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This paper presents the results of reliability life testing performed on commercial electronic devices that experienced non-destructive single-event latch-up (SEL) during heavy-ion testing.
Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.
The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.
Self-healing offers promise for addressing structural failures, increasing lifespan, and improving durability in polymeric materials. Implementing self-healing in thermoset polymers faces significant manufacturing challenges, especially due to the elevated temperature requirements of thermoset processing. To introduce self-healing into structural thermosets, the self-healing system must be thermally stable and compatible with the thermoset chemistry. This article demonstrates a self-healing microcapsule-based system stable to frontal polymerization (FP), a rapid and energy-efficient manufacturing process with a self-propagating exothermic reaction (≈200 °C). A thermally latent Grubbs-type complex bearing two N-heterocyclic carbene ligands addresses limitations in conventional G2-based self-healing approaches. Under FP's elevated temperatures, the catalyst remains dormant until activated by a Cu(I) co-reagent, ensuring efficient polymerization of the dicyclopentadiene (DCPD) upon damage to the polyDCPD matrix. The two-part microcapsule system consists of one capsule containing the thermally latent Grubbs-type catalyst dissolved in the solvent, and another capsule containing a Cu(I) coagent blended with liquid DCPD monomer. Using the same chemistry for both matrix fabrication and healing results in strong interfaces as demonstrated by lap-shear tests. In an optimized system, the self-healing system restores the mechanical properties of the tough polyDCPD thermoset. Self-healing efficiencies greater than 90% via tapered double cantilever beam tests are observed.
A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.
The multi-scale features and latent space are connected by a nested autoencoder.
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Our control philosophy is to charge the NiH2 cell in such a way that the damage incurred during the charging period is minimized, thus extending its cycle life. This requires nonlinear dynamic model of NiH2 cell and a damage rate model. We must do this first. This control philosophy is generally considered damage mitigating control or life-extending control. This presentation covers how NiH2 cells function, electrode behavior, an essentialized model, damage mechanisms for NiH2 batteries, battery continuum damage modeling, and battery life models. The presentation includes graphs and a chart illustrating how charging a NiH2 battery with different voltages and currents affects damages the battery and affects its life. The presentation concludes with diagrams of control system architectures for tracking battery recharging.
The Conference Proceedings contains 31 papers presented at the Propulsion and Energetics Panel Symposium on Erosion, Corrosion and Foreign Object Damage Effects in Gas Turbines which was held from 25-28th April 1994, in Rotterdam, The Netherlands. The Technical Evaluation Report and the Keynote Address are included at the beginning and discussions follow most papers. The Symposium was arranged in the following Sessions: Operational Experience and Requirements (7); Deposition and Erosion (7); Foreign Object Damage (5); Coatings, Repairs and Materials Aspects - 1 (4); Coatings, Repairs and Materials Aspects - 2 (7); and Testing and Certification Procedures (1).
The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.
This conference, the fifth of its kind, was cosponsored by IEEE, AIAA, and the NASA-Goddard Space Flight Center. Facilities for meetings and other arrangements were the responsibility of NASA-Goddard. Presentations are included in the order in which delivered at the conference and were prepared from papers submitted to the Power Information Center (PIC) through the IEEE. Presentations are arranged in three volumes and five sections reflecting the arrangement of the conference into three days and five sessions. Contents of the volumes are as follows: - Volume I - Advanced Solar Cells - Volume II - Thin Film Solar Cells and Radiation Damage - Volume III - Solar Power Systems Considerations
A nomogram based on the number/distribution of micrometeoroid/debris hits recorded on the Long Duration Exposure Facility (LDEF) is presented. The nomogram permits the user to estimate the total number of impacts that a space structure would encounter as a function of time in orbit, exposed area, and angular location associated with the LDEF. Examples are presented on the application of the nomogram to satellites and space structures of different cross-sections to that of the LDEF. The effects of altitude and orbit inclination are also discussed. To further demonstrate the usefulness of this nomogram, results are compared to impact data measured on the UTIAS polymer matrix composite experiment (AO180). The impact damage occurring on these composite materials is examined, and data on hole size, penetration depth, and delamination area are presented.
Two-dimensional hexagonal boron nitride (hBN) is attractive for several emerging applications. Ion bombardment can be used to modify the hBN properties. However, the understanding of radiation damage buildup in hBN remains limited. Here, we investigate the effects of the dose rate and ion mass on radiation damage buildup by studying 40 nm-thick hBN films bombarded at room temperature with 500 keV 4 He, 15 N, 40 Ar, and 129 Xe ions and comparing with results for ion bombardment of polycrystalline hBN ceramics. Raman spectroscopy is used to quantify damage buildup, and transmission electron microscopy is used for microstructural analysis. Experiments are complemented by molecular dynamics simulations of the formation and evolution of point defects. Lighter ions are found to be more efficient at disordering hBN than heavier ions. This observation points to a critical role of intracascade defect processes. In contrast, a negligible dose rate effect observed suggests limited intercascade defect dynamic annealing processes for these irradiation conditions. These findings provide a fundamental basis for hBN defect engineering.
The present report follows on the cohesive fatigue damage model methodology proposed in NASA-TP-2018-219838. In that report, an empirical function describing the incremental damage due to cyclic loading was used to calculate fatigue damage within a cohesive formulation. The form of the function was developed such that, when integrated at a constant stress amplitude from no damage to failure, it produces a life versus load response that is consistent with an S-N diagram. Therefore, the parameters of the damage model could be obtained by fitting the model predictions to an S-N diagram. The finite element analyses performed demonstrate that the cohesive fatigue accumulation function provides a link between the S-N diagram that describes crack initiation, and the Paris law that characterizes the rate of crack propagation. However, when the model was proposed, it was not known whether the form of the damage accumulation function associated with a desired S-N diagram is unique and, if not, if the link between S-N and the Paris law is unique and independent of the fatigue function selected. In the effort described herein, several alternative forms of the damage function that reproduce the desired features of S-N diagrams were found and evaluated. The effects of each of these functions on the predicted parameters of the Paris law and the propagation threshold are discussed. The results indicate that the predicted exponent m of the Paris law is indeed independent of the damage accumulation function. However, different functions predict different values for the pre-factor C of the Paris law. Therefore, the proper damage accumulation function must be selected by comparison with experiments. One of the new damage accumulation functions proposed herein was found to be particularly useful for analysis because of the ease with which the model parameters can be determined with a minimal amount of experimental information. The effectiveness of the proposed methodology and damage function was demonstrated by conducting analyses of a double cantilever beam test, a mixed-mode bending test, and a three-point bending test of a skin/doubler specimen. The results indicate that the same set of model parameters can provide accurate predictions of the rate of fatigue crack propagation for a variety of material interfaces, mode mixities, load levels, and stress ratios.
Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.
Spirit Aerosystems recently conducted screening tests on thermoplastic skin/stringer panels manufactured with Automated Fiber Placement (AFP) and stamp forming which were joined using Spirit’s Co-Fusion process. Skin and stringer gauge were both set to 0.168 cm. The resulting panel design is 48 cm tall, 49 cm wide, and has 3 stringers welded at a 21.5 cm pitch. Barely Visible Impact Damage (BVID) and Residual Compressive Strength were chosen to conduct a damage tolerance study. Zee stringers, made with IM8/PEKK-FC material, were Co-Fusion welded to a T1100/TC1225 skin. Out of these 5 panels, 1 was chosen to be a BVID survey panel, 1 for pristine compressive strength determination, and 3 to be impacted to create BVID followed by testing for residual compressive strength. On the survey panel, impacts ranged in energy from 40J – 65J and dent depth measurements were conducted at 24 hours, 48 hours, and two weeks after impacting. The residual compressive strength panel failed near the predicted load of 214 MPa. For the residual compressive strength panels, BVID was created at two different mid-bay locations: 1) striking the OML coincident with the stringer and 2) striking the skin IML 2.5 cm away from the stringer flange. Pulse Echo Ultrasonic Testing was used to inspect the panels before and after creating BVID.
Additively manufactured (AM) metals offer the potential for customizable, cost-effective components, but qualification and certification are crucial. Key to this process is understanding pore dynamics under stress, typically analyzed using micro-computed tomography. This study introduces laboratory-scale “betatron” x-rays from laser wakefield acceleration as a high-throughput alternative for x-ray tomography of advanced materials, such as AM AlSi10Mg alloys. Coupled with 3D finite element modeling, this method provides detailed insights into stress-porosity interactions. The approach delivers high-resolution scans, revealing that pore shape and local triaxiality significantly influence fracture dynamics, supporting advanced material characterization. This work also demonstrates the potential and versatility of laser-betatron x-ray μCT for generating large datasets to accelerate our understanding of the stochastic, process-specific nature of pore formation in AM alloys.