Aerospace Data Analysis

Advanced aerodynamic and flight data analysis project focused on optimizing aeronautical performance through data science and machine learning techniques.

Project Overview

This project focuses on the systematic analysis of large datasets from wind tunnel tests, numerical simulations, and real flight data to identify patterns and optimize aerodynamic performance.

Analysis Methodologies

Advanced Statistical Analysis

• Multivariate regression for aerodynamic parameter correlation
• Analysis of Variance (ANOVA) for critical factor identification
• Principal Component Analysis (PCA) for dimensionality reduction
• Machine learning for operational performance prediction

Processing and Data Handling

• Digital filtering and preprocessing of high-frequency telemetry signals
• Spatial interpolation of 3D velocity and pressure fields
• Environmental corrections for flight data standardization
• Advanced visualization of complex multidimensional datasets

Technology Stack

• Matlab: Primary environment for numerical and statistical analysis
• Python: Machine learning, automation and visualization with matplotlib/seaborn
• Julia: Intensive numerical calculations and algorithmic optimization
• Excel: Reporting and dashboards for non-technical stakeholders

Achieved Results

The analysis enabled significant improvements:

Aerodynamic Performance

• 15% improvement in aerodynamic efficiency (L/D ratio)
• Identification of optimal configurations for different operational conditions
• 8% reduction in parasitic drag through shape optimization

Predictive Models

• ML models with >95% accuracy for performance prediction
• Multi-objective optimization algorithms for design trade-offs
• Normalized database for cross-platform correlations between different aircraft

Practical Applications

• Design optimization of airfoils for UAVs and commercial aircraft
• Validation of CFD models through comparison with experimental data
• Development of empirical correlations for quick aerodynamic assessment
• Decision support for configuration choices in preliminary design phase