Development of a Predictive Model for Runway Water Film Depth
Peida Lin, Chiapei Chou
TL;DR
This paper develops a more accurate model for predicting water film depth on runways during rain to reduce hydroplaning risks.
Contribution
A new empirical model for runway water film depth prediction is developed and validated with high-accuracy data.
Findings
The NTU model outperforms existing models in predicting water film depth on runways.
High-accuracy WFD data was collected using a laser displacement sensor and programmable logic controller.
The model is suitable for integration into runway warning and management systems.
Abstract
Water film depth (WFD) on runways is a key factor contributing to aircraft hydroplaning during takeoff and landing. Thus, the early measurement or prediction of WFD during rain is critical for reducing accidents. Most existing WFD prediction models are derived from experiments conducted on road surfaces. However, an accurate prediction of WFD on runways and reduced hydroplaning risk require a precise empirical prediction model. This study conducted experiments involving four parameters—rainfall intensity, pavement mean texture depth, drainage length, and transverse slope—to develop a WFD dataset specific to different runway conditions. The multiple linear regression method is employed to establish a model for WFD predictions. The proposed National Taiwan University (NTU) model’s predictability is compared with three existing empirical models using NTU and Gallaway datasets. The results…
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Taxonomy
TopicsSmart Materials for Construction · Infrastructure Maintenance and Monitoring · Wind and Air Flow Studies