In the practical application of hydraulic rotating machinery, it is essential to thoroughly explore drag reduction and rheological characteristics of drag-reducing additives to optimize machinery efficiency and reduce equipment consumption. This paper combines simulation and experimental approaches to investigate the drag-reduction performance and rheological properties of drag-reducing additives. Numerical simulations are initially conducted to investigate the shear-thinning properties of drag-reducing fluid and explore variations in drag-reduction rate. Turbulent phenomena characteristics are described by analyzing turbulent statistical quantities. Subsequently, the rheological behaviors of polyethylene oxide (PEO), cetyltrimethyl ammonium chloride (CTAC), and their mixed solutions under different conditions are scrutinized using a rotational rheometer. The findings indicate that the drag reduction effect amplifies as the rheological index n and characteristic time λ decrease. The numerical simulations show a maximum drag reduction rate of 20.18%. In rheological experiments, a three-stage viscosity variation is observed in single drag-reducing additives: shear thickening, shear thinning, and eventual stabilization. Composite drag-reducing additives significantly reduce the apparent viscosity at low shear rates, thereby strengthening the shear resistance of the system.

The migration of arsenic in groundwater is influenced by the heterogeneity of the medium, and the presence of iron minerals adds complexity and uncertainty to this effect. In this study, a stratified heterogeneous sand column with an embedded hematite lens at the coarse-to-medium sand interface was designed. We introduced an arsenic-laden solution and controlled groundwater flow to investigate the spatiotemporal characteristics of arsenic migration and the impact of hematite dissolution. The results showed that the medium structure significantly influenced the arsenic migration and distribution within the lens-containing sand column. The clay layers directed the lateral migration of arsenic, and the arsenic concentrations in deeper layers were up to seven times greater than those on the surface. The extraction experiments of solid-phase arsenic revealed that the main adsorption modes on quartz sand surfaces were the specific adsorption (F2) and adsorption on weakly crystalline iron–aluminum oxides (F3), correlating to the specific and colloidal adsorption modes, respectively. Monitoring the total iron ions (Fe(aq)) revealed rapid increases within the first 14 days, reaching a maximum on day 15, and then gradually declining; these results indicate that hematite did not continuously dissolve. This study can aid in the prevention and control of arsenic contamination in groundwater.

River hydrodynamics are influenced by numerous factors that traditional models often fail to fully capture. Simulating complex hydrographs can benefit from parsimonious upscaling models, such as fractional derivative equations, that reduce the need to account for all variables. While fractional Saint-Venant equations (SVEs) have been mathematically explored, they lack clear physical interpretation and have not been applied in practical scenarios. This study introduces novel fractional-order Saint-Venant equations (FSVEs) for simulating river flow dynamics, addressing limitations in conventional modeling. Three models—constant, tempered, and variable time-fractional SVEs (CtFSVE, TtFSVE, and VtFSVE)—are developed to capture peak attenuation and tailing more effectively. Numerical experiments indicate that lower time-fractional derivative values enhance retention, producing a lower peak, delayed peak arrival, and pronounced late-time tailing. TtFSVE models transient tailing in hydrographs, VtFSVE captures transient evolution where inflow and outflow differ, and CtFSVE balances accuracy and simplicity with a single added parameter for various hydrographs. In the simulation of real-world hydrograph data, the fractional SVEs show high predictive accuracy. However, they should be regarded as effective proxy models that require parameter calibration and are not yet fully ‘plug-and-play’ predictive models. Comparative analysis with the Long Short-Term Memory (LSTM) machine learning model and distributed domain coupling model (DDCM) shows CtFSVE’s superior performance in capturing complex flow dynamics with minimal data, while field validation demonstrates its accuracy over traditional SVE, underscoring its practicality for complex river networks. The fractional engine shows promise as an effective tool for upscaling surface flow without the prohibitive burden of mapping detailed system heterogeneity.

Arsenic contamination in polymetallic mining areas is closely linked to surrounding iron-rich manganese minerals. However, conclusive evidence remains limited regarding the retention and migration process of As(V) in naturally manganese-rich manganese ores (especially those with different manganese/iron mass ratios) under dynamic flow conditions. This study investigated As(V) adsorption and transport by four natural manganese minerals (FM1–FM4) through batch/column experiments, characterization, and numerical modeling. Their Mn/Fe mass ratios were 22.7 for FM1, 4.2 for FM2, 3.7 for FM3, and 16.4 for FM4. Batch experiments showed that As(V) adsorption on FM1–FM3 was better described by the Freundlich model, indicating heterogeneous adsorption behavior. Under the tested experimental conditions, the apparent Langmuir q_m values of these minerals decreased from 0.066 to 0.015 mmol·g⁻¹ with decreasing Mn/Fe ratio. However, As(V) adsorption on FM4, which had the lowest Mn and Fe contents, followed the Langmuir model (q_m = 0.012 mmol·g⁻¹), suggesting monolayer adsorption. Column experiments demonstrated rapid As(V) retention for all minerals. In the time domain, increasing the flow rate from 0.5 to 2.0 mL·min⁻¹ generally advanced breakthrough and shortened the desorption tail, although the breakthrough behavior expressed in pore-volume coordinates was not strictly monotonic for all minerals. The Two-Site Kinetic Attachment Model (TSKAM) successfully simulated these dynamics (R² > 0.90, RMSE < 0.05), revealing adsorption controlled by fast and slow kinetic sites, with slow-site contributions diminishing at higher flow rates. Characterization results indicated that adsorbed arsenic on FM1 remained mainly as As(V) and was immobilized primarily through surface complexation involving surface hydroxyl and Fe/Mn–O groups. XRD and SEM-EDS suggested the participation of Fe/Mn-bearing phases, while XPS on FM1 showed pronounced changes in Mn surface species during adsorption. Therefore, As(V) removal by these natural manganese minerals is a coupled physicochemical process influenced by both mineral properties, including Mn/Fe ratio, specific surface area, pore structure, pH_PZC, and Mn surface-state changes, and hydrodynamic conditions in the polymetallic mining areas.