Riverine Flow Observations and Modeling- Sensitivity of Delft3D River Model to Bathymetric Variability

Long-term goals: The goal of our effort is to understand river and inlet fluid dynamics through in situ field observations and model validation.NPS Award Number: (N0001410WX21049; N0001411WX20962)UM Award Number: (N000141010379

Riverine Flow Observations and Modeling- Sensitivity of Delft3D River Model to Bathymetric Variability

Long-term goals: The goal of our effort is to understand river and inlet fluid dynamics through in situ field observations and model validation.NPS Award Number: (N0001410WX21049; N0001411WX20962)UM Award Number: (N000141010379

New River Inlet DRI: Observations and Modeling of Flow and Material Exchange

LONG-TERM GOALS: The goal of our effort is to understand river and inlet fluid dynamics through in situ field observations and model validation.N0001411WX20962; N0001412WX20498; N000141010409, N00014101037

A python tool for AUV-borne ADCP current data processing

Most Autonomous Underwater Vehicles (AUVs) mount Doppler sensors to navigate precisely underwater, where the Global Positioning System (GPS) is unavailable. These sensors -aside of providing accurate AUV velocity with respect to the ground-, can perform currents profiling, measuring currents along the water column. It has been shown that currents measurements taken by AUVs are very close to those taken by bottom-mounted ADCPs and that a 3D approach can yield differences between both instruments of about 0.07 ms-1, averaging AUV data in 90 second time windows. In this paper we present an OceanServer Iver2 AUV 3D water currents processing tool, developed in Python 2.7. The tool outputs .csv files for further data processing/representation as well as plots of the main variables, along with water currents plots.Peer Reviewe

New River Inlet DRI: Observations and Modeling of Flow and Material Exchange & Field and Numerical Study of the Columbia River Mouth

LONG-TERM GOALS: The goal of our effort is to understand river and inlet fluid dynamics through in situ field observations and model validation.N0001411WX20962; N0001412WX20498; N0001413WX20480; N000141110376, N000141010379, N00014131018

Diseño y validación del sistema de control para la navegación de un vehículo semiautónomo de superficie /

Los vehículos marinos no tripulados representan una solución cada vez más atractiva a todas las misiones que se desarrollan sobre ambientes acuáticos como inspección, vigilancia, investigación, monitoreo ambiental, desminado, entre otras. Los problemas involucrados en su desarrollo se pueden agrupar bajo tres áreas, las cuales son: guiado, navegación y control. Estas otorgan inteligencia, lectura del exterior más el movimiento y son ampliamente estudiadas con el fin de mejorar las técnicas utilizadas. En este estudio se presenta un repaso del modelamiento de las embarcaciones con el fin de implementar un control que satisfaga las necesidades de movimiento a lo largo de una ruta. También se presenta el filtrado de las señales del sistema de navegación mediante el filtro extendido de kalman (EKF). Se realiza una simulación del sistema y se valida experimentalmente donde se ve que la metodología propuesta es suficiente.Incluye referencia bibliográfic

The use of autonomous vehicles for spatially measuring mean velocity profiles in rivers and estuaries

The article of record as published may be located at http://dx.doi.org/10.1007/s11370-011-0095-6Autonomous vehicles (AVs) are commonly used in oceanic and more recently estuarine and riverine environments because they are small, versatile, efficient, moving platforms equipped with a suite of instruments for measuring environmental conditions. However, moving vessel observations, particularly those associated with Acoustic Doppler Current Profiler (ADCP) measurements, can be problematic owing to instrument noise, flow fluctuations, and spatial variability. A range of ADCPs manufactured by different companies were integrated on to an Unmanned Surface Vehicle (USV), an Unmanned Underwater Vehicle (UUV), and some additional stationary platforms and were deployed in a number of natural riverine and estuarine environments to evaluate the quality of the velocity profile over the depth, minimum averaging time interval requirements, and AV mission planning considerations. Measurements were obtained at fixed locations to eliminate any spatial variations in the mean flow characteristics..

The use of autonomous vehicles for spatially measuring mean velocity profiles in rivers and estuaries

Autonomous vehicles (AVs) are commonly used in oceanic and more recently estuarine and riverine environments because they are small, versatile, efficient, moving platforms equipped with a suite of instruments for measuring environmental conditions. However, moving vessel observations, particularly those associated with Acoustic Doppler Current Profiler (ADCP) measurements, can be problematic owing to instrument noise, flow fluctuations, and spatial variability. A range of ADCPs manufactured by different companies were integrated onto an Unmanned Surface Vehicle (USV), an Unmanned Underwater Vehicle (UUV), and some additional stationary platforms, and were deployed in a number of natural riverine and estuarine environments to evaluate the quality of the velocity profile over the depth, minimum averaging time interval requirements and AV mission planning considerations. An appropriate averaging window, T*, was determined using the Kalman Algorithm with a Kalman gain equal to 1%. T* was found to be independent of depth, flow velocity, and environment. There was no correlation (R2=0.18) for T* between flow magnitude and direction. Results from all measurements had a similar T* of approximately 3 minutes. Based on this, an averaging window of 4 minutes is conservatively suggested to obtain a statistically confident measure of the mean velocity profile.http://archive.org/details/theuseofutonomou109455538Approved for public release; distribution is unlimited

Dynamics of Tidally-Driven Flows in Coral Reef Shelves: Observations from Autonomous and Fixed Instruments

The present work examines the hydrodynamics of the inner-shelf region, focusing on tidally-driven alongshore flows over coral reef shelves. This study draws on field data collected in O’ahu, Hawai’i using fixed and mobile assets to develop new modes of observational research.First, a theoretical model is developed to describe how autonomous underwater vehicle (AUV)-based water velocity measurements are influenced by a surface wave field. The model quantifies a quasi-Lagrangian, wave-induced velocity bias as a function of the local wave conditions, and the vehicle’s depth and velocity using a first-order correction to the linear wave solution. The theoretical bias is verified via field experiments over a range of wave and current conditions. The analysis considers velocity measurements made using a REMUS-100 AUV, but the findings apply to any small AUV (vehicle size ≪ wavelength) immersed in a wave field. The observed wave-induced biases [O(1–5) cm/s] can be significant, and can be comparable to steady flow velocities for inner-shelf regions.Second, a new approach to estimate lateral turbulent Reynolds stresses (u′v′) in wavy coastal environments using acoustic Doppler current profilers (ADCPs) is described. The performance of the proposed method is evaluated via comparisons with independent acoustic Doppler velocimeter (ADV)-based stress estimates at two sites, and the vertical structure of the tidally-averaged turbulent Reynolds stresses is examined for an unstratified, tidally-driven flow over a rough coral reef seabed in weak waves. Observations and analysis indicate that lateral stresses are sustained by the cross-shore gradient of the mean alongshore flow, and driven by bottom-generated turbulence. Scaling considerations suggest that cross-shore transport by lateral turbulent mixing could be relevant to coral reef shelves with steep cross-reef slopes and rough bottoms.Finally, a tidally-driven alongshore flow over a forereef shelf is examined using AUV-based spatial velocity measurements along with time series data of the alongshore pressure gradient. Ensemble phase averages of AUV-based velocities reveal characteristics akin to an oscillatory boundary layer, with the nearshore flow leading the offshore flow in phase and with a corresponding velocity magnitude attenuation near the shallower regions of the reef. Analysis of the depth-averaged alongshore momentum equation indicates that the cross-shore structure and evolution of the alongshore flow is well described by a balance between local acceleration, barotropic pressure gradient, and bottom drag. This primary balance allows the estimation of a spatially-averaged drag coefficient as a function of cross-shore distance over depths spanning from 24 to 6 m. Seabed roughness data suggest that larger scales, with wavelengths of O(10 m), are more relevant than smaller meter-scale roughness for drag