CDS&E: Collaborative Research: Fast Numerical Simulations of Low Void Fraction Disperse Multiphase Systems using Event-Triggered Communication
CDS&E: Collaborative Research: Fast Numerical Simulations of Low Void Fraction Disperse Multiphase Systems using Event-Triggered Communication
批准号:
1953090
负责人:
Vijay Gupta
金额:
$27.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-01 至 2022-05-31
中文摘要
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英文摘要
Disperse flows involve multiphase fluids where one phase consists of particles, including bubbles and drops, suspended in another contiguous phase. Disperse flows arise in many problems of practical interest in engineering and the natural environment. Direct numerical simulations (DNS) have proven to be indispensable in understanding such flows. However, for flows with more concentrated particles (i.e. low void fractions), it becomes computationally expensive to simulate interactions between particles. Even state-of-the-art simulations take much too long for them to become commonplace. This project will develop new computational methods, based on ideas from control theory, to speed up such simulations and use them to conduct an in-depth study of disperse multiphase systems with low void fractions. The primary intellectual merit of the project lies in using computation algorithms that reduce communication among multiple processing elements for conducting large-scale simulations of heterogeneous multiphase systems - in particular, low void fraction disperse systems where the inclusion of many particles requires large computational domains and long computational times to collect converged statistical quantities. Such averaged data is critical for the construction of reduced order models used for industrial predictions. This project will aid in better understanding of multiphase flows and systems that are critical in energy conversion, material processing, the chemical industry, atmospheric processes, and living systems, as well as develop novel computational strategies based on event-triggered communications between processors to improve the efficiency of parallel computations. Further, people will be educated in both their development and use, and educational material aimed at lowering the barrier to entry for new researchers will be developed.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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1109/mlhpcai4s51975.2020.00008
发表时间:
2020-11
期刊:
2020 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) and Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)
影响因子:
--
作者:
[Soumyadip Ghosh;V. Gupta]
通讯作者:
Soumyadip Ghosh;V. Gupta
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批准号:2225978
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