A Comparison of Propagation Model Predictions with EPLRS (Enhanced Position Location and Reporting System) UHF Network Measurements

1988 ◽  
Author(s):  
William M. Brennan ◽  
Paul F. Sass
Keyword(s):  
Reporting System ◽  
Propagation Model ◽  
Network Measurements ◽  
Position Location ◽  
Model Predictions

scholarly journals Assimilating Global Wave Model Predictions and Deep-Water Wave Observations in Nearshore Swell Predictions

2017 ◽  
Vol 34 (8) ◽  
pp. 1823-1836 ◽  
Author(s):  
Sean C. Crosby ◽  
Bruce D. Cornuelle ◽  
William C. O’Reilly ◽  
Robert T. Guza
Keyword(s):  
Wave Propagation ◽  
High Resolution ◽  
Wave Energy ◽  
Propagation Model ◽  
Linear Wave ◽  
Time Lags ◽  
Energy Propagation ◽  
Nearshore Processes ◽  
Model Predictions ◽  
Linear Wave Propagation

AbstractNearshore wave predictions with high resolution in space and time are needed for boating safety, to assess flood risk, and to support nearshore processes research. This study presents methods for improving regional nearshore predictions of swell-band wave energy (0.04–0.09 Hz) by assimilating local buoy observations into a linear wave propagation model with a priori guidance from global WAVEWATCH III (WW3) model predictions. Linear wave propagation, including depth-induced refraction and shoaling, and travel time lags, is modeled with self-adjoint backward ray tracing techniques. The Bayesian assimilation yields smooth, high-resolution offshore wave directional spectra that are consistent with WW3, and with offshore and local buoy observations. Case studies in the Southern California Bight (SCB) confirm that the nearshore predictions at independent (nonassimilated) buoy sites are improved by assimilation compared with predictions driven with WW3 or with a single offshore buoy. These assimilation techniques, valid in regions and frequency bands where wave energy propagation is mostly linear, use significantly less computational resources than nonlinear models and variational methods, and could be a useful component of a larger regional assimilation program. Where buoy locations have historically been selected to meet local needs, these methods can aid in the design of regional buoy arrays by quantifying the regional skill improvement for a given buoy observation and identifying both high-value and redundant observations. Assimilation techniques also identify likely forward model error in the Santa Barbara Channel, where permanent observations or model corrections are needed.

scholarly journals Modeling of Liquid Injectivity in Surfactant-Alternating-Gas Foam Enhanced Oil Recovery

SPE Journal ◽  
2019 ◽  
Vol 24 (03) ◽  
pp. 1123-1138 ◽  
Author(s):  
J.. Gong ◽  
S.. Vincent-Bonnieu ◽  
R. Z. Kamarul Bahrim ◽  
C. A. Che Mamat ◽  
R. D. Tewari ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Flow Model ◽  
Radial Flow ◽  
Gas Injection ◽  
Small Region ◽  
Propagation Model ◽  
Injection Well ◽  
Wellbore Pressure ◽  
Model Predictions ◽  
Experimental Findings

Summary Surfactant alternating gas (SAG) is often the injection strategy used for injecting foam into a reservoir. However, liquid injectivity can be very poor in SAG, and fracturing of the well can occur. Coreflood studies of liquid injectivity directly following foam injection have been reported. We conducted a series of coreflood experiments to study liquid injectivity under conditions more like those near an injection well in a SAG process in the field (i.e., after a period of gas injection). Our previous experimental results suggest that the injectivity in a SAG process is determined by propagation of several banks. However, there is no consistent approach to modeling liquid injectivity in a SAG process. The Peaceman equation is used in most conventional foam simulators for estimating the wellbore pressure and injectivity. In this paper, we propose a modeling approach for gas and liquid injectivity in a SAG process on the basis of our experimental findings. The model represents the propagation of various banks during gas and liquid injection. We first compare the model predictions for linear flow with the coreflood results and obtain good agreement. We then propose a radial-flow model for scaling up the core-scale behavior to the field. The comparison between the results of the radial-propagation model and the Peaceman equation shows that a conventional simulator based on the Peaceman equation greatly underestimates both gas and liquid injectivities in a SAG process. The conventional simulator cannot represent the effect of gas injection on the subsequent liquid injectivity, especially the propagation of a relatively small region of collapsed foam near an injection well. The conventional simulator's results can be brought closer to the radial-flow-model predictions by applying a constant negative skin factor. The work flow described in this study can be applied to future field applications. The model we propose is based on a number of simplifying assumptions. In addition, the model would need to be fitted to coreflood data for the particular surfactant formulation, porous medium, and field conditions of a particular application. The adjustment of the simulator to better fit the radial-flow model also would depend, in part, on the grid resolution of the near-well region in the simulation.

Shipping Noise Predictions: Capabilities and Limitations

2003 ◽  
Vol 37 (4) ◽  
pp. 54-65 ◽  
Author(s):  
Richard M. Heitmeyer ◽  
Stephen C. Wales ◽  
Lisa A. Pflug
Keyword(s):  
Deep Water ◽  
Ambient Noise ◽  
Classical Model ◽  
Propagation Model ◽  
Environmental Data ◽  
Noise Model ◽  
Propagation Models ◽  
Littoral Region ◽  
Model Predictions ◽  
Noise Models

This paper addresses shortcomings in the ability to predict either current levels of the ambient noise generated by shipping or future trends in those levels that might result from changes in the world's shipping fleet. In particular, experimental evidence is presented that predictions of increases in the sound generated by the world's ships based on increases in their speeds and lengths are not justified. This is because, contrary to the classical model of shipping source levels (Ross, 1976), there is a negligible correlation between the source levels of an ensemble of ships and the speeds and lengths of those ships. We also present two examples of noise model predictions that result in large errors. The first shows that two state-of-the-art noise models can yield significantly different noise predictions (5 dB) for the same deep-water, open ocean site. About two dB of this difference is attributed to an approximation inherent in the acoustic propagation model of one of the noise models that is acceptable in some deep-water regions, but not in others. The remaining discrepancy is attributed to differences in the acoustic environmental databases (sound speeds, bathymetries, and geo-acoustic models) between the two noise models. The second example shows that neglecting the local shipping component in a littoral region near a port can result in a noise prediction that is over 15 dB less than a measured value. Taken together, these examples indicate that large errors can result because of inappropriate propagation models or incomplete or inaccurate shipping and environmental data bases. [Work supported by ONR.]

scholarly journals Evaluating anthropogenic noise impacts on animals in natural areas

2017 ◽  
Author(s):  
Alexander C. Keyel ◽  
Sarah E. Reed ◽  
Kathryn Nuessly ◽  
Elizeth Cinto-Mejia ◽  
Jesse R. Barber ◽  
...  
Keyword(s):  
Animal Species ◽  
Sound Propagation ◽  
Propagation Model ◽  
Anthropogenic Noise ◽  
Natural Areas ◽  
Noise Sources ◽  
Modeling Tools ◽  
Propagation Models ◽  
Model Predictions ◽  
Noise Impacts

SummaryNoise pollution is detrimental to a diversity of animal species and degrades natural areas, raising concern over the expanding footprint of anthropogenic noise on ecosystems. To guide management of noise sources, modeling tools have been developed to quantify noise levels across landscapes.We demonstrate how to model anthropogenic noise using sound propagation models, including noise from point, line, and polygon sources. In addition, we demonstrate three ways of evaluating spatially-explicit noise impacts, by identifying where noise 1) exceeds a sound level threshold, 2) is audible, or 3) has the potential to mask species communications. Finally, we examine approaches to mitigate these noise impacts on animal species.Noise sources in locations more favorable to sound propagation (e.g., locations with long, unobstructed lines-of-sight) will have a disproportionate impact on the surrounding area. We demonstrate how propagation models can identify sites with smaller acoustic footprints or sites that would benefit from additional noise-control measures.Modeling decisions, such as choice of sound propagation model, sound source information, and the quality of the input data, strongly influence the accuracy of model predictions. These decisions can be guided by comparing model predictions to empirical data when it is available.Synthesis and applications: Here, we demonstrate an approach for modeling and assessing anthropogenic noise sources across a landscape. Our versatile approach allows refining propagation outputs for species-specific questions as well as the quantitative evaluation of management alternatives. While the results are presented in the context of particular species, these approaches can be applied more generally to a wide range of taxa or used for multispecies assessments.

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