August 2013 - Allocations-admins - mailman3.cels.anl.gov

New LCRC report submitted for NuclearStructure
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=NuclearStruct…

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New LCRC report submitted for Nek5000
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=Nek5000

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New LCRC report submitted for Dissolution
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=Dissolution

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New LCRC report submitted for LiBattModeling
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=LiBattModeling

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New LCRC report submitted for MnO2_interfaces
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=MnO2_interfac…

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New LCRC report submitted for MnO2_interfaces
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=MnO2_interfac…

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New LCRC report submitted for Comp_Soft_Catalysts
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=Comp_Soft_Cat…

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New LCRC report submitted for Multi-physics-Foam
by support＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2013/?project=Multi-physics…

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[LCRC Accounts] Yearly Allocation Request from UQCLM
by accounts＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Emil M Constantinescu Project Name: UQCLM Division: MCS Project title: Uncertainty Quantification for Land Model in Climate Science Associated funding: Climate Science for a Sustainable Energy Future; PI: Emil Constantinescu; site PI: Ian Foster Other Systems: Science: In this project we study uncertainty quantification (UQ) methods applied to Community Land Model (CLM). CLM is the land model for the Community Earth System Model (CESM) and the Community Atmosphere Model (CAM). It consists of several sets of large and nonlinear complex systems. We aim to develop intrusive uncertainty quantification methods and to demonstrate their computational advantage over a differentiated version of CLM with automatic differentiation (OpenAD) which will open the way for gradient-based optimization for calibration in land testbed. Project description: In order to improve carbon cycling within Earth System Models, crop representation for corn, spring wheat, and soybean species has been incorporated into the latest version of the Community Land Model (CLM), the land surface model in the Community Earth System Model. As a means to evaluate and improve the CLM-Crop model, we have implemented a calibration strategy that will be used to fine-tune model specific parameters. The calibration process uses a series of observational data of the process, and is used to set of parameters in the computer model as well as estimate their uncertainty. The computer model that we are working on is the crop model component of the CESM model tag 26 improved by Argonne process model development team. To calibrate parameters of the crop model, we use the MCMC method. However, running MCMC samplers for complex models such as crop model typically requires thousands of model evaluations. This model evaluation is usually computationally intensive. Hence thousands of model evaluations exhaust available computer resources. And the situation is even worse if we only have a faint idea about the parameters because we don't know how to choose a well-proposed distribution. By using an efficient MCMC method, we will save a lot of computing cost. This method will automatically provide the variance for the next-step proposal distribution by communications between parallel chains. Project URL: http://www.mcs.anl.gov/~emconsta/Projects_CSSEF.php Current FY Hours Used: undetermined amount New FY Requested allocation: 499999 Q1: 100000 Q2: 125000 Q3: 150000 Q4: 124999 Justification: Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request from uncertainty-climate
by accounts＠lcrc.anl.gov 29 Aug '13

29 Aug '13

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Emil M Constantinescu Project Name: uncertainty-climate Division: MCS Project title: Uncertainty in weather forecasting for energy systems Associated funding: Other Systems: NFS @ MCS Science: The theoretical part of the project consists in developing uncertainty models for numerical weather and climate prediction systems for use in the operation and planning of energy systems. Fusion is used to run several scenarios and obtain forecasts and ensemble-based uncertainty information about ambient variables by running weather/climate simulation codes. We also perform adjoint sensitivity analyses that indicate suitable locations for meteorological sensor siting. This information is used in energy management applications such as stochastic optimization control loops that provides optimal operational point for energy systems. A secondary objective is to develop robust data assimilation methods (i.e., correct model predictions with observations) in order to calibrate weather and climate forecasts for effective operation and planning of energy systems. Project description: Due to increasingly strong economic and environmental pressures, the operation of next-generation industrial energy and power systems will require the use of sophisticated optimization models able to accommodate forecasts and uncertainty information of highly dynamic trends of demands, weather, and prices in order to improve efficiency, and minimize costs and emissions. Wind and solar power are becoming worldwide a significant component of the power generation portfolio. In the U.S. an adoption level of 20% wind power is expected by the year 2030. Such a large-scale adoption casts many challenges to the operation of the electrical power grid because wind power is highly intermittent and difficult to predict. In particular, unit commitment (UC) and energy dispatch (ED) operations are of great importance because of their strong economic impact. >From an operational point of view, we ask how suitable are the forecast capabilities of state-of-the-art numerical weather prediction (NWP) models for the management of energy systems and power grids? This is an important question because NWP models are expected to be used to make real-time operational decisions with important economic implications. To this end, we analyze and develop simulation codes that form the dynamical core of NWP models at the interaction with wind turbines. Project URL: http://www.mcs.anl.gov/~emconsta/Projects_ScalableIMEX.php Current FY Hours Used: undetermined amount New FY Requested allocation: 499999 Q1: 100000 Q2: 125000 Q3: 150000 Q4: 124999 Justification: Thank You, The LCRC Accounts System

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