Near-surface turbulence and buoyancy induced by heavy rainfall

2017 ◽  
Vol 830 ◽  
pp. 602-630 ◽  
Author(s):  
E. L. Harrison ◽  
F. Veron
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Energy Flux ◽  
Terminal Velocity ◽  
Near Surface ◽  
Wave Channel ◽  
Surface Turbulence ◽  
Rain Drop ◽  
The Impact

We present results from experiments designed to measure near-surface turbulence generated by rainfall. Laboratory experiments were performed using artificial rain falling at near-terminal velocity in a wind–wave channel filled with synthetic seawater. In this first series of experiments, no wind was generated and the receiving seawater was initially at rest. Rainfall rates from 40 to $190~\text{mm}~\text{h}^{-1}$ were investigated. Subsurface turbulent velocities of the order of $O(10^{-2})~\text{m}~\text{s}^{-1}$ are generated near the interface below the depth of the cavities generated by the rain drop impacts. The turbulence appears independent of rainfall rates. At depth larger than the size of the cavities, the turbulent velocity fluctuations decay as $z^{-3/2}$. Turbulent length scales also appear to scale with the size of the impact cavities. In these seawater experiments, a freshwater lens is established at the water surface due to the rain. At the highest rain rate studied, the resulting buoyancy flux appears to lead to a shallower subsurface mixed layer and a slight decrease of the turbulent kinetic energy dissipation. Finally, direct measurements and inertial estimates of the turbulent kinetic energy dissipation show that approximately 0.1–0.3 % of the kinetic energy flux from the rain is dissipated in the form of turbulence. This is consistent with existing freshwater measurements and suggests that high levels of dissipation occur at depths and scales smaller than those resolved here and/or that other phenomena dissipate a considerable amount of the total kinetic energy flux provided by rainfall.

Estimating turbulent kinetic energy dissipation rate using microstructure data from the ship-towed Surface Salinity Profiler

2020 ◽  
Author(s):  
Suneil Iyer ◽  
Kyla Drushka ◽  
Luc Rainville
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Spatial Scales ◽  
Energy Dissipation Rate ◽  
Upper Ocean ◽  
Surface Salinity ◽  
Near Surface ◽  
Surface Turbulence

AbstractAs part of the second Salinity Processes in the Upper Ocean Regional Study (SPURS-2), the ship-towed Surface Salinity Profiler (SSP) was used to measure near-surface turbulence and stratification on horizontal spatial scales of tens of kilometers over time scales of hours, resolving structures outside the observational capabilities of autonomous or Lagrangian platforms. Observations of microstructure variability of temperature were made at approximately 37 cm depth from the SSP. The platform can be used to measure turbulent kinetic energy dissipation rate when the upper ocean is sufficiently stratified by calculating temperature gradient spectra from the microstructure data and fitting to low wavenumber theoretical Batchelor spectra. Observations of dissipation rate made across a range of wind speeds under 12 m s−1 were consistent with the results of previous studies of near-surface turbulence and with existing turbulence scalings. Microstructure sensors mounted on the SSP can be used to investigate the spatial structure of near-surface turbulence. This provides a new means to study the connections between near-surface turbulence and the larger scale distributions of heat and salt in the near-surface layer of the ocean.

scholarly journals Diurnal Dynamics of the Umov Kinetic Energy Density Vector in the Atmospheric Boundary Layer from Minisodar Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1347
Author(s):  
Alexander Potekaev ◽  
Nikolay Krasnenko ◽  
Liudmila Shamanaeva
Keyword(s):  
Energy Density ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Energy Flux ◽  
Wind Effect ◽  
Kinetic Energy Density ◽  
Near Surface ◽  
Density Vector ◽  
The Mean ◽  
Umov Vector

The diurnal hourly dynamics of the kinetic energy flux density vector, called the Umov vector, and the mean and turbulent components of the kinetic energy are estimated from minisodar measurements of wind vector components and their variances in the lower 200-meter layer of the atmosphere. During a 24-hour period of continuous minisodar observations, it was established that the mean kinetic energy density dominated in the surface atmospheric layer at altitudes below ~50 m. At altitudes from 50 to 100 m, the relative contributions of the mean and turbulent wind kinetic energy densities depended on the time of the day and the sounding altitude. At altitudes below 100 m, the contribution of the turbulent kinetic energy component is small, and the ratio of the turbulent to mean wind kinetic energy components was in the range 0.01–10. At altitudes above 100 m, the turbulent kinetic energy density sharply increased, and the ratio reached its maximum equal to 100–1000 at altitudes of 150–200 m. A particular importance of the direction and magnitude of the wind effect, that is, of the direction and magnitude of the Umov vector at different altitudes was established. The diurnal behavior of the Umov vector depended both on the time of the day and the sounding altitude. Three layers were clearly distinguished: a near-surface layer at altitudes of 5–15 m, an intermediate layer at altitudes from 15 m to 150 m, and the layer of enhanced turbulence above. The feasibility is illustrated of detecting times and altitudes of maximal and minimal wing kinetic energy flux densities, that is, time periods and altitude ranges most and least favorable for flights of unmanned aerial vehicles. The proposed novel method of determining the spatiotemporal dynamics of the Umov vector from minisodar measurements can also be used to estimate the effect of wind on high-rise buildings and the energy potential of wind turbines.

scholarly journals A Statistical Evaluation of WRF-LES Trace Gas Dispersion Using Project Prairie Grass Measurements

2021 ◽  
Author(s):  
Alex Rybchuk ◽  
Caroline B. Alden ◽  
Julie K. Lundquist ◽  
Gregory B. Rieker
Keyword(s):  
Kinetic Energy ◽  
Natural Gas ◽  
Turbulent Kinetic Energy ◽  
Similarity Theory ◽  
Trace Gas ◽  
Near Surface ◽  
Gas Dispersion ◽  
Prairie Grass ◽  
Strong Convection ◽  
The Impact

AbstractIn recent years, new measurement systems have been deployed to monitor and quantify methane emissions from the natural gas sector. Large-eddy simulation (LES) has complemented measurement campaigns by serving as a controlled environment in which to study plume dynamics and sampling strategies. However, with few comparisons to controlled-release experiments, the accuracy of LES for modeling natural gas emissions is poorly characterized. In this paper, we evaluate LES from the Weather Research and Forecasting (WRF) model against Project Prairie Grass campaign measurements and surface layer similarity theory. Using WRF-LES, we simulate continuous emissions from 30 near-surface trace gas sources in two stability regimes: strong and weak convection. We examine the impact of grid resolutions ranging from 6.25 m to 52 m in the horizontal dimension on model results. We evaluate performance in a statistical framework, calculating fractional bias and conducting Welch’s t-tests. WRF-LES accurately simulates observed surface concentrations at 100 m and beyond under strong convection; simulated concentrations pass t-tests in this region irrespective of grid resolution. However, in weakly convective conditions with strong winds, WRF-LES substantially overpredicts concentrations – the magnitude of fractional bias often exceeds 30%, and all but one C-test fails. The good performance of WRF-LES under strong convection correlates with agreement with local free convection theory and a minimal amount of parameterized turbulent kinetic energy. The poor performance under weak convection corresponds to misalignment with Monin-Obukhov similarity theory and a significant amount of parameterized turbulent kinetic energy.

scholarly journals A Numerical Study of the Effect of Dissipative Heating on Tropical Cyclone Intensity

2007 ◽  
Vol 22 (5) ◽  
pp. 950-966 ◽  
Author(s):  
Yi Jin ◽  
William T. Thompson ◽  
Shouping Wang ◽  
Chi-Sann Liou
Keyword(s):  
Energy Dissipation ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Mesoscale Model ◽  
Dissipative Heating ◽  
Northwest Pacific ◽  
Forecast Errors ◽  
Maximum Wind ◽  
The Impact

Abstract The impact of dissipative heating on tropical cyclone (TC) intensity forecasts is investigated using the U.S. Navy’s operational mesoscale model (the Coupled Ocean/Atmosphere Mesoscale Prediction System). A physically consistent method of including dissipative heating is developed based on turbulent kinetic energy dissipation to ensure energy conservation. Mean absolute forecast errors of track and surface maximum winds are calculated for eighteen 48-h simulations of 10 selected TC cases over both the Atlantic basin and the northwest Pacific. Simulation results suggest that the inclusion of dissipative heating improves surface maximum wind forecasts by 10%–20% at 15-km resolution, while it has little impact on the track forecasts. The resultant improvement from the inclusion of the dissipative heating increases to 29% for the surface maximum winds at 5-km resolution for Hurricane Isabel (2003), where dissipative heating produces an unstable layer at low levels and warms a deep layer of the troposphere. While previous studies depicted a 65 m s−1 threshold for the dissipative heating to impact the TC intensity, it is found that dissipative heating has an effect on the TC intensity when the TC is of moderate strength with the surface maximum wind speed at 45 m s−1. Sensitivity tests reveal that there is significant nonlinear interaction between the dissipative heating from the surface friction and that from the turbulent kinetic energy dissipation in the interior atmosphere. A conceptualized description is given for the positive feedback mechanism between the two processes. The results presented here suggest that it is necessary to include both processes in a mesoscale model to better forecast the TC structure and intensity.

scholarly journals Wave-turbulence scaling in the ocean mixed layer

Ocean Science ◽  
2013 ◽  
Vol 9 (4) ◽  
pp. 597-608 ◽  
Author(s):  
G. Sutherland ◽  
B. Ward ◽  
K. H. Christensen
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Energy Dissipation Rate ◽  
Wall Layer ◽  
Near Surface ◽  
Microstructure Measurements ◽  
Oceanic Boundary Layer

Abstract. Microstructure measurements were collected using an autonomous freely rising profiler under a variety of different atmospheric forcing and sea states in the open ocean. Here, profiles of turbulent kinetic energy dissipation rate, ε, are compared with various proposed scalings. In the oceanic boundary layer, the depth dependence of ε was found to be largely consistent with that expected for a shear-driven wall layer. This is in contrast with many recent studies which suggest higher rates of turbulent kinetic energy dissipation in the near surface of the ocean. However, some dissipation profiles appeared to scale with the sum of the wind and swell generated Stokes shear with this scaling extending beyond the mixed layer depth. Integrating ε in the mixed layer yielded results that 1% of the wind power referenced to 10 m is being dissipated here.

scholarly journals Measuring Turbulent Kinetic Energy Dissipation at a Wavy Sea Surface

2015 ◽  
Vol 32 (8) ◽  
pp. 1498-1514 ◽  
Author(s):  
Peter Sutherland ◽  
W. Kendall Melville
Keyword(s):  
Surface Wave ◽  
Energy Dissipation ◽  
Kinetic Energy ◽  
Wave Field ◽  
Turbulent Kinetic Energy ◽  
Velocity Fields ◽  
Sea Surface ◽  
Stereo Pair ◽  
Near Surface ◽  
Surface Wave Field

AbstractWave breaking is thought to be the dominant mechanism for energy loss by the surface wave field. Breaking results in energetic and highly turbulent velocity fields, concentrated within approximately one wave height of the surface. To make meaningful estimates of wave energy dissipation in the upper ocean, it is then necessary to make accurate measurements of turbulent kinetic energy (TKE) dissipation very near the surface. However, the surface wave field makes measurements of turbulence at the air–sea interface challenging since the energy spectrum contains energy from both waves and turbulence over the same range of wavenumbers and frequencies. Furthermore, wave orbital velocities can advect the turbulent wake of instrumentation into the sampling volume of the instrument. In this work a new technique for measuring TKE dissipation at the sea surface that overcomes these difficulties is presented. Using a stereo pair of longwave infrared cameras, it is possible to reconstruct the surface displacement and velocity fields. The vorticity of that velocity field can then be considered to be representative of the rotational turbulence and not the irrotational wave orbital velocities. The turbulent kinetic energy dissipation rate can then be calculated by comparing the vorticity spectrum to a universal spectrum. Average surface TKE dissipation calculated in this manner was found to be consistent with near-surface values from the literature, and time-dependent dissipation was found to depend on breaking.

scholarly journals Wave-turbulence scaling in the ocean mixed layer

2012 ◽  
Vol 9 (6) ◽  
pp. 3761-3793
Author(s):  
G. Sutherland ◽  
K. H. Christensen ◽  
B. Ward
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Energy Dissipation Rate ◽  
Wall Layer ◽  
Stokes Drift ◽  
Near Surface ◽  
Oceanic Boundary Layer

Abstract. Microstructure measurements were collected using an autonomous freely rising profiler under a variety of different atmospheric forcing and sea states in the open ocean. Here, profiles of turbulent kinetic energy dissipation rate, ε, are compared with various proposed scalings. In the oceanic boundary layer, the depth dependence of ε was found to be consistent with that expected for a purely shear-driven wall layer. This is in contrast with many recent studies which suggest higher rates of turbulent kinetic energy dissipation in the near surface of the ocean. However, many dissipation profiles scaled with a Stokes drift-generated shear, suggesting there may be occasions where the shear in the mixed layer are dominated by wave-induced currents, which often causes turbulence to extend beyond the mixed layer depth. Integrating ε in the mixed layer yielded results that 1% of the wind power referenced to 10 m is being dissipated here.

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.

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