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

University Of Connecticut, Storrs CT

Investigators

Abstract

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.