scholarly journals A new mean-field plasma edge transport model based on turbulent kinetic energy and enstrophy

2020 ◽  
Vol 60 (5-6) ◽  
pp. e201900156 ◽  
Author(s):  
Reinart Coosemans ◽  
Wouter Dekeyser ◽  
Martine Baelmans
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Transport Model ◽  
Mean Field ◽  
Plasma Edge ◽  
Model Based ◽  
Plasma Edge Transport

Development and Validation of a Porous Medium Computational Fluid Dynamics Model for a Jet

2008 ◽  
Author(s):  
Timothy J. Drzewiecki ◽  
Brian L. Mount ◽  
Martin Lopez de Bertodano
Keyword(s):  
Porous Medium ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Transport Model ◽  
Constitutive Relations ◽  
Symmetric Domain ◽  
Jet Mixing ◽  
Negative Reactivity ◽  
An Array Of Cylinders ◽  
Development And Validation

The fast boron shutdown injection in a PHWR consists of a jet flowing through a very large moderator tank that contains an array of cylindrical coolant channels. The accurate prediction of the turbulent jet mixing is required to determine an accurate distribution of boron inside the moderator tank to model the insertion of negative reactivity into the reactor during fast shutdown. A CFD code is used to determine the distribution of boron in the moderator tank. The flow is analyzed with a porous medium model based on volume averaged momentum, turbulent kinetic energy, and turbulence dissipation equations. The additional source terms arise due to the averaging must be constituted. The constitutive relations that are implemented in the present model are: (i) the drag force on an array of cylinders for the momentum equations and (ii) the additional mixing effect of the cylinders which results in the sources of turbulent kinetic energy and turbulence dissipation transport model. The CFD analysis is performed on a porous, axis symmetric domain. The CFD results are finally compared with data for the boron concentration distribution obtained in a scaled geometrically similar experiment, demonstrating the validity of the approach.

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.

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