DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

Eldred, Michael; Vigil, Dena M.; Dalbey, Keith; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia; Hough, Patricia Diane; Eddy, John P.

doi:10.2172/1031910

Cited by 26 publications

(31 citation statements)

References 100 publications

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“…Subsequent (refined) UQ may then be performed by resolving the more sensitive parameters more accurately, thereby reducing the computational effort significantly. In this work, we use the open-source UQ and optimization toolkit, DAKOTA [17], for performing the uncertainty quantification and the subsequent optimization.…”

Section: Methodsmentioning

confidence: 99%

“…1 must be automated. We use the in-built preprocessor in DAKOTA called DPREPRO [17] for the purpose. Once the computational model (meshing and subsequent analysis) is parameterized, actual values for the parameters may be obtained directly using DPREPRO, with little or no human intervention.…”

Section: Solution Procedurementioning

confidence: 99%

“…A Smolyak sparse grid of level 2 as described in Ref. [17] is used for this purpose and based on evaluations at the collocation points, the polynomial response surface is obtained. This response surface is then used as a surrogate model and samples based on the LHS algorithm are drawn to cover the input variable range.…”

Section: Probabilistic Optimizationmentioning

confidence: 99%

“…In the present work, the number of fins is fixed at n ¼ 5 Â 5 fins spanning the entire base area for the optimization problem and the continuous parameter, the fin width W, is optimized using a more formal optimization approach based on the Fletcher-Reeves conjugate gradient method [17]. Simulations are first performed for the set of conditions considered by Ledezma et al [10] …”

Section: Pin-finmentioning

confidence: 99%

See 3 more Smart Citations

Optimization Under Uncertainty Applied to Heat Sink Design

Bodla¹,

Murthy²,

Garimella

2012

Journal of Heat Transfer

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Optimization under uncertainty (OUU) is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, yields output uncertainties that help the designer choose appropriate values for input parameters to produce safe designs. Apart from providing basic statistical information, such as mean and standard deviation in the output quantities, uncertainty-based optimization produces auxiliary information, such as local and global sensitivities. The designer may thus decide the input parameter(s) to which the output quantity of interest is most sensitive, and thereby design better experiments based on just the most sensitive input parameter(s). Another critical output of such a methodology is the solution to the inverse problem, i.e., finding the allowable uncertainty (range) in the input parameter(s), given an acceptable uncertainty (range) in the output quantities of interest. We apply optimization under uncertainty to the problem of heat transfer in fin heat sinks with uncertainties in geometry and operating conditions. The analysis methodology is implemented using DAKOTA, an open-source design and analysis kit. A response surface is first generated which captures the dependence of the quantity of interest on inputs. This response surface is then used to perform both deterministic and probabilistic optimization of the heat sink, and the results of the two approaches are compared.

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Section: Methodsmentioning

confidence: 99%

Section: Solution Procedurementioning

confidence: 99%

Section: Probabilistic Optimizationmentioning

confidence: 99%

Section: Pin-finmentioning

confidence: 99%

See 2 more Smart Citations

Optimization Under Uncertainty Applied to Heat Sink Design

Bodla¹,

Murthy²,

Garimella

2012

Journal of Heat Transfer

View full text Add to dashboard Cite

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“…Eldred et al, 2006;Izzo, 2012;Matott, 2005;Pohlheim, 2007;Vesselinov and Harp, 2012). Various implementation approaches have been taken, including: binaries written in C/C++ or FORTRAN (e.g.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

530

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

Flora,

Khosronejad

2023

Earth Surf Processes Landf

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Bank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. In this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.

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DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

Cited by 26 publications

References 100 publications

Optimization Under Uncertainty Applied to Heat Sink Design

Optimization Under Uncertainty Applied to Heat Sink Design

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

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