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Related Concept Videos

Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

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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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Bernoulli's Equation for Flow Along a Streamline01:30

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Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...
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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
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Laminar flow represents a smooth, orderly fluid motion where particles move along parallel paths, resulting in minimal mixing between layers. Streamlined particle paths characterize this flow regime and occur under conditions where viscous forces dominate over inertial forces. The distinction between laminar, transitional, and turbulent flow is primarily determined by the Reynolds number, a dimensionless quantity calculated as:
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Application of data-driven RANS model in simulating indoor airflow.

Bingqian Chen1, Sumei Liu1, Junjie Liu1

  • 1Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, China.

Indoor Air
|October 28, 2022
PubMed
Summary
This summary is machine-generated.

A new data-driven Reynolds-averaged Navier-Stokes (RANS) model accurately predicts indoor anisotropic airflow. This artificial neural network (ANN) approach improves predictions of air velocity, temperature, and turbulent kinetic energy for better indoor environments.

Keywords:
ANNCFDanisotropic flowgeneralizabilityturbulence flow

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Area of Science:

  • Computational fluid dynamics (CFD)
  • Building science
  • Artificial intelligence in engineering

Background:

  • Indoor environment quality significantly impacts occupant wellbeing.
  • Accurate indoor air distribution prediction is crucial for optimal indoor environments.
  • Traditional Reynolds-averaged Navier-Stokes (RANS) models struggle with anisotropic indoor flows due to the Boussinesq hypothesis limitations.

Purpose of the Study:

  • To develop a data-driven RANS model capable of accurately predicting indoor anisotropic flows.
  • To enhance the prediction of indoor air velocity, temperature, and turbulent kinetic energy distributions.
  • To improve the generalizability of RANS models for diverse indoor airflow scenarios.

Main Methods:

  • Developed a data-driven RANS model incorporating a nonlinear model from existing literature.
  • Utilized an artificial neural network (ANN) to determine coefficients for high-order terms within the RANS model.
  • Trained the model using three typical indoor airflow cases and validated its generalizability with four additional test cases.

Main Results:

  • The data-driven RANS model demonstrated superior prediction accuracy for air velocity, temperature, and turbulent kinetic energy compared to the original RANS model.
  • Accurate simulation of nonlinear terms by the ANN was key to the improved predictive performance.
  • The model successfully predicted indoor anisotropic flows, outperforming traditional methods.

Conclusions:

  • The developed data-driven RANS model effectively predicts indoor anisotropic flows.
  • The integration of ANN for nonlinear terms enhances the accuracy and generalizability of airflow predictions.
  • This approach offers a promising method for creating better indoor environments through improved air distribution modeling.