Probability Distribution of Turbulent Kinetic Energy Dissipation Rate in Stratified Turbulence: Microstructure Measurements in the Southern California Bight

Lozovatsky, Iossif; Shearman, Kipp; Pirro, Annunziata; Fernando, Harindra J. S.

doi:10.1029/2019jc015087

Cited by 9 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even the median values of the dissipation rate ε ∼ 10 −8 W kg −1 are high compared to data collected in open-ocean regions, where turbulence generation is not directly affected by bathymetry unlike in the region close to the Nova Scotia shelf break. A comparison between the Nova Scotia measurements and data obtained recently in the deep waters of the Southern California Bight (SCB, Lozovatsky et al 2019a) and in the Sri Lanka dome region in the Bay of Bengal (SLD, Lozovatsky et al 2019b) is given in Fig. 8.…”

Section: Statistics Of the Richardson Number And The Dissipation Rate In The Pycnoclinementioning

confidence: 97%

“…Therefore, particular attention was paid to the variation of turbulence and turbulence diffusivities over and across the shelf. To this end, ocean microstructure measurements were taken from 29 September until 2 October 2018 using a vertical microstructure profiler (VMP-500, Rockland Scientific, Victoria, Canada), which has been successfully employed in our numerous previous field campaigns (e.g., Lozovatsky et al 2017Lozovatsky et al , 2019a. The VMP survey was conducted by the R/V Hugh R. Sharp in a gridded pattern across and along the Nova Scotia shelf break (Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ocean Turbulence and Mixing Near the Shelf Break South-East of Nova Scotia

Lozovatsky

Wainwright

Creegan

et al. 2020

Boundary-Layer Meteorol

Self Cite

View full text Add to dashboard Cite

Formation of coastal fog was observed near the southern tip of Nova Scotia when warm, humid air was advected towards the shore over an area of colder water. The sea-surface temperature in the colder and higher salinity patch near the coast was below 14°C compared to that of the surrounding sea of ≈ 17-18°C. Measurements of stratification, currents, and the dissipation rate of turbulence kinetic energy (ε) on the Nova Scotia shelf, shelf break, and continental slope revealed the frequent occurrence of shear instability in the pycnocline. The probability of the gradient Richardson number (Ri) being less than a critical value of Ri cr 0.25 in the depth range 30-90 m exceeded 50% on the shelf and over the slope, and it was above 75% along the shelf break, while in the open ocean pycnocline the probability of Ri < 0.25 is below 5%. The cumulative distribution functions of the dissipation rate in the pycnocline south-east of Nova Scotia followed the Burr probability model with median values from 7.9 × 10 −9 W kg −1 on the slope to 1.9 × 10 −8 W kg −1 at the shelf break. The eddy diffusivity (K N ) estimates follow a generalized extreme value distribution with a high median value of K N 5.8 × 10 −5 m 2 s −1 at the shelf break. The diffusivity K N depended on Ri in general agreement with the parametrization previously suggested by Lozovatsky and Fernando (Philos Trans R Soc A 371:20120213, 2013) but with a higher level of mixing at Ri > 1. This could be relevant to the nature of turbulence generation near the shelf break by random wave instabilities in the pycnocline at high Richardson and high Reynolds numbers.

show abstract

Section: Statistics Of the Richardson Number And The Dissipation Rate In The Pycnoclinementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Ocean Turbulence and Mixing Near the Shelf Break South-East of Nova Scotia

Lozovatsky

Wainwright

Creegan

et al. 2020

Boundary-Layer Meteorol

Self Cite

View full text Add to dashboard Cite

show abstract

“…For further discussion see Vallis (2017) and Meredith and Naveira Garabato (2022). and shear probes, which have most commonly been deployed as vertically dropped microstructure profilers, resolve isotropic turbulent 5 fluctuations on length scales as small as ∼0(1) mm (Figure 5; e.g., Schmitt et al, 1988;Lueck et al, 2002;Lozovatsky et al, 2019). However, microstructure profiles are uncommon and sparsely distributed.…”

Section: Turbulent Mixingmentioning

confidence: 99%

The Next Decade of Seismic Oceanography: Possibilities, Challenges and Solutions

Dickinson

Gunn²

2022

Front. Mar. Sci.

View full text Add to dashboard Cite

Seismic reflection profiling of thermohaline structure has the potential to transform our understanding of oceanic mixing and circulation. This profiling, which is known as seismic oceanography, yields acoustic images that extend from the sea surface to the sea bed and which span horizontal distances of hundreds of kilometers. Changes in temperature and salinity are detected in two, and sometimes three, dimensions at spatial resolutions of ~O(10) m. Due to its unique combination of extensive coverage and high spatial resolution, seismic oceanography is ideally placed to characterize the processes that sustain oceanic circulation by transferring energy between basin-scale currents and turbulent flow. To date, more than one hundred research papers have exploited seismic oceanographic data to gain insight into phenomena as varied as eddy formation, internal waves, and turbulent mixing. However, despite its promise, seismic oceanography suffers from three practical disadvantages that have slowed its development into a widely accepted tool. First, acquisition of high-quality data is expensive and logistically challenging. Second, it has proven difficult to obtain independent observational constraints that can be used to benchmark seismic oceanographic results. Third, computational workflows have not been standardized and made widely available. In addition to these practical challenges, the field has struggled to identify pressing scientific questions that it can systematically address. It thus remains a curiosity to many oceanographers. We suggest ways in which the practical challenges can be addressed through development of shared resources, and outline how these resources can be used to tackle important problems in physical oceanography. With this collaborative approach, seismic oceanography can become a key member of the next generation of methods for observing the ocean.

show abstract

“…Matrix V includes n×m recorded ocean velocities (i.e., m velocity vectors each as a function of depth). The ocean velocity and especially the ocean turbulence are modeled with different probability distributions, including lognormal distribution [27] and Burr distribution [28]. In this paper, different methods for modelling the ocean velocity are developed.…”

Section: A Statistical Ocean Current Shear Profile Characterizationmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

View full text Add to dashboard Cite

This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

show abstract

Probability Distribution of Turbulent Kinetic Energy Dissipation Rate in Stratified Turbulence: Microstructure Measurements in the Southern California Bight

Cited by 9 publications

References 44 publications

Ocean Turbulence and Mixing Near the Shelf Break South-East of Nova Scotia

Ocean Turbulence and Mixing Near the Shelf Break South-East of Nova Scotia

The Next Decade of Seismic Oceanography: Possibilities, Challenges and Solutions

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Contact Info

Product

Resources

About