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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Saturation of fishbone instability through zonal flows driven by energetic particle transport in tokamak plasmas

Gyrokinetic and kinetic-MHD simulations are performed for the fishbone instability in the DIII-D discharge #178631, chosen for validation of first-principles simulations to predict the energetic particle (EP) transport in an ITER prefusion baseline scenario. Fishbone modes are found to generate zonal flows, which dominate the fishbone saturation. The underlying mechanisms of the two-way fishbone-zonal flows nonlinear interplay are discussed in details. Numerical and analytical analyses identify the fishbone-induced EP redistribution as the dominant generation mechanism for zonal flows. The zonal flows modify the nonlinear dynamics of phase space zonal structures, which reduces the amount of EPs able to resonate with the mode, leading to an early fishbone saturation. Simulation results including zonal flows agree quantitatively with DIII-D experimental measurements of the fishbone saturation amplitude and EP transport, supporting this novel saturation mechanism by self- generated zonal flows. Moreover, the wave-particle mode-locking mechanism is shown to determine quantitatively the fishbone frequency down-chirping, as evident in GTC simulation results in agreement with predictions from analytical theory. Finally, the
fishbone-induced zonal flows are possibly responsible for the formation of an ion-ITB in the DIII-D discharge. Based on the low EP transport and the large zonal flow shearing rates associated with the fishbone instability in gyrokinetic simulations of the ITER scenario, it is conjectured that high performance scenarios could be designed in ITER burning plasmas through fishbone-induced ITBs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Effects of radiation heat loss on planar and spherical hydrofluorocarbon/air flames

Hydrofluorocarbons (HFC), which are mildly flammable and pose potential fire risks, have received greater attention as a viable low global warming potential alternative to traditional refrigerant and fire-suppressant compounds. Therefore, there is a demand to accurately quantify their flammability and re-activity to establish proper safety metrics. Here, this study investigates the effects of radiation heat loss on slowly-propagating HFC/air laminar flames. Planar 1-D simulations of R-32/air and R-1234yf/air flames show significant reductions in laminar flame speed due to radiative heat losses from the flame zone. Simulations of spherically expanding flames (SEF) revealed that the radiation-induced flow needs to be considered when interpreting data from experiments. To this end, a Spherical-flame RADiation-Induced Flow (SRADIF) model was developed to estimate the burned gas inward flow velocities in constant-pressure SEFs, utilizing the optically thin limit assumption to model radiation heat loss. The model was validated against results from detailed numerical simulations of SEFs, from which radiation-induced inward flow was derived using a new formulation considering both the radiation heat loss and convective flow effects. Results show that SRADIF accurately predicts the inward flow velocity for R-32/air mixtures over a range of conditions and performs significantly better compared to existing analytical models. However, the model was unable to accurately predict flow velocities for R-1234yf/air flames and the reason for this is discussed. (224 words)

42 ENGINEERING↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Evaluating the potential for bird‐habitat models to support biodiversity‐friendly urban planning

Abstract Urban expansion poses a major threat to wildlife populations. Biodiversity‐friendly urban landscapes could deliver benefits for both wildlife and people, by incorporating conservation and ecosystem services objectives. Well‐designed urban developments could also soften the ecological impacts of urbanization. However, delivering urban landscapes that integrate biodiversity requirements effectively remains challenging. Ecological models, designed to predict wildlife population responses to alternative urban designs, could prove effective in supporting the creation of biodiversity‐friendly urban landscapes. Here, we combine national‐scale bird abundance data with high resolution, spatially explicit habitat data to characterize relationships between bird densities and urban landscape form in Britain. From these analyses and cross validation, we evaluate the potential for well‐parameterized, species‐specific models to be used to predict bird densities in novel or modified urban areas. Our analyses indicate that responses of bird abundance to urban habitat are species‐specific and complex, with few variables consistently affecting a large proportion of species. However, contiguous areas of greenspace within urban sites are preferential for accommodating breeding birds, compared to a more fragmented arrangement of multiple, small greenspace patches. In combination, the bird‐habitat relationships identified could successfully predict observed variation in abundance for most bird species considered. Further evaluation of habitat descriptor variables, spatial scales of species' habitat use and analytical modelling approaches may be needed to improve the predictive ability of bird‐habitat models for certain species, particularly waterbirds and those observed less frequently in urban areas. Synthesis and applications . We modelled breeding bird abundance in built‐up areas with respect to the characteristics and contexts of urban environments. While most variables were important for multiple species, responses overall were species‐specific, so simple assemblage metrics, like diversity, will not describe the variation in bird communities well. However, the results illustrate the potential of an evidence‐based, spatially explicit evaluation of urban development impacts on biodiversity, by predicting the consequences for bird numbers. Subject to verification of predictive ability, practitioners can apply the models to compare, for example, land‐sparing and sharing within developments, or to quantify the biodiversity requirements for effective offsetting. This would be facilitated by incorporation into an online tool allowing user‐determined input scenarios.

Plummer, Kate E.↗
DOE OSTI2024

Analytic Gradients for Equation-of-Motion Coupled Cluster with Single, Double, and Perturbative Triple Excitations

Understanding the process of molecular photoexcitation is crucial in various fields, including drug development, materials science, photovoltaics, and more. The electronic vertical excitation energy is a critical property, for example in determining the singlet-triplet gap of chromophores. However, a full understanding of excited-state processes requires additional explorations of the excited-state potential energy surface and electronic properties, which is greatly aided by the availability of analytic energy gradients. Owing to its robust high accuracy over a wide range of chemical problems, equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) is a powerful method for predicting excited state properties, and the implementation of analytic gradients of many EOM-CCSD (excitation energies, ionization potentials, electron attachment energies, etc.) along with numerous successful applications high- lights the flexibility of the method. In specific cases where a higher level of accuracy is needed or in more complex electronic structures, the inclusion of triple excitations becomes essential, for example, in the EOM-CCSD* approach of Saeh and Stanton. In this work, we derive and implement for the first time the analytic gradients of EOMEE-CCSD*, which also provides a template for analytic gradients of related ex- cited state methods with perturbative triple excitations. Here, the capabilities of analytic EOMEE-CCSD* gradients are illustrated by several representative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Spatiotemporal measurements of striations in a glow discharge’s positive column using laser-collisional induced fluorescence

Here we have observed the behavior of striations caused by ionization waves propagating in low-pressure helium DC discharges using the non-invasive laser-collision induced fluorescence (LCIF) diagnostic. To achieve this, we developed an analytic fit of collisional radiative model (CRM) predictions to interpret the LCIF data and recover quantitative two-dimensional spatial maps of the electron density, n e , and the ratios of LCIF emission states that can be correlated with T e with the use of accurate distribution functions at localized positions within striated helium discharges at 500 mTorr, 750 mTorr, and 1 Torr. To our knowledge, these are the first spatiotemporal, laser-based, experimental measurements of n e in DC striations. The n e and 447:588 ratio distributions align closely with striation theory. Constriction of the positive column appears to occur with decreased gas pressure, as shown by the radial n e distribution. We identify a transition from a slow ionization wave to a fast ionization wave between 750 mTorr and 1 Torr. These experiments validate our analytic fit of n e , allowing the implementation of an LCIF diagnostic in helium without the need to develop a CRM.

42 ENGINEERING↗
DOE OSTI2020

Aerosol Optical Tweezers Elucidate the Chemistry, Acidity, Phase Separations, and Morphology of Atmospheric Microdroplets

Aerosol particles represent unique chemical environments because of their high surface area-to-volume ratio that promotes the effects of interfacial chemistry in confined environments. Properties such as viscosity, diffusivity, water content, pH, and morphology—following liquid–liquid phase separation—can strongly alter how a particle interacts with condensable vapors and reactive trace gases, thus modifying its continual evolution and environmental effects. Our understanding of this chemical evolution of atmospheric particulate matter and its environmental impacts is largely limited by our ability to directly observe how these critical particle properties respond to the addition or reactive uptake of new chemical components. Aerosol optical tweezers (AOT) stably trap particles in focused laser beams, providing positional control and the retrieval of many of these critical properties required to understand and predict the chemistry of aerosolized microdroplets. The analytical power of the AOT stems from the retrieval of the cavity-enhanced Raman spectrum induced by the trapping laser. Analysis of the whispering gallery modes (WGMs) that resonate as a standing wave around the droplet’s interface, provide high accuracy measurements of the droplet’s size, refractive index (and thus a measurement of composition), and can distinguish between core–shell, partially engulfed, and homogeneous morphologies. In this study, we have advanced the ability to determine the properties of the core and shell phases in biphasic droplets, including obtaining high-accuracy pH measurements. These capabilities were applied to perform AOT physical chemistry experiments on authentic secondary organic aerosol (SOA) produced directly in the AOT chamber by ozonolysis of terpene vapors. The propensity of the SOA to phase separate as a shell from a wide range of nonpolar to polar core phases was observed, along with the discovery of a stable emulsified state of SOA particles in an aqueous salt droplet. Micron-thick SOA shells did not impede the gain or loss of water or squalane from the core to the surrounding air, indicating no significant diffusional limitations to condensational growth or partitioning even under dry conditions. These experiments formed the foundation of a new framework that predicts how the phase-separated morphology of complex aerosols containing organic carbon evolves during continual atmospheric oxidation processes. Increases in oxidation state will quickly drive conversion from a partially engulfed to core–shell morphology that has dramatically different chemical reactivity since the core phase is completely concealed by the shell. The recent advances in the experimental capabilities of the AOT technique such as presented here enable novel experimental methodologies that provide insights into the chemistry and multidimensional properties of aerosol microdroplets, and how these coevolve and respond to continual chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A species’ response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

In October 2021, the U.S. Department of Energy (DOE) welcomed participants to the Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, hosted by the Office of Biological and Environmental Research (BER)—Advanced Scientific Computing Research (ASCR). The workshop is part of BER-ASCR’s ambition to more radically and aggressively advance prediction capabilities in the climate, Earth, and environmental sciences through the use of modern data analytics and artificial intelligence (AI). Advances in these capabilities are needed to improve predictions of climate change and extreme events that provide actionable information for planning and building resilience to their impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

Mass absorption cross-section of black carbon (MAC BC ) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MAC BC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MAC BC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MAC BC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MAC BC using different statistical metrics (such as coefficient of determination (R 2 ), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MAC BC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MAC BC , we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Rate-Dependent Dynamic Cylindrical Cavity Expansion Equations for Conical- and Ogival-Nosed Projectiles

The dynamic cylindrical cavity expansion of rate-dependent materials was previously derived by Warren to examine the effects of strain-rate sensitivity on the radial stress acting on a perforating projectile. However, the equations presented were largely analytical and were not further applied to predict the ballistic performance of ductile target plates. The current work expands on Warren’s derivation to model the dynamics of conical and ogival geometries, and the rate-dependent model is compared to prior experimental results of 7.62-mm APM2 rounds impacting 6061-T6511 aluminum alloy plates. The results show that including rate effects improves the ballistic performance prediction, even for a marginally strain-rate sensitive material such as Al6061-T651. However, existing semi-empirical variations of the cavity expansion model can provide the same degree of accuracy if target material rate-sensitivity parameters are not readily available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geomechanical response due to nonisothermal fluid injection into a reservoir

The geomechanical response of a porous reservoir due to injection of fluid can result from a complex interplay between the changes in porepressure and temperature near the wellbore. As a result, predictions are usually made using either simplified analytical models, which may apply unrealistic assumptions in order to produce a tractable model, or detailed numerical simulations that can be computationally expensive.

58 GEOSCIENCES↗
DOE OSTI2020

Hydro-mechanical coupled modeling of hydraulic fracturing using the mesh fragmentation technique

The inclusion of special interface elements between standard finite elements is becoming a common technique to model discontinuities in porous media using the finite element method (FEM). Recently, it was proved that high aspect ratio (HAR) elements can reproduce very satisfactorily the main features of behavior observed in a single hydraulically-induced fracture. In this paper, the mesh fragmentation technique (MFT) is proposed to extend the application of HAR elements to simulate more general problems involving the formation of hydraulic fractures in rocks by introducing this type of element in-between the standard FE of a typical mesh. The proposed framework makes use of standard FE techniques. Therefore, it is relatively easy to upgrade an existing FE program for continuous porous media to deal with evolving discontinuities. The proposed approach is verified and validated against available analytical and numerical solutions. The crack patterns predicted by the MFT are also compared with the ones observed in the laboratory. The formation of multiple fractures is also analyzed. In all cases, the proposed technique was able to properly capture the main features of rock behavior subjected to hydraulic fracturing.

58 GEOSCIENCES↗
DOE OSTI2024

Testing and modeling of CLT-to-CLT joints made using hardwood dowels and screws with varying spacing

With the increasing popularity of mass timber, utilizing large-diameter wood screws and hardwood dowels can offer an easy and cost-efficient way to join structural elements. However, the existing research on hardwood dowels is limited to single fastener joints under monotonic loading. No research is available on the group effects of joints with multiple hardwood dowels. Here, this paper presents the results of 96 monotonic and cyclic single-shear plane CLT-to-CLT joint tests with hardwood dowels (Red Oak and Yellow Birch, 25.4 mm diameter) and self-tapping wood screws (8 mm diameter) in Grand-Fir CLT, where screws were inserted both perpendicular and at a 45-degree angle to the grain for comparison to hardwood dowels. The study evaluated the mechanical properties of the joints by changing fastener spacing. Mechanical properties such as yielding and peak strength, displacement, elastic and yielding stiffness, and ductility were evaluated. The results showed equal strength and stiffness properties for hardwood dowels comparable with large-diameter screws. Additionally, the hardwood dowel joints demonstrated moderate ductility properties. The strength and stiffness of the tested joints were compared with analytical equations found in the literature by considering the group action factor of the fasteners. Lastly, a nonlinear force-displacement model was presented and compared to the experimental results and analytical calculations, which gives the possibility to predict and optimize the mechanical behavior of hardwood dowel and screw joints with varying numbers and spacing between fasteners.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Theoretical priors in scalar-tensor cosmologies: Thawing quintessence

The late time acceleration of the Universe can be characterized in terms of an extra, time-dependent, component of the Universe - dark energy. The simplest proposal for dark energy is a scalar-tensor theory - quintessence - which consists of a scalar field, φ, whose dynamics is solely dictated by its potential, V(φ). Such a theory can be uniquely characterized by the equation of state of the scalar field energy momentum-tensor. We find the time dependence of the equation of state for a broad family of potentials and, using this information, we propose an analytic prior distribution for the most commonly used parametrization. Here, we show that this analytic prior can be used to accurately predict the distribution of observables for the next generation of cosmological surveys. Including the theoretical priors in the comparison with observations considerably improves the constraints on the equation of state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗