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Photoexcitation of Complex Molecular Systems through Combined FirstPrinciples...
全球变化科学紫荆论坛第439期:基于850hPa相对涡度的热带气旋路径追踪识别方法
Controlling the Structure of Inference and Learning in Neural Networks
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报告题目:
On the singularity formation of a 3D model for incompressible Euler and Navier-Stokes equations
 报告人:
王术
北京工业大学教授
报告时间:
2010-06-11 10:00
报告地点:
清华大学科学馆104报告厅
主办单位:
清华大学周培源应用数学研究中心
  简介:
Abstract: Abstract: In this talk, the singularity formation of a recently proposed 3D model for incompressible Euler and Navier-Stokes equations is discussed. This 3D model is derived from the axisymmetric Navier-Stokes equations with swirl using a set of new veriables. The model preserves almost all the properties of the full 3D Euler or Navier-Stokes equations except for the convection term which is neglected. If we add the convection term back to our model, we would recover the full Navier-Stokes equations.  Some numerical evidence supports that the 3D model may develop a potential finite time singularity. We will prove rigorously that the 3D model develops finite time singularities for a large class of initial data with finite energy and approriate boundary conditions. This work may shed interesting light into the stabilizing effect of convection for 3D incompressible Euler and Navier-Stokes equations. We also prove the global reguarity for a class of smooth initial data.
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