Modeling Exhaust-Generated Aerodynamic Pressure Loads on Airfield Matting Repair Systems
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Date
2021-11-30Type of Degree
Master's ThesisDepartment
Civil and Environmental Engineering
Restriction Status
EMBARGOEDRestriction Type
Auburn University UsersDate Available
11-30-2026Metadata
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Airfield matting systems are commonly used for rapid repair of damaged runways to facilitate continuity of critical operations. Under normal service conditions, matting repair systems are subject not only to wheel loads exerted by airfield traffic but also aerodynamic pressure loads resulting from high-speed, turbulent exhaust plumes produced by jets during taxi and take-off. Matting systems employed by the U.S. Air Force have been tested with respect to wheel loads, but probabilities of failure due to pressure loads produced by jet exhaust have not yet been established. This thesis presents a numerical approach for preliminary estimation of worst-case matting anchor forces resulting from exhaust-generated pressure loads. Using a two-dimensional computational fluid dynamics model, system behavior is evaluated by means of a parametric study of six system variables, of which the most significant are (1) distance between engine and matting, (2) depth of cavity openings at matting edges, and (3) engine exhaust velocity. The results demonstrate that matting systems are likely to experience net uplift in typical service scenarios, driven by a combination of flow separation, cavity pressurization, and spatio-temporal pressure gradients. Worst-case anchor pull-out forces, computed according to a tributary-area approach, are estimated to fall in the range of 130 lb. [581 N] to 979 lb. [4,350 N], depending on assumed load-sharing behavior among anchors and the size of the repair site. Load estimates may be considered along with published anchor pull-out capacities for different runway pavement structures to evaluate probabilities of anchor failure. Field testing of instrumented matting systems during jet taxi and take-off sequences is recommended as the best next step toward understanding system behavior.