stress, 2 mass, and thermal contraction are added to that of the previous work in an effort to find a more realistic feasible design. Several computational experiments show a substantial improvement in power required to maintain the system, as compared to the previous literature. The addition of the new constraints leads to a very different design without significantly changing the power required. The results demonstrate that the new algorithm can be applied to a very broad class of optimization problems, for which no previous algorithm with provable convergence results could be applied. Report Documentation Page

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Section: Introductionmentioning

confidence: 99%

Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm

Abramson

2004

Optimization and Engineering

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“…Others [YOY91] studied the effect of the number of intercepts and the types of insulator on the temperature distribution and insulation efficiency. Yet others [LLMB89] considered different substances such as liquid nitrogen or neon for cooling the intercepts and compared different types of insulators.…”

Section: Designing Thermal Insulation Systemmentioning

confidence: 99%

Ant Colony Optimization for Mixed-Variable Optimization Problems

Liao

Socha

et al. 2014

IEEE Trans. Evol. Computat.

220

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In this paper, we show how ant colony optimization (ACO) may be used for tackling mixed-variable optimization problems. We show how a version of ACO extended to continuous domains (ACO R ) may be further used for mixed-variable problems. We present different approaches to handling mixed-variable optimization problems and explain their possible uses. We propose a new mixed-variable benchmark problem. Finally, we compare the results obtained to those reported in the literature for various real-world mixed-variable optimization problems.

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“…Yamaguchi et al (1991) studied the effect of the number of intercepts and the insulator types (by varying the density of the multi-layers insulators used as the insulating material) on the temperature distribution and the resulting heat losses. Li et al (1989) considered the use of liquid nitrogen and neon instead of liquid helium as the cooling media for the intercepts and compared different insulator types including multi-layer insulators.…”

Section: Introductionmentioning

confidence: 99%

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Kokkolaras

Audet

Dennis

2001

Optimization and Engineering

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In the literature, thermal insulation systems with a fixed number of heat intercepts have been optimized with respect to intercept locations and temperatures. The number of intercepts and the types of insulators that surround them were chosen by parametric studies. This was because the optimization methods used could not treat such categorical variables. Discrete optimization variables are categorical if the objective function or the constraints can not be evaluated unless the variables take one of a prescribed enumerable set of values. The key issue is that categorical variables can not be treated as ordinary discrete variables are treated by relaxing them to continuous variables with a side constraint that they be discrete at the solution.A new mixed variable programming (MVP) algorithm makes it possible to optimize directly with respect to mixtures of discrete, continuous, and categorical decision variables. The result of applying MVP is shown here to give a 65% reduction in the objective function over the previously published result for a thermal insulation model from the engineering literature. This reduction is largely because MVP optimizes simultaneously with respect to the number of heat intercepts and the choices from a list of insulator types as well as intercept locations and temperatures. The main purpose of this paper is to show that the mixed variable optimization algorithm can be applied effectively to a broad class of optimization problems in engineering that could not be easily solved with earlier methods.

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Minimization of Total Refrigeration Power of Liquid Neon and Nitrogen Cooled Intercepts for SMES Magnets

Cited by 3 publications

References 2 publications

Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm

Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm

Ant Colony Optimization for Mixed-Variable Optimization Problems

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