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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The National Virtual Biotechnology Laboratory Symposium: Harnessing the unique strengths of DOE to tackle the biotechnology challenges of COVID-19, October 28, 2020

This virtual symposium was held to highlight the impact the U.S. Department of Energy’s (DOE) National Virtual Biotechnology Laboratory (NVBL) has had utilizing the unique capabilities of the DOE to tackle the science and technology challenges associated with COVID-19, and to discuss areas in which the NVBL can have impact in the future. With perspectives from Chris Fall, Director, Office of Science, DOE, and William A. Bookless, Principal Deputy Administrator of the National Nuclear Security Administration, the event featured Presentations from lead investigators reporting progress in: Epidemiological modeling, Therapeutics, Testing, Understanding transport of the virus, and Solving issues around supply chain challenges. In addition, keynote speakers from outside the NVBL discussed the upcoming science and technology needs in computing, testing and surveillance, and vaccines. The event was aimed at the S&T community, media, and general public.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An Investigation of Liquefaction in Irradiated TRIGA Fuel Exposed to Relatively High Temperatures

This report presents the findings of an investigation into high temperature fuel cladding chemical interactions (FCCI) in Training, Research, Isotopes, General Atomics (TRIGA) fuel rods. A TRIGA fuel-rod core or meat is principally composed of uranium (U) particles dispersed in a zirconium hydride (Zr H) matrix. The fuel is clad in sealed 304SS or Incoloy 800 tubes. At high temperatures, the fuel will interact with the cladding, resulting in FCCI. To investigate the FCCI (in this case, liquefaction), irradiated TRIGA fuel rods were exposed to relatively high temperatures in furnace tests. The tests were performed at different temperatures using segments from an irradiated TRIGA fuel rod at the Hot Fuel Examination Facility located at Idaho National Laboratory (INL). The data from this study is important for developing a better understanding of the performance of U-ZrH fuel type during transient events (e.g., a Loss of Coolant Accident (LOCA) event) that may result in high temperatures while operating a TRIGA reactor. Furnace tests were run for 6 hours at 730, 800, 900, 950, and 1,000°C. One test was run for 12 hours at 950°C. Post-test microstructural characterization was performed on the heat-treated samples using optical metallography to look for evidence of liquefaction. Additionally, microstructural characterization was performed using scanning electron microscopy to investigate the microstructure of the as-irradiated fuel before heat treatment and the microstructural changes that occurred after heat treating for the 730 and 950°C heat treatments for 6 hours. Results of the analysis were compared to those produced during diffusion studies performed using unirradiated TRIGA fuel meat and type 304 stainless steel. A gap is typically present in as-irradiated fuel that limits any possibility for chemical interaction between the fuel and cladding. Optical metallography (OM) was used to identify areas in the specimens heat treated at 950 and 1000°C where fuel and cladding contact was satisfactory for the FCCI to transpire. The bulk of the interaction zones that form due to FCCI developed in the fuel meat and not as much in the cladding. Even in the sample tested at 1,000°C, no evidence was found that gross fuel meat or cladding melting had occurred. Only limited porosity was found in the fuel meat that possibly could have been due to melting of a particular precipitate phase or phases that had formed during FCCI. These results agreed with those reported for the unirradiated diffusion couple studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Towards multi-fidelity deep learning of wind turbine wakes

We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

17 WIND ENERGY↗
DOE OSTI2021

Shock-ramp analysis test problem

Quasi-isentropic (ramp) compression is now a well-established experimental method and so are the analysis techniques to give Lagrangian sound speed, pressure, and density along the sample material's isentrope. A shock followed by ramp compression is a natural extension to investigate, for example, shock melt and refreeze on compression, or isentropes of states off the Hugoniot or principal isentrope. In practice, graded-density impactors produce initial shocks, compression by shaped laser pulses may be unable to produce a smooth pressure increase from zero, and incidental perturbations on the drive pulse may also give rise to shocks, so robust shock-ramp analysis methods will be needed. Appropriate analysis methods are needed for shock-ramp experiments, based on those for quasi-isentropic compression, and these require validation. This paper describes three different analyses of a shock-ramp test problem, including an assessment of their estimated errors. The methods tested were based on hydrodynamic characteristics or integration backward in space. All methods gave the known Lagrangian sound speed to within ~1%, and pressure and volume to within less than 2% and 1%, demonstrating that the analysis methods of isentropic compression experiments can be confidently extended to the analysis of shock and ramp compression.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride

Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. However, many aspects of its mechanical behavior, particularly at reactor-relevant conditions, remain unclear. In this study, molecular dynamics (MD) simulations were employed to investigate the deformation behavior and dislocation motion in UN. We found that the Kocevski potential predicts the principal slip system as $\frac{1}{2}$ $\langle110\rangle${110}, aligning with experimental data. On the other hand, the Tseplyaev potential predicts slip to primarily occur on $\frac{1}{2}$ $\langle110\rangle${111}. MD simulations of stress–strain behavior were used to estimate the nanoindentation hardness, revealing that the Kocevski potential accurately predicts hardness even though it fails to model dynamic plasticity. Complete dislocation mobility functions have been fitted for the edge and screw dislocations in both the thermally activated and phonon-drag regimes. The 300 K linear mobility of the edge dislocation using the Tseplyaev potential was found to be 817 Pa -1 ·s -1 , whereas that of the screw dislocation using the Kocevski potential was found to be 4546 Pa -1 ·s -1 . At intermediate stresses, we observed that the subsonic steady-state motion of the edge dislocation in UN is intermittently interrupted by velocity jumps, reaching the average sound velocity. Finally, the threshold Schmid stress is calculated as 179–197 MPa, which gives an upper-limit estimate of the uniaxial yield stress of polycrystalline UN of 548–603 MPa. These findings, including the fitted dislocation mobility function, provide essential input for future plasticity and dislocation dynamics models of nuclear fuels.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Adaptive Time Step Control for Multirate Infinitesimal Methods

Multirate methods have been used for decades to temporally evolve initial-value problems in which different components evolve on distinct time scales, and thus use of different step sizes for these components can result in increased computational efficiency. Generally, such methods select these different step sizes based on experimentation or stability considerations. For problems that evolve on a single time scale, adaptivity approaches that strive to control local temporal error are widely used to achieve numerical results of a desired accuracy with minimal computational effort, while alleviating the need for manual experimentation with different time step sizes. However, there is a notable gap in the publication record on the development of adaptive time step controllers for multirate methods. In this paper, we extend the single-rate controller work of Gustafsson [ACM Trans. Math. Software, 20 (1994), pp. 496-517] to the multirate method setting. Specifically, we develop controllers based on polynomial approximations to the principal error functions for both the "fast" and "slow" time scales within multirate infinitesimal (MRI) methods. We additionally investigate a variety of approaches for estimating the errors arising from each time scale within MRI methods. We then numerically evaluate the proposed multirate controllers and error estimation strategies on a range of multirate test problems, comparing their performance against an estimated optimal performance. Through this work, we combine the most performant of these approaches to arrive at a set of multirate adaptive time step controllers that robustly achieve desired solution accuracy with minimal computational effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modeling efficient and equitable distribution of COVID-19 vaccines

Producing and distributing COVID-19 vaccine during the pandemic is a major logistical challenge requiring careful planning and efficient execution. This report presents information on logistical, policy and technical issues relevant to rapidly fielding a COVID-19 vaccination program. For this study we (a) conducted literature review and subject matter expert elicitation to understand current vaccine manufacturing and distribution capabilities and vaccine allocation strategies, (b) designed a baseline vaccine distribution strategy and modeling strategy to provide insight into the potential for targeted distribution of limited initial vaccine supplies, and (c) developed parametric interfaces to enable vaccine distribution scenarios to be analyzed in depth with Sandias Adaptive Recovery Model that will allow us evaluate the additional sub- populations and alternative distribution scenarios from a public health benefit and associated economic disruption Principal issues, challenges, and complexities that complicate COVID-19 vaccine delivery identified in our literature and subject matter expert investigation include these items: The United States has not mounted an urgent nationwide vaccination campaign in recent history. The existing global manufacturing and distribution infrastructure are not able to produce enough vaccine for the population immediately. Vaccines, once available will be scarce resources. Prioritization for vaccine allocation will be built on existing distribution networks. Vaccine distribution may not have a universal impact on disease transmission and morbidity because of scarcity, priority population demographics, and underlying disease transmission rates. Considerations for designing a vaccine distribution strategy are discussed. A baseline distribution strategy is designed and tested using the Adaptive Recovery Model, which couples a deterministic compartmental epidemiological model and a stochastic network model. We show the impact of this vaccine distribution strategy on hospitalizations, mortality, and contact tracing requirements. This model can be used to quantitatively evaluate alternative distribution scenarios, guiding policy decisions as vaccine candidates are narrowed down.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analysis of Superconducting Magnet Quench Antenna Data

Quenching poses a serious problem for superconducting magnets operating at high currents. It occurs when the material transitions from the superconducting to the normal state, which leads to heating and potential damage to the magnet. To understand and mitigate quenching, the Magnet Department at Fermilab is developing and testing superconducting magnet quench antenna arrays. This study delves into the anomalous events preceding the quench during magnet training by analyzing the collected data. With the moving average and Fast Fourier Transform techniques, we investigate the trends and frequency patterns of the data. Moreover, we introduce an unsupervised anomaly detection algorithm based on Principal Component Analysis and DBSCAN clustering. It can autonomously identify events within background noise, without relying on any predefined event features. Our analysis reveals that the spatio-temporal distribution of these anomalous events has little connection to the quench location, indicating that a majority of them bear no relation to the quenching process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent unit (GRU) networks have similar prediction performance in constitutive modelling problems, and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path. Furthermore, the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

42 ENGINEERING↗
DOE OSTI2020

3D Nanotomography of calcium silicate hydrates by transmission electron microscopy

Calcium silicate hydrate (C-S-H), is the principal hydration product of Portland cement that mainly contributes to the physical and mechanical properties of concrete. This paper aims to investigate the three-dimensional structure of C-S-H with Ca/Si ratios of 1.0 and 1.6 at the nanoscale using electron tomography. The 3D reconstructions and selected region of interest analysis confirm that the morphology of both C-S-H materials are foil-like structures. Additionally, the difference between the two materials is the density of elongated structures. C-S-H with Ca/Si ratio 1.6 is clearly composed of denser particles compared to the other C-S-H material due to overlapping of the foil-like structure. Pore analysis shows that C-S-H 1.0 and C-S-H 1.6 have porosities 69.2% and 49.8% respectively. Pore size distribution also reveals that C-S-H 1.0 has pore size range between 0-250 nm and C-S-H 1.6 between 0-100 nm. The pore network's size of C-S-H 1.0 is significantly larger than 1.6. This study illustrates the capability of using electron tomography to determine the 3D nanoscale structure of cementitious products and to distinguish between C-S-H 1.0 and 1.6.

42 ENGINEERING↗
DOE OSTI2022

The concentration of BTEX in selected urban areas of Malaysia during the COVID-19 pandemic lockdown

Volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene and xylene (BTEX) are air pollutants that harm human health. This study aims to identify BTEX concentrations before the lockdown known as the Movement Control Order was imposed (BMCO), during the implementation of the Movement Control Order (MCO), and then during the Conditional Movement Control Order (CMCO). These orders were introduced during the COVID-19 pandemic in Malaysia. The study utilised data measured by the continuous monitoring of BTEX using online gas chromatography instruments located at three urban area stations. Here, the results showed that the BTEX concentrations reduced by between –38% and –46% during the MCO compared to the BMCO period. The reduction of human mobility during the MCO and CMCO influenced the lower BTEX concentrations recorded at a station within the Kuala Lumpur area. The results of the BTEX diagnostic ratios and principal component analysis showed that the major source of BTEX, especially during the BMCO and CMCO periods, was motor vehicle emissions. Further investigation, using correlation analysis and polar plots, showed that the BTEX concentrations were also influenced by meteorological variables such as wind speed, air temperature and relative humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2021

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Experimental Investigation of Hydraulic Fracturing and Stress Sensitivity of Fracture Permeability Under Changing Polyaxial Stress Conditions

Understanding and predicting fracture propagation and subsequent fluid flow characteristics is critical to geoenergy technologies that engineer and/or utilize favorable geological conditions to store or extract fluids from the subsurface. Fracture permeability decreases nonlinearly with increasing normal stress, but the relationship between shear displacement and fracture permeability is less well understood. We utilize the new Geo–Reservoir Experimental Analogue Technology (GREAT cell), which can apply polyaxial stress states and realistic reservoir temperatures and pressures to cylindrical samples and has the unique capability to alter both the magnitude and orientation of the radial stress field by increments of 11.25° during an experiment. We load synthetic analogue materials and real rock samples to stress conditions representative of 500–1,000 m depth, investigate the hydraulic stimulation process, and then conduct flow experiments while changing the fluid pressure and the orientation of the intermediate and minimum principal stresses. High–resolution circumferential strain measurements combined with fluid pressure data indicate fracture propagation can be both stable (no fluid pressure drop) and unstable (fluid pressure drop). The induced fractures exhibit both opening and shear displacements during their creation and/or during fluid flow with changing radial stress states. Flow tests during radial stress field rotation reveal that fracture normal effective stress has first–order control on fracture permeability but increasing fracture offset can lead to elevated permeabilities at maximum shear stress. Here, the results have implications for our conceptual understanding of fracture propagation as well as fluid flow and deformation around fractures.

58 GEOSCIENCES↗