Machine Learning Predictions for MHD Second-Grade Fluid Flow with Activation Energy

A dataset containing results from a numerical simulation of magnetohydrodynamic (MHD) flow of a second-grade fluid over a vertical surface. The data was generated by Ruchika Mehta using the Bvp4c solver and includes predictions from a Multiple Linear Regression model for engineering quantities. The dataset was last updated on June 4, 2026.

Use Cases

• Developing surrogate models for skin friction coefficient based on numerical simulation results.
• Predicting Nusselt number for thermal system design using regression analysis.
• Estimating Sherwood number for mass transfer applications in coating technologies.
• Analyzing the combined effects of thermophoresis and Brownian motion on fluid flow.

Strengths

• The regression model demonstrates good prediction ability with coefficients of determination of 0.89.
• The dataset provides a computationally inexpensive surrogate modeling scheme for complex phenomena.
• The numerical analysis includes combined influences of thermophoresis, Brownian motion, activation energy, and Joule heating.

Limitations

• The dataset is very small at 20.8 KB, indicating limited scope.
• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Numerical simulation solved with Bvp4c solver, followed by data-driven Multiple Linear Regression.

Freshness

Last updated 2026-06-04 11:02:48; freshness should be verified.