Design and evaluation of high-speed train head shape based on data-driven analysis.

Area of Science:

• Engineering
• Artificial Intelligence
• Data Science

Background:

• Optimizing high-speed train head shapes is crucial for aerodynamic efficiency and operational performance.
• Existing evaluation methods may lack comprehensive data-driven insights.

Purpose of the Study:

• To develop and validate a data-driven system for evaluating high-speed train head shapes.
• To identify key design factors and establish a quantitative evaluation index system.

Main Methods:

• Utilized a convolutional neural network algorithm for data analysis.
• Applied the Maximal Information Coefficient (MIC) principle for quantifying index values.
• Developed a comprehensive evaluation model for train head shapes.

Main Results:

• Identified key factors influencing high-speed train head shape design.
• Established a data-driven evaluation index system and a comprehensive model.
• The ALFA-X train achieved a high score (1.835), confirming superior operational performance.

Conclusions:

• The developed data-driven evaluation system accurately reflects the performance of high-speed train heads.
• The methodology provides a robust framework for future train head shape optimization.
• Findings align with operational data, validating the model's effectiveness.