Multi-Path Fusion: A Hierarchical Machine Learning Approach for Combining Diverse Data Sets for A Forest Monitoring New Observing System
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Far-field high-energy X-ray diffraction microscopy (ff-HEDM) and the crystal plasticity finite element method (CPFEM) are used to investigate the role of grain-scale (Type-II) residual stresses on the fatigue life of additively manufactured (AM) Inconel alloy 625 (IN-625). Grain-averaged orientations, centroids, and residual elastic strain tensors from ff-HEDM data are used to instantiate a crystal plasticity model to simulate the effect of residual stresses at the grain scale. Simulation results indicate that the presence of tensile residual strains increase stress localization and heterogeneity within grains, triggering an earlier onset of plasticity. A microscale fatigue indicator parameter (FIP) is computed to model the impact of these residual strains on the cycles to fatigue crack nucleation. The crack nucleation model, based on the computed FIPs, predicts a significant reduction in the number of cycles for fatigue crack nucleation for mid- and high-cycle fatigue due to the residual strain induced localization, while the residual strains have minimal impact on low-cycle fatigue life.
The goals of NASA’s Thermoplastic Development for Exploration Applications (TDEA) Project include an assessment of thermoplastic composite joints for space structures by developing and maturing design capabilities, analysis tools and techniques, and manufacturing processes for thermoplastic composites. Through this effort the TDEA project will expand NASA’s in-house thermoplastic composite manufacturing capabilities, develop an understanding of advanced thermoplastic joining techniques relevant to space environments, evaluate the feasibility of reconfigurable composite structures, and advance structural analysis capabilities.1
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N/A - this ANS summary is an abstract in itself. Please see the paper.
Original - A slide package that summarizes research progress of 2025 summer high school internship. To be presented at an internal group presentation to all the high school interns and mentors within C200, plus potential more interested audience from INL. Revision - Based on the original LRS-approved slide package, an updated slide package and a poster were prepared with slight changes (e.g., adjusting the layout of slides and reproducing some plots for better clarity) for the interns' final showcase on August 13. The showcase will be public-facing (i.e., not limited to INL attendees).
The prompt detection of explosions is a key element of the nuclear non-proliferation mission. With traditional sensors being limited in number and scale, smartphones as compact and economical multi-modal sensors are gaining traction and are being deployed. To address the flood of heterogeneous smartphone data, our team proposes a feature extraction using standardized constant-Q frequency bands across acoustic, barometric, and accelerometer data. The work presented in this poster utilizes three explosions collected on smartphones with Idaho National Lab. More explosions are planned in the near future at INL and NNSS. These extracted features will be used in machine learning methods in future iterations.