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Patrick Leser

Publications and source records attributed to Patrick Leser.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Composite Overwrapped Pressure Vessel (COPV) Damage Tolerance Life Analysis Methodology and Test Best Practices

The NASA Engineering and Safety Center (NESC) Deputy Director requested an independent assessment to develop data to understand the limitations of linear elastic fracture mechanics (LEFM) computational methods used to predict fatigue crack growth rate (da/dN) behavior of small detectable cracks in thin metal liners for composite overwrapped pressure vessels (COPVs). The NESC assessment team was also requested to demonstrate a test-based methodology for validating damage tolerance requirements for COPVs with elastically responding metal liners where LEFM methods are not appropriate. This report contains the outcome of the NESC assessment.

Composite Overwrapped Pressure Vessels; Linear Ela↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35322-DND

This talk discusses emerging methods that seek to fuse and integrate physics-based modeling with machine learning. With the recent rise of machine learning and artificial intelligence, there has been a huge surge in data-driven approaches to solve computational science and engineering problems. However, neglecting a priori knowledge of established physical laws and relying solely on data-driven methods can yield unreliable, less interpretable, and/or non-physical results, especially when data is sparse or predictions are required outside of the training data domain. This two part talk presents two distinct approaches for accelerating predictions with machine learning that are grounded and constrained by relevant physics and their application to problems at NASA.

Julian Cuevas Paniagua↗
NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗