Abstract Using extensive numerical simulation of the Navier–Stokes equations, we study the transition from the Darcy’s law for slow flow of fluids through a disordered porous medium to the nonlinear flow regime in which the effect of inertia cannot be neglected. The porous medium is represented by two-dimensional slices of a three-dimensional image of a sandstone. We study the problem over wide ranges of porosity and the Reynolds number, as well as two types of boundary conditions, and compute essential features of fluid flow, namely, the strength of the vorticity, the effective permeability of the pore space, the frictional drag, and the relationship between the macroscopic pressure gradient$${\varvec{\nabla }}P$$ and the fluid velocityv. The results indicate that when the Reynolds number Re is low enough that the Darcy’s law holds, the magnitude$$\omega _z$$ of the vorticity is nearly zero. As Re increases, however, so also does$$\omega _z$$ , and its rise from nearly zero begins at the same Re at which the Darcy’s law breaks down. We also show that a nonlinear relation between the macroscopic pressure gradient and the fluid velocityv, given by,$$-{\varvec{\nabla }}P=(\mu /K_e)\textbf{v}+\beta _n\rho |\textbf{v}|^2\textbf{v}$$ , provides accurate representation of the numerical data, where$$\mu$$ and$$\rho$$ are the fluid’s viscosity and density,$$K_e$$ is the effective Darcy permeability in the linear regime, and$$\beta _n$$ is a generalized nonlinear resistance. Theoretical justification for the relation is presented, and its predictions are also compared with those of the Forchheimer’s equation.
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On the stationarity for nonlinear optimization problems with polyhedral constraints
Abstract For polyhedral constrained optimization problems and a feasible point$$\textbf{x}$$ , it is shown that the projection of the negative gradient on the tangent cone, denoted$$\nabla _\varOmega f(\textbf{x})$$ , has an orthogonal decomposition of the form$$\varvec{\beta }(\textbf{x}) + \varvec{\varphi }(\textbf{x})$$ . At a stationary point,$$\nabla _\varOmega f(\textbf{x}) = \textbf{0}$$ so$$\Vert \nabla _\varOmega f(\textbf{x})\Vert $$ reflects the distance to a stationary point. Away from a stationary point,$$\Vert \varvec{\beta }(\textbf{x})\Vert $$ and$$\Vert \varvec{\varphi }(\textbf{x})\Vert $$ measure different aspects of optimality since$$\varvec{\beta }(\textbf{x})$$ only vanishes when the KKT multipliers at$$\textbf{x}$$ have the correct sign, while$$\varvec{\varphi }(\textbf{x})$$ only vanishes when$$\textbf{x}$$ is a stationary point in the active manifold. As an application of the theory, an active set algorithm is developed for convex quadratic programs which adapts the flow of the algorithm based on a comparison between$$\Vert \varvec{\beta }(\textbf{x})\Vert $$ and$$\Vert \varvec{\varphi }(\textbf{x})\Vert $$ .
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- Award ID(s):
- 2031213
- PAR ID:
- 10415087
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Mathematical Programming
- Volume:
- 205
- Issue:
- 1-2
- ISSN:
- 0025-5610
- Format(s):
- Medium: X Size: p. 107-134
- Size(s):
- p. 107-134
- Sponsoring Org:
- National Science Foundation
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