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Related Experiment Video

Updated: Aug 13, 2025

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames

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Published on: June 1, 2016

Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode.

Fahad Sameer Alshammari¹, Hamad Jan², Muhammad Sulaiman²

¹Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces an intelligent hybrid technique using neural networks (NN) and the Levenberg-Marquardt algorithm (LMA) to accurately model ion concentrations in rotating-disc electrodes (RDEs). The method offers a reliable approach for analyzing electrochemical processes in various machines.

Keywords:

entropy hydrogen and hydroxide ion concentration machine learning mathematical modeling neural networks non-linear equations numerical solutions rotating-disc electrode

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Last Updated: Aug 13, 2025

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames

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Ultrasound Velocity Measurement in a Liquid Metal Electrode

Ultrasound Velocity Measurement in a Liquid Metal Electrode

Published on: August 5, 2015

Area of Science:

Electrochemistry
Computational Fluid Dynamics
Applied Mathematics

Background:

Rotating-disc electrodes (RDEs) are crucial for analyzing electrochemical processes in machinery like engines and generators.
Modeling these processes often involves complex nonlinear entropy convection-diffusion equations.
Accurate characterization of ion concentrations is vital for performance optimization.

Purpose of the Study:

To investigate surrogate solutions for non-dimensional OH- and H+ ion concentrations at RDEs.
To develop and validate an intelligent hybrid technique for this modeling.
To assess the accuracy and reliability of the proposed computational method.

Main Methods:

Utilized nonlinear entropy convection-diffusion equations with semi-boundaries.
Employed an intelligent hybrid technique combining neural networks (NN) and the Levenberg-Marquardt algorithm (LMA).
Calculated reference solutions using the RK-4 numerical method for training, validation, and testing.

Main Results:

The NN-BLMA approximations accurately represented ion concentrations at the RDE.
Error histograms, absolute error, curve fitting, and regression graphs confirmed the method's resilience and accuracy.
Comparison graphs demonstrated the NN-BLMA procedure's reliability against reference solutions.

Conclusions:

The intelligent hybrid technique (NN-BLMA) provides a reliable and accurate paradigm for modeling ion concentrations in RDEs.
This approach enhances the analysis of electrochemical processes in revolving machines.
The study validates the efficacy of AI-driven methods in complex scientific modeling.