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zhangbo760木虫 (小有名气)
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北伊利诺伊大学(Northern Illinois University)机械系招收全奖博士生
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北伊利诺伊大学(Northern Illinois University)机械工程系张波教授实验室招收全奖博士生，2024秋季或2025春季入学。主要研究方向为计算流体力学以及机器学习在科学及工程上的应用等。 欢迎对科学计算、流体力学和机器学习等领域有兴趣的同学申请，有意者请将简历、成绩单以及相关资料发送导师：baylorbo@gmail.com或者bzhang9@nd.edu。 NIU位于美国伊利诺伊州DeKalb市，距离芝加哥约一个半小时车程。 Dr. Bo Zhang will join the Department of Mechanical Engineering at Northern Illinois University (NIU) in August 2024 as an Assistant Professor. Currently he is a Postdoctoral Research Associate at the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He obtained his Ph.D. degree at Baylor University and afterwards, he studied as a postdoctoral fellow at Georgia Institute of Technology. His research focuses on liquid fuel atomization, droplet dynamics, shock waves, aerodynamics, reacting flows, cavitating flows, advanced multiphase forming for next-generation paper-making machine, and machine learning methods for scientific and engineering problems. Dr. Zhang's ongoing research also includes studying swarm robots and soft robot by integrating control, machine learning and theories of partial differential equations. His representative publications and conference presentations are as follows, 1. B. Zhang, P.-H. Tsai, A.-B. Wang, S. Popinet, S. Zaleski and Y. Ling, “Short-term oscillation and falling dynamics for a water drop dripping in quiescent air”, Phys. Rev. Fluids 4, 123604 (2019). 2. B. Zhang, S. Popinet, and Y. Ling, “Modeling and detailed numerical simulation of the primary breakup of a gasoline surrogate jet under non- evaporative operating condition”, Int. J. Multiphase Flow 130, 103362 (2020). 3. B. Zhang, B. Boyd, and Y. Ling, “Direct numerical simulation of com- pressible interfacial multiphase flows using a mass-momentum-energy con- sistent volume-of-fluid method”, Comput. Fluids 236, 105267 (2022). 4. B. Zhang, M. Usta, I. Khan, D. Ranjan, and C. K. Aidun, “Chemically reacting mixing in coaxial miscible liquid jets under variable viscosities and reaction rates”, Chem. Eng. Sci. 268, 118412 (2023). 5. B. Zhang, “Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil”, Comput. Fluids. 258, 105883 (2023). 6. B. Zhang, “Nonlinear mode decomposition via physics-assimilated con- volutional autoencoder for unsteady flows over an airfoil”, Phys. Fluids. 35, 095115 (2023). 7. B. Zhang, X. Fan, J. Wang, “A differentiable hybrid neural solver for efficient simulation of cavitating flows”, 76th Annual Meeting of the APS Division of Fluid Dynamics 2023 (Washington, DC, 2023). |
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