International Journal for Numerical Methods in Fluids最新文献

Direct numerical simulation (DNS) and implicit large-eddy simulation (LES) of turbulent channel flows with isothermal walls, with and without shock trains, are performed using a recently proposed high-order optimized targeted essentially non-oscillatory (TENO) scheme. Mean flow and turbulence statistics are presented and compared with those previously obtained from DNS using a bandwidth-optimized weighted essentially non-oscillatory (WENO) scheme with limiter. It is observed that the TENO scheme performs better than the WENO scheme in predicting the mean flow and Reynolds stresses in these flows. The optimized TENO scheme used here is found to be very suitable for performing implicit LES on a relatively coarse grid.

This work is devoted to the numerical simulation of Shallow Water Equations involving dry areas, a moving shoreline and in the context of mesh adaptation. The space and time discretization using the Runge–Kutta Discontinuous Galerkin approach is applied to nonlinear hyperbolic Shallow Water Equations. Problems with dry areas are challenging for such methods. To counter this issue, special treatment is applied around the shoreline. This work compares three treatments, one based on Slope Modification, one based on p-adaptation and the last one based on eXtended Finite Element methods and mesh adaptation.

Based on large eddy simulations, intermittent airflow within an urban street canyon was simulated. The practice of time-varying inflow conditions (TVIC) required a time series of inflow wind velocity, which could be collected on a varying curve of the moving averaged measured data. The influences of the time interval of the wind series and the varying trend (or molded line) between adjacent data on airflow within the street canyon were analyzed. The results showed that TVIC would result in larger average wind velocity and turbulence intensity than that simulated under steady inflow conditions (SIC). The simulated total vertical air exchanges under TVIC would be one order of magnitude higher than that simulated under SIC. Airflow characteristics within street canyons were influenced by the varying trends and the time intervals of the time-series inflow wind. Average vertical wind velocity and turbulent kinetic energy (TKE) simulated under the stepped varying trend was higher than that under the jagged varying trend. The shorter the time interval, the larger the TKE within the street canyon. Vertical air exchanges induced by turbulence (ACH′) at the roof level simulated under the stepped molded lines were twice that of the jagged molded line. Under the time interval of 30 s, the ACH′ was significantly increased, which was 2.558 times that simulated with a time interval of 1 min. Thus, the suggested practical approach for time-varying inflow simulations is to obtain time-series wind data with a time interval of 1 min or less, and the linearly molded line would be critical; for larger time intervals, reasonable molded lines would be required.

Physics-informed neural network (PINN) has become a potential technology for fluid dynamics simulations, but traditional PINN has low accuracy in simulating incompressible flows, and these problems can lead to PINN not converging. This paper proposes a physics-informed neural network method (KA-PINN) based on the Kolmogorov–Arnold Neural (KAN) network structure. It is used to solve two-dimensional and three-dimensional incompressible fluid dynamics problems. The flow field is reconstructed and predicted for the two-dimensional Kovasznay flow and the three-dimensional Beltrami flow. The results show that the prediction accuracy of KA-PINN is improved by about 5 times in two dimensions and 2 times in three dimensions compared with the fully connected network structure of PINN. Meanwhile, the number of network parameters is reduced by 8 to 10 times. The research results not only verify the application potential of KA-PINN in fluid dynamics simulations, but also demonstrate the feasibility of KAN network structure in improving the ability of PINN to solve and predict flow fields. This study can reduce the dependence on traditional numerical methods for solving fluid dynamics problems.

Following the proposition of the original AWENO (Alternative Formulation of Weighted Essentially Non-Oscillatory) FD (Finite Difference) scheme, we construct the new AMDCD FD scheme, an Alternative formulation of the linear FD scheme with Minimized Dispersion and Controllable Dissipation, in this article. Spectral analysis shows that the proposed AMDCD FD scheme can be more efficient in resolving smooth solutions due to the flexibility in controlling dissipation. To efficiently solve compressible flows with discontinuities, we further combined the proposed AMDCD FD scheme with the original AWENO FD scheme using a hybrid interpolation scheme, in which the optimized linear MDCD (Minimized Dispersion and Controllable Dissipation) interpolation scheme would be switched to the nonlinear WENO (Weighted Essentially Non-Oscillatory) type interpolation scheme gradually as the flow structures are in transition from smooth region towards the vicinity of discontinuities. Therefore, the resulting hybrid AWENO-AMDCD FD scheme is suitable for solving compressible flows with broad-scale flow structures and/or shock waves. A series of one-, two-, and three-dimensional compressible flow problems are numerically tested to demonstrate the accuracy, superior resolution, as well as the robustness of the proposed hybrid AWENO-AMDCD FD scheme.

We present an augmented Lagrangian trust-region method to efficiently solve constrained optimization problems governed by large-scale nonlinear systems with application to partial differential equation-constrained optimization. At each major augmented Lagrangian iteration, the expensive optimization subproblem involving the full nonlinear system is replaced by an empirical quadrature-based hyperreduced model constructed on-the-fly. To ensure convergence of these inexact augmented Lagrangian subproblems, we develop a bound-constrained trust-region method that allows for inexact gradient evaluations, and specialize it to our specific setting that leverages hyperreduced models. This approach circumvents a traditional training phase because the models are built on-the-fly in accordance with the requirements of the trust-region convergence theory. Two numerical experiments (constrained aerodynamic shape design) demonstrate the convergence and efficiency of the proposed work. A speedup of $��12�.�7�\times �$ (for all computational costs, even costs traditionally considered “offline” such as snapshot collection and data compression) relative to a standard optimization approach that does not leverage model reduction is shown.

This article develops a high-order finite element scheme in an approximate solution of the two-dimensional Rayleigh–Stokes problem for a heated generalized second-grade fluid with fractional derivatives. The constructed approach consists of approximating the exact solution by interpolation in time while the finite element technique is used in the approximation of the spatial derivatives. This combination is simple and easy to implement. The stability and error estimates of the developed strategy are deeply analyzed in the $��{�L�}^{�\infty �}�$-norm. The theoretical studies suggest that the proposed method is unconditionally stable, convergent with order $��O�(�{�\sigma �}^{�1�+�\gamma �}�+�{�h�}^{�p�}�)�$, faster, and more efficient than a broad range of numerical schemes discussed in the literature for the considered time fractional partial differential equation. Some numerical examples are carried out to show the applicability and viability of the new algorithm.

In coastal and offshore engineering, the intense water–soil motion poses significant challenges to the safety of buildings and structures. The smoothed particle hydrodynamics (SPH) method, as a mesh-free Lagrangian solver, has considerable advantages in the numerical resolution of such problems. SPH models for the water–soil two-phase flow can be categorized into the multilayer type and the single-layer type. Although the single-layer model envisions a simpler algorithm and higher computational efficiency, its accuracy, stability, and recovery of interfacial details are far from satisfactory. In the present work, an improved single-layer model is established to alleviate these limitations. First, the soakage function, which takes effect near the phase interface, is introduced to characterize the two-phase coupling status. Additionally, the stress diffusion term and a modified density diffusion term applicable in density discontinuity scenario are introduced to ease the numerical oscillation. Finally, to remove the unphysical voids in the interfacial region, the particle shifting technique with special treatment tailored for free-surface particles is implemented. Validations of the proposed model are carried out by a number of numerical tests, including the erodible dam-break problem, the wall-jet scouring, the flushing case, and the water jet excavation. Appealing agreements with either experimental data or published numerical results have been achieved, which verifies the accuracy, stability, and robustness of the proposed model for water–soil two-phase flows.