Sampling Techniques

Diwekar, Urmila M.; Ulas, Saadet

doi:10.1002/0471238961.sampdiwe.a01

Cited by 11 publications

(4 citation statements)

References 131 publications

(117 reference statements)

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“…Finally, the Response Surface Optimization is executed based on the Response Surface and the defined objectives. In our case, the objectives are to minimize the pressure drop and maximize the velocity uniformity and the Screening method based on Shifted Hammersley Sampling is used [6,7]. As results, DX provides the best candidates for defined objectives and in the case of multiple objectives, it also shows the graph of Pareto fronts.…”

Section: Designxplorermentioning

confidence: 99%

Optimization of pressure drop in curved conduits using Ansys DesignXplorer

Palkovič¹,

Pečený²,

Teichman³

2022

MATEC Web Conf.

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This paper describes the effects of guide vanes installed in curved conduits with the main emphasis on pressure loss reduction. In curved parts of conduits, eddy zones are created due to the flow separation resulting in an increased value of pressure drop. Installation of guide vanes can prevent the creation of large eddy zones. On the other hand, the cross-section of the conduit is decreased by placing the vanes, leading to the growth of the pressure drop. That is why the properties of guide vanes needed to be selected carefully. The goal of this paper is to describe the process of finding the optimal number of vanes in order to obtain the lowest value of pressure drop in the curved conduit. This is achieved by a parametric case study in Ansys DesignXplorer combined with CFD simulations in Ansys Fluent. The case study parameters are the number and thickness of vanes, dimensions of the conduit, and the inlet velocity. Our results are compared with recommendations on the optimal number of vanes from Idelchik [1].

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Section: Designxplorermentioning

confidence: 99%

Optimization of pressure drop in curved conduits using Ansys DesignXplorer

Palkovič¹,

Pečený²,

Teichman³

2022

MATEC Web Conf.

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“…Even the derivative information is calculated using reweighing. We use the efficient Hammersley sequence sampling (HSS) [45,46] for the base distributions to increase the computational efficiency further. The algorithm we have developed is essentially a sequential quadratic programming (SQP) algorithm that replaces the sampling loop and uses a reweighting scheme to estimate the objective function and gradient at each iteration.…”

Section: The Bonus Algorithmmentioning

confidence: 99%

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

2020

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Renewable energy use can mitigate the effects of climate change. Solar energy is amongst the cleanest and most readily available renewable energy sources. However, issues of cost and uncertainty associated with solar energy need to be addressed to make it a major source of energy. These uncertainties are different for different locations. In this work, we considered four different locations in the United States of America (Northeast, Northwest, Southeast, Southwest). The weather and cost uncertainties of these locations are included in the formulation, making the problem an optimization-under-uncertainty problem. We used the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve these problems. The performance and economic models provided by the System Advisory Model (SAM) system from NREL were used for this optimization. Since this is a black-box model, this adds difficulty for optimization and optimization under uncertainty. The objective function and constraints in stochastic optimization (stochastic programming) problems are probabilistic functionals. The generalized treatment of such problems is to use a two-loop computationally intensive procedure, with an inner loop representing probabilistic or stochastic models or scenarios instead of the deterministic model, inside the optimization loop. BONUS circumvents the inner sampling loop, thereby reducing the computational intensity significantly. BONUS can be used for black-box models. The results show that, using the BONUS algorithm, we get 41%–47% of savings on the expected value of the Levelized Cost of Electricity (LCOE) for Parabolic Trough Solar Power Plants. The expected LCOE in New York is 57.42%, in Jacksonville is 38.52%, and in San Diego is 17.57% more than in Las Vegas. This difference is due to the differences in weather and weather uncertainties at these locations.

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“…Therefore, in order to reach high accuracy, larger sample sizes are needed, which adversely affects the computational efficiency (Diwekar & Kalagnanam, 1997;Diwekar & Ulas, 2007). algorithm.…”

Section: Efficient Ant Colony Optimization (Eaco) Algorithmmentioning

confidence: 99%

Efficient ant colony optimization for computer aided molecular design: Case study solvent selection problem

Gebreslassie

Diwekar

2015

Computers & Chemical Engineering

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In this paper, we propose a novel computer-aided molecular design (CAMD) methodology for the design of optimal solvents based on an efficient ant colony optimization (EACO) algorithm. The molecular design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a solvent performance measure is maximized (solute distribution coefficient) subject to structural feasibility, property, and process constraints. In developing the EACO algorithm, the better uniformity property of Hammersley sequence sampling (HSS) is exploited. The capabilities of the proposed methodology are illustrated using a real world case study for the design of an optimal solvent for extraction of acetic acid from waste process stream using liquid-liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. New solvents with better targeted properties are proposed.

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Sampling Techniques

Cited by 11 publications

References 131 publications

Optimization of pressure drop in curved conduits using Ansys DesignXplorer

Optimization of pressure drop in curved conduits using Ansys DesignXplorer

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

Efficient ant colony optimization for computer aided molecular design: Case study solvent selection problem

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