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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks

Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.

09 BIOMASS FUELS↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Status of the Fermilab Muon g - 2 Experiment

The muon anomalous magnetic moment, aµ = ( g - 2 )/2, is a low-energy observable which can be both measured and computed with very high precision, making it an excellent test of the Standard Model (SM) and a sensitive probe of new physics. Recent efforts improved the precision of both the theoretical prediction and the experimental measurement. On the theory side, the Muon g - 2 Theory Initiative, an international team of more than 130 physicists, reached in 2020 a consensus on the SM prediction for aµ. On the experimental side, the E989 Muon g - 2 Collaboration at Fermilab (FNAL) published in April 2021 a new measurement of aµ from the Run-1 dataset (2018) with 0.46 ppm precision, corroborating the previous Brookhaven National Laboratory (BNL) measurement and increasing the discrepancy with the SM value to 4.2 standard deviations. In this paper, I will discuss the experimental setup and report on the current status of the experiment.

Girotti, Paolo↗
DOE OSTI2020

Teaching Software Sustainability for High Performance Computing at ATPESC

The Argonne Training Program in Extreme Scale Computing (ATPESC) was started by Argonne National Laboratory with the objective of expanding the ranks of better-prepared users of high-performance computing (HPC) machines. One of the unique aspects of the program was inclusion of a track on software engineering and community codes. The inclusion was motivated by the observation that the projects with good software processes were better able to meet their scientific goals. Over the years, with greater awareness of software sustainability issues in the community, the track has evolved into a software productivity and sustainability track. In this paper we present our experience in choosing and disseminating the content related to the topic of software engineering in high performance computing science from the beginning of the program until now. We discuss the motivations and the reception of the tracks. We also document the evolution of the track over the years based on student feedback and also the growth of awareness about software productivity in high performance computing.

Dubey, Anshu↗
DOE OSTI2022

Development of the TREAT M2 experiment as a transient benchmark

A transient benchmark based on the Transient Reactor Test Facility (TREAT) M2 Calibration experiment (M2-CAL) is under development. TREAT, at Idaho National Laboratory, is a graphite moderated air-cooled research reactor which has been used extensively for fuel material testing under extreme and accident conditions. Accurate benchmark models are a beneficial component in the operations, experimental planning, and development of TREAT. In this work, we present a transient benchmark model for the M2-CAL experiment core loading. The benchmark model incorporates the coupling between the Monte Carlo Code SERPENT and the computational fluid dynamics code OpenFOAM to capture the temperature feedback mechanism. The M2-CAL transient 2580 was simulated in this work. A pre-transient analysis was performed to determine the optimum core composition and conditions before the beginning of the transient. The analysis was validated against historic TREAT kinetic measurements and the worth of the transient rod T-2. The axial power distribution in the flux wire for the M2-CAL experiment was determined and contrasted with the experiment. The model was then used to simulate the M2-CAL transient 2580 experiment based on the reported pre-transient and transient conditions. Several transient observables were calculated and compared to the experiment. The model shows good agreement with the experimental power traces, period, and average increase of power at the power ramp (time > 7.8 s). Inverse point kinetics analysis was performed during the period of the power ramp for the model and the experiment based on that, the temperature feedback component was isolated and contrasted with the experimental feedback. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s ρ coefficients as well as Kendall’s τ and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R 2 , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spear man’s ρ, Kendall’s τ, and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R 2 coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Tracking ion intercalation into layered Ti 3 C 2 MXene films across length scales

Enhancing the energy stored and power delivered by layered materials relies strongly on improved understanding of the intricate interplay of electrolyte ions, solvents, and electrode interactions as well as the role of confinement. Here we report a highly integrated study with multiscale theory/modelling and experiments to track the intercalation of aqueous Li + , Na + , K + , Cs + , and Mg 2+ ions into Ti 3 C 2 MXene. The integrated analysis of experiments assisted by theory/modelling allows for a deep understanding of energy storage processes highlighting the importance of the dynamics of cations, their positionings between MXene sheets, their effects on mechanical properties and capacitive energy storage. Computational simulations and operando calorimetry measurements prove the processes involving cation dehydration and H+ rehydration, showing a good correlation for heat variations between experiments and theory. Operando liquid AFM mapped energy dissipation of ions appears non-uniformly across the MXene surface, indicating heterogeneities of ions inside the MXene and confirming partially the ion behaviour obtained in theory. We directly demonstrate that the average distance between the cation and MXene surface follows a modified two-sided Helmholtz model when plotted versus the open circuit potential capacitance, revealing a different electrical double layer mechanism in confinement. This new fundamental understanding lays the foundation for improved functional devices utilizing electrodes and membranes made of two-dimensional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

NSUF Melt Wire Evaluations for BSU 8242 and GE Hitachi-10393 Irradiation Experiments

The Nuclear Science User Facilities (NSUF) is the United States Department of Energy Office of Nuclear Energy's only designated nuclear energy user facility. Its mission is to provide nuclear energy researchers access to world-class capabilities and to facilitate the advancement of nuclear science and technology. This mission is supported by providing access to state-of-the-art experimental irradiation testing, post-irradiation examination facilities, and high-performance computing capabilities as well as technical and scientific assistance for the design and execution of projects. Two NSUF irradiation experiments, Boise State University-8242 and General Electric Hitachi, used melt wire packs to determine peak temperatures reached during irradiation testing. This report will discuss the temperature evaluation process and present the associated data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Thermodynamic stability of a spin microemulsion in Rashba spin-orbit-coupled bosons

Recent finite-temperature numerical simulations have unveiled a quantum “spin” microemulsion analog, found by raising the temperature of a stripe supersolid phase in a Rashba spin-orbit coupled Bose gas. This microemulsion state is a highly correlated, isotropic normal fluid where atoms self-arrange based on their internal pseudospin into patterns that resemble bicontinuous microemulsions. This finding leaves several open questions regarding the broader accessibility of this phase in experiments. Here, we use equilibrium finite-temperature numerical simulations based on a coherent-state path integral representation to perform a computational investigation into the thermodynamic stability of the spin microemulsion state. Numerical simulations emphasize the requirement of a nearly, but not perfectly, isotropic spin-orbit coupling in order to achieve the microemulsion phase in cold-atom experiments. Moreover, the microemulsion state exists independent of miscibility of the pseudospin components and for a wide range of pseudospin population imbalance, suggesting a high degree of flexibility in choosing the atom and hyperfine states in an experimental realization. Lastly, we demonstrate this feasibility by mimicking a Rashba spin-orbit-coupled 87 Rb experiment in an isotropic harmonic trap, where we confirm the microemulsion's existence via its density profile and equilibrium quasimomentum distribution.

Complex Langevin dynamics↗
DOE OSTI2020

Metabolic Pathway Engineering

Modern microbial and enzyme engineering and their advancement are increasingly dependent on the marriage of a wide range of sophisticated technologies. For students entering the field of biotechnology, the outlook is indeed daunting. Expertise at levels beyond that of simple familiarity will be needed to conduct competitive research. It goes without saying that to be competitive, all new researchers in this field will need basic preparation in molecular biology, biochemistry, and genetics. Further, experience working with concepts and experimental tools in enzyme biochemistry and kinetics, gene editing, computational metabolic pathway modeling, experimental pathway flux analysis, and computational clustering tools to process complex data sets will be vital for success. We speculate that most biotechnology researchers in early career at this time will build teams of collaborators to address these disparate science fields rather than attempt to become experts in one lab.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of grassland fire dynamics on particulate emission factors

Understanding particulate emission factors in grassland fires is critical for improving air quality assessments and refining emissions inventories. This study explores the dynamic and heterogeneous nature of fire processes and their influence on particulate emissions. Traditional emission factor inventories often rely on static values, overlooking the spatially and temporally variable dynamics of fire behavior. Through field experiments conducted at Konza Prairie Biological Station measuring emissions with uncrewed aerial vehicles, and computational modeling using HIGRAD/FIRETEC, this research highlights the dependency of emission production on localized fire dynamics, including ignition patterns, fuel properties, and atmospheric conditions. The results demonstrate a need for fire dynamic considerations to accurately capture emissions variability.

09 BIOMASS FUELS↗
DOE OSTI2021

Peridynamic Model for Microballistic Perforation of Multilayer Graphene

The peridynamic theory of solid mechanics is applied to the continuum modeling of the impact of small, high-velocity silica spheres on multilayer graphene targets. The model treats the laminate as a brittle elastic membrane. The material model includes separate failure criteria for the initial rupture of the membrane and for propagating cracks. Material variability is incorporated by assigning random variations in elastic properties within Voronoi cells. Here, the computational model is shown to reproduce the primary aspects of the response observed in experiments, including the growth of a family of radial cracks from the point of impact.

42 ENGINEERING↗
DOE OSTI2020

Quantum Accurate Prediction of Plutonium–Plutonium Dihydride Phase Equilibrium Using a Lattice Gas Model

Plutonium-based materials are vital for use as nuclear fuels and as portable power sources for space vehicles. However, elucidating their sensitivity to hydriding corrosion represents an extreme challenge because of the toxicity of Pu as well as their anomalous magnetic properties. In this work, we develop a lattice gas model of plutonium–plutonium dihydride (Pu–PuH 2 ) phase equilibrium that retains the accuracy of density functional theory, while yielding many orders of magnitude improvement in computational efficiency. Using Monte Carlo and free-energy sampling algorithms, we compute a number of Pu–PuH 2 equilibrium properties that are difficult to probe experimentally, including equilibrium pressures and phase compositions and the PuH 2 heat of formation. In conclusion, our method will have particular impact on these types of materials studies, where there is a strong need for computationally efficient approaches to bridge time and length scale gaps between quantum calculations and experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Direct Observations of Solute Dispersion in Rocks With Distinct Degree of Sub‐Micron Porosity

Abstract The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X‐ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four‐dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore‐scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub‐micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi‐dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one‐dimensional transport models have limited predictive ability toward the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The data set generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Kurotori, Takeshi [Department of Chemical Engineer↗