Large-eddy simulation（LES） was originally proposed for simulating atmospheric flows in the 1960 s and has become one of the most promising and successful methodology for simulating turbulent flows ...with the improvement of computing power. It is now feasible to simulate complex engineering flows using LES. However, apart from the computing power, significant challenges still remain for LES to reach a level of maturity that brings this approach to the mainstream of engineering and industrial computations. This paper will describe briefly LES formalism first, present a quick glance at its history, review its current state focusing mainly on its applications in transitional flows and gas turbine combustor flows, discuss some major modelling and numerical challenges/issues that we are facing now and in the near future, and finish with the concluding remarks.
Transition from laminar flow to turbulent flow is of great practical interest as it occurs in many engineering flows and often plays a critical role in aerodynamics and heat transfer performance of ...those flow devices. There could be many routes through transition, depending on flow configuration, geometry and the way in which transition is initiated by a wide range of possible background disturbances such as free-stream turbulence, pressure gradient, acoustic noise, wall roughness and obstructions, periodic unsteady disturbance and so on. This paper presents a brief overview of wall bounded flow transition in general and focuses more on the transition process in the free shear layer of separation bubbles, demonstrating that at elevated free-stream turbulent intensity the so called bypass transition could occur in geometrically induced separation bubbles where the separation point is fixed.
In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simulations can be carried out to investigate the ...airflow and heat transfer taking place inside a neonatal intensive care unit (NICU). In this present study, the NICU is modeled based on the realistic dimensions of a single-patient room in compliance with the appropriate square footage allocated per incubator. The physics of flow in NICU is predicted based on the Navier-Stokes conservation equations for an incompressible flow, according to suitable thermophysical characteristics of the climate. The results show sensible flow structures and heat transfer as expected from any indoor climate with this configuration. Furthermore, machine learning (ML) in an artificial intelligence (AI) model has been adopted to take the important geometric parameter values as input from our CFD settings. The model provides accurate predictions of the thermal performance (i.e., temperature evaluation) associated with that design in real time. Besides the geometric parameters, there are three thermophysical variables of interest: the mass flow rate (i.e., inlet velocity), the heat flux of the radiator (i.e., heat source), and the temperature gradient caused by the convection. These thermophysical variables have significantly recovered the physics of convective flows and enhanced the heat transfer throughout the incubator. Importantly, the AI model is not only trained to improve the turbulence modeling but also to capture the large temperature gradient occurring between the infant and surrounding air. These physics-informed (Pi) computing insights make the AI model more general by reproducing the flow of fluid and heat transfer with high levels of numerical accuracy. It can be concluded that AI can aid in dealing with large datasets such as those produced in NICU, and in turn, ML can identify patterns in data and help with the sensor readings in health care.
Two-dimensional and three-dimensional computational fluid dynamics studies of a spherical bubble impacted by a supersonic shock wave (Mach 1.25) have been performed to fully understand the complex ...process involved in shock–bubble interaction (SBI). The unsteady Reynolds-averaged Navier–Stokes computational approach with a coupled level set and volume of fluid method has been employed in the present study. The predicted velocities of refracted wave, transmitted wave, upstream interface, downstream interface, jet, and vortex ring agree very well with the relevant available experimental data. The predicted non-dimensional bubble and vortex velocities are also in much better agreement with the experiment data than values computed from a simple model of shock-induced Rayleigh–Taylor instability (the Richtmyer–Meshkov instability). Comprehensive flow visualization has been presented and analyzed to elucidate the SBI process from the beginning of bubble compression (continuous reflection and refraction of the acoustic wave fronts as well as the location of the incident, refracted and transmitted waves at the bubble compression stage) up to the formation of vortex rings as well as the production and distribution of vorticity. Furthermore, it is demonstrated that turbulence is generated with some small flow structures formed and more intensive mixing, i.e., turbulent mixing of helium with air starts to develop at the later stage of SBI.
The aerodynamic efficiency of trucks is very low because of their non-streamlined box shape, which is subject to practical constraints, leaving little room for improvement in terms of aerodynamic ...efficiency. Hence, other means of improving the aerodynamic efficiency of trucks are needed, and one practical yet relatively simple method to reduce aerodynamic drag is deploying drag reduction devices on trucks. This paper describes a numerical study of flow over a simplified truck with drag reduction devices. The numerical approach employed was Reynolds-averaged Navier–Stokes (RANS). Four test cases with different drag reduction devices deployed around the tractor–trailer gap region were studied. The effectiveness of those drag reduction devices was assessed, and it was demonstrated that in all four cases, the aerodynamic drag was reduced compared with the baseline case without any drag reduction devices. The most effective device was case 4 (about 24% reduction), with a roof deflector, side extenders, and five cross-flow vortex trap devices (CVTDs). Flow field analysis was performed to shed light on drag reduction mechanisms, which confirmed our previous findings that the main reason for the drag reduction was the reduced pressure on the front face of the trailer, while the reduction in the turbulence level in the tractor–trailer gap region contributed much less to the overall drag reduction.
Aimed at the problem of large frequency deviation caused by the source load uncertainty and the communication delay in the interconnected power system, a frequency control strategy for interconnected ...power systems with time-delay considering energy storage regulation is proposed. An interconnected power grid model with time delay which includes a steam turbine generator, a wind turbine generator, and energy storage equipment is established. According to the area control error (ACE), the energy storage device coordinates the steam turbine generator to participate in the frequency control, and the modified particle swarm optimization (MPSO) algorithm is used to optimize the proportional integral derivative (PID) load frequency controller to realize the secondary frequency adjustment, which improves the frequency stability of the load frequency control (LFC) system in a certain time-delay interval. A fractional order PID (FOPID) controller is designed for the energy storage device to adjust the output power and
Three-dimensional (3D) computational fluid dynamics (CFD) simulations have been carried out to investigate the complex interaction of a planar shock wave (Ma = 1.22) with a cylindrical bubble. The ...unsteady Reynolds-averaged Navier–Stokes (URANS) approach with a level set coupled with volume of fluid (LSVOF) method has been applied in the present study. The predicted velocities of refracted wave, transmitted wave, upstream interface, downstream interface, jet, and vortex filaments are in very good agreement with the experimental data. The predicted non-dimensional bubble and vortex velocities also have great concordance with the experimental data compared with a simple model of shock-induced Rayleigh–Taylor instability (i.e., Richtmyer–Meshkov instability) and other theoretical models. The simulated changes in the bubble shape and size (length and width) against time agree very well with the experimental results. Comprehensive flow analysis has shown the shock–bubble interaction (SBI) process clearly from the onset of bubble compression up to the formation of vortex filaments, especially elucidating the mechanism on the air–jet formation and its development. It is demonstrated for the first time that turbulence is generated at the early phase of the shock cylindrical bubble interaction process, with the maximum turbulence intensity reaching about 20% around the vortex filament regions at the later phase of the interaction process.
•An optimal ML model was identified to predict fuel consumptions in diesel engines.•Various parameters were determined to reflect fuel consumptions using sensitivity analysis.•Different ML models ...were performed to consider the complexity of fuel consumption prediction.•ML models demonstrated good prediction performance for both flow and heat transfer characteristics of fuel combustion.
This paper describes the utilization of artificial intelligence (AI) techniques to identify an optimal machine learning (ML) model for predicting dodecane fuel consumption in diesel combustion. The study incorporates sensitivity analysis to assess the impact levels of various parameters on fuel consumption, thereby highlighting the most influential factors. In addition, this study addresses the impact of noise and implements data cleaning techniques to ensure the reliability of the obtained results. To validate the accuracy of the predictions, the study performs several metrics and validation process, including comparisons with computational fluid dynamics (CFD) results and experimental data. Comprehensive comparisons are made among neural networks (NN), random forest regression (RFR), and Gaussian process regression (GPR) models, taking into account the complexity associated with fuel consumption predictions. The findings demonstrate that the GPR model outperforms the others in terms of accuracy, as evidenced by metrics such as mean absolute error (MAE), mean squared error (MSE), Pearson coefficient (PC), and R-squared (R2). The GPR model exhibits superior predictive ability, accurately detecting and predicting even individual data points that deviate from the overall trend. The significantly lower absolute error values also consistently indicate its higher accuracy compared with the NN and RFR models. Furthermore, the GPR model shows a remarkable speedup, approximately 1.7 times faster than traditional CFD solvers, and physically captures the momentum and thermal characteristics in a surface field prediction. Finally, the target optimization is assessed using the Euclidean distance as a fitness function, ensuring the reliability of predicted data.
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A complex flow field is created when a vertical/short take-off and landing aircraft is operating near ground. One major concern for this kind of aircraft in ground effect is the possibility of ...ingestion of hot gases from the jet engine exhausts back into the engine, known as hot gas ingestion, which can increase the intake air temperature and also reduce the oxygen content in the intake air, potentially leading to compressor stall, low combustion efficiency and causing a dramatic loss of lift. This flow field can be represented by the configuration of twin impinging jets in a cross-flow. Accurate prediction of this complicated flow field under the Reynolds averaged Navier-Stokes (RANS) approach (current practise in industry) is a great challenge as previous studies suggest that some important flow features cannot be captured by the Steady-RANS (SRANS) approach even with a second-order Reynolds stress model (RSM). This paper presents a numerical study of this flow using the Unsteady-RANS (URANS) approach with a RSM and the results clearly indicate that the URANS approach is superior than the SRANS approach but still the predictions of Reynolds stress are not accurate enough.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
To understand how the long connections of a brain functional network come from the short connections of its corresponding structural network, remote synchronization (RS) was recently studied in star ...graph networks. However, the motif of the star graph cannot completely characterize the features of brain networks as the leaf nodes of a star graph may also be connected to each other to some extent in real brain networks. Especially, the dynamics of a star motif in a brain network will be seriously influenced by its surrounding nodes, i.e., other parts of the brain network. To study RS of real brain networks, we here present a model of star-like networks by considering both the partial connections among leaf nodes and the influence of other parts of the brain network. We find that RS will not appear in all leaf nodes and instead appears only in the group of indirectly connected leaf nodes when the frequency difference between the hub and leaf nodes is not large enough, resulting in the concept of partial RS (PRS). Further, we find that the partial connections among leaf nodes favor PRS, implying that PRS can more easily appear in real brain networks than RS and thus provides a different way to understand the mechanism of long connections in brain functional networks. Moreover, we find another kind of PRS, i.e., double PRS, and discuss the dependence of PRS on system parameters. Finally, a brief theoretical analysis is provided to explain the results.
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