Download Numerical Methods in Fluid Dynamics: Initial and Initial by Gary A. Sod PDF

By Gary A. Sod

Here's an creation to numerical tools for partial differential equations with specific connection with those who are of value in fluid dynamics. the writer provides an intensive and rigorous therapy of the ideas, starting with the classical tools and resulting in a dialogue of recent advancements. for less complicated interpreting and use, the various in simple terms technical effects and theorems are given individually from the most physique of the textual content. The presentation is meant for graduate scholars in utilized arithmetic, engineering and actual sciences who've a uncomplicated wisdom of partial differential equations.

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Extra info for Numerical Methods in Fluid Dynamics: Initial and Initial Boundary-Value Problems

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Finite difference methods can be divided into two classes. One class is explicit methods, one which involves only one grid point at the advanced time level (n + 1)k. The other class is implicit method, one which involves more than one grid point at the advanced time level (n + 1)k. 3) given in Chapter I is an example of an explicit finite difference method since the only grid point at the advanced time level (n + 1)k is x - ih. The method has advantages in that it is easy to program and the number of operations (multiplications) per grid point is low.

In this equation, v(x,t) denotes the temperature, k denotes the thermal conductivity of the rod, p denotes the density, and c denotes the specific heat. The function <^-(x,t) is a source term, that is, it is the strength of heat sources located in the rod. In general, the coefficients p , c , and k depend on x and t. Furthermore, k and c may depend on the temperature v which makes the equation nonlinear. Consider the case where p , c , and k are constant. ,(x,t) . 2) (x,t) = 0 and equation 3tv = a 2 3 x v .

What about the accuracy of the method? j # The term kD + D -2 D + D_^u^. is an extra term introduced by using the two fractional steps. jU1?. approximate? j = 9 2 3 2 V + 0(hx) + 0(h 2 ) . Thus the local truncation error becomes vn+1 ^4 -t A vn = a v + 0(k) - a2v + 0(h2) - a2v + o(h2) L A A y y - k(aVv) + o(h2) + o(h2)) y x x y = o(k) + o(h2) + o(h2) + k a V v x y y x = 0(k) + 0(h 2 ) + O(h^) . u. adds k3 3 v to the truncation error which is harmless since it is absorbed in the 0(k) term. It can be seen from the local truncation error that the fractional step method also solves the equation atv = axv + ajv + ka^ axv 0(k 2 ) + 0(h 2 ) + 0(h 2 ).

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