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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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The goal of this project is to develop technologies for further improving our understanding of marine animal interactions with marine hydrokinetic (MHK) devices and for reducing the cost and time to obtain scientifically rigorous behavioral information of fish around hydro turbines.
Linking the physics of the relaxation behavior of viscoelastic fluids as they form arrested gel states to the underlying chemical changes is essential for developing predictive controls on the properties of the suspensions. In this study, 3 wt.% laponite suspensions are studied as model systems to probe the influence of salt-induced relaxation behavior arising from the assembly of laponite nanodisks. X-ray Photon Correlation Spectroscopy (XPCS) measurements show that laponite suspensions prepared in the presence of 5 mM concentrations of CaCl2, MgCl2 and CsCl salts accelerate the formation of arrested gel states, with CaCl2 having a significant impact followed by CsCl and MgCl2 salts. The competing effects of ion size and charge on relaxation behavior are noted. For example, the relaxation times of laponite suspensions in the presence of Mg2+ ions are slower compared to Cs+ ions despite the higher charge, suggesting that cation size dominates in this scenario. The faster relaxation behavior of laponite suspensions in the presence of Ca2+ ions compared to Cs+ ions shows that a higher charge dominates the size of the ion. The trends in relaxation behavior are consistent with the cluster formation behavior of laponite suspensions and the electrostatic interactions predicted from MD simulations. Charge balance is achieved by the intercalation of the cations at the negatively charged surfaces of laponite suspensions. These studies show that the arrested gel state of laponite suspensions is accelerated in the presence of salts, with ion sizes and charges having a competing effect on relaxation behavior.
Atomistic molecular dynamics (MD) and a microstructural dislocation density-based crystalline plasticity (DCP) framework were used together across time scales varying from picoseconds to nanoseconds and length scales spanning from angstroms to micrometers to model a buried copper–nickel interface subjected to high strain rates. The nucleation and evolution of defects, such as dislocations and stacking faults, as well as large inelastic strain accumulations and wave-induced stress reflections were physically represented in both approaches. Both methods showed similar qualitative behavior, such as defects originating along the impactor edges, a dominance of Shockley partial dislocations, and non-continuous dislocation distributions across the buried interface. The favorable comparison between methods justifies assumptions used in both, to model phenomena, such as the nucleation and interactions of single defects and partials with reflected tensile waves, based on MD predictions, which are consistent with the evolution of perfect and partial dislocation densities as predicted by DCP. This substantiates how the nanoscale as modeled by MD is representative of microstructural behavior as modeled by DCP.
The microbial reduction of Fe(III) is a major component of Fe cycling in terrestrial and aquatic environments and is affected by the Fe(III) mineralogy of the system. The majority of the research examining the bioreduction of Fe(III) oxides by Fe(III)-reducing bacteria (IRB) has focused on the reduction of poorly crystalline Fe(III) phases, primarily ferrihydrite; however, crystalline Fe(III) oxides like goethite (α-FeOOH) and hematite (α-Fe 2 O 3 ) comprise the majority of Fe(III) oxides in soils. This study examined the bioreduction of goethite and hematite of geogenic and synthetic origin by Shewanella putrefaciens CN2, a well-studied model IRB, in laboratory incubations. Overall, the rate and extent of Fe(II) production were greater for goethite than for hematite, and for geogenic Fe(III) oxides relative to their synthetic analogs. Although there was substantial production of Fe(II) (i.e., >5 mM Fe(II)) in many of the systems, X-ray diffraction analysis of the solids at the end of the incubation did not indicate the formation of any Fe(II)-bearing secondary minerals (e.g., magnetite, siderite, green rust, etc.). The results of this study demonstrate the variability in the extent of bioreduction of geogenic goethite and hematite, and furthermore, that synthetic goethite and hematite may not be good analogs for the biogeochemical behavior of Fe(III) oxides in aquatic and terrestrial environments.
Defect structure, mechanical behavior, electrical and optical properties, magnetic, thermodynamic, and thin film properties of solid materials and crystals
Understanding the progression of irradiation-induced defects in real materials and alloys requires insight into the interaction between point defects, defect clusters, and the existing microstructure. This is particularly true when it comes to additively manufactured (AM) materials with complicated microstructures and residual stresses. In this work, we employ molecular dynamics (MD) to simulate the creation of point defects via primary radiation damage modeling and point defect interactions with extended defects such as voids and dislocations using simplified but representative alloy compositions. We analyze the effect of alloying elements on the formation energies of the defect clusters and measure interactions such as sink strengths and binding energies. This study provides insight into the unique behaviors of irradiation damage in AM materials and information necessary to bridge the gap in length and time scales to connect the atomistic behavior of defects to longer length scale simulations such as cluster dynamics.
Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.
A broad and vigorous science program in NASA is vital for the advancement of knowledge through space research.Scientists at NASA in collaboration with outside scientists play a key role in conceiving new space missions, providing mission requirements, and carrying out research oriented towards explaining the behavior of Earth's and other planetary systems. NASA scientists also supply outside scientists with technical assistance and scientific data.The Laboratory for Atmospheres serves as the focal point of a broad theoretical and experimental research program. The goal of the program is to study all aspects of the atmospheres of the Earth and other planets, including their structural, dynamical, radiative, and chemical properties. The Laboratory is one of four science laboratories of the at the Goddard Space Flight Center (GSFC). The Laboratory is located in Greenbelt, MD along with the and the. , located in New York, NY, is the fourth science Laboratory in the Directorate. The Directorate also includes the Global Change Data Center and the Space Data and Computing Division (SDCD). The GSFC is the lead center for science and management of the, which is one of the four NASA enterprises. Earth Sciences Directorate Laboratory for Terrestrial Physics Laboratory for Hydrospheric Processes The Goddard Institute for Space Studies (GISS) Mission to Planet Earth (MTPE)This report is a general statement of the purpose and philosophy of the Laboratory and of its role within NASA. It also includes a broad description of the research areas; information on human resources, scientific interactions, and outreach activities with the outside community; and a selection of the major accomplishments achieved in the Laboratory in calendar year 1996.
The deformation and failure mechanisms of Al-9Cu-6Ce (wt%) based alloys fabricated with laser powder bed fusion were investigated from room temperature to 400°C. The yield and ultimate tensile strengths decreased monotonically with increase in temperature, but the tensile elongation dipped unexpectedly at elevated temperatures and exhibited a minimum at 300°C. The dip in tensile elongation occurred with a concomitant dip in strain-rate sensitivity (SRS) of deformation. The as-fabricated alloy microstructure was heterogeneous, and the heat affected zone (HAZ) underneath the melt pool boundary was prone to strain localization. At 300 °C, the reduced SRS promoted the progression of strain localization in the HAZ leading to failure initiation and the dip in tensile elongation. Furthermore, a higher SRS or strain-hardening rate at other temperatures improved the tensile elongation by slowing the progression of strain localization in the HAZ such that failure initiated by other mechanisms elsewhere in the microstructure. Notably, the tensile elongation was limited by the defect structure only in a narrow temperature range (150 - 200 °C) while at other temperatures it was limited by the inherent microstructural features. This investigation exemplifies unexpected deformation and failure mechanisms possible in heterogeneous microstructures that result from additive manufacturing.
Predicting the liquid compositions that will vitrify at experimentally accessible quench rates remains one of the grand challenges in the field of condensed matter physics. This glass-forming ability can be quantified as the critical quench rate needed to suppress crystallization. Knowledge of this critical quench rate also informs which glass composition could be used for new applications. There have been several physical and empirical models presented in the literature to predict the critical quench rate/glass forming ability. These models range from those theoretically derived to those quantified only through experimental characterization. In this work, we instead propose a new method to calculate the critical quench rate using the recently developed toy landscape model combined with machine learning. The toy landscape model accesses the underlying physics that control the vitrification behavior by directly simulating the liquid thermodynamics and kinetics. In conclusion, the results are discussed in terms of industrial impact, physical insights, and how the glass science community can develop improved predictions of glass-forming ability.
Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.
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Here, the memory effect in a single crystal spin glass (Cu 0.92 Mn 0.08 ) has been measured using 1 Hz ac susceptibility measurements over a reduced temperature range of 0.4 - 0.7 T g and a model of the memory effect has been developed. A double-waiting-time protocol is carried out where the spin glass is first allowed to age at a temperature below T g , T w$_{1}$ , followed by a second aging 4 K lower, T w$_{2}$ . The 4 K separation is sufficient to ensure rejuvenation has occurred. The model is based on calculating overlaps between the growth of the correlation lengths at the two temperatures. It accounts for the absolute magnitude of the memory effect as a function of both waiting times and temperatures. The data can be explained by the memory loss being a function of the relative change in the correlated volume at the first waiting temperature due to growth in the correlations at the second waiting temperature.
A case study was conducted on a mechanistic model development that predicted tensile strength deterioration with thermal aging of 9Cr-1Mo-V steel in supporting the 60-year design life expected for advanced nuclear reactors. For property prediction beyond practical testing times, mechanistic modeling is highly desired, as it taps into the physics of structure–property relationships and therefore can generate reliable results for extrapolation. Meanwhile, as mechanistic models are often complicated, reflecting the intricacy of microstructure and strengthening mechanisms, pitfalls that are difficult to detect often exist. Here, this paper discusses latent pitfalls that are common in mechanistic modeling or specific in this 9Cr-1Mo-V case development through using the American Society of Mechanical Engineers verification and validation in computational solid mechanics (ASME V&V 10) standard for evaluating credibility of modeling in materials engineering. Suggestions are also made for enhancing reliability of microstructure-based modeling.
The main objective of this cooperative research with NASA's Goddard Laboratory for Atmospheric Sciences (GLAS) is to use information about the atmosphere acquired from new types of observations, especially from satellites, in order to deepen our understanding of its behavior on time scales from hours to years. This is achieved by: (1) improving the methods for processing point observations into complete fields in space and time, using sequential estimation theory; and (2) analyzing low-frequency atmospheric variability by the methods of dynamical system theory, suitably modified to enhance practical predictability.
NASA is studying advanced technologies for a future robotic exploration mission to the asteroid belt. The prospective ANTS (Autonomous Nano Technology Swarm) mission comprises autonomous agents including worker agents (small spacecra3) designed to cooperate in asteroid exploration under the overall authoriq of at least one ruler agent (a larger spacecraft) whose goal is to cause science data to be returned to Earth. The ANTS team (ruler plus workers and messenger agents), but not necessarily any individual on the team, will exhibit behaviors that qualify it as an autonomic system, where an autonomic system is defined as a system that self-reconfigures, self-optimizes, self-heals, and self-protects. Autonomic system concepts lead naturally to realistic, scalable architectures rich in capabilities and behaviors. In-depth consideration of a major mission like ANTS in terms of autonomic systems brings new insights into alternative definitions of autonomic behavior. This paper gives an overview of the ANTS mission and discusses the autonomic properties of the mission.
In a study of lunar and Mars settlement concepts, an analysis was made of fundamental design assumptions in five technical areas against a model list of occupational and environmental health concerns. The technical areas included the proposed science projects to be supported, habitat and construction issues, closed ecosystem issues, the "MMM" issues (mining, material processing, and manufacturing), and the human elements of physiology, behavior, and mission approach. Four major lessons were learned. First it is possible to relate public health concerns to complex technological development in a proactive design mode, which has the potential for long-term cost savings. Second, it became very apparent that prior to committing any nation or international group to spending the billions to start and complete a lunar settlement, over the next century, that a significantly different approach must be taken from those previously proposed, to solve the closed ecosystem and "MMM" problems. Third, it also appears that the health concerns and technology issues to be addressed for human exploration into space are fundamentally those to be solved for human habitation of the Earth (as a closed ecosystem) in the 21st century. Finally, it is proposed that ecosystem design modeling must develop new tools, based on probabilistic models as a step up from closed circuit models.
Abstract Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO 2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO 2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.