Oct 2026· Journal of Applied Fluid Mechanics· 0 citations· 29 references
Abstract
Accurate prediction of centrifugal-pump performance under viscous operating conditions remains challenging, particularly for low specific-speed pumps operating at low Reynolds numbers. This study develops a Reynolds-number-based correction-factor framework derived from a physically based energy-loss analysis. The method explicitly accounts for major internal loss mechanisms, including hydraulic losses, disk friction, leakage flow, recirculation, mixing and diffusion losses, slip-factor deviation, and blade blockage. The model was calibrated using water-test data from an FM-50 centrifugal pump at 1200 rpm and validated using an independent dataset at 900 rpm. The validation results showed accurate head prediction, with and . Efficiency prediction showed larger deviation, with percentage points, reflecting the sensitivity of efficiency to measurement uncertainty and combined loss mechanisms. After validation, the model was extended to viscous-flow conditions and used to derive compact analytical correction factors for head and efficiency as functions of Reynolds number. The proposed expressions showed strong cross-validation performance within the investigated range, with mean for the head correction factor and for the efficiency correction factor. Comparison with ANSI/HI, KSB, and Gülich methods shows that the proposed formulation follows the expected Reynolds-number-dependent trend while providing a more physically interpretable basis for viscous-performance correction. The proposed method offers a practical alternative to conventional correction charts for low specific-speed centrifugal pumps operating under viscous or low-Reynolds-number conditions.
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 1 citation
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
To address uneven air supply among multiple needle tubes during the drying of high-density forage bales, this study investigated the airflow characteristics and structural optimization of the upper and lower air distribution chambers of a needle-type forage dryer. A three-dimensional CFD model was established, and airflow performance was evaluated using the velocity non-uniformity coefficient M and the inlet-to-outlet total pressure drop Δp. Response surface methodology was used to optimize the key structural parameters. For the upper chamber, installation of a T-shaped baffle and optimization of the cavity height Hc, diffuser angle α, and top-plate opening area ratio Ra yielded an optimal combination of Hc = 133.29 mm, α = 12.51°, and Ra = 1.12, reducing M from 11.2264% to 3.3886%. For the lower chamber, a strip-perforated airflow equalizing plate with Hb = 74.82 mm, D = 23.79 mm, and W = 25.03 mm reduced M from 9.8772% to 1.5484%, with Δp of approximately 130 Pa. Mesh-refinement and turbulence-model sensitivity analyses supported the robustness of the numerical predictions. Repeated outlet-velocity measurements yielded mean absolute relative errors of 3.09%–4.58%. Smoke visualization and grayscale analysis further indicated that the optimized structures enhanced airflow diffusion and redistribution. The results provide guidance for air distribution chamber design in needle-type forage dryers.
X. T. Liu, R. Wang, T. C. Ding· Journal of Applied Fluid Mec...· 0 citations
SyntheGrAnon is introduced, a framework for evaluating synthetic graph anonymity that primarily targets the singling out, linkability, and inference risks outlined in the EU GDPR at the node and community levels, while also including edge-level attacks as an extension of the node-level setting.
Abele Malan, Ahmad Al Kurdi, Stefanie Roos et al.· Proceedings on Privacy Enhan...· 0 citations
CHAI (Compliant Human-centered Adaptive Interaction), a novel language-driven framework for real-time modulation of a robot’s kinematics and mechanical compliance in real-world environments, introduces on-the-fly language-driven impedance (compliance) modulation along both translational and rotational directions.
Junhui Huang, Xingguang Duan, A. Bucker et al.· IEEE Robotics and Automation...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.