International Journal for Numerical Methods in Fluids最新文献

We construct novel flux approximation schemes for the semidiscretized incompressible Navier–Stokes equations by finite-volume method on a staggered mesh. The calculation of the cell-face fluxes has been done by solving appropriate local non-linear boundary value problems (BVP). Consequently, the cell-face fluxes are represented as the sum of a homogeneous and an inhomogeneous flux; the homogeneous part represents the contribution of convection and viscous-friction, while the inhomogeneous part represents the contribution of the source terms. We derive three flux approximation schemes to include the impact of the source terms on the numerical fluxes. The first one is based on a homogeneous 1-D local BVP without source. The second scheme is based on an inhomogeneous 1-D local BVP considering only the pressure gradient term in the source. Finally, a complete flux scheme is derived which is based on an inhomogeneous 2-D local BVP. It takes into account both the gradient of the cross-flux and the pressure gradient in the source term. The numerical validation of the schemes is done for the benchmark lid-driven cavity flow for considerably high $��\text{Reynolds}�$ numbers along with a numerical convergence test for the exact solution of the Taylor–Green vortex problem.

We consider the moving least squares method to solve compressible two-phase water-water vapor flow with surface tension. A diffuse interface model based on the Navier–Stokes and Korteweg equations is coupled with a suitable system of state equations that allows for a more realistic estimation of the pressure jump across the liquid–vapor interface as a function of temperature. We propose a simple formulation for computing the capillarity coefficient $��\lambda �$ based on the surface tension and the thickness of the diffuse interface. A convergence analysis using pressure jump in the test case of static bubble is conducted to verify our solver. We present several numerical test cases that illustrate the ability of our model to reproduce qualitatively and quantitatively the effects of surface tension on cavitation bubbles in general situations.

High computational complexity due to rapidly increasing numerical stiffness is a difficult problem for simulating a supersonic reactive flow by using the uncoupled method. On the basis of our previous work, this paper proposes a dual adaptive method to ensure high calculation efficiency and good robustness in simulating stiff cases. The principle of this method is to realize adaptive coordination for the advection and reaction time steps in accordance with the non-uniform feature of stiffness in the space and time dimensions. The proposed method can advance by a small time step in strong stiffness while with a large one in weak stiffness through the “prediction-correction-recovery” strategy. Some classical problems are chosen to verify the performance of the proposed method. The proposed method improved the computation efficiency by at least $��30�\%�$ comparing with the previous method [1] and widened the error tolerance of the initial time step.

The seepage equation plays a crucial role in fields such as groundwater management, petroleum engineering, and civil engineering. Currently, physical-informed neural networks (PINNs) have become an effective tool for solving seepage equations. However, practical applications often involve variable flow rates, which pose significant challenges for using neural networks to find solutions. Inspired by Deep Operator Network (DeepONet), this paper proposes a new model named Simulation Net (Sim-net) to deal with unsteady sources or sinks problems. Sim-net is designed to simulate and solve seepage equations without the need for retraining. This model integrates potential spatial and temporal features based on spatial pressure distribution and well bottom–hole pressure, respectively, which serve as additional signposts to guide neural networks in approximating seepage equations. Sim-net exhibits transfer learning capabilities, enabling it to handle variable flow rate problems without retraining for new flow conditions. Numerical experiments demonstrate that the trained model can directly solve seepage equations without the need for retraining, indicating its superior applicability compared to existing PINNs-based methods. Additionally, in comparison to the DeepONet, Sim-net achieves higher accuracy.

Cavitation erosion would degrade the performance of the fluid machinery. To improve the reliability and prolong the life span of fluid machinery, it is necessary to study the mechanism of cavitation erosion and predict the possibility of erosion. Since the erosion power to be measured and calculated is closer to the actual state of cavitation, a new cavitation erosion indicator e_pp model based on erosion power is proposed, which can reflect the size and region of the erosion generated by cavitation more precisely. Concerning the cases of the axisymmetric nozzle and venturi tube, the prediction of cavitation erosion based on the newly proposed indicator is illustrated. It is found that cavitation erosion mainly occurs near the maximum margin of the cavitation region. This research indicates the possible erosion state of fluid machinery in a cavitation environment and provides a new approach to estimate the state of cavitation erosion.

The inspiration for this study originates from a recognized research gap within the broader collection of studies on nanofluids, with a specific focus on their interactions with different surfaces and boundary conditions (BCs). The primary purpose of this research is to use an artificial neural network to examine the combination of Alumina-Engine oil-based nanofluid flow subject to electro-magnetohydrodynamic effects, within a porous medium, and over a stretching surface with an impermeable structure under convective BCs. The flow model incorporates Thermophoresis and Brownian motion directly from Buongiorno's model. Accounting for the porous medium's effect, the model integrates the Forchheimer number (depicting local inertia) and the porosity factor developed in response to the presence of the porous medium. The conversion of governing equations into non-linear ordinary differential systems is achieved by implementing transformations. A highly non-linear ordinary differential system's final system is solved using a numerical scheme (Runge–Kutta fourth-order). Findings indicate that the porosity factor positively impacts the skin friction and the momentum boundary layer. The influence suggests an increment in the frictional force and a decline in the velocity profile. The volume fraction, Prandtl number, and magnetic number significantly impact the flow profiles. The skin friction data is tabulated with some physical justifications.