International Journal for Numerical Methods in Fluids最新文献

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.

Ship form optimization poses a complex and high-dimensional engineering challenge. Therefore, when conducting multi-objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare-bones multi-objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi-objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi-objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi-objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low-carbon shipping.

This work aims at presenting a new computational approach to study two and three dimensional two-phase flows and two dimensional coalescence phenomenon using direct numerical simulation. The flows are modeled by the incompressible Navier–Stokes equations, which are approximated by the finite element method. The Galerkin formulation is used to discretize the Navier–Stokes equations in the spatial domain and the semi-Lagrangian method is used to discretize the material derivative. In order to satisfy the Ladyzhenskaya–Babuška–Brezzi condition, high-order stable pair of elements are used, with pressure and velocity fields being calculated on different degrees of freedom in the unstructured mesh nodes. The interface is modeled by an unfitted adaptive moving mesh, where interface nodes are tracked in a Lagrangian fashion and moved with the velocity solution of the fluid motion equations. The surface tension is computed using the interface curvature and the gradient of a Heaviside function, and added in the momentum equations as a body force. In order to avoid undesired spurious modes at the interface due to high property ratios, a smooth transition between fluid properties is defined on the interface region. Several benchmark tests have been carried out to validate the proposed approach, and the obtained results have demonstrated agreement with analytical solutions and numerical results reported in the literature. A coalescence model is also proposed based on geometric criteria and results show interesting dynamics.