skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Neural network burst pressure prediction in impact damaged Kevlar/epoxy bottles from acoustic emission amplitude data

Conference ·
OSTI ID:482048
;  [1];  [2]
  1. Univ. of Alabama, Huntsville, AL (United States)
  2. National Aeronautics and Space Administration, AL (United States); and others

Acoustic emission (AE) signal analysis has been used to measure the effect of impact damage on the burst pressure of 5.75 inch diameter filament wound Kevlar/epoxy pressure vessels. A calibrated dead weight drop fixture, featuring both sharp and blunt hemispherical impact tups, generated impact damages with energies up to twenty ft-lb{sub f} in the mid hoop region of each vessel. Burst pressures were obtained by hydrostatically testing twenty-seven damaged and undamaged bottles, eleven of which were filled with inert propellant to simulate a rocket motor. Burst pressure prediction models were developed by correlating the differential AE amplitude distributions, Generated during the first pressure ramp to 25% of the expected burst pressure for the undamaged vessels, to known burst pressures using back propagation neural networks. Independent networks were created for the inert propellant filled vessels and the unfilled vessels using a small subset of each during the training phases. The remaining bottles served as the test sets. The eleven filled vessels had an average prediction error of 5.6%, while the unfilled bottles averaged 5.4%. Both of these results were within the 95% prediction interval, but a portion of the vessel burst pressure errors were greater than the {+-}5% worst case error obtained in previous work. in conclusion, the AE amplitude distribution data collected at low proof loads provided a suitable input for neural network burst pressure prediction in damaged and undamaged Kevlar/epoxy bottles. This included pressure vessels both with and without propellant backing. Work is ongoing to decrease the magnitude of the prediction error through network restructuring.

OSTI ID:
482048
Report Number(s):
CONF-960503-; TRN: 97:002904-0080
Resource Relation:
Conference: 1. international conference on nonlinear problems in aviation and aerospace, Daytona Beach, FL (United States), 9-11 May 1996; Other Information: PBD: 1994; Related Information: Is Part Of First international conference on nonlinear problems in aviation & aerospace; Sivasundaram, S. [ed.]; PB: 729 p.
Country of Publication:
United States
Language:
English