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Gradually Varying Flow01:29

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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...
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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...
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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Couette flow represents the flow of fluid between two parallel plates, with one plate fixed and the other moving with a constant velocity. This configuration allows for a simplified analysis using the Navier-Stokes equations, which govern fluid motion under conditions of viscosity and incompressibility. For Couette flow, the assumptions include a steady, laminar, incompressible flow with a zero-pressure gradient in the flow direction. This flow type is beneficial for understanding shear-driven...
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Laminar Flow: Problem Solving01:24

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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Coarse-Grained Molecular Dynamics with Normalizing Flows.

Samuel Tamagnone1, Alessandro Laio1,2, Marylou Gabrié3

  • 1International School for Advanced Studies (SISSA), Via Bonomea 265, Trieste 34136, Italy.

Journal of Chemical Theory and Computation
|September 3, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new sampling algorithm using normalizing flows and nonequilibrium dynamics for efficient molecular simulations. This method enables rapid exploration of complex energy landscapes and generation of thermalized configurations.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Machine Learning

Background:

  • Molecular simulations are crucial for understanding complex systems.
  • Sampling complex energy landscapes with high free energy barriers remains a challenge.
  • Existing methods struggle with efficient exploration of metastable states.

Purpose of the Study:

  • To introduce a novel sampling algorithm leveraging normalizing flows and nonequilibrium dynamics.
  • To enable efficient exploration of high-dimensional systems and overcome energy barriers.
  • To generate thermalized configurations and free energy landscapes.

Main Methods:

  • A Markov chain Monte Carlo algorithm with nonlocal updates is proposed.
  • Normalizing flows model a collective variable (CV) of midsize dimension.
  • Nonequilibrium dynamics are used to propose full configurational moves based on CV updates.
  • The flow is trained to reproduce the free energy landscape.

Main Results:

  • The algorithm successfully samples thermalized configurations.
  • It demonstrates efficient exploration across energy barriers and metastable states.
  • Successful application to a polymer in solution system with high free energy barriers was shown.

Conclusions:

  • The proposed algorithm offers an efficient approach for molecular simulations.
  • It effectively handles systems with complex energy landscapes and metastable states.
  • The trained normalizing flow can be reused for generating configurations.