scholarly journals NEARSHORE SEDIMENT TRANSPORT STUDY EXPERIMENTS

1980 ◽  
Vol 1 (17) ◽  
pp. 87
Author(s):  
R.J. Seymour ◽  
C.G. Gable
Keyword(s):  
Sediment Transport ◽  
Large Data ◽  
Data Sets ◽  
General Description ◽  
Institutional Research ◽  
Santa Barbara ◽  
Transport Study ◽  
Similar Work ◽  
Nearshore Sediment Transport ◽  
Waves And Currents

The Nearshore Sediment Transport Study (NSTS) is a multi-institutional research program with the objective of developing improved engineering predictive models for transport of sediment. both longshore and cross shore, by waves and currents. The program is sponsored by the Office of Sea Grant (OSG), an agency of the National Oceanic and Atmospheric Administration (NOAA). A general introduction to the objectivesi schedule and organization of the NSTS program was presented at the 16th ICCE by Seymour and Duane (1979). Shortly after the Hamburg Conference, the first major field experiment was conducted at Torrey Pines Beach. California, in November, 1978. The second major experiment was conducted 14 months later in February, 1980 at Leadbetter Beach, in Santa Barbara, California. Each of these experiments involved levels of measurement intensity that appear to exceed by a large factor those of any similar work. Because of the large data sets obtained and the importance of these data to other investigators in coastal processes, the NSTS project is making them available promptly. In the following sections, a general description will be given for each of these experiments and information supplied on how the data may be obtained.

scholarly journals THE NEARSHORE SEDIMENT TRANSPORT STUDY

1978 ◽  
Vol 1 (16) ◽  
pp. 92 ◽  
Author(s):  
Richard J. Seymour ◽  
David B. Duane
Keyword(s):  
Sediment Transport ◽  
Large Scale ◽  
Surf Zone ◽  
Coastal Engineering ◽  
Predictive Tools ◽  
Transport Study ◽  
Sediment Size ◽  
Forcing Function ◽  
Nearshore Sediment Transport ◽  
Waves And Currents

The models for predicting longshore transport of sediment along straight coastlines that are presently in general use were derived empirically from very sparse measurements of both the forcing function (waves and currents) and the response function (sediment motions). A detailed treatment of these data sets is contained in Greer and Madsen (1978). In addition to the generally unsatisfactory nature of the basic measurements upon which they were based, the models may be deficient because they fail to employ such potentially significant factors as wind stress, sediment size distribution, bottom slope and spatial variations in waves and currents, including the effects of rip currents. Although these models have served certain engineering needs, there is a strong measure of uncertainty in the coastal engineering community about their general applicability. Certainly, because they are empirical rather than physically reasoned models, there is no rational means for extending their usefulness to predicting transport where coastlines are not straight -- such as the case of a tidal inlet. The economic impact of sediment transport in the nearshore regime is enormous and the need for improved predictive tools appears to be universally accepted. To be most useful, these improved models must be globally applicable. This implies very strongly that they must be based upon a thorough understanding of surf zone dynamics and the details of the response of the sediment. The surf zone flow fields are highly complex and nonlinear, implying an equally complex and difficult system of sediment responses. Characterizing the entire forcing and response functions simultaneously requires large and expensive field measurement programs that greatly exceed the present state of the art of measurement and analysis.' The approach of the last two decades of single investigators working at laboratory scale or in the ocean with a few single point measurements would not appear to ever meet these needs. However, the present costs for coastal dredging and shoreline protection, which can be measured in billions of dollars on a world scale, argue for a major undertaking to develop better predictive tools. In an attempt to satisfy these needs, an ad hoc group was formed at the Fifteenth Coastal Engineering Conference in Honolulu to plan a large scale and coordinated series of investigations leading to improved sediment transport predictive models. Less than a year later, the Nearshore Sediment Transport Study was initiated under the sponsorship of the Office of Sea Grant.

scholarly journals LONGSHORE TRANSPORT DETERMINED BY AN EFFICIENT TRAP

1982 ◽  
Vol 1 (18) ◽  
pp. 60 ◽  
Author(s):  
R.G. Dean ◽  
E.P. Berek ◽  
C.G. Gable ◽  
R.J. Seymour
Keyword(s):  
Sediment Transport ◽  
Field Component ◽  
Santa Barbara ◽  
Wave Characteristics ◽  
Nearshore Sediment Transport ◽  
Longshore Sediment Transport ◽  
Longshore Transport ◽  
Correlation Constant ◽  
The Relationship ◽  
Best Fit

The Nearshore Sediment Transport Study (NSTS), sponsored by the National Sea Grant Office included a field component to quantify the total longshore sediment transport relationship. This component was conducted at Santa Barbara, California and encompassed a period of eighteen months during which ten surveys were conducted. To date, eight of these surveys have been analyzed, yielding seven intersurvey periods. A total of 288,600 m3 of net sediment transport was documented by these eight surveys. The wave characteristics are based on one of two Sxy gages located in a water depth of 7 m. The most widely used correlation constant, K, in the relationship I = KP, is 0.77. The values found from the data were 0.93 and 1.23 for linear and log best-fit values, respectively. The corresponding values of K4 relating I and S are 2.60 and 2.63 m/s, respectively.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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