This paper presents a thermal study on chest-freezers, the small refrigerators used in domestic and supermarket applications. A thermal and energy model of a particular kind of these refrigerators, the "hot-wall&...
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This paper presents a thermal study on chest-freezers, the small refrigerators used in domestic and supermarket applications. A thermal and energy model of a particular kind of these refrigerators, the "hot-wall" (or "skin condenser") refrigerator, is developed and used to perform sensitivity and design optimisation analysis for given working temperatures and useful volume of the refrigerated cell. A finite-element heat transfer model of the refrigerator box is coupled to the complete thermodynamic model of the refrigerating plant, including real working conditions (compressor efficiency, friction pressure losses and so on). A sensitivity study of the main design parameters affecting the global refrigerator performance has been developed (for fixed working temperatures) with reference to the thickness of the metallic plates, to the evaporator and condenser tube diameters and to the evaporator tube pitch (with fixed evaporator-to-condenser tube pitch ratio). The results obtained show that the proposed sensitivity analysis can yield quite reliable results (in comparison with much more complex, albeit more accurate mathematical optimisation algorithms) using small computational resources. The great importance of 2-D heat conduction in the metallic plates is shown, evidencing how the plate thickness and the evaporator and condenser tube diameters affect the global performance of the system according to the well-known "fin efficiency" effect. The influence of the evaporator and condenser tube diameters on the friction pressure losses is also outlined. Some practical suggestions are made in conclusion, regarding the criteria which should be adopted in the thermal design of a hot-wall refrigerator.
In this paper, estimation of distribution algorithms (EDAs) are used to solve nuclear reactor fuel management optimisation (NRFMO) problems. Similar to typical population based optimisation algorithms, e.g. genetic al...
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In this paper, estimation of distribution algorithms (EDAs) are used to solve nuclear reactor fuel management optimisation (NRFMO) problems. Similar to typical population based optimisation algorithms, e.g. genetic algorithms (GAs), EDAs maintain a population of solutions and evolve them during the optimisation process. Unlike GAs, new solutions are suggested by sampling the distribution estimated from all the solutions evaluated so far. We have developed new algorithms based on the EDAs approach, which are applied to maximize the effective multiplication factor (K-eff) of the CONSORT research reactor of Imperial College London. In the new algorithms, a new 'elite-guided' strategy and the 'stand-alone' K-eff with fuel coupling is used as heuristic information to improve the optimisation. A detailed comparison study between the EDAs and GAs with previously published crossover operators is presented. A trained three-layer feed-forward artificial neural network (ANN) was used as a fast approximate model to replace the three-dimensional finite element reactor simulation code EVENT in predicting the K-eff. Results from the numerical experiments have shown that the EDAs used provide accurate, efficient and robust algorithms for the test case studied here. This encourages further investigation of the performance of EDAs on more realistic problems. (c) 2006 Elsevier Ltd. All rights reserved.
As a result of previous large, multipoint linkage studies there is a substantial amount of existing marker data. Due to the increased sample size, genetic maps estimated from these data could be more accurate than pub...
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As a result of previous large, multipoint linkage studies there is a substantial amount of existing marker data. Due to the increased sample size, genetic maps estimated from these data could be more accurate than publicly available maps. However, current methods for map estimation are restricted to data sets containing pedigrees with a small number of individuals, or cannot make full use of marker data that are observed at several loci on members of large, extended pedigrees. In this article, a maximum likelihood (ML) method for map estimation that can make full use of the marker data in a large, multipoint linkage study is described. The method is applied to replicate sets of simulated marker data involving seven linked loci, and pedigree structures based on the real multipoint linkage study of Abkevich et al. (2003, American Journal of Human Genetics 73, 1271-1281). The variance of the ML estimate is accurately estimated, and tests of both simple and composite null hypotheses are performed. An efficient procedure for combining map estimates over data sets is also suggested.
This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid sol...
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This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy search and the introduction of randomization and de-randomization within our combination algebra. This algebra is made of terms that represent optimization algorithms, following the approach of SALSA[1], which can be generated or tuned automatically using a learning meta-strategy [2]. In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search.
An algorithm for solving feedback min-max model predictive control for discrete-time uncertain linear systems with constraints is presented in this note. The algorithm is based on applying recursively a decomposition ...
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An algorithm for solving feedback min-max model predictive control for discrete-time uncertain linear systems with constraints is presented in this note. The algorithm is based on applying recursively a decomposition technique to solve the min-max problem via a sequence of low complexity linear programs. It is proved that the algorithm converges to the optimal solution in finite time. Simulation results are provided to compare the proposed algorithm with other approaches.
The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learning (RL) seems to be limited because it often converges to suboptimal policies. We apply no...
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The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learning (RL) seems to be limited because it often converges to suboptimal policies. We apply noise for preventing early convergence of the cross-entropy method, using Tetris, a computer game, for demonstration. The resulting policy outperforms previous RL algorithms by almost two orders of magnitude.
Algorithmic cooling (AC) is a recent spin-cooling approach that ernploys entropy compression methods in open systems. AC reduces the entropy of spins on suitable molecules beyond Shannon's bound on the degree of e...
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Algorithmic cooling (AC) is a recent spin-cooling approach that ernploys entropy compression methods in open systems. AC reduces the entropy of spins on suitable molecules beyond Shannon's bound on the degree of entropy compression by reversible manipulations. Remarkably, AC makes use of thermalization, a generally destructive facet of spin systems, as an integral part of the cooling scheme. AC is capable of cooling spins to very low temperatures and provides significant cooling for molecules containing as few as 5-7 spins. Application of AC to slightly larger molecules Could lead to breakthroughs in high-sensitivity NMR spectroscopy in the near future. Furthermore, AC may be germane to the development of scalable NMR quantum computers. We introduce here a new practicable algorithm, "PAC3'', and several new exhaustive cooling algorithms, Such as the Tribonacci and k-bonacci algorithms, In particular, we present the "all-bonacci" algorithm, which appears to reach the maximal degree of cooling obtainable by the optimal AC approach. AC is potentially beneficial for NMR-derived biomedical applications, which involve bio-molecules with isotope enrichments, such as C-13- and (IN)-I-15-labeled amino acids. We briefly Survey AC experiments, including a recent 3-spin experiment in which Shannon's bound was bypassed. The difficulties associated with cooling molecules hearing a greater number of spins are explained. Finally, the potential of selected cooling algorithms (practicable, exhaustive, and optimal algorithms) is illustrated with regard to a highly relevant bio-medical target-C-13-labeled glucose.
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response...
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ISBN:
(纸本)9781901502589
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective and provides a better coverage of the Pareto optimal solutions at a lower computational cost.
We describe rational Bezier spline-based parametric description of non-imaging collimators and implementation into optical design software. Together with non-linear optimization routines and adapted merit functions th...
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ISBN:
(纸本)0819464279
We describe rational Bezier spline-based parametric description of non-imaging collimators and implementation into optical design software. Together with non-linear optimization routines and adapted merit functions this provides flexible tools for development of light sources with LEDs.
Multi-robot path planning is a challenge for mobile robots in AI. Multi-objective optimized algorithm based on cooperative co-evolution and CGA is brought up in this paper. Shortest path length, minimum time cost, smo...
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ISBN:
(纸本)9780769527482
Multi-robot path planning is a challenge for mobile robots in AI. Multi-objective optimized algorithm based on cooperative co-evolution and CGA is brought up in this paper. Shortest path length, minimum time cost, smoothest and limited speed, obstacle-collide free and robot-collide free are the objectives and constraints to optimize. Linear combination of them is designed as evaluation function for CGA with self-adaptive crossover and mutation rate, combined with chaos disturbs. Finally 2D dynamic simulation has proved the efficiency of the algorithm.
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