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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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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Effect of Flow on CO2 Corrosion of Self-Healing Metallic Coatings

Internal corrosion is an issue that affects natural gas pipelines, a significant part of the United States energy infrastructure. Over time, this corrosion has worn away longstanding pipelines due to the original construction materials used for the lines and impurities in the gas and liquid streams flowing through them. The main impurities in natural gas are H2O, CO2, H2S, and O2. One of the leading causes of corrosion is the CO2 dissolved in water, giving rise to carbonic acid formation, which can further dissolve the steel pipe. The National Energy Technology Laboratory (NETL) has been studying different solutions to this problem. One potential answer is a self-healing sacrificial metallic coating applied by a novel cold spray technique. Previous studies at NETL have shown that the zinc-rich self-healing coating can withstand corrosion through passivation and create a barrier to the diffusion of corrosive species. This study explored the impact of flow on corrosion with this coating and carbon steel when exposed to a saturated CO2 environment by simulating the pipeline flow profile in a small-scale lab setting. The samples were evaluated using multiple electrochemical techniques that found corrosion rates and were backed up with surface analysis to conclude the behavior of the corrosion mechanisms.

CO2 corrosion↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long-distance migrants where the use of landscapes varies by season and, sometimes, age cohort. In this study, we tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age-structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion-sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between-year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first-winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age-related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex-based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Chickpea protein hydrolysate as a novel plant-based cryoprotectant in frozen surimi: Insights into protein structure integrity and gelling behaviors

Chickpea protein (CP) and its enzymatic hydrolysates are one of the most widely consumed pulse ingredients manifesting versatile applications in food industry, such as binders, emulsifiers, and meat protein substitutes. Other than those well-known functionalities, however, the use of CP as a cryoprotectant remained unexplored. Here, in this study, we prepared the chickpea protein hydrolysate (CPH) and investigated its cryoprotective effects to frozen surimi in terms of the protein structure integrity and gelling behaviors. Results indicated that CPH could inhibit myofibrillar protein (MP) denaturation and oxidation during the freeze–thaw cycling, as evidenced by their increased solubility, Ca 2+ -ATPase activity, sulfhydryl concentration, and declined content of disulfide bonds, carbonyl concentration and surface hydrophobicity. Freezing-induced changes on MP secondary structures were also retarded. Moreover, gels prepared from CPH-protected frozen surimi demonstrated more stabilized microstructure, uniform water distribution, enhanced elasticity, gel strength and water holding capacity. The CPH alone, at a reducing addition content of 4% (w/w), exhibited comparable cryoprotective performance to that of the commercial formulation (4% sucrose and 4% sorbitol). Therefore, this study provides scientific insights for development of pulse proteins as novel and high-performance food cryoprotectants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Imaging from Macro to Nanoscale: Multimodal Advances in Chemical and Biomedical Imaging

Imaging increasingly serves as a multiscale framework for linking molecular mechanisms to cellular behavior, tissue architecture, and organ phenotypes in biology and unraveling fundamental processes in chemistry, physics and materials science. This Perspective highlights recent advances in chemical and biomedical imaging across macro-, micro-, and nanoscales, using representative examples published in Chemical and Biomedical Imaging (CBMI). At the macroscale, we discuss chemically selective MRI, including endogenous and exogenous CEST strategies, together with photoacoustic imaging as a hybrid modality with functional and chemical contrast. At the microscale, we consider fluorescence, label-free optical and vibrational imaging, and selected X-ray approaches that expand sensitivity, specificity, and temporal resolution in biological and materials systems. At the nanoscale, we highlight super-resolution fluorescence microscopy, single-molecule methods, tip-enhanced Raman spectroscopy, and correlative imaging strategies that resolve local heterogeneity and molecular organization. Across scales, a common theme emerges that advances in probes, contrast mechanisms, instrumentation, and sample handling are enabling chemically informed imaging that connects molecular specificity with biological context.

multiscale imaging↗
DOE OSTI2024

LANSCE Science Overview [Slides]

LANSCE is primarily focused on three science questions for NNSA and contributes to several other programs. We will primarily consider the NNSA questions: (1) How can we advance our understanding of dynamic material behavior using focused experiments? (2) What tools are needed to address Advanced Manufacturing and Aging? (3) How do we constrain the nuclear reaction networks involved in weapons?

36 MATERIALS SCIENCE↗
DOE OSTI2021

Anomalous behavior in high-pressure carbonaceous sulfur hydride

A new experimental study reported behavior in a high-pressure carbon-sulfur-hydrogen system that has been interpreted by the authors as superconductivity at room temperature. The sudden change of electrical resistance at a critical temperature and the change of the R vs. T behavior with an applied magnetic field point to superconductivity. This is a very exciting study in one of the most important areas of science, hence, it is crucial for the community to investigate these findings and hopefully reproduce these results. In this paper, we present calculations that expand upon the arguments put forth, and offer some speculations about physical mechanisms that might explain the observed data. Here, we show that there are errors in the analysis presented in the experimental paper, and with the correct analysis, the reported R vs. T data significantly deviate from the expected behavior. In particular, the extremely sharp change in resistance at the superconducting transition is not consistent with a strongly type II superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

CVEN 5899 Internship report

The internship is under the project of Center for Space and Earth Science student fellow program of Los Alamos National Laboratory (LANL). The topic of the project is grain-scale prediction of hypervelocity projectile penetration into terrestrial and extraterrestrial granular materials, which is also my PhD research topic. In this research, we would like to use discrete element method (DEM) to simulate the hyper-speed penetration into granular materials and compare with experiment results. The experiment is done by the Proton Radiography (pRad) team of LANL. In the experiment, a jet traveled through the sand with velocity of 4 km/s (fig.1). Our goal is to develop the large deformation constitutive model by the stress and strain upscaling, study the fluid (air) behavior and particle fracture during the hyperspeed penetration.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

Increasing population and resource-intensive lifestyles are driving enhanced demands for clean water, food, and energy. In parallel, land-use change, climate change, and perturbations—including drought, floods, fires, and early snowmelt—are significantly reshaping interactions within watersheds throughout the world. While watersheds are the Earth's key functional unit for assessing and managing water resources, hydrological processes in watersheds also mediate biogeochemical interactions that support terrestrial life on Earth (Kaushal, Gold, Bernal, & Tank, 2018; National Research Council, 2012). Although society is dependent upon clean water availability, tractable prediction of watershed hydrobiogeochemical behavior, including watershed response to perturbations, remains a challenge. Central to the challenge are complex, multiscale interactions between plants, microorganisms, organic matter, minerals, dissolved constituents, and migrating fluids, which occur within and across bedrock-to-canopy compartments and along extensive lateral gradients of a watershed. Several recent community reports have synthesized formidable challenges associated with watershed science and technology (AGU, 2018; Blöschl et al., 2019). Here, we discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex watershed hydrobiogeochemical behavior, which are important for underpinning optimized natural resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerated oxidation of epoxy thermosets with increased O 2 pressure

Polymer oxidation is usually accelerated with temperature, which is therefore applied in nearly every experimental approach dealing with predictive materials aging. Because of this simple approach, we may tend to neglect that the effective concentration of oxygen also acts as a rate multiplier for oxidation. Increasing the oxygen partial pressure in an aging environment accelerates oxidation, and while there is often a near proportional increase initially, the effect of additional oxygen usually transitions to a saturation level at some elevated pressure. This has been theoretically described in the general autoxidation scheme and is well recognized. However, for many materials the exact rate behavior under moderately increased oxygen concentration remains to be established. We therefore review epoxy oxidation and offer a broader overview of its behavior under increased O 2 partial pressure. Experimental data are given for a few thermoset materials demonstrating their rate behavior under O 2 partial pressure up to 4 atm, meaning approximately 20 times more than under standard atmospheric conditions. Confirmative evidence suggests that epoxy materials will reach saturation oxidation rates only at significantly higher O 2 partial pressure. In such a high-pressure regime it is theoretically possible to not only accelerate oxidation, but to transition into a condition where O 2 diffusion can be increased without further accelerating the oxidation rate. Finally, this can reduce diffusion limited oxidation effects under specific accelerated aging conditions as a combination of temperature and O 2 partial pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition

The Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophysics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of the Rayleigh-Taylor instability, and therefore, numerical simulation of the full problem is required. However, in order to capture the growth of amplitude of perturbations accurately, both the spatial and temporal discretizations need to be extremely fine for traditional numerical methods, and long-time simulation may become prohibitively expensive. In this paper, we propose efficient reduced order model techniques to accelerate the simulation of the Rayleigh-Taylor instability in compressible gas dynamics. Here, we introduce a general framework for decomposing the solution manifold to construct the temporal domain partition and temporally-local reduced order model construction with varying Atwood number. We propose two practical approaches in this framework, namely decomposition by physical time and by penetration distance. Numerical results are presented to examine the performance of the proposed approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploring pressure-dependent inelastic deformation and failure in bonded granular composites: An energetic materials perspective

In polymer-filled granular composites, damage may develop in mechanical loading prior to material failure. Damage mechanisms such as microcracking or plastic deformation in the binder phase can substantially alter the material’s mesostructure. For energetic materials, such as solid propellants and plastic bonded explosives, these mesostructural changes can have far reaching effects including degraded mechanical properties, potentially increased sensitivity to further insults, and changes in expected performance. Unfortunately, predicting damage is nontrivial due to the complex nature of these composites and the entangled interactions between inelastic mechanisms. In this work, we assess the current literature of experimental knowledge, focusing on the pressure-dependent shear response, and propose a simple simulation framework of bonded particles to study four limiting-case material formulations at both meso- and macro-scales. Further, to construct the four cases, we systematically vary the relative interfacial strength between the polymer binder and granular filler phase and also vary the polymer’s glass transition temperature relative to operating temperature which determines how much the binder can plastically deform. These simulations identify key trends in global mechanical response, such as the emergence of strain hardening or softening regimes with increasing pressure which qualitatively resemble experimental results. By quantifying the activation of different inelastic mechanisms, such as bonds breaking and plastically straining, we identify when each mechanism becomes relevant and provide insight into potential origins for changes in mechanical responses. The locations of broken bonds are also used to define larger, mesoscopic cracks to test various metrics of damage. We primarily focus on triaxial compression, but also test the opposite case of triaxial extension to highlight the impact of Lode angle on mechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A review of the metastable omega phase in beta titanium alloys: the phase transformation mechanisms and its effect on mechanical properties

Since its discovery in 1954, the omega (ω) phase in titanium and its alloys has attracted substantial attention from researchers. The β-to-ω and ω-to-α phase transformations are central to β-titanium alloy design, but the transformation mechanisms have been a subject of debate. With new generations of aberration-corrected transmission electron microscopy and atom probe tomography, both the spatial resolution and compositional sensitivity of phase transformation analysis have been rapidly improving. Furthermore, this review provides a detailed assessment of the new understanding gained and related debates in this field enabled by advanced characterization methods. Specifically, new insights into the possibility of a coupled diffusional-displacive component in the β-to-ω transformation and key nucleation driving forces for the ω-assisted α phase formation are discussed. Additionally, the influence of ω phase on the mechanical properties of β-titanium alloys is also reviewed. Lastly, a perspective on open questions and future direction for research is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum Machine Learning Applications in High-Energy Physics

Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.

Delgado, Andrea↗