Invited Talk: Understanding Radiative Transport in Flowing Particles Via Monte Carlo Ray Tracing and Data-Driven Techniques

Particle-laden flows comprising assemblies of individual grains/agglomerates or fluidized gas-solid flows have widespread applications in particle-based concentrated solar power thermal energy storage systems^1–4. At high temperatures, the contribution of radiation to the overall energy transport intensifies. However, quantitative predictions and mechanistic insights to evaluate the coupled impacts of the flow regimes, underlying morphology, and optical properties on radiative transport, and therefore the overall heat transfer behavior are lacking in the literature. To fully resolve radiative energy transport in particle-laden multiphase flows, the challenge, in part, is to compute absorption, emission, and scattering of radiative energy as a function of dynamically evolving particle positions. In this talk, I will highlight two ongoing research activities to address these challenges. First, we investigate the influences of spatial correlations of a packed-bed of particles on radiative intensity transport . Spatial distributions of the particles were statistically quantified and radiation-particle interactions were calculated by performing collision based Monte-Carlo ray tracing simulations. Results from this study indicated that the transmittance is strongly influenced by the spatial correlation of particles in the medium. Next, we leverage results obtained from the fully resolved ray tracing simulations to inform the development of data-driven correlations for the predictions of view factors using least-square regression and Gaussian kernel regression techniques. Three distinct regimes were identified for the dependence of the particle-particle view factors on the dimensionless distance between particles—a monotonic decay regime for small distances, a shading-dominated regime for medium distances, and a large-distance regime, where the view factors have negligibly small values. Results from this study reveal the potential for data-driven techniques to be applied to deduce correlations that are computationally more efficient to model radiative transport and overall heat-transfer, especially for flowing particle systems.

References

1. Ho, C. K. et al. On-sun testing of an advanced falling particle receiver system. in 030022 (2016). doi:10.1063/1.4949074
2. Ma, Z. & Martinek, J. Fluidized-Bed Heat Transfer Modeling for the Development of Particle/Supercritical-CO 2 Heat Exchanger. in ASME 2017 11th International Conference on Energy Sustainability V001T05A002 (ASME, 2017). doi:10.1115/ES2017-3098
3. Ge, W. et al. Discrete simulation of granular and particle-fluid flows: from fundamental study to engineering application. Rev. Chem. Eng. 0, (2017).
4. Mehos, M. et al. Concentrating Solar Power Gen3 Demonstration Roadmap. (2017).