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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Galaxy zoo builder: Morphological dependence of spiral galaxy pitch angle

ABSTRACT Spiral structure is ubiquitous in the Universe, and the pitch angle of arms in spiral galaxies provide an important observable in efforts to discriminate between different mechanisms of spiral arm formation and evolution. In this paper, we present a hierarchical Bayesian approach to galaxy pitch angle determination, using spiral arm data obtained through the Galaxy Builder citizen science project. We present a new approach to deal with the large variations in pitch angle between different arms in a single galaxy, which obtains full posterior distributions on parameters. We make use of our pitch angles to examine previously reported links between bulge and bar strength and pitch angle, finding no correlation in our data (with a caveat that we use observational proxies for both bulge size and bar strength which differ from other work). We test a recent model for spiral arm winding, which predicts uniformity of the cotangent of pitch angle between some unknown upper and lower limits, finding our observations are consistent with this model of transient and recurrent spiral pitch angle as long as the pitch angle at which most winding spirals dissipate or disappear is larger than 10°.

Lingard, Timothy↗
DOE OSTI2024

Enhancers in Plant Development, Adaptation and Evolution

Understanding plant responses to developmental and environmental cues is crucial for studying morphological divergence and local adaptation. Gene expression changes, governed by cis-regulatory modules (CRMs) including enhancers, are a major source of plant phenotypic variation. However, while genome-wide approaches have revealed thousands of putative enhancers in mammals, far fewer have been identified and functionally characterized in plants. This review provides an overview of how enhancers function to control gene regulation, methods to predict DNA sequences that may have enhancer activity, methods utilized to functionally validate enhancers and the current knowledge of enhancers in plants, including how they impact plant development, response to environment and evolutionary adaptation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Impact of Heat Input and Step Over Distance on Part Geometry for Multi-Bead WAAM Components

Abstract This study investigates the impact of heat input and overlap ratio or stepover distance on part geometry in wire arc additive manufacturing (WAAM). In this work, an experimental approach was taken to quantify the variation in height and aspect ratio of walls at different combinations of overlap and heat input. Multi-bead walls were deposited with the overlap ratio varying from ½, ⅔, and 1 at a low, medium, and high-power set point on the welding power supply. A relationship between heat input, overlap ratio, and the resultant part geometry is established. Trials with an overlap ratio of one and low to medium heat input exhibited greater lack of fusion defects than the high heat input trials. At higher heat input levels, the as-deposited geometry was found to have less variation in height and an increase in aspect ratio compared to lower heat input trials. The aspect ratio of deposited walls ranged from approximately 1, for the low-power trial with an overlap ratio of ½, to approximately 7, for the high-power trial with an overlap ratio of 1. Additionally, the low and medium power trials with an overlap ratio of ⅔ exhibited the least height variation in the top surface for those heat input levels with an average range of 2.9 mm and 1.8 mm respectively. For the high heat input trials, an overlap ratio of 1 was observed to have the least variation in height with a standard deviation of 0.42 mm and an average range of 1.7 mm; however, lack of fusion defects were present in the top surface and the part cross section.

Hussein, Zaky↗
DOE OSTI2022

Unlocking Failure Mechanisms and Improvement of Practical Li-S Pouch Cells Through in Operando Pressure Study

For Lithium-sulfur (Li-S) battery commercialization, the study at a pouch cell level is essential, as some problems ignored or deemed minimal at the smaller level could have a greater effect on the performance of the larger pouch cell. Herein, the failure mechanisms of Li-S pouch cells are deeply investigated via in operando pressure analysis. It is found that highly porous structures of cathodes/separators and slow electrolyte diffusion through cathodes/separators can both lead to poor initial wetting. Additionally, Li-metal anode dominates the thickness variation of the whole pouch cell, which is verified by in situ measured pressure variation. Consequently, a real-time approach that combined normalized pressure with dP/dV analysis is proposed and validated to diagnose the morphology evolution of Li-metal anode. Moreover, applied pressure and porosity/tortuosity ratio of the cathode are both identified as independent factors that influence anode performance. In addition to stabilizing anodes, high pressure is proven to improve the cathode connectivity and avoid cathode cracking over cycling, which improves the possibility of developing cathodes with high sulfur mass loading. This work provides insights into Li-S pouch cell design (e.g., cathode and separator) and highlights pathways to improve cell capacity and cycling performance with applied and monitored pressure

25 ENERGY STORAGE↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Towards A Geo-Data Science Method for Assessing Rare Earth Element and Critical Mineral Occurrences in Coal and Other Sedimentary Systems

While preliminary analyses of data from open-source resources (Ekmann, 2012) show promising concentrations of REE in individual coal samples from a number of sites and basins in the U.S., other sparse data for REE in domestic coal-related strata suggest that many occurrences are low, “subeconomic” concentrations. At present, there is no method for systematically assessing potential sedimentary occurrences of REE. However, the geologic processes responsible for REE occurrences in coal-related strata are systematic; the unpredictability of REE resources in coal-related strata is due to poorly quantified spatial resource trends and the lack of an exploration method tailored to these resources. Thus, there exists a need for a systematic assessment approach that incorporates knowledge of geological variation in the mechanisms of REE enrichment within coal basins to help minimize geologic uncertainty and reduce commercial exploration risk.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Report on Hydrogen Content Measurements of Yttrium Hydrides

This report describes the hydrogen content measurements of yttrium samples as irradiated at Advanced Test Reactor as part of the Microreactor Program. Irradiated samples were prepared at the facilities of Analytical Research Laboratory (ARL) of Materials and Fuels Complex (MFC). Hydrogen content measurements were performed on reduced size specimens using an inert gas fusion analyzer. For a single sample, replicate samples were prepared and tested to improve statistics of results. Uncertainty analyses were conducted using two approaches to determine the hydrogen content variations in the samples. Hydrogen content of irradiated specimens were both lower and higher than the expected values, indicating stoichiometry variations. Hydrogen was detected in all samples, even for the cracked capsule’s specimens. Results also suggested a potential hydrogen redistribution is present between samples inside the capsules.

08 HYDROGEN↗
DOE OSTI2024

Stability optimization of energetic particle driven modes in nuclear fusion devices: the FAR3d gyro-fluid code

The development of reduced models provide efficient methods that can be used to perform short term experimental data analysis or narrow down the parametric range of more sophisticated numerical approaches. Reduced models are derived by simplifying the physics description with the goal of retaining only the essential ingredients required to reproduce the phenomena under study. This is the role of the gyro-fluid code FAR3d, dedicated to analyze the linear and nonlinear stability of Alfvén Eigenmodes (AE), Energetic Particle Modes (EPM) and magnetic-hydrodynamic modes as pressure gradient driven mode (PGDM) and current driven modes (CDM) in nuclear fusion devices. Such analysis is valuable for improving the plasma heating efficiency and confinement; this can enhance the overall device performance. The present review is dedicated to a description of the most important contributions of the FAR3d code in the field of energetic particles (EP) and AE/EPM stability. FAR3d is used to model and characterize the AE/EPM activity measured in fusion devices as LHD, JET, DIII-D, EAST, TJ-II and Heliotron J. In addition, the computational efficiency of FAR3d facilitates performing massive parametric studies leading to the identification of optimization trends with respect to the AE/EPM stability. This can aid in identifying operational regimes where AE/EPM activity is avoided or minimized. This technique is applied to the analysis of optimized configurations with respect to the thermal plasma parameters, magnetic field configuration, external actuators and the effect of multiple EP populations. In addition, the AE/EPM saturation phase is analyzed, taking into account both steady-state phases and bursting activity observed in LHD and DIII-D devices. The nonlinear calculations provide: the induced EP transport, the generation of zonal structures as well as the energy transfer towards the thermal plasma and between different toroidal/helical families. Finally, FAR3d is used to forecast the AE/EPM stability in operational scenarios of future devices as ITER, CFETR, JT60SA and CFQS as well as possible approaches to optimization with respect to variations in the most important plasma parameters.

Alfv én Eigenmodes↗
DOE OSTI2022

Evaluation of interactive and prescribed agricultural ammonia emissions for simulating atmospheric composition in CAM-chem

Abstract. Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of 6-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+, and NH4+ wet deposition) in situ observations. Over East Asia, Europe and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200 %–300 % higher than in the inventories, and the available in situ observations from western and central Africa, as well as NH3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories cannot capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the US, resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the US may be related to unresolved temporal variability in the NH3 emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Empirical Bounding Analysis and User Recommendations for a Neutron Multiplicity Detector

Neutron multiplicity detectors are useful for a variety of applications including nuclear emergency response, nuclear nonproliferation, safeguards, and criticality safety. When measuring black-box problems (i.e., when the system being measured is completely unknown) with systems that have relatively low detection efficiency, expert analysis is frequently required to determine appropriate bounds for system parameters such as neutron multiplication and neutron leakage. This is because the detection efficiency can vary wildly, which also means that the resulting system parameters can have large variations. This work applies an empirical approach to provide bounds on system parameters (such as system multiplication, neutron leakage, and detector efficiency). The results of this bounding analysis are then used as inputs to provide recommendations to users regarding criticality safety. In addition to describing the method used, this work provides sample results from a measurement campaign performed at the National Criticality Experiments Research Center (NCERC).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Noisy Intermediate-Scale Quantum Applications on a Pathfinder System

Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A standard convention for particle-level Monte Carlo event-variation weights

Streams of event weights in particle-level Monte Carlo event generators are a convenient and immensely CPU-efficient approach to express systematic uncertainties in phenomenology calculations, providing systematic variations on the nominal prediction within a single event sample. But the lack of a common standard for labelling these variation streams across different tools has proven to be a major limitation for event-processing tools and analysers alike. Here we propose a well-defined, extensible community standard for the naming, ordering, and interpretation of weight streams that will serve as the basis for semantically correct parsing and combination of such variations in both theoretical and experimental studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Microstructure characterization of electric field assisted sintering (EFAS) sintered metallic and ceramic materials using local thermal diffusivity measurement

Electric Field Assisted Sintering (EFAS, also referred to as spark plasma sintering) is a powerful technology for the consolidation of powder materials. The high heating rate during the sintering process is critical for minimizing energy consumption, but it can also cause microstructure heterogeneities in sintered parts, such as spatially varied porosity. The examination of localized porosity usually requires the use of a scanning electron microscope with a carefully prepared surface. In this paper, photothermal radiometry is used to measure local thermal diffusivity and extract localized porosity of EFAS-sintered parts by using a percolation-threshold model. Applying this approach, we identified the radial position-dependent porosity variation in EFAS parts, which is likely formed due to the large temperature gradient during the sintering process. This approach has a unique advantage because it can measure samples with minimal or no surface preparation, enabling the possibility of in situ characterization in EFAS with proper system modification. Necessary modifications on the measurement approach for EFAS deployment and in situ characterization are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Extreme drought impacts have been underestimated in grasslands and shrublands globally

Climate change is increasing the frequency and severity of short-term (~1 y) drought events—the most common duration of drought—globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function—aboveground net primary production (ANPP)—was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity

Abstract Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.

60 APPLIED LIFE SCIENCES↗