Transport equations, numerical methods
Methods for solving integro-differential equations describing particle or radiation transport. The equations take the following form for stationary problems:
where , is a unit vector, is the particle flux at a point for particles with velocity , and the positive functions and describe the interaction of the particles with matter, while is the source. Two basic aspects are considered: 1) finding the solution to (1) in a (convex) domain such that at its boundary ,
where is the outward normal to ; and 2) finding the largest eigenvalue , and the corresponding eigenfunction of (1)–(2), in which
Equation (1) contains six independent variables; as it is essentially higher dimensional, it must be reduced to simpler equations. In (1) and (3) one replaces the integral with respect to by a quadrature formula containing terms and assumes that the scattering is isotropic, which gives a system of so-called multi-velocity equations:
are the zero moments, while the coefficients , and are obtained by applying averaging methods and using the solutions to conjugate problems. For the problem (2) one similarly gets
For , one gets the single-velocity equation
for the function . Equation (6) takes the following form for a planar layer , where the solution depends only on one coordinate and one angular variable , :
where , , . The characteristics of the left-hand side in (6) are all the straight lines , , and along each of them, (6) takes the form
If one makes the substitution in (6), it becomes
The solution to (9) minimizes the quadratic Vladimirov functional
Let the boundary value problems be written in operator form:
A characteristic feature of the transport problem, which is used in numerical algorithms, is that the value of is found from a given by a direct method involving the integration of (8) along the characteristics. On this basis, from (11) one obtains the Peierls integral equation
for the zero moment .
The method of spherical harmonics (a form of Galerkin's method) has been substantially developed for solving transport problems. An approximate solution (approximation ) is found in the form
where are unknown functions while are spherical harmonics of order . Substituting (13) into (6), multiplying the result by and integrating with respect to leads to a system of partial differential equations for (cf. Spherical harmonics, method of). In approximation , the system takes the form
where , . For (14) implies the diffusion approximation
where , which is an elliptic problem whose solution can be found by variational or grid methods.
To solve one-dimensional cases, analytic methods have been developed based on expanding the solution in terms of generalized eigenfunctions. The Monte-Carlo method is used to find functionals in the solutions to complex multi-dimensional problems.
Finite-difference approximation methods are widely used for transport equations. For example, one can use a quadrature formula for and replace (12) by a system of linear equations. One can approximate the integral operator in (4), (5), (6), or (8) by means of quadrature formulas for a sphere. The Gauss quadrature formula for a sphere is known up to the 35th algebraic order of accuracy. In the method of characteristics, a family of characteristics is drawn through each point in the spatial grid along directions corresponding to the nodes of the quadrature for a sphere, and the differential operator in (8) is replaced by a difference one. The difference equations of the method are obtained by integrating (6) over a grid cell in the phase space on the assumption that the solution is linear in the independent variables within the cell. In Galerkin's method, the solution is sought in the form
If the are given, one obtains a system of degenerate integral equations for the ; if the are functions of compact support, one obtains the finite-element method; and if the are given functions of compact support and (16) minimizes (10), one gets the so-called equations.
Iterative methods for solving difference transport problems have the specific feature that the convergence usually becomes slower as , and that to derive the next approximation one uses only part of the information on a preceding approximation of substantially-fewer dimensions — one stores and uses only the values of . In iterative methods an intermediate operation (an operation ) is often that of solving the following problem:
Then the error satisfies (11) with source which is, as well as the discrepancy, independent of . This feature enables one to accelerate the convergence. Consider a periodic problem for (7) with constant coefficients, with a source that is even in , and let . In this application, below the following iterative methods are considered. For (7) one constructs a grid with nodes in and angular directions in . Let
For convergent iterative methods, , where . Let be the price (number of operations) in the operation , while is the price of a complete iteration and . The following relationships apply for the various methods.
1) Simple iteration: , where and .
2) Lyusternik's method: For certain , one uses the simple iteration
in which is the largest eigenvalue of ; then and ( for , ).
3) The method of estimating iterative deviations: , where is the solution of the equation
( as , ).
4) The balancing-multiplier method: , where
Here and ( as , ).
5) The mean-flux method (rebalance method):
where the function is selected to minimize the functional (10) or else to minimize it in some finite-dimensional subspace: , and then the satisfy a certain system of equations.
6) The quasi-diffusion method:
7) Splitting methods:
The methods 4)–6) are non-linear, and their convergence may slow down as and ; method 7) requires the storage of ( as , ).
8) -methods: The correction is determined as the solution in of the boundary value problem
where are second-order linear differential operators, and one puts . In one of the forms of the -method, (18) takes the form
Then for (19); for , while for , where are the roots of the Jacobi polynomial , , the geometric mean of over iterations is close to . In the -method, the convergence of the iterations does not slow down as , .
Seidel's iterative method is used to solve the multi-velocity problem (4):
and the solution in each equation in (20) is found by an iterative method for the one-velocity equation.
To solve multi-velocity problems for the eigenvalues of (4) and (5), these two iterative loops are supplemented with a further outer one to find the maximal value and the corresponding eigenfunction . If and , then problem (4), (5) becomes
To find and , iterative methods are used with the Chebyshev parameters:
and and are parameters. One assumes that the spectrum is non-negative and first finds and , which are the largest eigenvalues of (21), on the assumption that , , where is a lower bound for , and takes as a -sequence (see below). The values of and are determined by the generalized Aitken method, which incorporates the shifts . When and have been found, is found from (22) and (23) with . The infinite -sequence is formed, correspondingly, from the specially ordered roots of the Chebyshev polynomials of the first kind , while the initial segment of the -segment of length consists of all numbers of the form , :
Any segment of the -sequence of length ensures an optimal suppression in a certain sense of the error and stability in the iterative method (22), (23).
The following are used to solve non-stationary problems
the method of characteristics in -space, Galerkin's method, and finite-difference methods amounting to explicit and implicit difference schemes or to operator splitting methods. In the case of implicit schemes, the solution on the upper layer may be found by the -method.
|||V.S. Vladimirov, "Mathematical methods of uniform-velocity transport theory" Trudy Mat. Inst. Steklov. , 61 (1961) pp. 1–158 (In Russian)|
|||G.I. Marchuk, V.I. Lebedev, "Numerical methods in the theory of neutron transport" , Harwood (1986) (Translated from Russian)|
|||V.I. Lebedev, S.A. Finogenov, "Utilization of ordered Chebyshev parameters in iterative methods" USSR Comp. Math. Math. Phys. , 16 : 4 (1976) pp. 70–83 Zh. Vychisl. Mat. i Mat. Fiz. , 16 : 4 (1976) pp. 895–907|
|||V.I. Lebedev, "An iterative method with Chebyshev parameters for finding the maximum eigenvalue and corresponding eigenfunction" USSR Comp. Math. Math. Phys. , 17 : 1 (1977) pp. 92–101 Zh. Vychisl. Mat. i Mat. Fiz. , 17 : 1 (1977) pp. 100–108|
|[a1]||B. Davison, "Neutron transport theory" , Oxford Univ. Press (1957)|
|[a2]||G.J. Bell, S. Glasstone, "Nuclear reactor theory" , v. Nostrand (1971)|
Transport equations, numerical methods. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Transport_equations,_numerical_methods&oldid=16010