Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

Adams, Brian M.; Ebeida, Mohamed S.; Eldred, Michael; Jakeman, John Davis; Swiler, Laura Painton; Stephens, J. A.; Vigil, Dena M.; Wildey, Timothy Michael; Bohnhoff, William J.; Eddy, John P.; Hu, Kenneth T.; Dalbey, Keith; Bauman, Lara E; Hough, Patricia Diane

doi:10.2172/1177048

Cited by 242 publications

(179 citation statements)

References 91 publications

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“…An important reason for this is that the capability to morph models along flexibly specifiable paths of modification is difficult to obtain from the combination constituted by parametric exploration tools and building simulation tools: today's state-of-the-art tools for parametric analysis, both building-specific (Mourshed et al 2003, Christensen 2006, Caldas 2008, Zhang 2012, Palonen et al 2013, Ellis et al 2006, Attia et al 2012) and multi-purpose (Adams et al 2011, Wetter 2000a, require a more or less explicit description of all the "actions" to be performed on models, which may be long and difficult if the actions are complex and intertwined.…”

Section: Search In Building Designmentioning

confidence: 99%

Cyclic overlapping block coordinate search for optimizing building design

Brunetti¹

2016

Automation in Construction

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Section: Search In Building Designmentioning

confidence: 99%

Cyclic overlapping block coordinate search for optimizing building design

Brunetti¹

2016

Automation in Construction

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“…A genetic algorithm can therefore derive a diverse set of Pareto optimal solutions in a single optimization run, which is a great advantage over other methods that require multiple runs to characterize the multiobjective space. For our network design problem, we use the multi-objective genetic algorithm (MOGA) (Eddy and Lewis, 2001;Adams et al, 2010) to optimize performance and cost, and a single objective variation with the same genetic operators (SOGA -single objective genetic algorithm) (2001) and Adams et al (2010) for further details about these settings and other available options.…”

Section: Multiobjective Genetic Algorithmsmentioning

confidence: 99%

Designing optimal greenhouse gas observing networks that consider performance and cost

Lucas

Kwok

Cameron-Smith

et al. 2015

Geosci. Instrum. Method. Data Syst.

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Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH 2 FCF 3 , HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

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“…The heat transfer process is modelled using the volume averaging [19][20][21] approach that allows devising a single governing equation valid in both domains. The developed code is coupled to DAKOTA toolkit [22], aiming the elaboration of parametric/optimization studies.…”

Section: Introductionmentioning

confidence: 99%

“…Aiming to perform parametric and/or design optimization studies, the open-source software DAKOTA [22] was coupled with the developed numerical code. With the same purpose automatic geometry and mesh generators were implemented in SALOME [23].…”

mentioning

confidence: 99%

Development of a new 3D OpenFOAM® solver to model the cooling stage in profile extrusion

Fernandes¹,

Habla²,

Carneiro³

et al. 2016

AIP Conference Proceedings

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Development of a New 3D OpenFOAM® Solver to Model the Cooling Stage in Profile ExtrusionAbstract. In this work a new solver is developed in OpenFOAM® computational library, to model the cooling state in profile extrusion. The solver is able to calculate the temperature distribution in a two domain system, comprising the profile and calibrator, considering the temperature discontinuity at the interface. The derivation of the model is based on the local instantaneous energy conservation equation, in conjunction with the conditional volume averaging technique, which yields a single governing equation valid in both domains. Aiming the solution of automatic optimization/parameterization problems, the developed solver was coupled with the DAKOTA toolkit. The application of the novel calculation system is illustrated in a study of a complex geometry extruded profile cooling stage.

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Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

Cited by 242 publications

References 91 publications

Cyclic overlapping block coordinate search for optimizing building design

Cyclic overlapping block coordinate search for optimizing building design

Designing optimal greenhouse gas observing networks that consider performance and cost

Development of a new 3D OpenFOAM® solver to model the cooling stage in profile extrusion

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