Fluids

The aim of this study is to compare submerged vortical structures for a pump mounted in a pump intake without any anti-vortex devices (AVDs), with a trident-like AVD or with a cone AVD. Another aim is to compare the pump characteristics (head, efficiency, power input and radial forces) of these pump arrangements via CFD simulation along with experimental measurements in a closed circuit. The numerical simulation of unsteady multiphase flow is established by means of computational fluid dynamics (CFD) and the volume of fluid (VOF) method. To predict vortical structures in the vicinity of the pump suction bell, the unsteady Reynolds-averaged Navier–Stokes equations (URANS) are solved together with the scale-adaptive simulation (SAS) turbulence model. For each AVD configuration, integral characteristics like the head, power input, efficiency and forces acting on the pump rotor are also evaluated. The numerical results show that the configuration with the cone AVD exhibits the best performance (from the point of view of both hydraulic efficiency and vorticity strength), but it requires a larger distance between the intake bottom wall and the pump bellmouth. The submerged vortices are quite stable when using an AVD, but rather unsteady without any anti-vortex tool.

For capturing dynamic information about a filled-cave in the fractured reservoir, a novel Pressure Transient Analysis (PTA) analytical model for a well located at the filled-cave is established. In this new model, we consider the stress-sensitivity of the filled-cave and the inter-porosity flow of fracture. First, Perturbation transformation was used to obtain the pressure distribution in the filled-cave zone. Then, the Warren–Root model was applied to establish the pressure solution in the fractured reservoir. Next, the pressure and its derivative are obtained by the Laplace transformation and Steftest inversion. Lastly, the Bottomhole Pressure (BHP) and Bottomhole Pressure Derivative (BHPD) combined curve reveals the flow regimes of this novel model. The results show the composite model can be used to characterize the fractured reservoir with the filled-caves, and its flow follows the composite flow regimes. The spherical flow has an obvious slope of 0.5 on the BHPD curve, which can identify the size of the filled-caves. The boundary flow can be used to identify stress-sensitivity. Affected by the stress-sensitivity of the filled-cave, the BHPD’s slope of the boundary flow will be greater than 1. This research work provides technical support for capturing cave and fracture parameters in the fractured reservoir.

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number representing thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ( ) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action).