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

New LCRC report submitted for MRSA
by support＠lcrc.anl.gov 09 Aug '13

09 Aug '13

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

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New LCRC report submitted for Nek5000
by support＠lcrc.anl.gov 07 Aug '13

07 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 HSCD
by support＠lcrc.anl.gov 07 Aug '13

07 Aug '13

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2012/?project=HSCD

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Re: [allocations-admins] New project request
by Bair, Raymond A. 06 Aug '13

06 Aug '13

OK with me. Prio 3 seems correct. On 8/6/13 10:09 AM, "Shashi Aithal" <aithal(a)mcs.anl.gov> wrote: > > > Applicant's name: Edwin Campos > Project Name: Solar_Forecasting > Applicant's division: Environmental Sciences > Project title: Multi-Model Machine-Learning Solar Forecasting Technology > Project request date: Aug 5th 2013 > Category: Engineering Applications > Sub-Category: Env Sci > Priority: 3? > Time Request(Q4): 50,000 > POC: Ray Bair

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New project request
by Shashi Aithal 06 Aug '13

06 Aug '13

Applicant's name: Edwin Campos Project Name: Solar_Forecasting Applicant's division: Environmental Sciences Project title: Multi-Model Machine-Learning Solar Forecasting Technology Project request date: Aug 5th 2013 Category: Engineering Applications Sub-Category: Env Sci Priority: 3? Time Request(Q4): 50,000 POC: Ray Bair

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[LCRC Accounts] Project Request: Solar_Forecasting
by accounts＠lcrc.anl.gov 05 Aug '13

05 Aug '13

Hello, A new project on the LCRC cluster has been requested. Please forward the information on to the LCRC Allocation sub-committee. Applicant's name: Edwin Campos Applicant's institution: ANL Applicant's division: Environmental Sciences Project Name: Solar_Forecasting Project title: Multi-Model Machine-Learning Solar Forecasting Technology Associated funding: DOE EERE SunShot Other Systems: None. Science: The objectives of this 3-year project (starting Aug. 2013) are as follows: -Step 1: Perform seminal research to improve modeling and forecasting of solar irradiance. -Step 2: Apply research results obtained from Step 1 (previous) to develop solar power forecasts. -Step 3: Validate developed solar forecasts to meet target values of pre-existing standard metrics. Project description: Current weather forecast inaccuracies often result in substantial economic losses, as the electric grid operators must continuously balance supply and demand to maintain the reliability of the power grid. Failures to predict solar energy generation accurately will undoubtedly constrain the national expansion of renewable energy. The relevant weather variable to forecast here is solar-irradiance, and its rapid variations are primarily due to cloud movement, cloud formation and dissipation. There are several technology barriers – such as forecast inputs integration, forecasts output integration, scalability of forecasting tools, and numerical representation of relevant cloud processes – that must be overcome for improving solar forecast accuracies to a useful level, if solar energy were to be become a dependable source of energy This project proposes to overcome these barriers by using novel machine-learning algorithms and hi-performance computers. The project will demonstrate that transformational improvements are possible on solar forecasting – for periods between 0 to 6 hours (including power ramps) and the day ahead – by determining optimal combinations for observations of thermodynamic profiles measured over a solar power plant, measured cloud motions in the area (e.g., from remote sensing observations at high resolution) and routine forecast outputs from numerical weather models. We are a team of 6 Argonne researchers, working in coordination with a new Research Consortium lead by IBM. We will develop advanced cloud and radiative transfer computations, as well as machine learning algorithms based on the self-adjusting voting algorithms of the Watson computer (http://en.wikipedia.org/wiki/Watson_%28computer%29) Project URL: http://solarhighpen.energy.gov/project/ibm_thomas_j_watson_research_center_… Requested allocation: 200000 Q1: 50000 Q2: 50000 Q3: 50000 Q4: 50000 Justification: The requester has used undetermined amount hours of their initial startup project. In addition to approving an initial amount, please specify a Category and Subcategory for this project. For a list of the current selection of approved categories, please see: https://wiki.lcrc.anl.gov/wiki/Processes/Categories Once the Allocation committee has approved the project, please go to the Project Management page to create it: https://accounts.lcrc.anl.gov/projects.php Thank You, The LCRC Accounts System

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[LCRC Accounts] Project Allocation Request
by accounts＠lcrc.anl.gov 04 Aug '13

04 Aug '13

Hello, A change in allocation has been requested: Requester: ssom (Sibendu Som) Project: cavitation-modeling Title: Development of cavitation models for compression ignition engines Description: This research activity will mainly focus on comparing the cavitation models existing in the state-of-the-art engine modeling codes CONVERGE, OpenFOAM, and AVL-FIRE. Most cavitation models assume that vapor formation is initiated by the presence of cavitation nuclei which grow to become bubbles and then to form the complex structures also observed experimentally. The transport of vapor can be modeled either as discrete vapor bubbles or as a continuum. The Lagrangian tracked approach can handle natively different velocities for bubbles and liquid by solving the Newton law expressing the balance of forces acting on them. Among Eulerian methods, the multi-fluid model (implemented in AVL-FIRE) is conceptually superior to the single-fluid model (implemented in CONVERGE), since the latter is a homogeneous method therefore only one velocity is shared by all phases; while in a multi-fluid model each phase has its own velocity since momentum is solved separately for each fluid. In this case proper source terms accounting for the exchange of momentum between the phases have to be accounted for. In addition to that, a number of other physical processes have to be simulated. The most important one is the mechanism leading to bubble growth/collapse, i.e. the mass exchange model in cavitating conditions. The so-called Rayleigh-Plesset equation forms the basis for this modeling. The complete non-linear form, even if it is numerically stiff to be integrated, can be implemented in a multi-fluid model. Cavitation threshold can be expressed in terms of saturation pressure if the flow is quasi-steady, or adding viscous shear stress effects for high viscous or high velocity flows. By introducing a plenum for the outlet chamber and using a three-fluid model consisting of liquid, vapor and air, the nozzle outlet region can be resolved. This avoids the setting of unphysical boundary conditions at the nozzle exit and it allows reproducing the flow field accurately even in the proximity of the outlet. In case of super-cavitation the vapor plume can extend slightly beyond the outlet ; in case of hydraulic flip air can re-enter the hole region. An advantage of this approach is that the nozzle flow analysis is coupled to the in-cylinder flow, which results in the possibility of consideration of in-cylinder pressure variations, recirculation in cavitating flows (hydraulic flip), etc. Specific deliverables: -Comparison between single-fluid model (CONVERGE) and multi-fluid (FIRE) models and validation against experimental x-ray radiography data from Argonne. -Perform transient simulations of ECN injectors and perform validation against ECN data -Compare CONVERGE and FIRE results against transient ECN data -Assessment of transient effects: Series of steady simulations at fixed lift vs. transient simulations. -True needle movement (by x-ray visualization, Argonne) vs. idealized needle movement with n-dodecane as diesel surrogate -Coupling with outlet chamber in steady and time varying pressures (combustion chamber conditions) Current: undetermined amount Justification: Requested: 150000 A specific reason has been given: We are in the phase of debugging two phase flow models in CONVERGE. Several cases have been crashing after running for days. Hence we need to run additional cases. This needs to be approved and the final allocation amount decided upon. Thank You, The LCRC Accounts System

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