Using RANS Calculations of Turbulent Kinetic Energy to Provide Two Point Flow Velocity Correlations and Surface Pressure Spectra

2012 ◽  
Author(s):  
Stewart A. Glegg
Keyword(s):  
Kinetic Energy ◽  
Flow Velocity ◽  
Turbulent Kinetic Energy ◽  
Surface Pressure

scholarly journals Analysis of Turbulent Flow Structure with Its Fluvial Processes around Mid-Channel Bar

2021 ◽  
Vol 14 (1) ◽  
pp. 392
Author(s):  
Md. Amir Khan ◽  
Nayan Sharma ◽  
Jaan Pu ◽  
Faisal M. Alfaisal ◽  
Shamshad Alam ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Turbulent Flow ◽  
Flow Velocity ◽  
Turbulent Kinetic Energy ◽  
Production Rate ◽  
Energy Transport ◽  
Vertical Distance ◽  
Scour Hole ◽  
The Impact ◽  
Channel Bar

Researchers have recognized that the successive growth of mid-channel bar deposits can be entertained as the raison d’être for the initiation of the braiding process, which is closely interlinked with the growth, decay, and vertical distribution of fluvial turbulent kinetic energy (TKE). Thus, focused analysis on the underlying mechanics of turbulent flow structures in the proximity of a bar deposit occurring in the middle of the channel can afford crucial scientific clues for insight into the initiating fluvial processes that give rise to braiding. In the study reported herein, a physical model of a mid-channel bar is constructed in an experimental flume to analyze the turbulence parameters in a region close to the bar. Notably, the flow velocity plays an important role in understanding the flow behavior in the scour-hole location in the upstream flow divergence zone as well as near the downstream zone of flow convergence in a mid-channel bar. Therefore, the fluctuating components of turbulent flow velocity are herein discussed and analyzed for the regions located close to the bar. In the present study, the impact of the mid-channel bar, as well as its growth in turbulent flow, on higher-order velocity fluctuation moments are investigated. For near-bed locations, the results show the dominance of ejection events in upstream zones and the dominance of sweep events at locations downstream of the mid-channel bar. In scour-hole sections, the negative value of the stream-wise flux of turbulent kinetic energy and the positive value of the vertical flux of turbulent kinetic energy indicate energy transport in downward and forward directions, respectively. The downward and forward energy transport processes lead to scouring at these locations. The maximum turbulent production rate occurs in the wake region of the bar. The high rate of turbulence production has occurred in that region, which can be ascribed to the process of shedding turbulent vortices. The results show that the impact of the presence of the bar is mainly restricted to the lower layers of flow. The turbulent dissipation rate monotonically decreases with an increase in the vertical distance from the bed. The turbulent production rate first increases and then decreases with successive increases in the vertical distance from the bed. The paper concludes with suggestions for the future potential use of the present research for the practical purpose of examining braid bar occurrences in alluvial rivers to develop an appropriate response through training measures.

scholarly journals Effect of Mold Width on the Flow Field in a Slab Continuous-Casting Mold with High-Temperature Velocity Measurement and Numerical Simulation

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1943
Author(s):  
Jian-Qiu Liu ◽  
Jian Yang ◽  
Chao Ma ◽  
Yi Guo ◽  
Wen-Yuan He ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Flow Field ◽  
Flow Velocity ◽  
Turbulent Kinetic Energy ◽  
Flow Rate ◽  
Gas Flow ◽  
Immersion Depth ◽  
Gas Flow Rate ◽  
Argon Gas ◽  
Slag Entrainment

In this paper, the effects of the width of the mold on the surface velocity, flow field pattern, turbulent kinetic energy distribution, and surface-level fluctuation in the mold were studied with measurement of the flow velocity near the surface of the mold at high temperature with the rod deflection method and numerical calculation with the standard k-ε model coupled with the discrete-phase model (DPM) model for automobile exposed panel production. Under the conditions of low fixed steel throughput of 2.2 ton/min, a nozzle immersion depth of 140 mm, and an argon gas flow rate of 4 L/min, as the width of the mold increases from 880 mm to 1050 mm and 1300 mm, the flow velocity near the surface of the mold decreases. The flow direction changes from the positive velocity with the mold widths of 880 mm and 1050 mm to the unstable velocity with the mold width of 1300 mm. The calculated results are in good agreement with the measured results. The turbulent kinetic energy near the submerged entry nozzle (SEN) gradually increases, and the risk of slag entrainment increases. Under the conditions of high fixed steel throughput of 3.5 ton/min, the SEN immersion depth of 160 mm, and the argon gas flow rate of 10 L/min, as the width of the mold increases from 1600 mm to 1800 mm and 2000 mm, the velocity near the mold surface decreases. The flow velocity at 1/4 of the surface of the mold is positive with the mold width of 1600 mm, while the velocities are negative with the widths of 1800 mm and 2000 mm. The calculated results are basically consistent with the measured results. The high turbulent kinetic energy area near the nozzle expands to a narrow wall, and the risk of slag entrainment is significantly increased. In both cases of low and high fixed steel throughput, the change rules of the flow field in the mold with the width are basically the same. The argon gas flow rate and the immersion depth of SEN should be adjusted reasonably to optimize the flow field in the mold with different widths under the same fixed steel throughput in the practical production.

scholarly journals Effect of the Number of Leaves in Submerged Aquatic Plants on Stream Flow Dynamics

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1448
Author(s):  
Peiru Yan ◽  
Yu Tian ◽  
Xiaohui Lei ◽  
Qiang Fu ◽  
Tianxiao Li ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Flow Velocity ◽  
Turbulent Kinetic Energy ◽  
Aquatic Plants ◽  
Roughness Coefficient ◽  
Turbulence Structure ◽  
Momentum Exchange ◽  
Submerged Aquatic Plants ◽  
Number Of Leaves ◽  
Manning’S Roughness Coefficient

The main purpose of this study is to investigate the effects of aquatic plants with no leaves (L0), 4 leaves (L4), 8 leaves (L8), and 12 leaves (L12) on the mean streamwise velocity, turbulence structure, and Manning’s roughness coefficient. The results show that the resistance of submerged aquatic plants to flow velocity is discontinuous between the lower aquatic plant layer and the upper free water layer. This leads to the difference of flow velocity between the upper and lower layers. An increase of the number of leaves leads to an increase in the flow velocity gradient in the upper non-vegetation area and a decrease in the flow velocity in the lower vegetation area. In addition, aquatic plants induce a momentum exchange near the top of the plant and increase the Reynold’s stress and turbulent kinetic energy. However, because of the inhibition of leaf area on the momentum exchange, the Reynold’s stress and turbulent kinetic energy increase first and then decrease with the increase in the number of leaves. Quadrant analysis shows that ejection and sweep play a dominant role in momentum exchange. Aquatic plants can also increase the Reynold’s stress by increasing the ejection and sweep. The Manning’s roughness coefficient increases with the increasing number of leaves.

Probability law of turbulent kinetic energy in the atmospheric surface layer

2021 ◽  
Vol 6 (7) ◽  
Author(s):  
Mohammad Allouche ◽  
Gabriel G. Katul ◽  
Jose D. Fuentes ◽  
Elie Bou-Zeid
Keyword(s):  
Surface Layer ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Atmospheric Surface Layer

scholarly journals A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4136
Author(s):  
Clemens Gößnitzer ◽  
Shawn Givler
Keyword(s):  
Kinetic Energy ◽  
Flow Field ◽  
Turbulent Kinetic Energy ◽  
Negative Impact ◽  
Operating Conditions ◽  
Engine Operation ◽  
Gas Engine ◽  
Cycle Simulation ◽  
Simulation Results ◽  
The Impact

Cycle-to-cycle variations (CCV) in spark-ignited (SI) engines impose performance limitations and in the extreme limit can lead to very strong, potentially damaging cycles. Thus, CCV force sub-optimal engine operating conditions. A deeper understanding of CCV is key to enabling control strategies, improving engine design and reducing the negative impact of CCV on engine operation. This paper presents a new simulation strategy which allows investigation of the impact of individual physical quantities (e.g., flow field or turbulence quantities) on CCV separately. As a first step, multi-cycle unsteady Reynolds-averaged Navier–Stokes (uRANS) computational fluid dynamics (CFD) simulations of a spark-ignited natural gas engine are performed. For each cycle, simulation results just prior to each spark timing are taken. Next, simulation results from different cycles are combined: one quantity, e.g., the flow field, is extracted from a snapshot of one given cycle, and all other quantities are taken from a snapshot from a different cycle. Such a combination yields a new snapshot. With the combined snapshot, the simulation is continued until the end of combustion. The results obtained with combined snapshots show that the velocity field seems to have the highest impact on CCV. Turbulence intensity, quantified by the turbulent kinetic energy and turbulent kinetic energy dissipation rate, has a similar value for all snapshots. Thus, their impact on CCV is small compared to the flow field. This novel methodology is very flexible and allows investigation of the sources of CCV which have been difficult to investigate in the past.

scholarly journals Spatiotemporal Dynamics of the Kinetic Energy in the Atmospheric Boundary Layer from Minisodar Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 421
Author(s):  
Alexander Potekaev ◽  
Liudmila Shamanaeva ◽  
Valentina Kulagina
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Atmospheric Boundary Layer ◽  
Turbulent Kinetic Energy ◽  
Time Of Day ◽  
Linear Increase ◽  
Spatiotemporal Dynamics ◽  
Intermediate Layers ◽  
Subsequent Decrease ◽  
Stable Atmospheric Boundary Layer

Spatiotemporal dynamics of the atmospheric kinetic energy and its components caused by the ordered and turbulent motions of air masses are estimated from minisodar measurements of three velocity vector components and their variances within the lowest 5–200 m layer of the atmosphere, with a particular emphasis on the turbulent kinetic energy. The layered structure of the total atmospheric kinetic energy has been established. From the diurnal hourly dynamics of the altitude profiles of the turbulent kinetic energy (TKE) retrieved from minisodar data, four layers are established by the character of the altitude TKE dependence, namely, the near-ground layer, the surface layer, the layer with a linear TKE increase, and the transitive layer above. In the first layer, the most significant changes of the TKE were observed in the evening hours. In the second layer, no significant changes in the TKE values were observed. A linear increase in the TKE values with altitude was observed in the third layer. In the fourth layer, the TKE slightly increased with altitude and exhibited variations during the entire observation period. The altitudes of the upper boundaries of these layers depended on the time of day. The MKE values were much less than the corresponding TKE values, they did not exceed 50 m2/s2. From two to four MKE layers were distinguished based on the character of its altitude dependence. The two-layer structures were observed in the evening and at night (under conditions of the stable atmospheric boundary layer). In the morning and daytime, the four-layer MKE structures with intermediate layers of linear increase and subsequent decrease in the MKE values were observed. Our estimates demonstrated that the TKE contribution to the total atmospheric kinetic energy considerably (by a factor of 2.5–3) exceeded the corresponding MKE contribution.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

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