VEXTEC to Present at the 2015 Aircraft Airworthiness & Sustainment Conference

VEXTEC's SCCrack SoftwareBrentwood, TN, March 20, 2015 – VEXTEC Corporation co-founder Robert Tryon will be making two presentations at the 2015 Aircraft Airworthiness & Sustainment (AA&S) Conference. The conference will be held in Baltimore, Maryland March 30 – April 2. Representatives from the Air Force, Navy, NASA, Federal Aviation Administration and industry will be in attendance.

The first paper, “Probabilistic Software Tool to Predict Statistical Distribution of Stress Corrosion Crack Lifetimes” will demonstrate software, SCCrack, developed collaboratively by VEXTEC, University of Virginia and Navmar Applied Science Corporation. SCCrack predicts the statistical distribution of stress corrosion cracking (SCC) in cracked generic components fabricated from various materials [stress corrosion crack size vs. time and SCC life], in particular ultra-high strength steels such as AerMet100, 300M, and 5083 aluminum alloys. The necessary crack growth rate input data was obtained from an experimental effort as well as from the literature.  Random variables including the initial crack size and shape, applied polarization, applied degree of sensitization (when relevant), as well as material SCC rates are also considered.

The second paper, “Uncertainty Propagation in Multi-Disciplinary Computational Analysis” will discuss the uncertainty management analysis framework being developed for the US Air Force under the Airframe Digital Twin program. The analysis methodology, applied to an airframe canopy sill longeron, seeks to optimize computational and testing resources to drive robust predictions at the lowest cost. Uncertainty information is propagated through 3 key disciplines – aerodynamics (magnitude and time of application of the stick forces), structures (computation of the resultant stresses) and fatigue (combines stress input with material microstructural uncertainty). The presentation will also demonstrate how probabilistic virtual inspection can be incorporated in the analysis to predict a condition based maintenance forecast.