A fast GPU Monte Carlo Radiative Heat Transfer Implementation for Coupling with Direct Numerical Simulation

TL;DR

This paper presents a GPU-accelerated Monte Carlo algorithm for radiative heat transfer in turbulent flows, achieving significant speed-ups while maintaining accuracy, and enabling coupling with direct numerical simulations.

Contribution

The paper introduces a fast, GPU-based Monte Carlo method for radiative heat transfer that is optimized for coupling with DNS of turbulent flows, with substantial computational speed-up.

Findings

Achieves up to 1000x speed-up over traditional methods.

Maintains accuracy comparable to line-by-line Monte Carlo computations.

Successfully couples radiative transfer with DNS of turbulent flows.

Abstract

We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve radiative heat transfer in turbulent flows of non-grey participating media that can be coupled to fully resolved turbulent flows, namely to Direct Numerical Simulation (DNS). The spectrally varying absorption coefficient is treated in a narrow-band fashion with a correlated-k distribution. The implementation is verified with analytical solutions and validated with results from literature and line-by-line Monte Carlo computations. The method is implemented on GPU with a thorough attention to memory transfer and computational efficiency. The bottlenecks that dominate the computational expenses are addressed and several techniques are proposed to optimize the GPU execution. By implementing the proposed algorithmic accelerations, a speed-up of up to 3 orders of magnitude can be achieved, while maintaining the same accuracy.