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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Adjusting the pH of a pretreatment solution using carbon dioxide useful for integrating saccharification and fermentation

The present invention provides for a method of fermenting or saccharifying a biomass comprising: (a) (i) contacting a biomass comprising a polysaccharide, and an ionic liquid (IL) to form a first solution, or (ii) providing the first solution comprising the biomass and the IL, (b) contacting the first solution and carbon dioxide such that the first solution results in a lower pH, (c) introducing (i) an enzyme capable of enzymatically to breakdown at least one bond in the polysaccharide or a breakdown product of the polysaccharide, and/or (ii) a microorganism that capable of producing the enzyme and/or fermenting the polysaccharide or a breakdown product of the polysaccharide, such that the polysaccharide is at least partially broken down and the first solution is transformed into a second solution.

Sun, Jian↗
DOE OSTI2020

IDENTIFICATION OF ADSORBA TION OF ADSORBATE FT-IR BANDS USING IN-SI -IR BANDS USING IN-SITU TECHNIQUES: Pd SPECIATION AND ADSORPTION CHEMISTRY OF Pd-ZEOLITES FOR PASSIVE NOx ADSORPTION

To meet increasingly stringent automotive emissions standards, further improvements in catalytic converter design are necessary. Current automotive catalyst systems are effective at eliminating emission of nitrogen oxides (NOx) once the catalyst reaches operational temperature (~200 °C). NOx emitted at lower catalyst temperatures now comprises most of the NOx released during a typical test cycle. Referred to as “the cold start problem” this issue has come to the forefront of automotive catalyst development, as mitigating these emissions is necessary to further reduce automotive emissions. Passive NOx adsorbers present an appealing solution to the cold start problem, these being a class of materials that chemisorb exhaust components such as NOx, carbon monoxide (CO) and hydrocarbons at near-ambient temperatures, and then desorb these compounds once the downstream catalyst has reached operational temperature. An effective passive NOx adsorber must have several properties: high NOx adsorption at near-ambient temperatures, near-complete NOx desorption at temperatures within the operational range, high thermal stability, and resistance to automotive exhaust components at high temperatures. The potential environmental impact of such a system is substantial, as NOx emissions currently result in the formation of millions of tons of smog and acid rain each year. Pd-exchanged zeolites have shown promise for deployment as Passive NOx adsorbers, though much remains to be understood about their adsorption chemistry and deactivation. In-situ Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) provides a convenient probe of adsorbed species, most automotive exhaust components possessing IR-active chemical bonds. By examining the evolution of IR bands under various pretreatments and adsorbates, the overall Pd-speciation and adsorptive zeolite sites of each material can be characterized, and the identities of IR bands can be deduced. In this work, microreactor-MS analysis of the adsorption and desorption behavior of these materials was also examined, these results being coupled with in-situ DRIFTS temperature programmed desorption (TPD) to correlate desorption events with specific adsorbed species. A pair of zeolite frameworks of similar Si/Al ratio but differing pore size were examined, Beta zeolite (BEA) and Chabazite (CHA) representing a medium- and small-pore framework, respectively. The effect of Pd-loading on BEA was examined, as well as the various deactivation pathways and active sites of each material.

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

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗
DOE OSTI2024

Method of adjusting the pH of a pretreatment solution using carbon dioxide useful for integrating saccharification and fermentation of a biomass

The present invention provides for a method of fermenting or saccharifying a biomass comprising: (a) (i) contacting a biomass comprising a polysaccharide, and an ionic liquid (IL) to form a first solution, or (ii) providing the first solution comprising the biomass and the IL, (b) contacting the first solution and carbon dioxide such that the first solution results in a lower pH, (c) introducing (i) an enzyme capable of enzymatically to breakdown at least one bond in the polysaccharide or a breakdown product of the polysaccharide, and/or (ii) a microorganism that capable of producing the enzyme and/or fermenting the polysaccharide or a breakdown product of the polysaccharide, such that the polysaccharide is at least partially broken down and the first solution is transformed into a second solution.

Sun, Jian↗
DOE OSTI2022

Post-Combat-Injury Opioid Prescription and Alcohol Use Disorder in the Military

Previous studies have identified combat exposure and combat traumatic experience as problematic drinking risk factors. Increasing evidence suggests that opioid use increases the risk of alcohol use disorder. This study investigated the association between opioid prescription use after injury and (1) alcohol use disorder and (2) severity of alcohol use disorder among deployed military servicemembers. Deidentified health records data of 9,029 deployed servicemembers from a retrospective cohort study were analyzed. Data were randomly selected from the Department of Defense Trauma Registry and included servicemembers with combat injuries during deployment in Iraq or Afghanistan (2002–2016). Pharmacy records and International Classification of Diseases, Ninth and Tenth Revision diagnosis codes were used. Three groups were identified (no opioid prescription use, nonpersistent opioid prescription use, and persistent opioid prescription use) and were compared on the basis of alcohol use disorder risk using Cox proportional hazard models. Data analyses were performed in 2021. Of the 9,029 servicemembers with combat injury, 2,262 developed alcohol use disorder (1,322 developed severe alcohol use disorder). Compared with no opioid prescription use, increased alcohol use disorder risk was associated with persistent opioid prescription use, with a hazard ratio of 1.13 (95% CI=1.02, 1.26). After covariate adjustment, increased risk remained statistically significant (hazards ratio=1.24; 95% CI=1.10, 1.39). There was no significant difference in alcohol use disorder risk between no opioid prescription use and nonpersistent opioid prescription use. The risk of severe alcohol use disorder did not vary by opioid use among servicemembers with alcohol use disorder diagnosis. Furthermore, the findings of the study suggest that the incidence of alcohol use disorder was higher among injured servicemembers with persistent opioid prescription use than among those without opioid use. If replicated in prospective studies, the findings highlight the need for clinicians to consider the current and history of alcohol use of patients in initiating treatment involving opioids.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

(U) A General-Purpose Code for Correlated Sampling Using Batch Statistics with MCNP6 for Fixed-Source Problems

Correlated sampling can be used to reduce the uncertainty of a difference of tallies by taking advantage of the negative covariance term in the sandwich formula. Booth first showed how correlated sampling can be applied with batch statistics using MCNP’s tally fluctuation chart (TFC) to reduce the uncertainty of a difference of tallies in fixed-source problems. Booth presented a problem in which a 1273% uncertainty in a difference was reduced to 8% by accounting for correlations. Researchers He and Su recently studied correlated sampling using the TFC in MCNP version 5. They determined that the code did not print enough digits in the TFC tally means for accurate batch statistics in some cases. After modifying the source code, they concluded that “correlated sampling can yield a standard deviation of about one magnitude smaller than that predicted by the direct, un-correlated simulation when the changes in system response are small (say about 1%), which is equivalent to saving in CPU time by a factor of 100. Such saving [sic] becomes less significant as the change in system response becomes larger.” He and Su provided the formulas needed to apply batch statistics to compute the correlated uncertainty of a difference of tallies. In this report, we follow up on their work by providing the formulas needed to apply batch statistics to compute the correlated uncertainty of a ratio of tallies and of a difference of two tallies divided by a third tally. We extend these formulas to differences and ratios of ratios. These formulas are applied to reduce the uncertainty associated with calculating a relative sensitivity. He and Su did not investigate the accuracy of their correlated sampling uncertainty estimates. We use their test problems and evaluate the accuracy of the uncertainty estimates by comparing with results obtained from random sampling, and, in simple cases, with theoretical values of the “exact” uncertainties. We find that the uncertainties obtained from batch statistics are accurate as long as at least 100 batches are used. We present a new computer code, COSUBS (COrrelated Sampling Using Batch Statistics), that reads MCNP6 TFCs and applies correlated sampling using batch statistics for the tally combinations that the user specifies. COSUBS is a very general tool that compares all TFCs for a base case and one or two perturbed cases. It computes uncertainties for ratios if given only a base case. This report is organized as follows. The equations to apply batch statistics to the difference of random tallies are reviewed in Sec. II. Section III presents the equations for applying batch statistics to a ratio of random tallies; this is useful for computing relative sensitivities using a one-sided finite difference and the relative sensitivity using the differential operator method. Section IV presents the equations for applying batch statistics to a difference of two random tallies divided by a third; this is useful for computing a relative sensitivities using a central difference. Section V presents the equations for applying batch statistics to a difference of two ratios with four random tallies. Section VI presents the equations for applying batch statistics to a one-sided finite difference estimate of the relative sensitivity of a ratio (this uses four random tallies). Section VII presents the equations for applying batch statistics to a central difference estimate of the relative sensitivity of a ratio (this uses six random tallies). Section VIII presents the equations for applying batch statistics to a sum of random tallies. Section IX discusses how to apply batch statistics using MCNP6. Section X presents COSUBS, describing its command-line options and logic. Sections XI through XVI present numerical results for various test problems. Section XVII is a summary and conclusions. Appendix A derives the theoretical Monte Carlo tally variance given certain assumptions; these variances are used to verify the batch statistics for some of the problems. Appendix B lists the MCNP6 input for the unperturbed example problem. Appendix C presents modifications made to MCNP6.3 to support this work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Biogeophysical Effects of Land-Use and Land-Cover Change Not Detectable in Warmest Month

Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (t LU ) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that t LU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unlocking nighttime mobility: Land use and accessibility in public transit for night commuters

Night commuters are integral to urban transportation systems. Essential services such as healthcare and manufacturing rely on workers who travel at night, and reliable mobility options are crucial for them. A gap exists in understanding how land use and accessibility influence public transportation use among night commuters. This study addresses this gap by using public data to explore land use and accessibility factors that affect night commuters' public transportation use in New York State. We investigated (1) the demographic characteristics of night commuters; (2) the influence of land use and accessibility on nighttime public transportation use; and (3) potential improvements to increase public transportation use and their impact. We combined data from the National Household Travel Survey with the Smart Location Database to link home locations with land use characteristics. Using logistic regression, we found that although females are generally less likely to be night commuters, they are more likely to use public transportation. Longer commute distances are associated with higher use of public transportation. Increasing job density along fixed-guideway transit routes and improving overall job accessibility via public transportation significantly enhances public transportation use among night commuters. In conclusion, this research provides actionable insights for public transportation agencies and urban planners to support night commuters, improving access and encouraging nighttime employment.

Job accessibility↗