Journal of Aerospace Engineering

This paper discusses research to develop a computer modeling technique to predict the statistical behavior of fatigue in fully lamellar TiAl. Probabilistic micromechanics was used to relate the random nature of the microstructure to the fatigue response of smooth round bar specimens. The effort showed that models proposed in the literature addressing the microstructural mechanisms that govern fatigue damage accumulation could be combined with the concept of mesomechanics to properly account for the characteristic sizes and multiple phases of fatigue. The capability developed can be used to virtually test large numbers of specimens for which actual laboratory testing would be cost prohibitive.

This paper addresses the stochastic modeling for advanced jet engine applications and also presents an overview of the basic theoretical aspects of different useful stochastic field models and then illustrates their application to practical problems. It is shown that stochastic fields adequately capture key random aspects of engine environment and behavior. The illustrated applications address stochastic modeling issues that are typical for engine structural analysis, such as time-varying operating speed conditions, manufacturing geometry deviations, and engine vibration, that produce highly nonlinear responses. This paper discusses stochastic modeling for two situations often met in practice: (1) stochastic process/field models with known statistics; and (2) stochastic process/field models with unknown statistics. Illustrative examples are used to highlight features of different stochastic field modeling techniques.

This paper develops a probabilistic analysis framework to predict the fatigue reliability of laminated composites and applies it to tapered composites used in rotorcraft structures. The method combines finite-element analysis, composite laminate analysis, fatigue failure modeling, and first-order reliability analysis to achieve this objective. This methodology is demonstrated on a composite helicopter rotor hub test specimen with thick, thin, and tapered regions. The specimen is subjected to centrifugal and oscillatory bending loads. The failure mechanism observed for this type of fatigue loading is an initial tension crack followed by internal delamination at the thick-to-taper transition, where internal ply drop-offs occur. The proposed method predicts the probability of delamination initiation for such rotor hubs. The method is validated using full-scale test results.

The Comet Nucleus Tour (CONTOUR) is one of NASA's Discovery series missions in which a single spacecraft will be launched in July 2002 to journey to the coma of comets Encke and Schwassmann-Wachmann-3 at velocities of 28.2 and 14 km/s, respectively. A multishock bumper dust shield with four ceramic fabric bumpers and a Kevlar-based rear wall, normally aligned to the dust velocity field vector, will provide the protection. Various configurations of the prototype shield were shot with surrogate nylon particles of various diameters at about 7 km/s to determine empirical ballistic limit equations. The performance is scaled to flyby velocities and comet dust particles using laws of momentum and kinetic energy and is related to the expected comets' dust fluence to determine flyby distances from the nuclei for a desired level of reliability. Empirical damage models to predict the size of the holes and mass loss of each layer are developed from measurements of the damage on the prototype targets. For small dust particles, a shield erosion model is assumed to only affect the first bumper. The damage and erosion models are incorporated into a Monte Carlo probabilistic simulation that predicts the shield damage and the probability of no penetration of the shield. This paper describes the methodology to evaluate the shield performance.

Structural dynamic models are used to simulate the performance of structures in a dynamic environment. The predictive accuracy of a model has been defined as the accuracy of predicted structural response under conditions for which the structure has not been tested. This paper addresses the quantification of modeling uncertainty, not including the additional uncertainties that may be introduced by the dynamic environment itself. Uncertainty is quantified relative to measured quantities (i.e., experimentally derived modal frequencies and displacements). Although these “measurements” are known to be uncertain themselves, they are taken as the “truth” reference, so modeling uncertainty by definition includes experimental uncertainty as well as parametric uncertainty and the uncertainty of model form (the equations of motion). This paper shows how this modeling uncertainty can be derived by comparing analysis and test modes of generically similar structures and thereafter be used to evaluate the accuracy of numerical simulations based on prior analysis and test experience. Practical examples are given.

This paper presents approaches for integrating multidisciplinary optimization and probabilistic methods to perform reliability-based multidisciplinary optimization. The approaches are built into a framework that allows solution of optimization problems, wherein system parameters including dimensional tolerances, material properties, boundary conditions, loads, and model predictions are uncertain or variable. This approach directly supports quality engineering because it allows engineers to specify manufacturing tolerances required to achieve the desired product reliability, and it results in robust designs that are optimal over the range of variable conditions because it considers uncertainties during the optimization process. The basic reliability-based multidisciplinary optimization methodology has been demonstrated to design engine components, aircraft lap joints, and transport aircraft wings. Herein this methodology is reviewed and then the focus is on demonstrating a new framework that makes it possible to use these methods with commercial CAD/CAE tools and support commercial shape parameterization to enable shape optimization and consideration of manufacturing uncertainties.