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Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

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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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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
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Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
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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.
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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.
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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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Related Experiment Video

Updated: Mar 11, 2026

Evaluation of Coronary Flow Reserve After Myocardial Ischemia Reperfusion in Rats
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Fractional flow reserve: lessons from PLATFORM and future perspectives.

Gianluca Pontone1, Patrizia Carità2, Massimo Verdecchia3

  • 1Centro Cardiologico Monzino, IRCCS, Milan, Italy - gianluca.pontone@ccfm.it.

Minerva Cardioangiologica
|December 1, 2016
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Fractional flow reserve computed from coronary computed tomography angiography (FFR-CT) improves the diagnosis of coronary artery disease (CAD). This novel technology enhances the positive predictive value and specificity of cCTA, guiding better treatment decisions for patients with CAD.

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Area of Science:

  • Cardiovascular Imaging
  • Interventional Cardiology
  • Computational Fluid Dynamics

Background:

  • Stable coronary artery disease (CAD) management requires identifying patients who benefit most from invasive treatments.
  • Ischemia-guided revascularization improves outcomes and cost-effectiveness compared to anatomy-guided approaches.
  • Invasive fractional flow reserve (FFR) is the gold standard for assessing lesion-specific ischemia in intermediate lesions.

Purpose of the Study:

  • To review the technical aspects, clinical evidence, and limitations of FFR derived from coronary computed tomography angiography (FFR-CT).
  • To focus on the PLATFORM Trial's analysis of FFR-CT's effectiveness, clinical outcomes, and resource utilization.
  • To present future perspectives for FFR-CT technology.

Main Methods:

  • Review of current literature on FFR-CT technology and its clinical applications.
  • Analysis of data from the PLATFORM Trial regarding FFR-CT effectiveness and outcomes.
  • Discussion of computational fluid dynamics principles applied to cCTA data.

Main Results:

  • Coronary computed tomography angiography (cCTA) alone has limitations in specificity for diagnosing significant CAD.
  • FFR-CT, derived from cCTA using computational fluid dynamics, enhances diagnostic accuracy by improving positive predictive value and specificity.
  • The PLATFORM Trial demonstrated the clinical utility and resource implications of FFR-CT.

Conclusions:

  • FFR-CT represents a significant advancement in non-invasive assessment of coronary artery disease.
  • This technology has the potential to optimize patient selection for invasive procedures and improve healthcare resource allocation.
  • Further research and clinical integration are expected to solidify the role of FFR-CT in CAD management.