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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Shear localization in metallic materials at high strain rates

Three factors govern adiabatic shear localization: strain hardening (or softening), strain-rate hardening, and thermal softening. It is typically associated with large shear strains (>1), high strain rates (10 3 –10 7 s –1 ), and high temperatures (40–100% of melting point), all of which happen within narrow regions with widths of about 1–200 μm. It is often an undesirable phenomenon, leading to failure, but there are situations where it is desirable, e. g., the generation of machining chips. Here, we review the development of both theoretical and experimental achievements, from the initiation of shear bands to their propagation with emphasis on three aspects: novel experimental techniques, novel materials, and nano/microstructural effects. The principal characteristics of adiabatic shear bands in metallic materials at the nano- and micro-scale are described. Bands that were formerly identified as transformed actually consist of nanocrystalline/ultrafine grains. These grains result from the breakup of the microstructure by a rotational recrystallization process. The evolution of the microstructure inside shear bands and their interactions for hcp, bcc, and fcc alloys, high-entropy alloys, nanocrystalline alloys, and metallic glasses are analyzed mechanistically. In conclusion, the gaps in the field and opportunities for future research are identified. Modern experimental characterization and computational techniques enable a more profound and predictive understanding of adiabatic shear localization and its avoidance in advanced materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Tracking of Marangoni driven motion during laser powder bed fusion

Laser Powder Bed Fusion (LPBF) can be used to create Functionally Graded Materials (FGMs) by selectively distributing property augmenting particles throughout the build matrix. With this method parts can be created with nonhomogeneous properties tuned to spatially varied, pre-determined design constraints. To unlock this capability, the fluid motion that occurs in the molten liquid phase must be thoroughly understood. More importantly, the actual tracking of motion during the LPBF process is needed. Further, a numerical model of heat transfer and fluid flow in LPBF process was created. Using results from the model the motion of particles starting at many different locations was tracked. The average change in position for these particles based on starting location was calculated. The effects of non-dimensional parameters on melt pool size were examined. These results were validated experimentally and compared to what is available in the literature.

42 ENGINEERING↗
DOE OSTI2025

In situ probing of structure and deagglomeration of SnO 2 colloids via small-angle X-ray scattering

Transforming dry nanopowders into stable colloidal dispersions remains challenging due to the cohesive forces between the nanoparticles (NPs) which promote agglomeration. Effective dispersion and deagglomeration of these agglomerates is a critical process in the formulation and preparation of nanoparticle-based functional materials via the colloidal route. Understanding the deagglomeration dynamics provides information to improve microstructural quality in various applications by enabling engineering of agglomerate size, structure and morphology. However, the deagglomeration process dynamics with respect to the evolution of the fractal agglomerate structures, particularly for very small NPs, is still poorly understood and requires further investigation. This study employs in situ small-angle X-ray scattering (SAXS) to investigate the sonication-induced deagglomeration of SnO 2 NPs. Electrostatically stabilized SnO 2 colloids with varying primary particle size (6–21 nm) are investigated in a specifically designed in situ cell using synchrotron-based SAXS to study the influence of sonication time and intensity on the nanoscaled agglomerates. Complete structural analysis via SAXS reveals a direct correlation between changes in agglomerate size and structure, size-dependent deagglomeration behavior and a dependence on the overall energy introduced during sonication into the dispersion regardless of the actual power as well as ultrasonic process parameters in case of SnO 2 NPs. The results suggest that control of the dispersion process during ultrasonic deagglomeration results in tailoring agglomerates with respect to size and structure.

Deagglomeration↗
DOE OSTI2020

Green chemistry design in polymers derived from lignin: review and perspective

The use of lignin for material and chemical applications has gained interest in recent times due to the environmental focus on using biomass for renewable materials as well as the motivation to valorize lignin as part of an integrated biorefinery. While a wealth of publications can be found for lignin-derived materials, and several reviews can be found on the topic, there are no reviews that outline and critically assess the sustainability of these protocols based on the criterion of the 12 Principles of Green Chemistry. This review assesses the literature for lignin- derived polymers over the past 20 years and highlights attempts to use biobased accompanying reagents, green solvents or solvent-less protocols, reports that replace traditionally toxic precursors (e.g. diisocyanates for polyurethanes), use mild conditions, valorize additional waste streams and prepare materials particularly biodegradable or recyclable. Here, the following work is a comprehensive and critical assessment of the green and sustainable nature of the state-of-the art of lignin-derived polymers (polyurethane, polyester, epoxy, phenolic resin, and others) utilizing actual biomass in the synthetic protocol. Each section of this review highlights and assesses published protocols that use a particularly green approach in one or more steps of the synthetic sequence. While many reports still contain traditional petroleum-derived and toxic methods along with more sustainable methods, the outline of all such green methods is able to create a landscape amidst lignin-derived polymer synthesis that points the way toward safer and more environmentally friendly materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification

This paper describes a physics-guided Bayesian framework for identifying the milling stability boundary and system parameters through iterative testing. Prior uncertainties for the parameters are identified through physical simulation and literature reviews, without physical testing of the actual milling system. Those uncertainties are then propagated to the stability map using a physics-based stability model, which is used to suggest a test point. The uncertainties are updated based on the new information acquired from the cutting test to form a new probability distribution, called the posterior. Finally, the posterior are compared to measured values for the stability boundary and system parameters to evaluate the approach. Based on experimental observations, the advantages and disadvantages of using a physics-guided model are discussed.

Cornelius, Aaron↗
DOE OSTI2024

The Kalahari sediments and hominins in southern Africa

In this study, the temporal coupling of the structural evolution of the Kalahari Basin and the accumulation of the Kalahari Group sediments has been an accepted paradigm leading to the assumption that the Kalahari group sediments have been accumulating gradually since the mid-Cretaceous. Here we review the first actual ages for the Kalahari Group based on cosmogenic ages from six geological localities. These results demonstrate that Kalahari Basin infill was a more dynamic process than previously thought and that the Kalahari Group sediments are mostly Plio-Pleistocene in age (~4 Ma to 1–2 Ma). The hiatus between the initial structural subsidence of the basin, during the Cretaceous, and the general young age of the investigated sediments, implies a dynamic landscape in which significant phases of erosion occurred during the Mesozoic and Cenozoic. The magnitude of erosion is manifested by the fact that in many locations Kalahari Neogene to Quaternary sediments overlie Precambrian basement. The age of the present infill of the Kalahari thus falls within the temporal range of the genus Homo. In light of this new understanding, we provide a review of the archaeological evidence from the Kalahari Basin and along its southern fringe. Initial hominin presence is found at Wonderwerk Cave during the Olduvai Event and there is subsequent high-density occupation along the southern fringe of the Kalahari Basin during the Acheulean and the Fauresmith. Middle Stone Age occupation is limited to localities of limited size and small artifact counts and it appears that the focus of human occupation, particularly in the later stages of the Middle Stone Age, shifts southward, including along the coastal regions.

58 GEOSCIENCES↗
DOE OSTI2023

Evaluating Multimodel Ensemble Seasonal Climate Forecasts on Rangeland Plant Production in the California Annual Grassland

Seasonal precipitation and temperature directly affect total plant production in the California Annual Grassland (CAG). Technological advances have resulted in skillful seasonal climate forecasts (i.e., significant correlations between actual and forecasted climate), which could be input into plant production models to inform stocking and other rangeland management decisions. This study presents a procedure for forecasting plant production in the CAG ecosystem to predict annual plant production for grazing, restoration, or other rangeland management practices using a combination of historical gridMET climate data and seasonal hindcasts (i.e., retrospective forecasts, from the North American Multi-Model Ensemble program). Further, the results of this study first confirmed high forecast skill, throughout the growing season at all sites. We also identified skillful plant production forecasts across most of the growing season at two sites and in three of the seven forecasting months at one study site. Forecasting climate and end-of-year plant production across the growing season at three CAG sites allowed us to identify the places and times in the growing season when forecasting might be most helpful in informing management decisions. Integrating plant production forecasting into rangeland management practices could significantly improve rangeland management outcomes. These procedures provide a user guide for creating plant production forecasts for any given area of interest and may be applicable across a wide range of other agricultural and rangeland management systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Validating simulated mountain wave impacts on hub-height wind speed using SoDAR observations

The ascent of stably stratified air over a mountain barrier can trigger the generation of mountain waves. Mountain waves occur frequently over the Columbia River Gorge in western North America and can impact wind power generation over the area. Therefore, predicting the details of mountain waves events (e.g., dominant wavelength, timing, and duration) can be very valuable for the wind energy community. In this study, the ability of the Weather Research and Forecasting (WRF) model to simulate mountain waves and their impact on hub-height wind speed is investigated. Our results suggest that the WRF model has moderate skill in simulating observed mountain wave. Further, given WRF predictions of wavelength range and wave period, the Fast Fourier Transform can calculate the simulated mountain wave impact on hub-height wind speed. The resulting wind speeds agree well with SoDAR observations in terms of both magnitude and pattern. Finally, for the simulated cases, WRF consistently predicts impacts of significant mountain wave events about an hour earlier than the actual observations. Overall, in this work, the sensitivities as well as uncertainties associated with our methodology are discussed in detail.

17 WIND ENERGY↗
DOE OSTI2024

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

14 SOLAR ENERGY↗
DOE OSTI2021

United States plastics: Large flows, short lifetimes, and negligible recycling

Given increasing concerns for the marine environment and human health, as well as trade restrictions from Asian countries, plastics have become a great challenge for the United States. This study addresses the seven commonly used plastics: low-density polyethylene/linear low-density polyethylene, high-density polyethylene, polyethylene terephthalate (PET), polypropylene, polystyrene, polyvinyl chloride, and other plastics. Material flows of the seven polymers were tracked from production into fabrication, manufacturing, flow into use, waste management, and recycling in the United States in 2015. Low- and high-density polyethylene and polypropylene were found to be the largest in both production and product manufacture. More than 88% of the plastics went into three end-use sectors: Packaging, Consumer and Institutional Products, and Building and Construction. In-use lifetimes across the plastics are generally short. Virgin plastics were mainly exported, while intermediate plastic products were largely imported. The actual end-of-life recycling rate of the plastics as a group was no more than 6.2%, with PET and the polyethylene family the most recycled. Here, the high yearly plastic throughput and low recycling rate pose a serious challenge to the sustainability goals of the United States and is in stark contrast to the vision of a circular economy of plastics.

02 PETROLEUM↗
DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗
DOE OSTI2021

Probability of Loss of Assured Safety in Systems with Multiple Time-Dependent Failure Modes: Incorporation of Delayed Link Failure in the Presence of Aleatory Uncertainty

Probability of loss of assured safety (PLOAS) is modeled for weak link (WL)/strong link (SL) systems in which one or more WLs or SLs could potentially degrade into a precursor condition to link failure that will be followed by an actual link failure after some amount of elapsed time. The descriptor loss of assured safety (LOAS) is used because failure of the WL system places the entire system in an inoperable configuration while failure of the SL system before failure of the WL system, although undesirable, does not necessarily result in an unintended operation of the entire system. Thus, safety is “assured” by failure of the WL system before failure of the SL system. Here, the following topics are considered: (i) Definition of precursor occurrence time cumulative distribution functions (CDFs) for individual WLs and SLs, (ii) Formal representation, approximation and illustration of PLOAS with (a) constant delay times, (b) aleatory uncertainty in delay times, and (c) delay times defined by functions of link properties at occurrence times for link failure precursors, and (iii) Procedures for the verification of PLOAS calculations for the three indicated definitions of delayed link failure.

58 GEOSCIENCES↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stationary and drone-assisted methane plume localization with dispersion spectroscopy

This work presents stationary and mobile retroreflector-based remote sensing techniques for methane leak localization and quantification using chirped laser dispersion spectroscopy equipped with a custom laser transceiver capable of continuous tracking of a flying drone and coupled with inverse atmospheric gas dispersion modeling. The techniques demonstrate the ability to localize leaks as low as 0.13 g CH 4 ·s -1 , which are up to 25 times smaller than those typically observed at natural gas facilities, as well as actively track a moving retroreflector mounted on a lightweight (~250 g) drone to enable spatial plume reconstruction. This system exhibited a 2.3 ppm-m sensitivity over pathlengths of 40–150 m. Source localization to within ±7 m is demonstrated using a modified horizontal radial plume mapping technique with a stationary retroreflector grid. Meanwhile, the mobile system utilizing a drone-mounted retroreflector is able to localize a controlled release within ±1 m of its source location and estimate leak rates using inversion techniques assuming type B Gaussian plume stability class within ±30% error with respect to the actual low flow rate releases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

How do the research and public communities view biofuel development?

Understanding how public and research view biofuel development, especially the unsynchronized views, can explain the gaps between the actual biofuel production and consumption and the government mandates in the US. Applying a comparative text mining technique, these views are explored using 9924 news articles and 57,849 research abstracts. It is found both the public and research communities respond actively to major policy and incentive programs and market conditions and the public has been closely following important research advances. However, research and practice should be coordinated to achieve economies of scale for regular and alternative biofuels. Priority is particularly needed for policy research to support legislation and enforcement and remove technical barriers to commercialization; meanwhile, greater attention is needed to prompt the commercialization of mature technologies. In addition, sentiment analysis shows positive public perceptions in general and negative perceptions primarily stemming from fraud in biofuel tax-credit programs with minor concerns on unintended negative impacts of biofuel development and policy implementation issues. This study contributes to methodology by using cutting-edge text-mining techniques to the largest and most up-to-date datasets of public news articles and research publication abstracts so as to compare public and academic views on biofuel development. As a result, this study also contributes to knowledge discovery by providing new perspectives on biofuel development, which will support policy design and identify future research directions.

09 BIOMASS FUELS↗
DOE OSTI2023

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. In conclusion, the utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗