scholarly journals Measured and Predicted Turbulent Kinetic Energy in Flow Through Emergent Vegetation With Real Plant Morphology

2020 ◽  
Vol 56 (12) ◽  
Author(s):  
Yuan Xu ◽  
Heidi Nepf
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Plant Morphology ◽  
Emergent Vegetation ◽  
Flow Through ◽  
Real Plant

CHARACTERISTICS OF FLOW THROUGH RIGID, EMERGENT AND SPARSE VEGETATION

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Suraya Sharil ◽  
Wan Hanna Melini Wan Mohtar ◽  
Siti Fatin Mohd Razali
Keyword(s):  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulence Intensity ◽  
Transverse Velocity ◽  
Flow Region ◽  
Vegetation Density ◽  
Flow Depth ◽  
Flow Through ◽  
Fast Flow

This paper looks into the flow profiles in terms of longitudinal and transverse velocities, turbulence intensity and turbulent kinetic energy in relation to the vegetation density, flow depth and stem Reynolds number. An experimental study was conducted in a fully vegetated flume, whereby a control volume was selected for detailed velocity measurement using Acoustic Doppler Velocimeter (ADV). This research considered 0.97%, 3.90% and 7.80% vegetation density or solid volume fractions (SVF) which are categorised as sparse in the lab work. Series of experiments were conducted in uniform flow condition with stem Reynolds number, Red ranging between 1300 and 3000. Experimental results managed to capture the wake area (velocity deficit; < 1) and fast flow region (velocity enhance; > 1). The boundary between the wake area and fast flow region is reflected by the highest magnitude of the normalised longitudinal turbulence intensity and turbulent kinetic energy. Positive normalised transverse velocity represents the flow diversion away from the vegetation and the negative normalised transverse velocity indicates flux towards the centre of the wake. Both turbulence intensity and turbulent kinetic energy display no observable relation with the flow depth. This is probably because the characteristic length for turbulent flow through vegetation is the stem diameter.  

Data Assimilation of Steam Flow Through a Control Valve Using Ensemble Kalman Filter

2021 ◽  
Author(s):  
Peixun Fang ◽  
Chuangxin He ◽  
Peng Wang ◽  
Sihua Xu ◽  
YingZheng Liu
Keyword(s):  
Kalman Filter ◽  
Kinetic Energy ◽  
Data Assimilation ◽  
Turbulent Kinetic Energy ◽  
Flow Rate ◽  
Ensemble Kalman Filter ◽  
Control Valve ◽  
Operating Conditions ◽  
Constant Matrix ◽  
Flow Through

Abstract The present work concentrates on the simulation enhancement of steam flow through a control valve using novel data assimilation (DA) approach. Ensemble Kalman filter (EnKF) is applied to improve the performance of k-? shear stress transport (SST) model by optimizing its turbulence model constants. The selected measurement data at different operating conditions are used as observation, while the rest data are involved for validation. Firstly, four flow patterns, which arise on their respective operating conditions, are identified and analyzed to illustrate the basic characteristics of flow in the control valve. Then DA is performed based on the sample computation by perturbing the model constants and the EnKF process to determine the optimal constant matrix. This optimized constant matrix is subsequently used for the precomputation of the valve flow with significant improvement on the flow rate prediction. The velocity and turbulent kinetic energy fields with default and optimal model constants are also compared to illustrate the effect of DA. The results show that the DA enhanced model constants can significantly reduce the predicted volume flow rate error at all opening ratios presently concerned. With updated model constants, the velocity and turbulent kinetic energy distributions are greatly modified in the valve seat between main valve and control valve.

scholarly journals Hydraulic Features of Flow Through Local Non- Submerged Rigid Vegetation in the Y-shaped Confluence Channel

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 146 ◽  
Author(s):  
Xuneng Tong ◽  
Xiaodong Liu ◽  
Ting Yang ◽  
Zulin Hua ◽  
Zian Wang ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Secondary Flow ◽  
Turbulent Kinetic Energy ◽  
Flow Structure ◽  
High Velocity ◽  
Internal Flow ◽  
Acoustic Doppler Velocimeter ◽  
Flow Phenomenon ◽  
Rigid Vegetation ◽  
Flow Through

A laboratory measurement with acoustic Doppler velocimeter (ADV) was used to investigate the flow through a Y-shaped confluence channel partially covered with rigid vegetation on its inner bank. In this study, the flow velocities in cases with and without vegetation were measured by the ADV in a Y-shaped confluence channel. The results clearly showed that the existence of non-submerged rigid plants has changed the internal flow structure. The velocity in the non-vegetated area is greater than in the vegetated area. There is a large exchange of mass and momentum between the vegetated and non-vegetated areas. In addition, due to the presence of vegetation, the high-velocity area moved rapidly to the middle of the non-vegetated area in the vicinity of tributaries, and the secondary flow phenomenon disappeared. The presence of vegetation made the flow in non-vegetated areas more intense. The turbulent kinetic energy of the non-vegetated area was smaller than that of the vegetated area.

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.

Sign in / Sign up

Export Citation Format

Share Document