92 A <strong>Semi</strong>-<strong>Implicit</strong>, <strong>Three</strong>-<strong>Dimensional</strong> <strong>Model</strong> <strong>for</strong> <strong>Estuarine</strong> Circulation where A i, j, k Bi, j, k Ci, j, k Di, j, k = n – DV i, j, k – 1⁄ 2 n + 1 n = hi, j, k⁄ ( 2Δt ) + DV , , = = n – DVi , j, k+ 1⁄ 2 n Fi, j, k n n ⁄ ( hi, j, k – 1 + hijk , , ) , i j k+ 1⁄ 2 n n n ⁄ ( hijk , , + hi, j, k+ 1) + DV , , n n ⁄ ( hi, j, k+ hijk , , + 1) , and n – 1 n – 1 + ( hi, j, ksijk , , ) ⁄ ( 2Δt ) – + n DV n DV ijk , , + 1⁄ 2 ijk , , – 1⁄ 2 i j k – 1⁄ 2 sn – 1 i, j, k sn – 1 n n ( – i, j, k + 1) ⁄ ( hijk , , + hi, j, k+ 1) sn – 1 ijk , , – 1 sn – 1 n · n ( – i, j, k) ⁄ ( hi, j, k– 1 + hi, j, k) n n ⁄ ( hi, j, k – 1 + hi, j, k) , Applying equation 4.41 to each of the layers at a computational point iΔx, jΔy results in a system of km equations involving km n + 1 unknown values of sijk , , . The boundary conditions are satisfied by choosing Ai,j, 1 = 0, Ci,j, km = 0 and setting the diffusion coefficients to zero at the free surface and bottom. The matrix <strong>for</strong>m of the equations is tridiagonal, which can be efficiently solved with the double sweep algorithm. Once the new salinities are computed, they are used to update the density field using the equation of state presented in Appendix B. 4.3.2 <strong>Semi</strong>-<strong>Implicit</strong> Trapezoidal Scheme The finite-difference equations <strong>for</strong> the semi-implicit trapezoidal scheme are nearly identical to those of the semi-implicit leapfrog scheme, except the time interval over which the scheme is applied is halved to Δt from 2Δt. The integration procedure is centered at the time level (n + ½)Δt and no longer involves the time level (n - 1)Δt. The scheme, as used here, involves three time levels (n, n + ½, n + 1) but is referred to as a two-level scheme since it is executed over a single time step. The variables needed in the scheme at the (n + ½)Δt time level are determined by averaging; <strong>for</strong> example, + ⁄ 1 n + 1 n -- + , j, k= ⎛Ũi+ 1⁄ 2 , j, k+ U i 1 ⎞ 2 + ⁄ 2 , j, k , ⎝ ⎠ n 1 2 U i 1⁄ 2 where the symbol ( ˜ ) denotes the estimate of the unknown from the semi-implicit leapfrog solution. The <strong>for</strong>m of the explicit stage <strong>for</strong> the x-momentum finite-difference equation is Ûi + 1⁄ 2, jk , = U i 1⁄ 2 n + , j, k Δt ( ADVx) n + 1⁄ 2 ( CORx) n 1 [ – + + ⁄ 2 ( BCLINICx) n 1 + – + ⁄ 2 + HDIFFx ( ) n ] i 1⁄ 2 (4.42) + , jk , , (4.43)
5. Numerical Experiments 93 which is very similar to equation 4.23 <strong>for</strong> the leapfrog scheme. The bracketed terms are identical to those expanded in Appendix D but are evaluated at the time level indicated. The finite-difference equation <strong>for</strong> the implicit stage of the x-momentum equation is n + 1 + , j, k= U i 1⁄ 2 Ûi + 1⁄ 2, j, k --- Δt Δx – g 2 n ----- hi + 1 2 1 + ⁄ 2 ⁄ , jk , ρn + 1⁄ 2 ⎛ i + 1⁄ 2, j, 1⎞ n + 1 n + 1 n n ⎜--------------------- 1 n + ⁄ ⎟ ⋅ ( ζ 2 i + 1, j – ζi, j + ζi + 1, j – ζi, j) ⎝ + ⁄ , j, k⎠ ρ i 1 2 n + 1 n + 1 n n Δt⎛ n + 1⁄ ⎛( U⁄ h) 2 i + 1⁄ 2, j, k – 1– ( U⁄ h) i + 1⁄ 2, j, k ui + 1⁄ 2, j, k – 1 – ui + 1⁄ 2, j, k⎞ + ---- ⎜A 2 Vi ---------------------------------------------------------------------------- + 1⁄ 2, j, k– 1 ⋅ ⎜ + ------------------------------------------------------- ⁄ 2 n + 1 n ⎟ ⎝ ⎝ h i + 1⁄ 2, j, k – 1 h ⁄ 2 i + 1⁄ 2, j, k – 1 ⎠ ⁄ 2 n + 1 n + 1 n n n + 1⁄ ⎛( U⁄ h) 2 i + 1⁄ 2, j, k– ( U⁄ h) i + 1⁄ 2, j, k+ 1 ui + 1⁄ 2, j, k– ui + 1⁄ 2, j, k+ 1⎞⎞ – A ---------------------------------------------------------------------------- Vi ⋅ ⎜ + ------------------------------------------------------ + 1⁄ 2, j, k + 1⁄ n + 1 n ⎟⎟ 2 ⎝ ⎠⎠ ⁄ , , + ⁄ ⁄ , , + ⁄ , (4.44) hi + 1 2 jk 1 2 hi + 1 2 jk 1 2 which is analogous to the leapfrog equation 4.26. The continuity equation <strong>for</strong> the trapezoidal scheme is n + 1 ζi, j = n 1 ζ -- ij , 2 Δt – Δx km ( n + 1 + , j, k– n + 1 – ⁄ , j, k+ n + ⁄ , jk , – n – ⁄ , j, k) ∑ ----- Ui 1⁄ 2 k = 1 U i 1 2 U i 1 2 U i 1 2 1 -- 2 Δt km n + 1 n + 1 n n – ( , + , k – , + ⁄ , k + , + ⁄ , k – , – ⁄ , k ) Δx k = 1 ----- Vi j 1⁄ V 2 i j 1 V 2 i j 1 V 2 ij 1 ∑ 2 . It is straight<strong>for</strong>ward to develop all the equations <strong>for</strong> the trapezoidal step from those already presented in section 4.3.1. ⎧ n + 1⎫ The iteration <strong>for</strong> the matrix solution in the trapezoidal step is started with the earlier estimates <strong>for</strong> ⎨ζi, j ⎬ from the leapfrog ⎩ ⎭ step. The accuracy of these estimates causes the iterative convergence to be rapid. Experience with the 3-D model has shown that the trapezoidal step is not always needed. For example, the 3-D test case in this report could be solved accurately with just the leapfrog step. The trapezoidal step is needed mostly to stabilize the solution <strong>for</strong> markedly nonlinear problems and to improve the accuracy of the time integration when large time steps are used. If necessary to stabilize a solution, more than one iteration of the trapezoidal step can also be used; a sparing use of additional iterations is advis- able, however, to keep the computer run time of the model from becoming excessively long. 5. Numerical Experiments 5.1 Introduction Numerical experiments are useful in first verifying that the computer coding of a computational scheme can accurately solve the governing equations under at least some combinations of computational grid intervals and wave conditions. For this purpose, the test problems used in experiments must have solutions that are available either analytically or from another independently developed and verified computer model. Once the computer code is verified, numerical experiments are then useful in studying the stability and convergence properties of a numerical scheme by comparing solutions that are computed by using varying time and space steps and wave conditions. (4.45)
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