Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

498
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
498
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

2.2K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
2.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

843
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
843
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

176
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
176
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

145
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
145
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

164
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
164

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Consistent Nonlinear Mild-Slope Equation Models for Wide-Angle Water Waves Transformation.

Coastal engineering·2025
Same author

A Simplified Consistent Nonlinear Mild-Slope Equation Model for Random Waves Propagation and Dissipation.

Coastal engineering·2024
Same author

A Modified Frequency Distribution Function of Wave-Breaking-Induced Energy Dissipation.

Journal of geophysical research. Oceans·2023
Same author

A Consistent Nonlinear Mild-Slope Equation Model.

Coastal engineering·2022
Same author

Potential Human Health Hazard of Post-Hurricane Harvey Sediments in Galveston Bay and Houston Ship Channel: A Case Study of Using <i>In Vitro</i> Bioactivity Data to Inform Risk Management Decisions.

International journal of environmental research and public health·2021
Same author

Temporal and spatial analysis of per and polyfluoroalkyl substances in surface waters of Houston ship channel following a large-scale industrial fire incident.

Environmental pollution (Barking, Essex : 1987)·2020
See all related articles

Related Experiment Video

Updated: Oct 31, 2025

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

863

Optimization of Bathymetry Estimates for Nearshore Hydrodynamic Models Using Bayesian Methods.

Samira Ardani1, James M Kaihatu2

  • 1Research Assistant Professor, Dept. of Ocean Engineering, Texas A&M Univ., 200 Seawolf Parkway, Galveston, TX 77554; formerly, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843.

Journal of Waterway, Port, Coastal, and Ocean Engineering
|June 30, 2021
PubMed
Summary
This summary is machine-generated.

A Bayesian inverse framework optimizes numerical models by interpolating bathymetric data. This approach improves wave and flow predictions, reducing errors by 30% using Markov chain Monte Carlo (MCMC) optimization.

More Related Videos

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.5K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Related Experiment Videos

Last Updated: Oct 31, 2025

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

863
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.5K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Area of Science:

  • Coastal Engineering
  • Geophysical Fluid Dynamics
  • Computational Science

Background:

  • Predictive numerical models are crucial for understanding coastal processes.
  • Accurate bathymetry is essential for reliable wave and hydrodynamic predictions.
  • Existing models often face challenges with bathymetric resolution and data assimilation.

Purpose of the Study:

  • To develop and apply a Bayesian inverse framework for optimizing numerical model skill.
  • To improve the interpolation of bathymetric measurements for a more probable bathymetric surface.
  • To minimize residual errors between measured data and numerical model outputs.

Main Methods:

  • A Bayesian inverse framework coupled with Markov chain Monte Carlo (MCMC) optimization.
  • Interpolation of bathymetric measurements to determine the most probable bathymetric surface.
  • Monte Carlo simulation for uncertainty analysis of model output fields (wave height, flow velocity).

Main Results:

  • The Bayesian approach infers probable model parameters from observed data.
  • Model parameters estimated via Bayesian analysis lead to improved data comparisons.
  • Relative errors in significant wave height predictions were reduced by 30%.

Conclusions:

  • The developed Bayesian inverse framework effectively optimizes numerical model skill.
  • The method enhances the accuracy of wave and hydrodynamic predictions.
  • This approach provides a robust way to assimilate bathymetric data into coastal models.