SBIR Phase I: Hybrid Lattice Boltzmann Technique for Heat Transfer Prediction

SBIR 第一阶段：用于传热预测的混合格子玻尔兹曼技术

• 批准号: 0128618
• 负责人: Ilya Staroselsky
• 金额: $ 9.98万
• 依托单位: Exa Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0128618&HistoricalAwards=false
• 关键词: SBIR; Phase; Hybrid; Lattice; Boltzmann

This Small Business Innovation Research (SBIR) Phase I project will produce a unique computational tool for heat transport prediction. The novel approach to be used here will hybridize the Digital Physics technology based on Lattice Boltzmann Methods (LBM) for hydrodynamics with efficient partial differential equation (PDE) solution methods for heat transfer using grids of up to a hundred million computational cells thus allowing for quantitative prediction of heat transfer phenomena of interest in materials processing and manufacturing. With this platform, the highest standards of numerical accuracy, efficiency (including nearly perfect parallel scalability) and geometrical flexibility (including full integration with commercial CAD tools), as well as a user friendly interface, shall be naturally inherited. Upon algorithm optimization and benchmarking against test flow data, a complex heat transfer problem of industrial level complexity shall be simulated. The hybrid thermal transport prediction tool will open major new commercial markets for the PowerFLOW product, especially at the engineering design level. This new technology shall enable prediction of internal flow and heat transfer within the automotive industry. The ability of the proposed LBM-PDE methods to address microscale thermal transport problems in which Knudsen number effects are important should open important new markets for novel technologies in MEMS and related industries as well as broad new markets for computer aided engineering (CAE), especially in manufacturing industries.

这个小企业创新研究（SBIR）第一阶段项目将产生一个独特的计算工具，用于热传输预测。这里使用的新方法将混合数字物理技术的基础上格子玻尔兹曼方法（LBM）的流体动力学与有效的偏微分方程（PDE）的解决方案方法的热传递使用网格高达一亿计算单元，从而允许定量预测的热传递现象感兴趣的材料加工和制造。有了这个平台，最高标准的数值精度，效率（包括近乎完美的并行可扩展性）和几何灵活性（包括与商业CAD工具的完全集成），以及用户友好的界面，将自然继承。在算法优化和测试流量数据的基准测试后，应模拟工业级复杂性的复杂传热问题。混合热传输预测工具将为PowerFLOW产品打开主要的新商业市场，特别是在工程设计层面。这项新技术将能够预测汽车行业内的内部流动和传热。所提出的LBM-PDE方法解决其中努森数效应很重要的微尺度热传输问题的能力应该为MEMS和相关行业的新技术以及计算机辅助工程（CAE），特别是制造业的广阔新市场开辟重要的新市场。