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Heather Hickman

Publications and source records attributed to Heather Hickman.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

NASA Engineering and Safety Center Technical Bulletin No. 21-04: Evaluating Appropriateness of LEFM Tools for COPV and Metal Pressure Vessel Damage Tolerance Life Verification

Human spaceflight composite overwrapped pressure vessels (COPVs) and metal pressure vessels can use linear elastic fracture mechanics (LEFM) analysis to demonstrate damage tolerance life per ANSI/AIAA-S-081 for COPVs and ANSI/AIAA-S-080 for metal pressure vessels. LEFM analysis assumptions require that the crack tip plastic zone is small relative to the crack size and is completely surrounded by elastically responding material. Test and analysis have shown that LEFM tools (i.e., NASGRO*) can provide unconservative crack growth predictions for cracks in COPV liners that violate LEFM assumptions. COPV and metal pressure vessel designers should evaluate and address the violation of LEFM plasticity assumptions before using LEFM analysis tools for damage tolerance life verification.

COPV↗
NASA NTRSDate not supplied

Bayesian Symbolic Regression: Addressing Challenges in Estimating Fractional Bayes Factors and Application to Fatigue Crack Growth Modeling

This research pioneers advancements in computational mechanics by integrating Bayesian-based uncertainty quantification into symbolic regression, specifically focusing on the critical task of accurately estimating the fractional Bayes factor for selecting arbitrary equations. In our exploration, we rigorously study two prominent methods—sequential Monte Carlo and the Laplace approximation—employed for computing the fractional Bayes factor. Our findings underscore the limitations of the Laplace approximation, revealing its diminished accuracy in nonlinear and multimodal scenarios. Specifically, the Laplace approximation is shown to underpredict fractional Bayes factor on a wide set of equations associated with a symbolic regression benchmark. This comparative analysis sheds light on the nuanced performance of these techniques, guiding researchers toward more informed choices in uncertainty quantification within symbolic regression. Furthermore, we showcase the practical utility of these enhanced symbolic regression tools through their application to a real-world problem in fatigue crack growth modeling, emphasizing their efficacy in capturing the complexities of mechanical systems.

Geoffrey Bomarito↗