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Jacob Hochhalter

Publications and source records attributed to Jacob Hochhalter.

17 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-13792-DND

Extreme structural requirements of future aerospace vehicles motivate the development of new, ultra-durable materials and game-changing methodologies for material certification and sustainment. Computational simulations spanning many orders of magnitude in length and time scales—from the nanoscale of underlying damage processes to the larger scales of continuum cracks—are being developed to support these requirements. Results of these simulations are used to deduce key aspects of material response to loads and environments, including: - The microstructural mechanics that govern metal fatigue crack initiation and growth - The variety and complexity of the dislocation-precipitate interac- tions that underpin plastic behavior and damage evolution - The energetic principles that govern the interaction of water with crack surfaces within a structural component - The mechanics of grain boundary separation Modeling the physics behind these metal fatigue behaviors is extremely complex, and requires intensive, high-fidelity simulations. The knowledge gained through these simulations, however, is forming the keystone for advanced material design and will enable development of more capable, reliable structures for aerospace vehicles.

Jacob Hochhalter↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17800-DND

Currently, there are two national challenge problems that guide much of the research in durability and damage tolerance at NASA Langley. The first, Airframe Digital Twin, is a concept that combines as-built vehicle components, as-experienced loads and environments, and other vehicle-specific characteristics to enable ultrahigh fidelity modeling of aircraft and spacecraft throughout their service lives. The second, Materials Genome Initiative, is an analog to the Human Genome Project, and is intended to improve the rate at which materials scientists can discover, understand fundamental physics, and improve material systems. This presentation will highlight several research projects ongoing at NASA Langley that are in support of the above challenge problems. Two of those topics will be the subject of detailed discussion. First, investigations of microstructurally-small fatigue cracking (MSFC) in Al-2Cu and Al-4Cu, fabricated in-house, will be presented. Single- and oligo-crystals of Al-Cu specimens were loaded in uniaxial fatigue, while high-resolution in-situ measurements of deformation were made using image correlation (IC) in a scanning-electron microscope (SEM). The Al-Cu specimens were then replicated as crystal plasticity finite element models (CPFEM), where evolution of slip localization near grain boundaries was computed. Comparison among experiment and CPFEM is made. In addition, XRay diffraction measurements of the as-fabricated specimens were carried out, where direct measurements of the embedded copper precipitates were made, and their influence on growing MSFCs were directly observed. The second main topic will illustrate ongoing work in the area of so-called damage-sensing particles. In this work, shape-memory alloys are embedded in an aluminum alloy matrix. Upon the propagation of a fatigue crack, these particles undergo a strain-induced phase transformation which is detected using an acoustic sensor, providing real-time information on propagating cracks. Experiments and simulations regarding the development of this system will also be detailed.

Jacob Hochhalter↗
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↗
NASA NTRSDate not supplied

In-situ Testing to Acquire HR-EBSD and DIC Strain Data Within a Coincident Domain

For this project, an inked rubber stamp was applied to a small Inconel 625 specimen. The stamp transferred a thin pattern with microscale features for digital image correlation (DIC). The pattern is easily visible at lower voltages and thin enough to not obstruct backscattered electrons. The unique characteristics of the pattern enabled the concurrent acquisition of DIC and high-resolution electron backscatter diffraction (HR-EBSD) data while the specimen was loaded in-situ. The challenges of in-situ testing and combining EBSD with DIC measurements are discussed. By combining the elastic strains (from HR-EBSD) and total strains (from DIC) the result of this approach is an estimate of stress- strain behavior at points across the specimen surface. This combined dataset can then be used as higher-fidelity data in the calibration of crystal plasticity models.

Will Gilliland↗
NASA NTRSDate not supplied

Application of a Bayesian Framework for Plasticity Model Selection

Interpretable Machine Learning (IML) has performed well when tasked with deriving constitutive material models. However, IML has been shown to prefer models that overfit noise in data, which tends to lead to bloat and a decrease in interpretability. Due to these issues, the ability of IML to reliably derive models that fit the data and are both interpretable and generalizable is limited. A method developed recently has shown promise to improve upon traditional IML by using a Bayesian fitness definition for the evolution of free-form models with non-deterministic parameters. This framework was developed for genetic-programming-based symbolic regression(GPSR) and involves model parameter estimation using Sequential Monte Carlo sampling (SMC).The method has demonstrated a reduction in bloat when dealing with noisy data in comparison to conventional GPSR. The results of this framework applied to stress-strain data for copper show models that more effectively predict the experimental data better than was previously shown with GPSR.

plasticity↗
NASA NTRSDate not supplied

Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis

Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.

uncertainty quantification↗