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Differential equations 

Navier–Stokes differential equations used to simulate airflow around an obstruction. 
Classification 
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Solution 
General topics 
The finitevolume method (FVM) is a method for representing and evaluating partial differential equations in the form of algebraic equations [LeVeque, 2002; Toro, 1999]. Similar to the finite difference method or finite element method, values are calculated at discrete places on a meshed geometry. "Finite volume" refers to the small volume surrounding each node point on a mesh. In the finite volume method, volume integrals in a partial differential equation that contain a divergence term are converted to surface integrals, using the divergence theorem. These terms are then evaluated as fluxes at the surfaces of each finite volume. Because the flux entering a given volume is identical to that leaving the adjacent volume, these methods are conservative. Another advantage of the finite volume method is that it is easily formulated to allow for unstructured meshes. The method is used in many computational fluid dynamics packages.
Consider a simple 1D advection problem defined by the following partial differential equation
Here, represents the state variable and represents the flux or flow of . Conventionally, positive represents flow to the right while negative represents flow to the left. If we assume that equation (1) represents a flowing medium of constant area, we can subdivide the spatial domain, , into finite volumes or cells with cell centres indexed as . For a particular cell, , we can define the volume average value of at time and , as
and at time as,
where and represent locations of the upstream and downstream faces or edges respectively of the cell.
Integrating equation (1) in time, we have:
where .
To obtain the volume average of at time , we integrate over the cell volume, and divide the result by , i.e.
We assume that is well behaved and that we can reverse the order of integration. Also, recall that flow is normal to the unit area of the cell. Now, since in one dimension , we can apply the divergence theorem, i.e. , and substitute for the volume integral of the divergence with the values of evaluated at the cell surface (edges and ) of the finite volume as follows:
where .
We can therefore derive a semidiscrete numerical scheme for the above problem with cell centres indexed as , and with cell edge fluxes indexed as , by differentiating (6) with respect to time to obtain:
where values for the edge fluxes, , can be reconstructed by interpolation or extrapolation of the cell averages. Equation (7) is exact for the volume averages; i.e., no approximations have been made during its derivation.
We can also consider the general conservation law problem, represented by the following PDE,
Here, represents a vector of states and represents the corresponding flux tensor. Again we can subdivide the spatial domain into finite volumes or cells. For a particular cell, , we take the volume integral over the total volume of the cell, , which gives,
On integrating the first term to get the volume average and applying the divergence theorem to the second, this yields
where represents the total surface area of the cell and is a unit vector normal to the surface and pointing outward. So, finally, we are able to present the general result equivalent to (8), i.e.
Again, values for the edge fluxes can be reconstructed by interpolation or extrapolation of the cell averages. The actual numerical scheme will depend upon problem geometry and mesh construction. MUSCL reconstruction is often used in high resolution schemes where shocks or discontinuities are present in the solution.
Finite volume schemes are conservative as cell averages change through the edge fluxes. In other words, one cell's loss is another cell's gain!
