Navier-Stokes Equations
Version 1.0.0 · Updated 2026-07-28
CORE DEFINITION
The Navier-Stokes equations are a set of partial differential equations that describe the motion of fluids such as liquids and air. Named after French physicist Claude-Louis Navier and Irish physicist George Stokes, these equations describe the conservation of momentum and mass that Newtonian fluids must satisfy during motion. In some applications, the Navier-Stokes equations are listed together with the equation of state to illustrate the relationship among fluid pressure, temperature, and density. The Navier-Stokes equations result from applying Newton's second law to fluid dynamics. Unlike the Euler equations, the Navier-Stokes equations account for fluid viscosity. They assume that during fluid motion, the stress is the sum of a diffusive viscous force proportional to the velocity gradient and pressure. Therefore, the Navier-Stokes equations can describe the motion of viscous fluids, while the Euler equations can only describe inviscid flow. When fluid viscosity is zero, the Navier-Stokes equations reduce to the Euler equations. The Navier-Stokes equations have a wide range of applications.
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The Navier-Stokes equations are a set of partial differential equations that describe the motion of fluids such as liquids and air. Named after French physicist Claude-Louis Navier and Irish physicist George Stokes, these equations describe the conservation of momentum and mass that Newtonian fluids must satisfy during motion. In some applications, the Navier-Stokes equations are listed together with the equation of state to illustrate the relationship among fluid pressure, temperature, and density.
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The N-S equations describe the conservation of momentum for viscous fluids, including nonlinear convective terms. They are sensitive to initial conditions and prone to turbulence, and no general analytical solution has been found to date (one of the Millennium Prize Problems). They represent a class of systems that are inherently complex and difficult to predict precisely.
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