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Sensitivity of Precipitation to Parameter Values in the Community Atmosphere Model Version 5

Swiler, Laura P.; Wildey, Timothy

One objective of the Climate Science for a Sustainable Energy Future (CSSEF) program is to develop the capability to thoroughly test and understand the uncertainties in the overall climate model and its components as they are being developed. The focus on uncertainties involves sensitivity analysis: the capability to determine which input parameters have a major influence on the output responses of interest. This report presents some initial sensitivity analysis results performed by Lawrence Livermore National Laboratory (LNNL), Sandia National Laboratories (SNL), and Pacific Northwest National Laboratory (PNNL). In the 2011-2012 timeframe, these laboratories worked in collaboration to perform sensitivity analyses of a set of CAM5, 2° runs, where the response metrics of interest were precipitation metrics. The three labs performed their sensitivity analysis (SA) studies separately and then compared results. Overall, the results were quite consistent with each other although the methods used were different. This exercise provided a robustness check of the global sensitivity analysis metrics and identified some strongly influential parameters.

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TYPE SAND Report YEAR 2014
DOIOSTI

Dakota uncertainty quantification methods applied to the NEK-5000 SAHEX model

Weirs, Gregory

This report summarizes the results of a NEAMS project focused on the use of uncertainty and sensitivity analysis methods within the NEK-5000 and Dakota software framework for assessing failure probabilities as part of probabilistic risk assessment. NEK-5000 is a software tool under development at Argonne National Laboratory to perform computational fluid dynamics calculations for applications such as thermohydraulics of nuclear reactor cores. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. The goal of this work is to demonstrate the use of uncertainty quantification methods in Dakota with NEK-5000.

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TYPE SAND Report YEAR 2014
DOIOSTI

Optimality conditions for the numerical solution of optimization problems with PDE constraints: Theoretical framework and applications to parameter identification and optimal control problems

Valentin, Miguel A.; Ridzal, Denis

A theoretical framework for the numerical solution of partial differential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to efficiently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identification and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

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TYPE SAND Report YEAR 2014
DOIOSTI
Results 6226–6250 of 9,998
Results 6226–6250 of 9,998