| NSF Org: |
CBET Div Of Chem, Bioeng, Env, & Transp Sys |
| Recipient: |
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| Initial Amendment Date: | January 4, 2022 |
| Latest Amendment Date: | January 4, 2022 |
| Award Number: | 2145871 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Shahab Shojaei-Zadeh
sshojaei@nsf.gov (703)292-8045 CBET Div Of Chem, Bioeng, Env, & Transp Sys ENG Directorate For Engineering |
| Start Date: | May 1, 2022 |
| End Date: | April 30, 2027 (Estimated) |
| Total Intended Award Amount: | $543,686.00 |
| Total Awarded Amount to Date: | $443,172.00 |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
585 Purdue Mall West Lafayette IN US 47907-2088 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| NSF Program(s): | PMP-Particul&MultiphaseProcess |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Flows involving dense suspensions of particles in gas, which are known as granular flows, are common in nature and industry. In many cases of practical importance, the suspended particles have an irregular shape (i.e., non-spherical), which makes predicting the flow behavior of the suspension especially challenging with currently available methods. As a result, the design of many particulate processes often relies on costly empirical trial-and-error testing. This CAREER project will develop a physics-based stochastic model that accounts for irregular particle shapes to predict particle dynamics more accurately in large-scale systems. Results of the project will be useful in extending granular flow theory for idealized spherical particles to more realistic granular media and in providing new solutions to technical challenges that occur in particle technology. The project will involve research training for graduate and undergraduate students and will prepare them for possible careers involving particle technology. The research team will participate with the Purdue Engineering Outreach club to bring demonstrations of particulate flows for K-12 students in local schools.
The goal of this CAREER project is to use stochastic methods to develop a physics-based model for predicting particle flows in systems containing billions of particles. Current state-of-the-art discrete element methods for non-spherical particles are limited to fewer than one million particles. By comparison, a single cup of sand contains approximately 100 million particles. To achieve this goal, the project will develop high-fidelity simulations that capture the dynamics of colliding particles to construct a stochastic model for large-scale systems. Discrete element simulations will be performed to determine how non-spherical particles scatter during collisions and redistribute rotational and translational energies. Machine learning tools will then be employed to build probability distribution functions that relate the pre-collision to the post-collision states of particles. The probability distribution functions will then be incorporated into a direct simulation Monte Carlo solver that can simulate the dynamics of systems containing billions of particles. To validate the stochastic model, comparisons will first be made to deterministic discrete element simulations for relatively small-scale systems. The accuracy of the stochastic model will then be assessed for larger scale systems by comparing results with available experimental data in the literature and with data from in-house tests. In addition to capturing the complex physics that arise due to particle shape effects, the project will create a framework for improving predictions of other complex phenomena such as particle attrition and agglomeration.
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.
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