2018 Fluid Dynamics Conference 2018
DOI: 10.2514/6.2018-4160
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A New Formulation of Hyperbolic Navier-Stokes Solver based on Finite Volume Method on Arbitrary Grids

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Cited by 12 publications
(11 citation statements)
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“…These solvers will be particularly useful for allowing parameters (e.g., α g ) to be defined locally to adapt the gradients to local flow features while keeping different values in the Jacobian/preconditioner for robust convergence. A preliminary study indicates that the Newton solver is much more robust than the IDC-IGG and JFNK solvers, but requires a very efficient linear solver (e.g., multigrid) to minimize the overall computing time; it seems suggest that the hyperbolic Navier-Stokes formulation is better suited since the linear relaxation converges rapidly by the reduced condition number (due to the elimination of second derivatives) [26,27,28,29,30,31].…”
Section: Discussionmentioning
confidence: 99%
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“…These solvers will be particularly useful for allowing parameters (e.g., α g ) to be defined locally to adapt the gradients to local flow features while keeping different values in the Jacobian/preconditioner for robust convergence. A preliminary study indicates that the Newton solver is much more robust than the IDC-IGG and JFNK solvers, but requires a very efficient linear solver (e.g., multigrid) to minimize the overall computing time; it seems suggest that the hyperbolic Navier-Stokes formulation is better suited since the linear relaxation converges rapidly by the reduced condition number (due to the elimination of second derivatives) [26,27,28,29,30,31].…”
Section: Discussionmentioning
confidence: 99%
“…However, in this case, the residual Jacobian consists of 12×12 blocks for 4 conservative variables and 8 primitive-variable gradient components (20×20 blocks in three dimensions). The size of the discrete problem is equivalent to the P 1 discontinuous Galerkin method, where the solution gradients are introduced as additional discrete unknowns, and to the hyperbolic Navier-Stokes method [26,27,28,29,30,31], where the solution gradients are introduced as additional unknowns in the differential-equation-level. Clearly, it would require much more memory to store the Jacobian matrix although it is quite feasible and can be very effective for iterative convergence.…”
Section: Implicit Defect-correction (Idc) and Igg Solvermentioning
confidence: 99%
“…The 3 rd and 5 th order accurate polynomials for the left and right boundaries are given by Eqs. (32) and (33).…”
Section: Boundary Conditionsmentioning
confidence: 99%
“…[30], to construct a numerical dissipation matrix, the flux Jacobian does not have to be exactly derived, and the coefficients can be frozen and thus not needed to be differentiated. This type of formulation is very useful for developing high-order methods [31,32]. Note that the preconditioned formulation is necessary, for whatever variables are chosen, to apply the hyperbolic method to variable-coefficient and nonlinear equations because T r is not a constant and thus cannot be included in the flux vectors [30].…”
mentioning
confidence: 99%
“…Thus by solving the hyperbolic system (1.3) to steady state, we can obtain the solution to an elliptic problem at arbitrary precision. This idea has been successfully applied to develop finite volume-type methods, e.g., [20][21][22][23], as well as discontinuous Galerkin-type methods, e.g., [24,25], that approximate the solution of parabolic partial differential equations.…”
Section: Introductionmentioning
confidence: 99%