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Solvability of p-Laplacian Parabolic Equations with Constraints Coupled with Navier-Stokes Equations in 3D Domains by Using Largeness of p

机译：利用 P 的基本耦合的约束与3D域中的Navier-Stokes方程的约束的抛抛光型方程的可溶性

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Takeshi Fukao; Yutaka Tsuzuki; Tomomi Yokota;
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1. EXISTENCE AND UNIQUENESS OF SOLUTIONS TO NONLINEAR HEAT EQUATIONS WITH CONSTRAINTS COUPLED WITH NAVIER-STOKES EQUATIONS IN 2D DOMAINS [J] . Motohiro Sobajima, Yutaka Tsuzuki, Tomomi Yokota Advances in mathematical sciences and applications . 2012,第2期

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2. Higher-order in time "quasi-unconditionally stable" ADI solvers for the compressible Navier-Stokes equations in 2D and 3D curvilinear domains [J] . Bruno Oscar P., Cubillos Max Journal of Computational Physics . 2016,第Null期

机译：2D和3D曲线域中可压缩Navier-Stokes方程的高阶时间“准无条件稳定” ADI求解器
3. Application of the Laminar Navier-Stokes Equations for Solving 2D and 3D Pathfinding Problems with Static and Dynamic Spatial Constraints: Implementation and Validation in Comsol Multiphysics [J] . Ivorra Benjamin Journal of Scientific Computing . 2018,第2期

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4. Fully Coupled Solver for Incompressible Navier-Stokes Equations using a Domain Decomposition Method [C] . Jerome Breil, Rossitza S. Marinova, Hideaki Aiso, Parallel CFD 2002 Conference May 20-22, 2002 Kansai Science City, Japan . 2002

机译：利用域分解法求解不可压缩的Navier-Stokes方程的全耦合求解器
5. Normalized p-laplacian evolution: boundary behavior of non-negative solutions of fully nonlinear parabolic equations: Gradient bounds for p-harmonic systems with vanishing neumann (dirichlet) data in a convex domain. [D] . Banerjee, Agnid. 2014

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6. A Numerical Method for Solving the 3D Unsteady Incompressible Navier-Stokes Equations in Curvilinear Domains with Complex Immersed Boundaries [O] . Liang Ge, Fotis Sotiropoulos -1

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7. Higher-order in time "quasi-unconditionally stable" ADI solvers for the compressible Navier-Stokes equations in 2D and 3D curvilinear domains [O] . Bruno, Oscar, Cubillos, Max 2015

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Solvability of p-Laplacian Parabolic Equations with Constraints Coupled with Navier-Stokes Equations in 3D Domains by Using Largeness of p

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