An In-Depth Review on Sensing, Heat-Transfer Dynamics, and Predictive Modeling for Aircraft Wheel and Brake Systems.

Area of Science:

• Aerospace Engineering
• Thermal Science
• Data Science

Background:

• Accurate prediction of aircraft wheel and brake (W&B) temperatures is vital for landing gear safety, operational decisions, and condition monitoring.
• Existing thermal behavior studies are fragmented and lack operational relevance, necessitating a comprehensive review.

Purpose of the Study:

• To evaluate the state of knowledge for a non-intrusive, temperature-centric monitoring framework for aircraft W&B.
• To identify research gaps and opportunities in thermal modeling and data-driven prediction for W&B systems.

Main Methods:

• Surveyed sensing techniques including optical, electromagnetic, acoustic, and infrared (IR) thermography.
• Reviewed traditional machine learning, sequence-based models, and hybrid physics-data approaches.
• Synthesized findings on heat transfer pathways and cooling behaviors during different flight phases.

Main Results:

• Identified four core gaps: scarcity of real-flight thermal data, insufficient multi-physics integration, limited IR thermography use, and lack of advanced transient predictive models.
• Highlighted opportunities in emissivity-aware IR thermography and multi-modal datasets.
• Noted challenges including measurement uncertainty, environmental sensitivity, and model generalization.

Conclusions:

• Established a foundation for a thermography-enabled W&B temperature prediction framework.
• Emphasized the need for advanced predictive models for transient thermal dynamics.
• Proposed future directions focusing on multi-modal data and improved IR thermography applications.