Skip to main content
  • Home
  • Happenings
  • Events
  • Stochastic modeling and analysis of fin buffeting
Stochastic modeling and analysis of fin buffeting

Stochastic modeling and analysis of fin buffeting

Date1st Jul 2020

Time11:00 AM

Venue Google meet Link: uxy-utdb-ddr https://urlprotection-tko.global.sonicwall.com/click?PV=1&MSGID=2

PAST EVENT

Details

Buffeting occurs in a vertical tail fin of an aircraft when it is subjected to irregular oscillations due to the flow from the wings striking its surface. This phenomenon impacts the aerodynamic performance of the aircraft and may also affect the fatigue life of the fin. Due to the separated flow, the gust from a delta wing is stochastic (random in space and time) in nature and is known to be Gaussian. Past analyses in this regard involved experimental investigations of loads acting on the fin surface. A few numerical studies, carried out earlier, involve high fidelity models that need intensive computational efforts. The study quantifies the forces on the tail fin and its response using an appropriate simplified model of the fin. It also proposes a method to compute the fatigue damage caused due to the random loads and estimate the fatigue life of the fin.



A computational fluid dynamics solver is developed for flow over an airfoil, considering the 2D section of the fin, with a random boundary condition implementing the stochastic velocity input. It is observed that the forces acting on the fin profile are non-Gaussian in nature. Parameters for a dynamically equivalent 2D structure are calculated for the fin by equating the fundamental bending frequency. A solver that couples Navier-Stokes solver with Finite Element structural solver is developed to find the response of the fin using a one-way fluid-structure interaction approach. Further, random vibration analysis is done to estimate the random stresses developed in the fin due to buffeting. The rainflow fatigue damage and the expected life of the fin are calculated based on analytico-computational algorithms. The predictions are validated using Monte Carlo simulations.

Speakers

Mr. GHORPADE AVISHA ASHOK, (AM17S004)

Applied Mechanics