Search Results (910)

This study investigates the hydrodynamic loads of “Ningde No. 1” offshore aquaculture under current-only conditions using a fluid–structure interaction (FSI) approach with the computational fluid dynamics (CFD) solver OpenFOAM. A porous-media-based model is applied to simulate net-induced drag, while the rigid framework is resolved using a large eddy simulation (LES) turbulence model. A comprehensive set of 350 CFD simulations is performed, with varying flow velocities, flow directions, draft depths, and existence of nets. The results reveal that the load on this fishing facility in the streamwise direction (Fx) increases monotonically with flow velocity, direction, and draft. The lateral (Fy) and vertical (Fz) loads exhibit non-linear trends, peaking at a specific flow direction (approximately 60°) and draft levels (around 11.5 m). The fishing nets substantially increase the streamwise load by up to 80%, while their influence on the lateral forces is dependent on submergence depth. To efficiently predict hydrodynamic loads without performing additional and lengthy CFD simulations, a physics-informed neural network (PINN) is trained using the simulated data. The PINN model is found able to accurately reproduce the hydrodynamic force across a wide range of current conditions, offering a practical and interpretable surrogate approach for structural design optimization and mooring system development in offshore aquaculture industry. Full article

This study presents numerical simulations of ice crystal accretion on aircraft surfaces under mixed-phase icing conditions, where ice crystals coexist with supercooled water droplets. The Finite Element Navier–Stokes Analysis Program (FENSAP-ICE) suite, incorporating the Discrete Roughness Optimization Program in 3D (DROP3D) and Ice Accretion Simulation in 3D (ICE3D) solvers, was applied to the Common Research Model with Natural Laminar Flow (CRM-NLF) to examine the effects of crystal size, aspect ratio, and concentration on ice growth. The results show that the presence of ice crystals produces smoother, more uniform, and substantially thicker ice compared with droplet-only cases, where distinct horns and roughness dominate. At peak growth locations, the predicted ice thickness increases by up to 75% under mixed-phase conditions. Quantitative analyses reveal that increasing crystal diameter from 50 μm to 200 μm raises ice growth by 25%–75%, increasing aspect ratios from 0.05 to 1 increases growth by 20%–75%, and raising concentrations from 0.25 to 2 kg/m³ enhances growth by nearly 450%. These findings demonstrate the critical role of ice crystals in promoting layered ice accumulation, clarify the mechanisms driving mixed-phase icing, and provide theoretical guidance for advancing anti-icing and de-icing technologies in aviation. Full article

The role of wall roughness in heat and mass transfer for fully developed viscous flows in square microchannels is investigated here. Since the roughness, which is the key geometrical feature to be investigated, introduces high velocity gradients at the wall, the effect of the viscous dissipation is considered. A fully developed flow in the forced convection regime is assumed. This assumption allows the two-dimensional treatment of the problem; thus, the velocity and temperature fields are simulated on the microchannel cross-section. The boundary roughness is modeled by randomly throwing points around the nominal square cross-section perimeter and by connecting those points to generate a simple polygon. This modification of the nominal square shape of the cross-section influences the velocity and temperature fields, which are computed by employing a finite element method solver. The heat and mass transfer is studied by calculating the Nusselt and the Poiseuille numbers as a function of roughness amplitude at the boundary. Each Nusselt and Poiseuille number is obtained by employing an averaging procedure over a sample of a thousand cases. Full article

Using Excel and its Solver feature, a novel method of analyzing the component reactions of an overall reaction is outlined. As an example, autothermal reforming (300–700 °C) of acetic acid (AA), a significant component of pyrolysis oil, was considered. The overall reaction can be viewed as comprising five individual reactions: reforming, oxidation, water–gas shift, reverse Boudouard, and methanation. A laboratory apparatus was set up in which acetic acid, air, and water were continuously fed to a BASF dual-layer catalytic reactor in plug flow at 1 atm. For this setup, it is easy to construct a material balance in Excel in which five factors, f_i, are defined which represent the fraction of reactant going to each of the individual five reactions. Using the Solver feature of Excel, it can readily be determined which of the five factors f_i produce the best match of the calculated exit gas composition with the measured gas concentrations for CO, CO₂, H₂, CH₄, and O₂. Furthermore, a program such as GasEq or Aspen can then be used to calculate the theoretical equilibrium gas composition at a given condition. Using this equilibrium gas composition and Solver, the individual (f_i)_equilb can be calculated. Thus, the ratio f_i/(f_i)_equilb is an indication of how close each component reaction is to equilibrium. In this way, an idea is gained of which of the individual component reactions need to be improved or inhibited or if operating parameters should be adjusted. For the specific case of autothermal reforming of acetic acid, the steam reforming reaction requires at least 600 °C to approach equilibrium. In contrast, the oxidation reaction goes to equilibrium throughout the temperature range, completely consuming oxygen. The water–gas shift reaction appears to approach equilibrium to the extent of 71–90% throughout the temperature range. The reverse Boudouard reaction is favored at lower temperatures; in fact, coking was predicted and found at the low temperature of 300 °C. Full article

This study develops an incompressible two-phase flow solver based on the open-source OpenFOAM platform, employing the volume-of-fluid (VOF) method to track the gas–liquid interface and utilizing the MULES algorithm to suppress numerical diffusion. This study provides a comprehensive investigation of the spreading dynamics of droplet pairs near walls, along with the presentation of a corresponding mathematical model. The numerical model is validated through a two-dimensional axisymmetric computational domain, demonstrating grid independence and confirming its reliability by comparing simulation results with experimental data in predicting drConfirmedoplet collision, spreading, and deformation dynamics. The study particularly investigates the influence of surface wettability on droplet impact dynamics, revealing that increased contact angle enhances droplet retraction height, leading to complete rebound on superhydrophobic surfaces. Finally, a mathematical model is presented to describe the relationship between spreading length, contact angle, and Weber number, and the study proves its accuracy. Analysis under logarithmic coordinates reveals that the contact angle exerts a significant influence on spreading length, while a constant contact angle condition yields a slight monotonic increase in spreading length with the Weber number. These findings provide an effective numerical and mathematical tool for analyzing the spreading dynamics of droplet pairs. Full article

This study focuses on the analysis of the simultaneous impact of inclined magnetohydrodynamic Carreau hybrid nanofluid flow over a stretching sheet, including microorganisms with the effects of chemical reactions in the presence and absence of slip conditions for dilatant $(n>1.0)$ and quasi-elastic hybrid nanofluid $(n<1.0)$ limitations. Meanwhile, the transfer of energy is strengthened through the employment of heat sources and bioconvection. The analysis incorporates nonlinear thermal radiation, chemical reactions, and Arrhenius activation energy effects on different profiles. Numerical simulations are conducted using the efficient Bvp5c solver. Motile concentration profiles decrease as the density slip parameter of the motile microbe and Lb increase. The Weissenberg number exhibits a distinct nature depending on the hybrid nanofluid; the velocity profile, skin friction, and Nusselt number fall when $(n>1.0)$ and increase when $(n<1.0)$. For small values of inclination, the 3D surface plot is far the surface, while it is close to the surface for higher values of inclination but has the opposite behavior for the 3D plot of the Nusselt number. A detailed numerical investigation on the effects of important parameters on the thermal, concentration, and motile profiles and the Nusselt number reveals a symmetric pattern of boundary layers at various angles $\left(\alpha \right)$. Results are presented through tables, graphs, contour plots, and streamline and surface plots, covering both shear-thinning cases $(n<1.0)$ and shear-thickening cases $(n>1.0)$. Full article

This study investigates the stochastic variation in droplet size generated within a microfluidic flow-focusing cross-junction. A commercial micro cross-junction was used to experimentally analyze droplet formation under fixed flow rate conditions. An in-house machine learning-based algorithm was developed to automatically detect and measure droplet dimensions from high-speed video recordings. Despite constant flow rates, the analysis revealed fluctuations in droplet size, attributed to velocity oscillations induced by syringe pumps. To explore this phenomenon, micro-Particle Image Velocimetry (micro-PIV) was employed to capture velocity profiles, which were then used to define time-dependent boundary conditions for numerical simulations. Simulations were conducted using the OpenFOAM solver interFoam and validated against experimental data. The results demonstrate good agreement and confirm that velocity fluctuations significantly influence droplet formation. This combined experimental and numerical approach provides an innovative, robust framework for understanding and predicting droplet behavior in microfluidic systems. Full article

This study addresses the issues of unbalanced capacity allocation and rigid train formations in urban metro systems under tidal passenger flow conditions. By integrating temporal–spatial passenger demand with real-time dynamic train formation, we propose a virtual formation optimization method driven by carriage load factors. This method enhances the flexibility of train formation strategies by coordinating virtual coupling and decoupling operations between trains traveling in opposite directions. A mixed-integer linear programming (MILP) model is developed, with train unit allocation and turnover scheduling as the main decision variables. The model aims to minimize total passenger waiting time and system operating costs, while incorporating constraints related to unit allocation, turnover, and passenger assignment. The model can be efficiently solved using commercial solvers such as CPLEX. To evaluate the proposed method, a case study is conducted on a metro line in a major city. Numerical experiments demonstrate that, compared with a fixed 6-car formation scheme, the proposed method reduces total passenger waiting time by approximately 4.2% and operating costs by 11.6%. When compared to a fixed 8-car formation scheme, it achieves a 48.8% reduction in operating costs with only a 4.3% increase in passenger waiting time. These results highlight the potential of the proposed virtual formation strategy to enhance operational efficiency and resource utilization in urban metro systems, offering both practical value and implementation feasibility. Full article

Understanding and modeling the effect of magnetic fields on flows that present separation properties, such as those over a backward-facing step (BFS), is critical due to its role in metallurgical processes, nuclear reactor cooling, plasma confinement, and biomedical applications. This study examines the hydrodynamic and magnetohydrodynamic numerical solution of an electrically conducting fluid flow in a backward-facing step (BFS) geometry under the influence of an external, uniform magnetic field applied at an angle. The novelty of this work lies in employing an in-house finite-volume solver with a collocated grid configuration that directly applies a Newton–like method, in contrast to conventional iterative approaches. The computed hydrodynamic results are validated with experimental and numerical studies for an expansion ratio of two, while the MHD case is validated for Reynolds number $Re=380$ and Stuart number $N=0.1$. One of the most important findings is the reduction in the reattachment point and simultaneous increase in pressure as the magnetic field strength is amplified. The magnetic field angle with the greatest influence is observed at $\phi =\pi /2$, where the main recirculation vortex is substantially suppressed. These results not only clarify the role of magnetic field orientation in BFS flows but also lay the foundation for future investigations of three-dimensional configurations and coupled MHD–thermal applications. Full article

Land availability constraints limit the installation of conventional ground-mounted solar installations. As a result, Floating Photovoltaic (FPV) systems are gaining popularity as an alternative to renewable energy generation. FPV consist of individual solar panels that are commonly symmetrical and modular. However, the hydrodynamic behaviour of FPVs in water surface waves is understudied to ensure their stability and optimal performance under varying environmental conditions. This literature review examines various modelling techniques applied in studying FPV hydrodynamics. Specifically, the application of Computational Fluid Dynamics (CFD) solvers and potential flow theory solvers is investigated for their effectiveness in capturing the behaviour of FPVs and mooring dynamics under the impact of wind and waves. The review highlights the advantages and limitations of each approach. Findings suggest that a combined CFD-potential flow approach offers a perfect balance between accuracy and computational efficiency, offering valuable insights into the performance of FPVs. However, extensive research is notably absent in hydrodynamic modelling for large-scale FPVs. This lack of research represents a significant gap in our current study on multiscale FPV systems. Full article

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

This study investigates the numerical prediction of the sonic boom phenomenon in supersonic aircraft by evaluating the near-field pressure signatures of three different aeroshapes. Two computational fluid dynamics (CFD) solvers, the open-source SU2 Multiphysics code and ANSYS Fluent, were employed to assess their effectiveness in modeling the aerodynamic flow field. A preliminary validation of numerical methods was conducted against numerical data available from the Sonic Boom Prediction Workshops (SBPW) organized by NASA, ensuring simulation reliability. Particular attention is paid to the topology of the mesh grid, exploring hybrid approaches that combine structured and unstructured grids to optimize the accuracy of pressure wave transmission. In addition, different numerical schemes were analyzed to determine the best practices for sonic boom simulations. The proposed methodology was finally applied to three supersonic aircraft developed within the European project MORE&LESS, demonstrating the capability of the model to estimate shock wave generation, evaluate the aeroacoustic performance of different supersonic aeroshapes from Mach 2 to Mach 5, and provide predictions to support ground-level noise assessment. The findings of this study contribute to the definition of a comprehensive workflow for sonic boom evaluation, providing a reliable methodology for exploring future supersonic aircraft designs. Full article

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

As urban buildings become increasingly dense, indoor personnel are often exposed to noise disturbances from adjoining rooms which can reduce working efficiency and affect mental health. Closing the door is one of the ways to reduce noise transmission, but it can cause a decrease in indoor air circulation. This paper investigates the sound insulation effect and air ventilation performance of a door in a partially open state by numerical simulation. To acquire the effect of sound insulation, an acoustic–structural solver is employed to calculate the sound transmission losses with different door opening angles in the frequency domain. To evaluate the ventilation performance, the mass flow rates across door opening are calculated by computational fluid dynamics. The simulation results confirm the trade-off relation between the sound insulation effect and the ventilation performance. To calculate the effect of noise and ventilation on work efficiency, a comprehensive evaluation index workplace environmental score (WES) was introduced and calculated by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. A clear sound insulation effect corresponds to an opening angle (θ_d) of less than 15° with minimum air ventilation. Good ventilation performance could be obtained when the door opening angle is larger than 45°, while the sound insulation effect is negligible. A good compromise between the sound insulation effect and the air ventilation performance is found to be in the range of θ_d = 15°~25°, which provides practical recommendations in daily routines. Full article