Survival and Behaviors of Juvenile and Subadult Swainson’s Hawks
Survival and Behaviors of Juvenile and Subadult Swainson’s Hawks
54 ENVIRONMENTAL SCIENCES↗
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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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Understanding Multiscale Defect Formation Process and Phase-Switching Behavior in Shape Memory Functional Oxides
In the last 3 years, we have worked on both the nanoscale and microscale understanding of TMPT, defect formation and evolution, and plasticity in SMFOs, specifically in YSTZ. The validated computational investigations have provided unmatched insights into the deformation and shape memory behaviors of SMFOs that could not be obtained from experiments.
36 MATERIALS SCIENCE↗
Comprehensive Characterization of Helium-Induced Degradation of the Friction Stir Weld on Neutron-Irradiated 304L Stainless Steel
The report describes new experimental results on the mechanical performance of the friction stir welds made on neutron-irradiated 304L stainless steel with helium. The report focuses on helium-related issues (e.g., the helium-induced degradation in the welded joint), aiming at the need to repair irradiated components of nuclear power plants. The friction stir welds analyzed here were previously produced at the US Department of Energy’s Oak Ridge National Laboratory, and initial characterization work was performed, mostly addressing the microstructure and if macroscopic cracks and helium bubbles present. The present work attempts to perform a more comprehensive study to assess mechanical performance (i.e., microhardness distribution, tensile properties, tensile deformation behaviors, and fractography analysis).
36 MATERIALS SCIENCE↗
The role of unit cell topology in modulating the compaction response of additively manufactured cellular materials using simulations and validation experiments
Additive manufacturing has enabled a transformational ability to create cellular structures (or foams) with tailored topology. Compared to their monolithic polymer counterparts, cellular structures are potentially suitable for systems requiring materials with high specific energy-absorbing capability to provide enhanced damping. In this work, we demonstrate the utility of controlling unit-cell topology with the intent of obtaining a desired stress–strain response and energy density. Using mesoscale simulations that resolve the unit-cell sub-structures, we validate the role of unit-cell topology in selectively activating a buckling mode and thereby modulating the characteristic stress–strain response. Simulations incorporate a linear viscoelastic constitutive model and a hyperelastic model for simulating large deformation of the polymer under both tension and compression. Simulated results for nine different cellular structures are compared with experimental data to gain insights into three different modes of buckling and the corresponding stress–strain response.
36 MATERIALS SCIENCE↗
Development of a MOOSE-based crystal plasticity model with irradiation defect evolution for irradiation creep in 316
Irradiation creep and irradiation swelling are two of the lifetime limiting factors for structural materials in nuclear reactors. These mechanical effects are driven by irradiation defect evolution and the interaction of those defects with dislocations in the microstructure. We present here a coupled cluster dynamics – crystal plasticity approach to model irradiation swelling and creep behavior in 316 SS. The time-dependent evolution of irradiation defects is calculated with a cluster dynamics approach and passed to the crystal plasticity model to compute the dislocation evolution. We show the impact of the irradiation defect evolution on the stress state in the material, which drives inelastic deformation through dislocation motion. The inelastic deformation in the 316 SS is dependent on the dose rate, where the inelastic deformation driven by the early-stage irradiation defect evolution determines the mechanical behavior of the 316 SS.
316 Stainless Steels↗
A Theoretical Analysis on the Dynamic Behavior and Electromechanical Response of PZT
Piezoelectric materials are used as a power source in stockpile components for safety and reliability assurances. The objective of this project is to gain insights into the fundamental pressure-induced behavior and electromechanical response of lead zirconate titanate (PZT). Specifically, to establish the basis for an accurate, physics-based material model for robust model-based design to rapidly optimize PZT-based materials and components for performance studies. Established ab-initio methods are used to interrogate and understand the dynamic behavior of PZT as a function of composition (50/50, 65/35, 80/20, 95/5) and dopants (La, Nb) to overcome the costly and time-consuming experimental methodologies. New cold curves for pure and doped single-crystal PZT are obtained as the reference equation-of-state (EOS). A negligible change in the pressure responses was observed for the systems and strains studied. Dielectric and piezoelectric responses of pure and doped single-crystal PZT were also calculated as a function of pressure. For undoped PZT, there is a clear and orderly pressure and composition dependence. For doped PZT, there is a significant increase in the responses, but the behavior is very disordered and inconclusive.
36 MATERIALS SCIENCE↗
Developing a Deep Learning-Computer Vision Framework to Monitor Avian Interactions with Solar Energy Facility Infrastructure (Final Technical Report)
The project addressed an inability to monitor avian interactions with photovoltaic (PV) solar energy facilities necessary for understanding PV solar impacts on birds. In the project, machine-vision technology that continuously monitors avian activities at PV solar facilities was developed. The technology includes four machine-learning (ML) models, each of which accomplishes a specific task in detecting birds and classifying their activities in live or recorded videos—detecting and tracking moving objects, differentiating birds from other objects, detecting bird collisions with solar panels, and classifying non-collision bird activities around PV facilities. Major project outcomes include adoption by two of DOE SETO’s SolWEB projects, providing novel observational data on birds to promote co-location of PV solar development and habitat conservation, known as ecovoltaics.
14 SOLAR ENERGY↗
Advancements in Heat Transfer and Fluid Mechanics (Fundamentals and Applications)
Thermo-fluid science is a foundational discipline for numerous mechanical systems, particularly in energy production and building equipment, where thermal and mechanical energy transfer play critical roles. Advancements in heat transfer and fluid mechanics have significantly enhanced these systems, driving progress in associated market sectors. For instance, evaporator and condenser coils are essential for optimizing vapor compression cycles in building equipment. Heat pumps used for space and water heating constitute a major share of building systems, with microchannel heat exchanger technology at the forefront of these innovations. It is worth mentioning that advancements in heat transfer and fluid mechanics significantly alleviate the challenge of designing energy-efficient building equipment. The development of research techniques has contributed significantly to the advancement of science; for example, in the experimental field, non-intrusive measurement techniques such as Particle Image Velocimetry (PIV) are now capable of resolving flow behavior in a 3D format for different length and time scales.
42 ENGINEERING↗
On the regeneration of fish scales: structure and mechanical behavior
Fish scales serve as a dermal armor that provides protection from physical injury. Owing to a number of outstanding properties, fish scales are inspiring new concepts for layered engineered materials and next-generation flexible armors. Although past efforts have primarily focused on the structure and mechanical behavior of ontogenetic scales, the structure–property relationships of regenerated scales have received limited attention. In the present study, common carp ( Cyprinus carpio ) acquired from the wild were held live in an aquatic laboratory at 10°C and 20°C. Ontogenetic scales were extracted from the fish for analysis, as well as regenerated scales after approximately 1 year of development and growth. Their microstructure was characterized using microscopy and Raman spectroscopy, and the mechanical properties were evaluated in uniaxial tension to failure under hydrated conditions. We note the strength, strain to fracture and toughness of the regenerated scales were significantly lower than those of ontogenetic scales from the same fish, regardless of the water temperature. Scales that regenerated at 20°C exhibited significantly higher strength, strain to fracture and toughness than those regenerated at 10°C. The regenerated scales exhibited a highly mineralized outer layer, but no distinct limiting layer or external elasmodine; they also possessed a significantly lower number of plies in the basal layer than the ontogenetic scales. The results suggest that a mineralized layer develops preferentially during scale regeneration with the topology needed for protection, prior to the development of other qualities.
59 BASIC BIOLOGICAL SCIENCES↗
Solidification Behavior and Microstructure Evolution in Dissimilar Electron Beam Welds between Commercially Pure Iron and Nickel
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Hochanadel, Joris Erich↗
Perspectives for self-driving labs in synthetic biology
Self-driving labs (SDLs) combine fully automated experiments and data collection with artificial intelligence (AI) and control algorithms that decide not only the set of parameters for the next experiment, but also potentially which scientific hypotheses to test. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. Whereas there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single, easily accessible, target for affecting the incredibly wide repertoire of biological cell behavior. Since they can provide large amounts of high-quality data, SDLs can be a platform for AI to develop approaches to systematically convert data into scientific knowledge systems. These knowledge systems can be used both to understand the biological world and to design bioengineered systems to fit a desired specification (inverse design). However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
59 BASIC BIOLOGICAL SCIENCES↗
Scientific Discovery with Physics-Informed System Identification (Abbreviated Report)
My fellowship research focused on making physics-based simulations faster and more useful through machine learning. Many problems in science and engineering are governed by partial differential equations, but high-fidelity simulations are often too expensive to run repeatedly. I worked on improving Latent Space Dynamics Identification (LaSDI), a reduced-order modeling framework that compresses large simulation data sets into a smaller representation and then learns how that representation evolves over time. The motivation was to develop reduced models that remain accurate for more challenging systems, especially when predictions must remain reliable over long time intervals or when the underlying dynamics are more complicated than standard methods can easily handle. I also contributed to related work on Quandary, a high-performance software effort for simulation and control of open quantum systems, before focusing primarily on Latent Space Dynamics Identification methods. The main outcomes of the fellowship were two new algorithms (both of which were published), Rollout-LaSDI and Higher-Order LaSDI, together with supporting work on multi-stage Latent Space Dynamics Identification. Rollout-LaSDI improved long-term prediction by training the model to stay accurate over extended time horizons, and Higher-Order LaSDI broadened the method so it could model systems with higher-order time dynamics. My contributions to multistage Latent Space Dynamics Identification also helped show that its later training stages could be simplified without losing effectiveness, and that this behavior held across different model architectures and training strategies. Taken together, these advances improved the accuracy, flexibility, and practical value of reduced-order modeling tools for computational science.
97 MATHEMATICS AND COMPUTING↗
Supporting Co-Design of Extreme-Scale Systems with In Situ Visual Analysis of Event-Driven Simulations (Final Report)
Extreme-scale systems for compute- and data-centric science are pushing the boundaries of system architectures in order to achieve ambitious goals in terms of computing capability, resilience, and power efficiency. Future systems necessitate emerging designs that incorporate new technologies, system organizations, and computer science techniques, and these changes are not yet well understood. Discrete-event-driven simulation of computing system architectures and subsystems has emerged as a productive and cost-effective means to evaluating potential designs, along with capabilities for executing these simulations of extreme-scale systems. However, techniques for analyzing the behavior of these simulations have not kept pace with our ability to perform the simulations, preventing us from extracting the most value from this promising approach. The objective of this project is to support the codesign of extreme-scale system architectures for compute and data-centric science through research and development of novel methods for analysis and visualization of large-scale event-driven simulations.
97 MATHEMATICS AND COMPUTING↗
Direct growth of crystalline SiGe nanowires on superconducting NbTiN thin films
Novel heterostructures created by coupling one-dimensional semiconductor nanowires with a superconducting thin film show great potential toward next-generation quantum computing. Here, by growing high-crystalline SiGe nanowires on a NbTiN thin film, the resulting heterostructure exhibits Ohmic characteristics as well as a shift of the superconducting transition temperature (T c ). The structure was characterized at atomic resolution showing a sharp SiGe/NbTiN interface without atomic interdiffusion. Lattice spacing, as calculated from large-area x-ray diffraction experiments, suggests a potential preferred d-spacing matching between (200) NbTiN and (110) SiGe grains. The observed out-of-plane compressive strain within the NbTiN films coupled with SiGe nanowires explains the downward shift of the superconductivity behavior. The presented results post scientific insights toward functional heterostructures by coupling multi-dimensional materials, which could enable tunable superconductivity that benefits the quantum science applications.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Knowledge gaps for neuromorphic ionic computing
BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.
Neuromorphic↗
From Data to Discovery: AI's Transformative Role in Thin Film Research
The advancement of thin film technologies is pivotal for progress in numerous fields, including energy, electronics, and quantum computing. However, the traditional trial-and-error approach to materials discovery is inherently slow and inefficient. This presentation will showcase how artificial intelligence (AI) is transforming thin film research by enabling a data-driven paradigm shift. We will highlight our past successes in applying AI to understand radiation damage in thin film oxides, demonstrating how graph analytics can unravel complex material behavior. Additionally, we will provide insights into our current work at the National Renewable Energy Laboratory, where we are leading the charge in autonomous materials science. Backed by a $14M investment in our characterization facility, we are developing AI-guided workflows that seamlessly integrate experimentation and AI-guided decision-making. By harnessing the power of AI, we aim to accelerate the discovery and design of high-performance thin films, propelling innovation across a multitude of industries.
36 MATERIALS SCIENCE↗