This paper considers the problem of fluid flow and pressure drop through wire mesh screens using three-dimensional numerical approaches based on the finite volume method (FVM) and lattice Boltzmann method (LBM). The employed numerical methods were successfully validated and cross-verified for Plain Square, Full Twill, and Plain Dutch wire mesh screens, with a maximum deviation of 16.0% from experimental data over a wide Reynolds number range. Parametric simulations were performed for 20 wire mesh screens with different weave types and geometric parameters over the range 0.01≤ReAr≤100. Generalized relations were obtained for the pressure loss and for the relative contributions of pressure and viscous shear forces to the total drag for Plain Square and Full Twill screens. Correlations for the dimensionless pressure-loss coefficient showed maximum deviations of 21.0% for Full Twill and 23.6% for Plain Square relative to the numerical simulation results.
A pool fire is a turbulent diffusion fire that occurs when a flammable liquid, such as gasoline or oil, spills, forms a pool on a surface (or floats on water), and ignites. It is characterized by low-momentum, buoyancy-driven flames, significant thermal radiation, and a heat feedback mechanism that sustains fuel evaporation. The numerical simulation of PMMA pyrolysis using Computational Fluid Dynamics (CFD) and Quantum Machine Learning (Time Series Forecasting by using Pytorch and Pennylane libraries) represents a cutting-edge approach combining high-fidelity physical modeling with hybrid quantum machine learning (QML) for material optimization and durability enhancement. While CFD models predict transient thermal boundary conditions and heat transfer reduction, QML and classical machine learning (ML) accelerate design optimization. The PMMA surface recession obtained by using FDS software version 5 has been successfully validated against the analytical method. It has been shown that the calculated recession velocity of the PMMA obtained by FDS is 0.074 mm/s. The calculated recession velocity of the PMMA obtained by using the pyrolysis kinetic model is 0.079 mm/s. Thus, the error is about 6.12%. The coupled hybrid classical and quantum time forecasting algorithm predicts very well the temperature reading of TC1 thermo-couple obtained by FDS simulation. This theoretical work uses mass loss rate derived from non-isothermal pyrolysis kinetics. In the framework of this research, a coupled classical–quantum algorithm has been developed in order to solve the one-dimensional transient heat conduction in poly (methyl methacrylate) (PMMA), whose surface pyrolyzes away at a constant recession velocity, with a constant temperature imposed on both faces. The analytical and numerical obtained for the temperature profile are similar.
Tip leakage flow originates from the pressure difference between the pressure and suction sides of a blade, which drives fluid through the finite tip clearance. After leaving the clearance, the leakage jet interacts with the passage flow and casing boundary layer, forming a highly three-dimensional shear layer that rolls up into a tip leakage vortex (TLV). This review evaluates turbulence simulation approaches according to the TLV quantities they are intended to predict, the physical mechanisms they resolve, and the validation basis available for each quantity. Reynolds-averaged Navier–Stokes (RANS), unsteady RANS (URANS), large eddy simulation (LES), hybrid RANS–LES, and direct numerical simulation (DNS) are compared in terms of their ability to predict mean leakage characteristics, vortex trajectory and core properties, unsteady dynamics, spectra, and vortex breakdown, together with their associated computational requirements and validation needs. Experimental measurements and modal analysis techniques are treated separately as validation and diagnostic tools. The review further identifies unresolved challenges in engine-relevant scale-resolving simulation, model form uncertainty, standardized validation, and data-assisted prediction.
Modern wind turbines have adapted to increasing power demands through larger rotors and proportionally lighter blades. These lightweight designs are often more responsive to atmospheric fluctuations, resulting in complex rotor aeroelastic behavior and interactions between turbine wakes and background wind. In this study, wind measurements recorded at meteorological towers and turbine nacelles at Sandia National Laboratories’ Scaled Wind Farm Technology (SWiFT) facility are used to characterize propagating wind patterns within the farm flow. These measurements inform a resolved-scale atmospheric flow model using novel spatio-temporally variant (STV) wind input methods. Turbine aeroelastic response and farm-scale wake interactions are simulated within the Common Ordinary Differential Equation Framework (CODEF). Simulated wake structure and downstream flow velocity are compared with SWiFT Light Detection and Ranging (LiDAR) measurements to evaluate the influence and propagation of large-scale coherent atmospheric fluctuations as they mutually advect with turbine wake vortex structures. This investigation demonstrates the capabilities of the proposed STV atmospheric modeling methods and provides a validated approach for studying turbine aeroelastic behavior and wake evolution under realistic, space and time-varying atmospheric conditions.
Every proposed lunar in situ resource utilization (ISRU) project, from oxygen extraction by hydrogen reduction or molten regolith electrolysis to polar water recovery, begins with bulk transport of granular lunar soil. Such mixtures are expected to segregate, and segregation is the largest source of feedstock non-uniformity in terrestrial processing. Whether that transfers to the Moon is not obvious: lunar regolith is fine, angular, agglutinate-bearing and electrostatically active, and is handled in vacuum at one-sixth gravity, shifting the cohesive–gravitational balance on which classical segregation theory implicitly rests. We ask which size and density segregation mechanisms survive that transition. We argue that the appropriate organizing variable is not gravity but the granular Bond number, and that reduced gravity may displace regolith handling toward a cohesion-dominated regime in which agglomerate- and charge-mediated separation may replace terrestrial kinetic sieving. Recent low-gravity hopper and drop-tower experiments show that, for cohesive discharge, clogging becomes more likely at reduced gravity and the data are organized by a granular Bond number rather than by gravity alone; whether the same holds for segregation has not been tested. We present a conceptual regime map in the granular Bond number–inertial number plane, separating where terrestrial correlations are expected to apply from where they may not, and derive seven testable hypotheses. No lunar-surface segregation experiment has yet been performed on a flowing mixture; we close with a prioritized research agenda and the minimum instrumentation such a measurement would require.
