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谢晨月
单位：工程科学学院
地址：安徽省合肥市蜀山区黄山路443号中国科学技术大学(西校区)力学一楼5楼521办公室
邮编：230002
电话：18811461720
个人主页： http://dslx.ustc.edu.cn/?menu=expert_paper&expertid=6571369
 
个人简历 Personal resume 
  谢晨月，1990年生，博士，特任研究员。2012年6月获吉林大学数学学院理论与应用力学专业学士学位，2017年7月获北京大学工学院力学与工程科学系博士学位。之后分别在南方科技大学力学与航空航天工程系、普林斯顿大学应用与计算数学专业和香港科技大学海洋系从事博士后研究工作。主要从事基于机器学习的大涡模拟和雷诺平均模拟、流体力学界面失稳以及海洋中尺度涡参数化研究，相关工作在JFM、PRF、PRE、JPO、JAMES等国内外学术期刊上发表论文29篇。入选中科院人才引进项目（B类）候选人。参与国家自然科学基金重点项目和面上项目多项。
 
研究方向 Research direction
1) 湍流大涡模拟和雷诺平均模拟研究
  高雷诺数复杂边界湍流在风沙运动、飞行器湍流噪声、壁面阻力预测与控制等流动问题中发挥着关键作用。这种流动涉及多个过程和场的耦合，存在着建模复杂性、测量不准确性和计算挑战等多方面难题。由于高雷诺数湍流具有广泛的尺度变化和时间空间耦合特性，实验观测和数值模拟都面临着技术上的挑战。我们从跨学科的角度出发，基于多物理过程分解方法和机器学习技术，探索普适的湍流模型。通过雷诺平均湍流模拟，研究高雷诺数复杂边界流动规律，特别关注流动的分离和再附现象。相关研究将为解决高雷诺数复杂边界流动问题提供基础支持。
2) 海洋中尺度涡参数化研究
  跨陆架的物质和能量输运对我国近海环流的空间和时间分布发挥着重要的调节作用。跨陆架的中尺度涡（10-100km）一方面调制边界流，另一方面主导着陆坡和外海之间的物质交换和生物地球化学过程。复杂地形对中尺度涡的生成、传播和演化具有重要影响。当前海洋模拟缺乏适用于复杂地形流的模型。我们基于流动信息和地形参数，通过学习雷诺应力和湍动能，建立封闭的雷诺平均运动学方程，实现对近海速度场和浮力场预测。
3) 流体力学界面失稳研究
  密度差引起的流体力学界面失稳机制对高雷诺数下多相流模拟展向参数设置具有指导意义。例如，沙尘暴头部的沙墙具有一个关键特征，即展向具有大尺度的凸起-裂隙结构，这些结构影响了当地的动量、质量和能量传输过程。从连续介质的观点出发，沙尘暴可模化为由水平密度差引起的流动，目前对其头部凸起-裂隙结构的产生及演化机制缺乏深入研究。我们通过数值模拟和稳定性分析，探索界面不稳定性在异重流头部凸起-裂隙结构形成中的作用。
 
招生信息 Enrollment information 
招生学院：工程科学学院
招生专业：流体力学
研究方向：湍流模型、海洋中尺度涡参数化等
招生类型：博士/硕士研究生
所属学科：力学
招生人数：博士1人，硕士1人
 
论文专著 The monograph
1) Impact of parameterized isopycnal diffusivity on shelf‐ ocean exchanges under upwelling‐ favorable winds: Offline tracer simulations augmented by artificial neural network - Journal of Advances in Modeling Earth Systems - 2023
2) Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts - Journal of Physical Oceanography - 2023
3) Artificial neural network approach for turbulence models: A local framework - Physical Review Fluids - 2021
4) Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence - Physical Review Fluids - 2020
5) Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network - Physical Review Fluids - 2019
6) Origin of lobe and cleft at the gravity current front - Physical Review E - 2019
7) Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence - Physical Review E - 2019
8) Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence - Physics of Fluids - 2020
9) Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence - Physics of Fluids - 2019
10) An approximate second-order closure model for large-eddy simulation of compressible isotropic turbulence - Communications in Computational Physics - 2020
11) An approximate second-order closure model for large-eddy simulation of compressible isotropic turbulence - Communications in Computational Physics - 2020
12) Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence - Physics of Fluids - 2019
13) Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence - Physics of Fluids - 2020
14) Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence - Physical Review E - 2019
15) Origin of lobe and cleft at the gravity current front - Physical Review E - 2019
16) Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network - Physical Review Fluids - 2019
17) Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence - Physical Review Fluids - 2020
18) Artificial neural network approach for turbulence models: A local framework - Physical Review Fluids - 2021
19) Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts - Journal of Physical Oceanography - 2023
20) Impact of parameterized isopycnal diffusivity on shelf‐ ocean exchanges under upwelling‐ favorable winds: Offline tracer simulations augmented by artificial neural network - Journal of Advances in Modeling Earth Systems - 2023
21) Impact of parameterized isopycnal diffusivity on shelf‐ ocean exchanges under upwelling‐ favorable winds: Offline tracer simulations augmented by artificial neural network - Journal of Advances in Modeling Earth Systems - 2023
22) Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts - Journal of Physical Oceanography - 2023
23) Artificial neural network approach for turbulence models: A local framework - Physical Review Fluids - 2021
24) Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence - Physical Review Fluids - 2020
25) Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network - Physical Review Fluids - 2019
26) Origin of lobe and cleft at the gravity current front - Physical Review E - 2019
27) Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence - Physical Review E - 2019
28) Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence - Physics of Fluids - 2020
29) Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence - Physics of Fluids - 2019
30) An approximate second-order closure model for large-eddy simulation of compressible isotropic turbulence - Communications in Computational Physics - 2020
31) An approximate second-order closure model for large-eddy simulation of compressible isotropic turbulence - Communications in Computational Physics - 2020
32) Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence - Physics of Fluids - 2019
33) Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence - Physics of Fluids - 2020
34) Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence - Physical Review E - 2019
35) Origin of lobe and cleft at the gravity current front - Physical Review E - 2019
36) Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network - Physical Review Fluids - 2019
37) Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence - Physical Review Fluids - 2020
38) Artificial neural network approach for turbulence models: A local framework - Physical Review Fluids - 2021
39) Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts - Journal of Physical Oceanography - 2023
40) Impact of parameterized isopycnal diffusivity on shelf‐ ocean exchanges under upwelling‐ favorable winds: Offline tracer simulations augmented by artificial neural network - Journal of Advances in Modeling Earth Systems - 2023
 
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