Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Riccardo Scarcelli Project Name: FOA_dual_fuel Division: ES Project title: Efficiency-Optimized Dual Fuel Engine with In-Cylinder Gasoline/CNG Blending Associated funding: DOE - Office of Vehicle Technology Other Systems: Science: This project proposal, led by Argonne’s Center for Transportation Research, was submitted in response to the FY 2014 Vehicle Technologies Program Wide Funding Opportunity Announcement DE-FOA-0000991 (0991-1822) and was awarded with a total of $1,000,000 for FY2015, FY2016, and FY2017. Main goal of the project is to demonstrate that the proposed gasoline/CNG blending concept yields improved engine efficiencies over both gasoline and CNG-only operation by tailoring fuel properties based on engine load. 3D-CFD simulation plays a key role in carrying out fundamental analysis of the mixture formation process, with particular regard to the direct injection (DI) of natural gas within the cylinder, and in optimizing the engine operating parameters for a cost-effective engine design. Project description: This project focuses on investigating mixture formation and combustion in a single-cylinder engine fueled by port fuel injection of gasoline and direct injection of natural gas. To this aim, the numerical simulation of supersonic gaseous jets represent the main challenge from a CFD standpoint. At the current stage, the project has identified minimum mesh requirements for a fair representation of the gaseous jet (by comparison with x-ray radiography data) and provided guidelines on how to improve the agreement between modeling and experiments.
From now on, the project will follow two parallel paths: 1) DI of NG will be implemented on a single-cylinder engine and mixture formation and combustion under dual-fuel operation will be investigated to provide useful input for subsequent optimization of the engine hardware, and 2) Improvements in the accuracy of numerical results with respect x-ray data will be pursued especially in the near-injector region where the flow is under-expanded and supersonic. In fact, it is well known that this is the critical domain for CFD predictions of high-pressure gaseous jets.
Simulations will be performed with the commercial code CONVERGE and computational grids of the order of some millions of cells will require the use of 32-128 cores depending on the specific test case – whether cylindrical chamber or actual engine – and on the mesh resolution. The project has two members (scarcell and barlo) working full-time on it, with 2-4 cases running regularly. With an average estimate of 48 cores per run and 3 cases in parallel, the expected amount of core-hrs needed for this project for each quarter is: 48 cores x 24 hrs x 90 days x 3 jobs = 311,040. Considering some queue time, our request is for 450,000 hrs total for the first two quarters, when the project will have the largest effort. The effort will slightly decrease in Q3 and Q4. Industry partnership: This project is supported by 2 of the 3 most important US automotive OEMs, i.e. Ford Motor Company and Fiat Chrysler Automobile Group under a CRADA. Therefore this project has high potential of impact on industry development. Nevertheless, it is still mostly a DOE funded project through FOA, therefore data is still of public domain and can be published. Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 700000 Q1: 225000 Q2: 200000 Q3: 150000 Q4: 125000 Justification: Recent scalability test (Sept 2015) performed on Blues provided following results: Case 2: Simulation of gas jet from outward opening pintle. Mesh size = 5,000,000 cells 1 node (16 cores) – walltime = 60min 2 node (32 cores) – walltime = 35min 30sec 84% 3 node (48 cores) – walltime = 26min 50sec 75% Storage requirements: Currently the project folder has 1TB size. Due to recent increased number of people working on the project and expected increase of data for FY 2016 we kindly ask to expand the project folder size to 3TB. Thank You, The LCRC Accounts System