CDS&E: Collaborative Research: CDS&E: Advances in closure modeling for turbulent flows with finite-sized particles informed by massive simulations on heterogeneous architec

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Suspensions of solid particles immersed in a fluid – either gas or liquid – are important in many industrial systems and in many natural processes. Multiphase computational fluid dynamics to predict the behavior of these systems relies on making approximations to the appropriate equations. As a result, some important phenomena, such as the aggregation of solid particles in the flow, are not accurately predicted. The formation of particle clusters in gas-solid flows results from complex interactions of the particles with the gas and the particles with each other. This collaborative Computational and Data-Enabled Science and Engineering (CDS&E) project will develop a modeling framework for particle clustering in gas-flow problems enabled by massive simulations using highly scalable codes. Numerical techniques developed by the researchers at the three participating institutions will be integrated to formulate a consistent and scalable framework for solving problems in which particle clustering is significant. The results will enable researchers to tackle problems involving particle clusters such as chemical looping combustion, biomass fast pyrolysis and carbon dioxide capture applications. Computational data generated in the project will be available to researchers through a web-based interface. The researchers will use the project to develop educational tools to engage a broad public audience, especially middle and high school students, in displays of the fundamental principles underlying flows of particle suspensions in a gas.Particle-resolved Direct Numerical Simulation (PR-DNS), which can resolve microscale particle-fluid interactions and point-particle direct numeric simulation (PP-DNS), which can model inter-particle collisions, will be integrated with pseudo-spectral techniques, which have the potential to accelerate computations for gas-solid flows. Highly scalable PR–DNS and PP-DNS codes will be developed to leverage new algorithmic advances in (a) turbulence simulation using the pseudo-spectral approach on heterogeneous architectures and (b) efficient scaling with number of particles in Eulerian-Lagrangian multiphase flow solvers to perform massive PR–DNS and PP-DNS of canonical clustering flows on scales needed to demonstrate particle clustering. Consistent scalable closures will be developed for volume-filtered Eulerian-Lagrangian formulations and PP-DNS and PR-DNS. A consistent averaging operation will be applied on the PR–DNS data that explicitly takes the resolved scales into account, resulting in new unclosed terms that have yet to be quantified in a two-phase context. Novel closure techniques using machine learning will be employed to evaluate the functional form of the new terms. To maximize their impact on model development, the source codes and canonical multiphase flow data generated in this project will be archived in an open-access database available to the broader scientific community.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.