CRII:OAC: Novel techniques for improving convergence and scalability of a Monte Carlo radiation solver for large-scale combustion simulations

燃烧长期以来一直是一种重要的能源，并将在相当长的未来继续如此。在高性能计算(HPC)的帮助下，预测和准确的燃烧模拟具有巨大的潜力，成为实用燃烧系统(例如燃气轮机、内燃机、熔炉等)的一种高性价比和可靠的设计、评估和决策工具。燃烧系统的详细预测建模要求对热辐射进行详细和准确的建模。然而，燃烧模拟中使用的热辐射模型通常被过度简化。在HPC中使用详细的辐射模型的主要瓶颈是其较高的计算成本和较低的并行效率。本项目探索了几个新的想法，以提高HPC燃烧模拟中高保真辐射解算器的效率和稳健性，从而使在现实的时间框架内对实际的燃烧系统进行预测模拟和准确模拟成为可能。执行如此大规模和可靠的预测模拟的能力不仅在真实燃烧装置的设计过程中非常重要，而且对于进一步了解燃烧过程的基本原理也是至关重要的。考虑到对清洁燃烧装置的日益增长的需求，这种预测能力可能在学术研究中产生重大的影响。以及能源行业和交通运输行业的投资。该项目还将对在本科生中普及计算机编程产生影响。因此，这项研究与美国国家科学基金会推动科学进步、促进国家健康、繁荣和福祉的使命是一致的。因此，该项目中选择的辐射解算器是基于蒙特卡洛光线跟踪(MCRT)的解算器。它是现有的最精确的辐射解算器之一，随着问题的复杂程度的增加，它通常会使所有其他辐射解算器相形见绌。为了在HPC和大规模燃烧控制系统的模拟中实现MCRT解算器的效率和可扩展性的改进，这项研究集合了数学、统计理论和计算机科学的不同学科的想法，并将它们应用于解决工程问题。考虑到HPC中MCRT解算器的整体性能主要取决于底层统计算法和计算负载平衡的事实，目前的研究工作分为三个主要任务。首先，该项目正在开发新的算法，使用具有低偏差的特殊统计分布来改善收敛。第二，正在探索在计算时间和内存利用率方面进行MCRT负载管理的新策略，以进一步提高HPC模拟中求解器的可扩展性。第三，计划将改进的MCRT求解器创建为具有标准化接口的模块化、平台无关的求解器模块，以便它可以在不显著牺牲其性能的情况下与任何燃烧和/或CFD求解器一起使用。例如，通过提高MCRT的效率和可扩展性，这项工作旨在能够在现实的时间范围内对大规模燃烧系统进行更准确的预测性HPC模拟。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Combustion has been an important source of energy for ages and will continue to be so for considerable future. With the help of high-performance computing (HPC), predictive and accurate combustion simulations have a tremendous potential to emerge as a cost-effective and reliable design, assessment, and decision-making tool for practical systems (e.g., gas turbines, internal combustion engines, furnaces, etc.).  Detailed predictive modeling of combustion system requires, among other things, detailed and accurate modeling of thermal radiation. However, models for thermal radiation used in combustion simulations are usually over-simplified. The main bottlenecks in using detailed radiation model are its high computational cost and poor parallel efficiency in HPC.  This project explores several novel ideas to increase efficiency and robustness of a high-fidelity radiation solver in HPC combustion simulations, leading to the possibility of performing predictive and accurate simulations of practical combustion systems in a realistic time-frame.  The ability to perform such large-scale reliable predictive simulation is not only important in the design process of real combustion devices but also essential to further our understanding of fundamentals of combustion processes.  Considering the ever-increasing need for cleaner combustion devices, this predictive capability can potentially have a significant effect in academic research, as well as in energy and transportation industry. The project will also have an impact in popularizing computer programming in undergraduate students. Therefore, this research aligns with the NSF's mission to promote the progress of science and to advance the national health, prosperity, and welfare.  The radiation solver of choice in this project is a Monte-Carlo ray tracing-based (MCRT) solver. It is one of the most accurate radiation solver available, and typically outshines all other radiation solvers as the complexity of the problem increases.  To achieve improvements in efficiency and scalability of the MCRT solver in HPC simulations of large-scale combustion systems, this research brings together ideas from different disciplines of mathematics, statistical theory, and computer science and applies them to solve an engineering problem.  Considering the fact that the performance of an MCRT solver in HPC primarily depends on the underlying statistical algorithm and computational load-balancing, the current research is divided into three primary tasks. First, the project is developing new algorithms for improved convergence using special statistical distributions with low discrepancy. Second, novel strategies for MCRT load management, both in terms of computational time and memory utilization, are being explored to improve scalability of the solver in HPC simulations.  Third, the improved MCRT solver are planned to be created as a modular, platform-independent solver module with standardized interfaces so that it can be used with any combustion and/or CFD solver without significant sacrifice of its performance.  By enhancing efficiency and scalability of MCRT, this work aims to enable more accurate predictive HPC simulations of large-scale combustion systems in a realistic timescale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.