Scalable Algorithms for Multiscale Modeling and Analysis of Turbulent Combustion

湍流燃烧的精确模拟是一个主要的开放问题，需要千万亿级计算来解决在大范围的长度和时间尺度上的物理过程的高度非线性耦合。PIs的方法是为这个问题开发新的建模和算法方法，以有效地解决千兆级燃烧模拟的高性能计算（HPC）。PI的方法结合了三种技术：自动算法并行化，用于模型简化的多维数据分析，以及用于连接不同尺度模型的拓扑分析的多尺度建模。算法并行化是基于一种算法分析，检测计算阶段之间的依赖关系，使用图论比现有算法更有效地检测和利用并行性。这种方法独立于MPI分布式并行，并与之互补，允许实现更细粒度的并行，以利用每个计算节点上可用的多核资源。pi还计划了一种强大的新方法来模拟多物理场流动，例如利用直接数值模拟（DNS）和一维湍流（ODT）来提供替代的“真实集”的湍流燃烧。包含tb级数据的高维DNS数据集可以通过分析提取已知存在的低维流形。诸如主成分分析之类的技术可以确定在这种高维数据中表示流形的最佳基础。一旦从ODT生成的数据集中确定并提取了基，就可以在大涡模拟（LES）中推导和求解构成基的变量的输运方程。然后，可以使用LES来生成新的ODT模拟，这些模拟可以反馈给LES，从而创建一种动态建模方法，该方法使用小尺度、高分辨率的统计信息来构建模型，以用于更大尺度（LES）。这种建模方法是对千万亿级系统进行早期测试的主要候选方法。本研究的研究人员已经证明了将DNS和LES扩展到万亿级计算系统的能力，而万亿级计算的可用性将直接使这些建模方法成为可能。将算法和建模方面的进展应用于天然气的氧燃料燃烧。氧燃料燃烧是一种促进碳捕获和封存的技术，以减少发电厂燃烧化石燃料产生的二氧化碳排放。虽然将应用于天然气系统，但这里开发的技术和算法将直接应用于其他系统，包括煤炭和柴油、汽油等运输燃料。该项目将为学生提供独特的教育体验，包括在国家实验室的暑期实习。将在这个项目中吸取的经验教训纳入常规课程，将有助于教育未来的劳动力。此外，这项研究将加强大学研究人员与参与模拟和模型开发的国家实验室工作人员之间的合作，他们也将参与指导学生。

Pascucci0904631Accurate simulation of turbulent combustion is a major open problem requiring petascale computing to resolve highly nonlinear coupling of physical processes over a wide range of length and time scales. The PIs approach to develop new modeling and algorithmic approaches for this problem to tackle effectively High Performance Computing (HPC) for combustion simulation at the Petascale. The PI's approach combines three techniques: automatic algorithm parallelization, multidimensional data analysis for model reduction, and multi-scale modeling with topological analysis to connect models at different scales. The algorithm parallelization is based on an algorithmic analysis that detects dependencies among computing stages, using graph theory to detect and exploit parallelism more effectively than current algorithms. This approach is independent from and complimentary to MPI distributed parallelism and allows achieving the finer grain parallelism necessary to exploit the multi core resources available on each computing node. The PIs also plan a powerful new approach to model multiphysics flows, such as turbulent combustion that leverages direct numerical simulation (DNS) and one-dimensional turbulence (ODT) to provide surrogate 'truth sets'. High-dimensional DNS data sets, containing terabytes of data, can be analyzed to extract lower-dimensional manifolds known to exist. Techniques such as principal component analysis can identify the optimal basis for representing manifolds in this high-dimensional data. Once a basis has been identified and extracted from the data sets generated by ODT, transport equations for the variables forming the basis may be derived and solved in a large-eddy simulation (LES). The LES can then be used to generate new ODT simulations which can feed back to the LES, thereby creating a dynamic modeling approach that uses down-scale, highly resolved statistical information to construct models to be used on larger scales (LES). This modeling approach is a prime candidate for early testing on petascale systems. The researchers in this study have already demonstrated the ability to scale DNS and LES to terascale computing systems, and availability of petascale computing will directly enable these modeling approaches. Application of the algorithmic and modeling advances will be made to oxyfuel combustion of natural gas. Oxyfuel combustion is one technique to facilitate carbon capture and sequestration to mitigate carbon dioxide emissions from power plants burning fossil fuels. While application will be made to natural gas systems, the techniques and algorithms developed here will apply directly to other systems including coal and transportation fuels such as diesel and gasoline. This project will provide unique educational experiences for students, including summer internships at national laboratories. Incorporating in regular classes the lessons learned in this project will help educate the future work force. Additionally, the research will strengthen collaborations between university researchers and national laboratory staff involved in simulation and model development, who will also participate in mentoring students.