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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
38
Typical Model Studies01:30

Typical Model Studies

340
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.
340
Gradually Varying Flow01:29

Gradually Varying Flow

32
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
32
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

121
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
121
Modeling and Similitude01:12

Modeling and Similitude

245
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
245
Plane Potential Flows01:23

Plane Potential Flows

363
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
363

You might also read

Related Articles

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

Sort by
Same author

Learning place cells and remapping by decoding the cognitive map.

eLife·2025
Same author

Strong strain dependence of friction in graphene kirigami allows engineering a negative coefficient of friction.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

An Extended Energy-Biased Aggregation-Volume-Bias Monte Carlo (EB-AVBMC) Method for Nucleation Simulation of a Reactive Water Potential.

Journal of chemical theory and computation·2025
Same author

A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons.

PLoS computational biology·2018
Same journal

Molecular Ordering and Hydration in Model Lipid Rafts Studied by Vibrational Sum Frequency Spectroscopy.

The journal of physical chemistry. B·2026
Same journal

Vertical Segregation in Planar Multiblock Copolymer Brushes Induced by Collapse in a Nonselective Solvent.

The journal of physical chemistry. B·2026
Same journal

Optical Interactions and Excited-State Dynamics in γ-Irradiated Mn-Doped Phosphate Glasses.

The journal of physical chemistry. B·2026
Same journal

Dynamics of Intermediate Water in Biocompatible Poly(2-methoxyethyl acrylate) Revealed by Quasi-Elastic Neutron Scattering.

The journal of physical chemistry. B·2026
Same journal

Fluoride Electrolyte Discovery via Reactivity Guided Genetic Algorithms.

The journal of physical chemistry. B·2026
Same journal

Microscopic Factors That Dictate the Optimal Compositions of Mixed Solvent Systems for Lithium Ion Battery Applications: Revelation through a Combined Experimental and Simulation Study.

The journal of physical chemistry. B·2026
See all related articles
  1. Home
  3. Genetic Algorithm Workflow For Parameterization Of A Water Model Using The Vashishta Force Field.
  1. Home
  3. Genetic Algorithm Workflow For Parameterization Of A Water Model Using The Vashishta Force Field.

Related Experiment Video

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

Genetic Algorithm Workflow for Parameterization of a Water Model Using the Vashishta Force Field.

Anthony Val C Camposano1, Even Marius Nordhagen1, Henrik Andersen Sveinsson1

  • 1The Njord Centre, Department of Physics, University of Oslo, Sem Sælands vei 24, NO-0316 Oslo, Norway.

The Journal of Physical Chemistry. B
|January 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new, computationally inexpensive water model was developed using the Vashishta potential. This model accurately describes water properties and enables simulations of mineral-water interactions, crucial for understanding geological processes.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.7K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

Related Experiment Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.7K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

Area of Science:

  • Geochemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Water significantly alters mineral surface properties, impacting geological processes like fracture.
  • Accurate molecular-level water models are essential for simulating mineral behavior in aqueous environments.
  • Existing molecular dynamics water models are often computationally expensive or lack compatibility with mineral interaction models.

Purpose of the Study:

  • To develop a computationally efficient water model for molecular dynamics simulations.
  • To enable accurate modeling of mineral-water interactions, particularly in dynamic processes.
  • To parametrize a 3-point water model using the Vashishta potential form.

Main Methods:

  • Parametrization of a 3-point water potential using the Vashishta potential form.
  • Molecular dynamics simulations to study water properties and mineral interactions.
  • Comparison of model predictions with experimental transport and liquid-vapor properties.

    • Main Results:

    • The Vashishta-based water model shows good agreement with experimental transport and liquid-vapor properties.
    • The model is computationally inexpensive, facilitating large-scale simulations.
    • The Vashishta form ensures compatibility with existing silica glass models.

    Conclusions:

    • The developed Vashishta water model provides a computationally efficient and accurate method for simulating water properties.
    • This model is suitable for studying mineral-water interactions, advancing research in geochemistry and materials science.
    • Enables the simulation of dynamic processes involving mineral fracture in aqueous environments.