scholarly journals Can vertical mixing from turbulent kinetic energy mitigate coral bleaching? An application of high frequency ocean radar

2010 ◽  
Author(s):  
Diane D. DiMassa ◽  
Malcolm L. Heron ◽  
Alessandra Mantovanelli ◽  
Scott F. Heron ◽  
Craig Steinberg
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Coral Bleaching ◽  
High Frequency ◽  
Vertical Mixing ◽  
Ocean Radar

scholarly journals Turbulence and hypoxia contribute to dense biological scattering layers in a Patagonian fjord system

Ocean Science ◽  
2018 ◽  
Vol 14 (5) ◽  
pp. 1185-1206 ◽  
Author(s):  
Iván Pérez-Santos ◽  
Leonardo Castro ◽  
Lauren Ross ◽  
Edwin Niklitschek ◽  
Nicolás Mayorga ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Water Column ◽  
Turbulent Kinetic Energy ◽  
Tidal Flow ◽  
Vertical Mixing ◽  
Acoustic Doppler Current Profiler ◽  
Marine Species ◽  
Eddy Diffusivity ◽  
Diel Vertical Migrations

Abstract. The aggregation of plankton species along fjords can be linked to physical properties and processes such as stratification, turbulence and oxygen concentration. The goal of this study is to determine how water column properties and turbulent mixing affect the horizontal and vertical distributions of macrozooplankton along the only northern Patagonian fjord known to date, where hypoxic conditions occur in the water column. Acoustic Doppler current profiler moorings, scientific echo-sounder transects and in situ plankton abundance measurements were used to study macrozooplankton assemblages and migration patterns along Puyuhuapi Fjord and Jacaf Channel in Chilean Patagonia. The dissipation of turbulent kinetic energy was quantified through vertical microstructure profiles collected throughout time in areas with high macrozooplankton concentrations. The acoustic records and in situ macrozooplankton data revealed diel vertical migrations (DVM) of siphonophores, chaetognaths and euphausiids. In particular, a dense biological backscattering layer was observed along Puyuhuapi Fjord between the surface and the top of the hypoxic boundary layer (∼100 m), which limited the vertical distribution of most macrozooplankton and their DVM, generating a significant reduction of habitat. Aggregations of macrozooplankton and fishes were most abundant around a submarine sill in Jacaf Channel. In this location macrozooplankton were distributed throughout the water column (0 to ∼200 m), with no evidence of a hypoxic boundary due to the intense mixing near the sill. In particular, turbulence measurements taken near the sill indicated high dissipation rates of turbulent kinetic energy (ε∼10-5 W kg−1) and vertical diapycnal eddy diffusivity (Kρ∼10-3 m2 s−1). The elevated vertical mixing ensures that the water column is well oxygenated (3–6 mL L−1, 60 %–80 % saturation), creating a suitable environment for macrozooplankton and fish aggregations. Turbulence induced by tidal flow over the sill apparently enhances the interchange of nutrients and oxygen concentrations with the surface layer, creating a productive environment for many marine species, where the prey–predator relationship might be favored.

scholarly journals Measurements of Enhanced Turbulent Mixing near Highways

2012 ◽  
Vol 51 (9) ◽  
pp. 1618-1632 ◽  
Author(s):  
Mark Gordon ◽  
Ralf M. Staebler ◽  
John Liggio ◽  
Paul Makar ◽  
Shao-Meng Li ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Traffic Flow ◽  
Power Law ◽  
Vertical Mixing ◽  
Weather Forecast ◽  
Early Morning ◽  
Highway Traffic ◽  
Vertical Diffusion ◽  
Global Environmental

AbstractIn August and September of 2010, measurements of turbulent fluxes and turbulent kinetic energy were made on highways in the Toronto area (Ontario, Canada). In situ turbulence measurements were made with a mobile laboratory while driving on the highway with traffic. Results demonstrate that the turbulent kinetic energy (TKE) spectrum is significantly enhanced on and near the highway by traffic for frequencies above 0.015 Hz. The decay of TKE with distance behind vehicles is well approximated by power-law curves. The strongest increase in TKE is seen while following heavy-duty trucks, primarily for frequencies above 0.7 Hz. From these results, a parameterization of on-road TKE enhancement is developed that is based on vehicle type and traffic-flow rate. TKE with distance downwind of the highway also decays following a power law. The enhancement of roadside TKE is shown to be strongly dependent on traffic flow. The effect of vehicle-induced turbulence on vertical mixing was studied by comparing parameterized TKE enhancement with the typical TKE predictions from the Global Environmental Multiscale weather forecast to predict the potential increase in vertical diffusion that results from highway traffic. It is demonstrated that this increase in TKE by traffic may be locally significant, especially in the early morning.

scholarly journals Scaling of Turbulent Spectra Measured in Mobile Bed Flows and Estimation of Turbulent Kinetic Energy Budgets

2019 ◽  
Vol 8 (2) ◽  
pp. 291-298
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
High Frequency ◽  
Scaling Law ◽  
Power Spectra ◽  
Experimental Investigations ◽  
Bed Load ◽  
Acoustic Frequency ◽  
Load Transport ◽  
Bed Load Transport

This study aims at quantifying experimentally the influence of bed load transport on velocity power spectrum of acoustic Doppler Velocimeter (ADV) and turbulent kinetic energy budget. Hydraulic flume was used for experimental investigations for bed load transport. Uniform size of gravels (d50 = 4 mm) were injected into the flows without bed-forms development and compared with those in clear water flows. A 5 cm down looking Vectrino+ of 10 MHz acoustic frequency was used to measure the velocity of flows. The velocity signals produced spikes in the Vectrino+ data on account of instrument noise and high-frequency fluctuations. Therefore, de-spiking and removal of contaminated data were very much essential to obtain clear velocity power spectra for the ADV data sets. The velocity power spectra of filtered dataset followed the Kolmogorov “–5/3 scaling-law” in the inertial frequency range. At low frequencies, another scaling regime with spectral slope of about −1.0 is found resulting a shifting of turbulent power production subrange towards the high frequency regime and it is interpreted as the signature of sediment mobility without bed form development. Interestingly, the bed lad transport reduced the TKE dissipation rate and sharply changed the pressure energy diffusion rate which corroborated a gain in turbulence production.

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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