CRII: ACI: Efficient Radiative Heat Transfer Modeling In Large-Scale Combustion Systems

热辐射是一个重要的，但了解不够充分的大规模热系统的传热过程。在大规模火灾中，辐射传热占向周围环境传热总量的70%以上。燃烧系统产生的污染物，如颗粒物、NOx和SOx（酸雨的主要贡献者），对辐射传热的热效应非常敏感。为了正确预测火灾蔓延和污染物排放，并指导电厂改造，需要对大型燃烧系统进行高保真的辐射模拟。然而，昂贵的计算成本的高保真辐射模型，其密集的内存需求，和缩放性能差，传统上阻止他们的应用超出玩具或小规模的问题。现代高性能计算系统已经发展到可以利用大规模并行性但每核内存正在减少的阶段。因此，需要新的建模和并行热辐射预测策略，以利用当前和未来的网络基础设施的力量。为了加深对热辐射过程的理解，并使预测模型能够应用于实际工程系统，该CRII项目旨在优化现代异构众核高性能计算系统的高保真辐射模型的求解算法和并行策略。 因此，本研究符合美国国家科学基金会的使命，以促进科学的进步和促进国家的健康，繁荣和福利。本项目的总体目标是打破应用高保真辐射模型的实际大规模系统的障碍，利用现代网络基础设施。结果表明，该方法可以更好地预测计算边界上的热流以及污染物的排放，从而减少火灾损失，减轻环境污染问题。具体而言，该项目的重点是提高并行性的Monte Carlo为基础的高保真辐射模型，使用混合计算环境提供的多集成核（MIC）协处理器。根据提议，高保真辐射模型将与开源火灾模拟器相结合，并将根据记录良好的实验数据进行验证。通过识别不同物理过程的不同时间尺度，首先优化求解算法，以提高所提出的代码的整体效率。混合并行与消息传递接口（MPI）和OpenMP，然后提出了实现所需的减少在“时间解决方案。“最后，开发的火灾辐射代码的准确性和效率将通过大规模火灾模拟来证明。

Thermal radiation is an important but less adequately understood heat transfer process for large-scale thermal systems. Radiative heat transfer accounts for more than 70% of the total heat transfer to the ambient environment in large-scale fires. Pollutants that are produced by combustion systems, such as particulate matters, NOx and SOx (main contributor to acid rain), are highly sensitive to the thermal effects of radiative heat transfer. To correctly predict fire propagation and pollutant emission, and to guide power plant retrofit, high-fidelity radiation modeling for large-scale combustion systems is needed. However, the expensive computational cost of high-fidelity radiation models, their intensive memory requirements, and poor scaling performances have traditionally prevented their applications beyond toy or small-scale problems. Modern high performance computing systems have evolved to a stage where massive parallelism can be harnessed but memory-per-core is decreasing. Therefore, new modeling and parallelism strategies for thermal radiation prediction are required to leverage the power of current and future cyber-infrastructure. To advance the understanding of the thermal radiation processes, and to enable the application of predictive models to practical engineering systems, this CRII project aims at optimizing the solution algorithms and parallelism strategies of high-fidelity radiation models for the modern heterogeneous many-core high performance computing systems. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.The overarching goal of this project is to break the barrier of applying high-fidelity radiation models to practical large-scale systems, utilizing modern cyber-infrastructure. As a result, the heat flux on the computational boundaries as well as pollutant emissions can be better predicted, which can reduce the fire loss and alleviate the environmental concerns with pollutions. Specifically, the project focuses on enhancing the parallelism of a Monte Carlo based high-fidelity radiation model, using the hybrid computing environment provided by the many-integrated-core (MIC) co-processors. As proposed, the high-fidelity radiation model will be coupled to an open-source fire simulator, and will be validated against well-documented experimental data. By identifying the disparate time scales of different physical processes, solution algorithm is first optimized to enhance the overall efficiency of the proposed code. Hybrid parallelism with message passing interface (MPI) and OpenMP is then proposed to achieve the desired reduction in the "time to solution." Finally, the accuracy and efficiency of the developed fire-radiation code will be demonstrated through a large-scale fire simulation.