Design optimization using high-fidelity computational fluid dynamics simulations is becoming increasingly popular, sustaining the desire to make these methods more computationally efficient. A reduction in problem dim...
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Design optimization using high-fidelity computational fluid dynamics simulations is becoming increasingly popular, sustaining the desire to make these methods more computationally efficient. A reduction in problem dimensions as a result of improved parameterization techniques is a common contributor to this efficiency. The focus of this paper is on the high-fidelity aerodynamic design of airfoil shapes. A multifidelity design search method is presented which uses a parameterization of the airfoil pressure distribution followed by inverse design, giving a reduction in the number of design variables used in optimization. Although an expensive analysis code is used in evaluating airfoil performance, computational cost is reduced by using a low-fidelity code in the inverse design process. This method is run side by side with a method which is considered to be a current benchmark in design optimization. The two methods are described in detail, and their relative performance is compared and discussed. The newly presented method is found to converge towards the optimum design significantly more quickly than the benchmark method, providing designs with greater performance for a given computational expense.
A hybrid trajectory optimization procedure for a class of solar-electric-propulsion, gravity-assist, outer-planet missions is presented. The parameter space of a target mission is often nonconvex and a calculus-of-var...
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A hybrid trajectory optimization procedure for a class of solar-electric-propulsion, gravity-assist, outer-planet missions is presented. The parameter space of a target mission is often nonconvex and a calculus-of-variations-based optimization algorithm suffers difficulties efficiently exploring this space. A hybrid procedure using a genetic algorithm to drive a calculus-of-variations program is developed to automate searching over a reduced parameter space. Employing the hybrid procedure, the delivered mass profiles of a Uranus and Pluto mission are generated more quickly than by using the calculus-of-variations optimization algorithm alone.
Topology optimization method is a novel design method for MEMS actuators. In this paper, a multi-objective design method is introduced into topology optimization for MEMS actuators. Some important factors for multi-ob...
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ISBN:
(纸本)9781424401390
Topology optimization method is a novel design method for MEMS actuators. In this paper, a multi-objective design method is introduced into topology optimization for MEMS actuators. Some important factors for multi-objective design of MEMS actuators are deeply researched. A multi-objective topology optimization method based on minimal structural compliance and maximal structural output displacement of MEMS actuators is proposed and the corresponding governing equation for topology optimization is established. A sensitivity analysis of adjoint method is proposed to analyze topology optimization design of multi-objective MEMS actuators. Meanwhile, GCMMA (globally convergent version of the method of moving asymptotes) algorithm is used in optimization. Two numerical examples of MEMS actuators verified the effectiveness of above theory and algorithm.
In Taiwan, due to its relatively low development cost, groundwater has been the main source of water supply for most aquacultural industry in costal areas. The overdraft of groundwater has caused serious land-subsiden...
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In Taiwan, due to its relatively low development cost, groundwater has been the main source of water supply for most aquacultural industry in costal areas. The overdraft of groundwater has caused serious land-subsidence in many parts of Taiwan. In addition to providing enough surface water for aquaculture freshwater demand, revising the aquaculture structure is one approach to reduce the reliance on fresh groundwater. Due to the most serious land-subsidence in Tachen Village, Changhua County, Taiwan, which may be caused by overusing groundwater for mainly raising freshwater clams, alternative techniques, such as changing the method of water use or revising the kinds of fish with less freshwater demands and higher gross profits, were studied in the study to reduce the dependence on fresh groundwater. The fuzzy multi-objective function comprising three single-objectives, viz. reducing saltwater demand, reducing freshwater demand, and increasing the total fisheries gross profit, was coupled with a global optimization algorithm to find suitable aquaculture scenarios in the study area. Analytical results can be provided to the fisheries authorities as references for revising the aquaculture structure.
This paper investigates moral hazard issues using Markov processes with payoffs and strategy options, an algorithm developed by Howard [Howard, R.A., 1960. Dynamic Programming and Markov Processes. MIT Technology Pres...
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This paper investigates moral hazard issues using Markov processes with payoffs and strategy options, an algorithm developed by Howard [Howard, R.A., 1960. Dynamic Programming and Markov Processes. MIT Technology Press/John Wiley & Sons, NY]. An option consists of a probability vector and an expected payoff for a given state. Each state may have one or more options. Choice of options for each state, called "a strategy", must be fixed by the manager at the start. An "n-period" manager tries to maximize his/her cumulative payoff (undiscounted or discounted) over n periods. As n -> infinity, the manager's strategy becomes in line with owners' interest as the firm lasts indefinitely. Managerial implications of the analyses are examined. (c) 2006 Elsevier B.V. All rights reserved.
To have efficient data mining systems, we need powerful algorithms to extract and mine the data. In the case of genomes data mining system, the algorithms search for genomes/proteins that share similar properties. Pro...
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To have efficient data mining systems, we need powerful algorithms to extract and mine the data. In the case of genomes data mining system, the algorithms search for genomes/proteins that share similar properties. Proteins that have a significant biological relationship to one another often share only isolated regions of sequence similarity. When identifying relationships of this nature, the ability to find local regions of optimal similarity is advantageous over global alignments that optimize the overall alignment of two entire sequences. The paper describes a new method for genome sequence comparison. This algorithm can be used in a genomes data mining system. It provides a good theoretical improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is based on the popular progressive approach, the dot plot method, but avoids the most serious pitfalls caused by the greedy nature of this technique. The new approach pre-processes a data set of all pair-wise alignments between the sequences. This provides a library of alignment information that can be used to guide the comparison. The algorithm is based on the similar segment method, i.e. having n similar identities in window of size L. The paper presents some results about the termination and correctness of the algorithm and how to include this algorithm into other comparison algorithms. The paper introduces the mechanism to create random sequences. These data will be our main benchmarks for comparing our algorithms. (c) 2005 Elsevier Ltd. All rights reserved.
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on...
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A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information. (C) 2002 Elsevier Science Ltd. All rights reserved.
Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms intera...
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Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe-target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe-target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (< 0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on...
详细信息
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information. (C) 2002 Elsevier Science Ltd. All rights reserved.
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