The Evaporation of Oil Spills: Development and Implementation of New Prediction Methodology

1999 ◽  
Vol 1999 (1) ◽  
pp. 281-287 ◽  
Author(s):  
Mervin F. Fingas
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Evaporation Rate ◽  
Oil Spills ◽  
Initial Step ◽  
Transfer Coefficients ◽  
Sea Temperature ◽  
Step Over ◽  
Simple Equations ◽  
Velocity Turbulence

ABSTRACT Extensive experimentation was conducted on oil evaporation. The results of the regulation nature of oil evaporation experiments, show that oil is not strictly boundary-layer regulated specifically: (1) the evaporation rate of several oils with increasing wind speed shows that, unlike water, the evaporation rate does not change significantly except for the initial step over 0-level wind; (2) increasing area does not significantly change oil evaporation rate; (3) decreasing thickness does not increase oil evaporation rate; (4) the volume or mass of oil evaporating correlates with the evaporation rate; (5) evaporation of pure hydrocarbons with and without wind (turbulence) shows that compounds larger than nonane and decane are not boundary-layer regulated. The fact that oil evaporation is not strictly boundary-layer regulated implies a simplistic evaporation equation will suffice to describe the process. A simple equation of the form, evaporation = constant × logarithm of time, is sufficient to describe evaporation. The following processes do not require consideration: wind velocity, turbulence level, area, thickness, and scale size. The factors found to be important to evaporation are time and temperature. The equation parameters found experimentally for the evaporation of oils can be related to commonly-available distillation data for the oil. Specifically, it has been found that the distillation percentage at 180°C correlates well with the equation parameters. Relationships enabling calculation of evaporation equations directly from distillation data have been developed. These equations were combined with the equations generated to account for the temperature variations. The results have application in oil spill prediction and modelling. The simple equations can be applied using readily-available data such as sea temperature and time. Old equations required oil vapour pressure, specialised distillation data, spill area, wind speed, and mass transfer coefficients, all of which are difficult to obtain.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

Modeling mass transfer and substrate utilization in the boundary layer of biofilm systems

1998 ◽  
Vol 37 (4-5) ◽  
pp. 139-147 ◽  
Author(s):  
Harald Horn ◽  
Dietmar C. Hempel
Keyword(s):  
Boundary Layer ◽  
Mass Transfer ◽  
Reynolds Number ◽  
Film Theory ◽  
Substrate Utilization ◽  
The Other ◽  
Concentration Profiles ◽  
Hydrodynamic Conditions ◽  
Time Axis ◽  
Transfer Coefficients

The use of microelectrodes in biofilm research allows a better understanding of intrinsic biofilm processes. Little is known about mass transfer and substrate utilization in the boundary layer of biofilm systems. One possible description of mass transfer can be obtained by mass transfer coefficients, both on the basis of the stagnant film theory or with the Sherwood number. This approach is rather formal and not quite correct when the heterogeneity of the biofilm surface structure is taken into account. It could be shown that substrate loading is a major factor in the description of the development of the density. On the other hand, the time axis is an important factor which has to be considered when concentration profiles in biofilm systems are discussed. Finally, hydrodynamic conditions become important for the development of the biofilm surface when the Reynolds number increases above the range of 3000-4000.

Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

2014 ◽  
Vol 142 (11) ◽  
pp. 4284-4307 ◽  
Author(s):  
Natalie Perlin ◽  
Simon P. de Szoeke ◽  
Dudley B. Chelton ◽  
Roger M. Samelson ◽  
Eric D. Skyllingstad ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coupling Coefficient ◽  
Eddy Viscosity ◽  
Sea Surface ◽  
Coupling Coefficients ◽  
Wind Response ◽  
Speed Response

Abstract The wind speed response to mesoscale SST variability is investigated over the Agulhas Return Current region of the Southern Ocean using the Weather Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model. The SST-induced wind response is assessed from eight simulations with different subgrid-scale vertical mixing parameterizations, validated using Quick Scatterometer (QuikSCAT) winds and satellite-based sea surface temperature (SST) observations on 0.25° grids. The satellite data produce a coupling coefficient of sU = 0.42 m s−1 °C−1 for wind to mesoscale SST perturbations. The eight model configurations produce coupling coefficients varying from 0.31 to 0.56 m s−1 °C−1. Most closely matching QuikSCAT are a WRF simulation with the Grenier–Bretherton–McCaa (GBM) boundary layer mixing scheme (sU = 0.40 m s−1 °C−1), and a COAMPS simulation with a form of Mellor–Yamada parameterization (sU = 0.38 m s−1 °C−1). Model rankings based on coupling coefficients for wind stress, or for curl and divergence of vector winds and wind stress, are similar to that based on sU. In all simulations, the atmospheric potential temperature response to local SST variations decreases gradually with height throughout the boundary layer (0–1.5 km). In contrast, the wind speed response to local SST perturbations decreases rapidly with height to near zero at 150–300 m. The simulated wind speed coupling coefficient is found to correlate well with the height-averaged turbulent eddy viscosity coefficient. The details of the vertical structure of the eddy viscosity depend on both the absolute magnitude of local SST perturbations, and the orientation of the surface wind to the SST gradient.

scholarly journals Observed Boundary Layer Wind Structure and Balance in the Hurricane Core. Part I: Hurricane Georges

2006 ◽  
Vol 63 (9) ◽  
pp. 2169-2193 ◽  
Author(s):  
Jeffrey D. Kepert
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Radial Profile ◽  
Lower Altitude ◽  
Near Surface ◽  
Wind Structure ◽  
The Right ◽  
Tropical Cyclone Boundary Layer ◽  
Upper Boundary

Abstract The GPS dropsonde allows observations at unprecedentedly high horizontal and vertical resolution, and of very high accuracy, within the tropical cyclone boundary layer. These data are used to document the boundary layer wind field of the core of Hurricane Georges (1998) when it was close to its maximum intensity. The spatial variability of the boundary layer wind structure is found to agree very well with the theoretical predictions in the works of Kepert and Wang. In particular, the ratio of the near-surface wind speed to that above the boundary layer is found to increase inward toward the radius of maximum winds and to be larger to the left of the track than to the right, while the low-level wind maximum is both more marked and at lower altitude on the left of the storm track than on the right. However, the expected supergradient flow in the upper boundary layer is not found, with the winds being diagnosed as close to gradient balance. The tropical cyclone boundary layer model of Kepert and Wang is used to simulate the boundary layer flow in Hurricane Georges. The simulated wind profiles are in good agreement with the observations, and the asymmetries are well captured. In addition, it is found that the modeled flow in the upper boundary layer at the eyewall is barely supergradient, in contrast to previously studied cases. It is argued that this lack of supergradient flow is a consequence of the particular radial structure in Georges, which had a comparatively slow decrease of wind speed with radius outside the eyewall. This radial profile leads to a relatively weak gradient of inertial stability near the eyewall and a strong gradient at larger radii, and hence the tropical cyclone boundary layer dynamics described by Kepert and Wang can produce only marginally supergradient flow near the radius of maximum winds. The lack of supergradient flow, diagnosed from the observational analysis, is thus attributed to the large-scale structure of this particular storm. A companion paper presents a similar analysis for Hurricane Mitch (1998), with contrasting results.

scholarly journals Meteorological observations of the coastal boundary layer structure at the Bulgarian Black Sea coast

2011 ◽  
Vol 6 (1) ◽  
pp. 251-259 ◽  
Author(s):  
D. Barantiev ◽  
M. Novitsky ◽  
E. Batchvarova
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Black Sea ◽  
Vertical Structure ◽  
Sonic Anemometer ◽  
Sea Breeze ◽  
Turbulence Measurements ◽  
Black Sea Coast ◽  
Meteorological Observatory ◽  
Sea Coast

Abstract. Continuous wind profile and turbulence measurements were initiated in July 2008 at the coastal meteorological observatory of Ahtopol on the Black Sea (south-east Bulgaria) under a Bulgarian-Russian collaborative program. These observations are the start of high resolution atmospheric boundary layer vertical structure climatology at the Bulgarian Black Sea coast using remote sensing technology and turbulence measurements. The potential of the measurement program with respect to this goal is illustrated with examples of sea breeze formation and characteristics during the summer of 2008. The analysis revealed three distinct types of weather conditions: no breeze, breeze with sharp frontal passage and gradually developing breeze. During the sea breeze days, the average wind speed near the ground (from sonic anemometer at 4.5 m and first layer of sodar at 30–40 m) did not exceed 3–4 m s−1. The onset of breeze circulation was detected based on surface layer measurements of air temperature (platinum sensor and acoustic), wind speed and direction, and turbulence parameters. The sodar measurements revealed the vertical structure of the wind field.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

A study of wind speed modification and internal boundary-layer heights in a coastal region

1988 ◽  
Vol 42 (4) ◽  
pp. 313-335 ◽  
Author(s):  
Hans Bergström ◽  
Per-Erik Johansson ◽  
Ann-Sofi Smedman
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Coastal Region ◽  
Internal Boundary Layer ◽  
Internal Boundary
Sign in / Sign up

Export Citation Format

Share Document