An in-depth review on sensing, heat-transfer dynamics, and predictive modelling for aircraft wheel and brake systems
An in-depth review on sensing, heat-transfer dynamics, and predictive modelling for aircraft wheel and brake systems
An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes.
aircraft wheels and brakes, brake temperature prediction, condition monitoring, heat transfer dynamics, machine learning prediction models, physics-informed and hybrid modeling
Ramachandra, Lusitha S.
8c3dd340-35aa-4978-80d5-ba2c6f381e14
Jennions, Ian K.
fe89d6c8-e7b6-4db3-8e13-5f7467c4ef38
Avdelidis, Nicolas P.
a3de63a8-48ff-4664-b6fa-8650721f39bb
30 January 2026
Ramachandra, Lusitha S.
8c3dd340-35aa-4978-80d5-ba2c6f381e14
Jennions, Ian K.
fe89d6c8-e7b6-4db3-8e13-5f7467c4ef38
Avdelidis, Nicolas P.
a3de63a8-48ff-4664-b6fa-8650721f39bb
Ramachandra, Lusitha S., Jennions, Ian K. and Avdelidis, Nicolas P.
(2026)
An in-depth review on sensing, heat-transfer dynamics, and predictive modelling for aircraft wheel and brake systems.
Sensors, 26 (3), [921].
(doi:10.3390/s26030921).
Abstract
An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes.
Text
sensors-26-00921-v2
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Accepted/In Press date: 27 January 2026
Published date: 30 January 2026
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© 2026 by the authors.
Keywords:
aircraft wheels and brakes, brake temperature prediction, condition monitoring, heat transfer dynamics, machine learning prediction models, physics-informed and hybrid modeling
Identifiers
Local EPrints ID: 510118
URI: http://eprints.soton.ac.uk/id/eprint/510118
ISSN: 1424-8220
PURE UUID: 11189cd8-ebee-458f-b8c4-4acbdd23558c
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Date deposited: 17 Mar 2026 18:09
Last modified: 18 Mar 2026 03:11
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Author:
Lusitha S. Ramachandra
Author:
Ian K. Jennions
Author:
Nicolas P. Avdelidis
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