基本信息
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职业迁徙
个人简介
Research Interests
Fluid-structure interaction
Computational methods and numerical analysis
Data-driven computing
Model order reduction
Bluff-body flows and flow-induced vibration
Flow control and drag reduction
Multiphase flows
Current Research Work
My current research program is concentrated on a diverse set of topics related to high-fidelity multiphysics and multiphase simulations, fluid-structure interaction (FSI), flow control techniques, model order reduction and data-driven computing. While the traditional high-fidelity full-order simulations provide valuable physical insight for coupled problems, these full-order models (FOM) are strongly mechanistic, computationally expensive, memory demanding and time-consuming for design space exploration, control, and optimization, even on supercomputing facilities. Another dimension of our research involves the development of efficient data-driven model order reduction (MOR) or dimensionality reduction techniques, which are of practical importance in a broad range of problems in aerospace and marine/offshore engineering. Further highlights of ongoing research themes are as follows:
Computational Methods and Numerical Analysis
Software Development and Practical Applications
Physics of Fluid-Structure Interaction and Aeroelasticity
Data-Driven Computing and Machine Learning
New Flow Control Techniques and Devices
Efficient Bio-inspired Structures and Feedback Flow Control
Fluid-structure interaction
Computational methods and numerical analysis
Data-driven computing
Model order reduction
Bluff-body flows and flow-induced vibration
Flow control and drag reduction
Multiphase flows
Current Research Work
My current research program is concentrated on a diverse set of topics related to high-fidelity multiphysics and multiphase simulations, fluid-structure interaction (FSI), flow control techniques, model order reduction and data-driven computing. While the traditional high-fidelity full-order simulations provide valuable physical insight for coupled problems, these full-order models (FOM) are strongly mechanistic, computationally expensive, memory demanding and time-consuming for design space exploration, control, and optimization, even on supercomputing facilities. Another dimension of our research involves the development of efficient data-driven model order reduction (MOR) or dimensionality reduction techniques, which are of practical importance in a broad range of problems in aerospace and marine/offshore engineering. Further highlights of ongoing research themes are as follows:
Computational Methods and Numerical Analysis
Software Development and Practical Applications
Physics of Fluid-Structure Interaction and Aeroelasticity
Data-Driven Computing and Machine Learning
New Flow Control Techniques and Devices
Efficient Bio-inspired Structures and Feedback Flow Control
研究兴趣
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arxiv(2024)
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AIAA JOURNALpp.1-18, (2024)
JOURNAL OF COMPUTATIONAL PHYSICS (2024): 112866
crossref(2024)
PHYSICS OF FLUIDSno. 1 (2024)
Nihar B. Darbhamulla,Rajeev K. Jaiman
Computers & Fluidspp.106283, (2024)
PHYSICS OF FLUIDSno. 3 (2024)
AIAA SCITECH 2023 Forum (2023)
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