Collaborative Research: CDS&E: Leveraging hardware acceleration for accurate particle dynamics in turbulent flows

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Nanoparticles are present in many aspects of everyday life, from food, drugs, cosmetics, textiles, and wood preservatives to tires, electronics, and engines. Regardless of a nanoparticle's type, its formation, growth, and eventual destruction involves physical processes that lead to distributions of particles of many different shapes and sizes. In most cases, industry wants better means to control the particle distributions; in other cases, the objective is to prevent the formation of particles. Either way, one needs to understand how particles behave and evolve over time to accomplish those goals. This project aims to improve the predictive capabilities of software for simulating the transport and evolution of populations of nanoparticles in complex flow fields (e.g., nanoparticles in a combustion engine). The improved tools developed in this project will benefit the combustion field by enabling better predictions of the formation, growth, and destruction of soot particles. This will support the design of cleaner internal combustion engines, gas turbine engines, and furnaces. Beyond combustion, the tools developed and understanding gained through this project could advance manufacturing by identifying which environmental properties can be leveraged to enhance/trigger certain particles, increase the formation of certain types of particles, or suppress them altogether. Finally, the software to be developed is not limited to solid-oxide nanoparticles and soot. In fact, any dispersed phase of nano- to micro-size materials can be described by the tools developed here, including dispersion of aerosols, dust, charged particles in plasmas, and even "particles" representing large objects in astrophysics. In addition to the scientific objectives of the project, the PIs will engage in public outreach and education efforts, including producing videos explaining computational fluid dynamics for the public, running workshops teaching software skills to researchers, and involving undergraduate students from diverse and underrepresented backgrounds in research. This project will improve the predictive capabilities of numerical frameworks for simulating the transport and evolution of populations of nanoparticles in complex flow fields. The research objectives include: (1) Solving the population balance equation (PBE) for nanoparticle number density functions with no compromise in physical accuracy. The approach provides not only the full distribution of particles but also all their relevant properties; (2) Evaluate a computationally efficient numerical implementation of a coupled flow solver and PBE solver; and (3) Enable new physical and chemical insights into nanoparticle distributions across a wide range of fields by sharing the software developed and working with other research groups. These objectives will be achieved by leveraging the strengths of the underlying computer architectures: using traditional central processing units (CPUs) to solve the flow fields and particle transport, while performing the temporal evolution of the population of particles on graphics processing units (GPUs) using a Monte Carlo solver. The PIs plan to release the NGA software as open source and build a user community around NGA by ensuring that interested researchers are able to contribute to the codebase. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.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.