CAMP March Newsletter: Page 6
Professor Yongming Liu, in Clarkson’s Department of Civil and Environmental Engineering, is investigating the fatigue damage prognosis of materials and structures. This topic is still a challenging problem despite tremendous progress made during the past several decades. Fatigue damage accumulation is a multi-scale phenomenon, which involves very different spatial and temporal scales. In addition, huge uncertainties are associated with the fatigue damage accumulation. The development of a general methodology for probabilistic multi-scale fatigue damage modeling would significantly enhance the nation’s aviation safety and the rulemaking of governmental authorities. Professor Liu is currently leading two projects related to probabilistic fatigue damage prognosis of materials and structures. One project, which is sponsored by the NASA Ames Research Center, focuses on aircraft structural materials, such as aluminum and titanium alloys. The other project, which is sponsored by the Federal Aviation Administration’s William J. Hughes Technical Center, is concerned with rotorcraft materials, such as steel and aluminum.
An integrated simulation and experimental approach were proposed to address the multi-scale and probabilistic nature of fatigue damage. In-situ fatigue testing under scanning electron microscopy and optical microscopy will be used to quantify the small crack initiation and propagation behavior at the nanometer to micrometer scales. This is critical for high-cycle fatigue analysis of aircrafts and rotorcrafts. Observed crack growth behavior will be used to develop and validate the mechanism modeling at the macro-level for material and component analysis. A newly developed small temporal scale crack growth model will be investigated for concurrent multi-scale damage analysis. The developed model is fundamentally different from existing approaches for fatigue analysis and has a great potential in unifying multi-scale fatigue damage analysis into a single framework. Following this, advanced statistical methods will be used for probabilistic fatigue damage analysis. Analytical reliability methods, an advanced surrogate modeling technique, random process theory, and Bayesian statistics will be integrated together to address the large uncertainties associated with the fatigue damage accumulation. A schematic presentation of the proposed methodology is shown in Figure 10.