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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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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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FlowMat: a toolbox for modeling flow reactors using physics-based and machine learning approaches for modular

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This study presents an open-source MATLAB/Simulink toolbox for flow reactor modeling and optimization. It aids researchers in parameter determination and reactor design, reducing costs and time.

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Area of Science:

  • Chemical Engineering
  • Computational Modeling
  • Process Optimization

Background:

  • Accurate modeling and optimization of flow reactors are crucial for efficient chemical processes.
  • Existing tools may lack versatility or integration for diverse modeling approaches.
  • The need for efficient parameter identification and operational optimization is significant.

Purpose of the Study:

  • To introduce a versatile, open-source MATLAB/Simulink toolbox for flow reactor modeling and optimization.
  • To support various modeling paradigms, including physics-based, data-driven, and hybrid methods.
  • To demonstrate practical applications in simulation, parameter identification, and optimization.

Main Methods:

  • Development of a modular, open-source MATLAB/Simulink toolbox.
  • Implementation of a drag-and-drop interface for user-friendliness.
  • Integration of physics-based, data-driven, and hybrid (e.g., physics-informed neural networks) modeling approaches.

Main Results:

  • Successful simulation of flow reactors using the toolbox.
  • Accurate identification of reaction parameters from experimental transient data.
  • Effective optimization of reactor operating points and configurations.
  • Experimental validation confirming the toolbox's practical applicability.

Conclusions:

  • The developed toolbox offers a valuable resource for researchers and engineers in flow reactor technology.
  • It streamlines the process of parameter determination and reactor optimization.
  • The toolbox has the potential to significantly reduce the cost and time associated with these tasks.