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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Simulating energy performance of buildings: a study using eQUEST and Energy Star ® portfolio manager

Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. In conclusion, the synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Analysis of Polarimetry Data with Angular Uncertainties

For a track based polarimeter, such as the Imaging X-ray Polarimetry Explorer (IXPE), the sensitivity to polarization depends on the modulation factor, which is a strong function of energy. In previous work, a likelihood method was developed that would account for this variation in order to estimate the minimum detectable polarization (MDP). That method essentially required that the position angles of individual events should be known precisely. In a separate work, however, it was shown that using a machine-learning method for measuring event tracks can generate track angle uncertainties, which can be used in the analysis. Here, the maximum likelihood method is used as a basis for revising the estimate of the MDP in a general way that can include uncertainties in event track position angles. The resultant MDP depends solely upon the distribution of track angle uncertainties present in the input data. Due to the physics of the IXPE detectors, it is possible to derive a simple relationship between these angular uncertainties and the energy-dependent modulation function as a step in the process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Oxygen enrichment combustion to reduce fossil energy consumption and emissions in hot rolling steel production

The reheating furnace operation in the hot mill is natural gas- and electricity-intensive. Oxygen enrichment combustion for reheating furnaces has been proposed to curb and replace natural gas use. In this study, heat transfer in steel slabs in the combustion environment of a push-type reheating furnace was simulated using a computational fluid dynamics (CFD) model. Two oxygen enrichment methods that optimized for performance were selected— a medium oxygen enrichment (MOE) case and an oxy-fuel (OF) case. A life cycle analysis (LCA) characterized the energy and emission profiles of an integrated iron and steel manufacturing process using the two oxygen enrichment cases for the hot mill. These conditions were evaluated for energy use and carbon intensity and compared with a baseline case. Results show that with oxygen enrichment, natural gas consumption can decrease by 19.6%–26.8%, total energy consumption (natural gas and electricity) can decrease by 15.1%–20.7% in the hot mill. Emissions of greenhouse gases can decrease by 11.1%–15.2% in the two optimized cases with 14%–27% reductions in regulated criteria pollutants (nitrogen oxides, carbon monoxide, particulate matter, volatile organic compounds, black carbon, organic carbon, and volatile organic carbons). There is a tradeoff between reducing natural gas consumption and increasing electricity demand from a life cycle perspective. Although the OF case resulted in higher energy- and emissions-related benefits, the MOE case showed the more desirable heat flux uniformity, which is key to maintaining product quality. The analysis suggests that oxygen enrichment in the reheating furnace process can have a significant impact on hot mill environmental performance and become a contributing factor in transitioning to low-carbon steel manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ideal MHD induced temperature flattening in spherical tokamaks

This paper extends the analysis first presented in Jardin et al. [Phys. Rev. Lett. 128, 245001 (2022)] to more thoroughly examine the stability of spherical torus equilibrium to ideal magnetohydrodynamic (MHD) infernal modes and their nonlinear consequences. Here we demonstrate that in a 3D resistive magnetohydrodynamic (MHD) simulation of a NSTX discharge, anomalous transport can occur due to these instabilities. Here we generate a family of equilibrium of differing β and use this to show that these instabilities could explain the experimentally observed flattening of the electron temperature profile at modest β. The modes studied in this paper are found to occur with poloidal mode number m and toroidal mode number n when the ratio m/ n is in the range of 1.2–1.5, when the central safety factor is in this range or slightly lower, and when the central region has very low magnetic shear. Our analysis gives some insight as to why the unstable linear growth rates are oscillatory functions of the toroidal mode number n. We present a simulation of an initially stable configuration that passes through a stability boundary at a critical β as it is heated. We also show that a particular NSTX discharge is unstable to these modes over a timescale of several hundred ms. We conclude that these modes must be taken into account when performing predictive modeling. An appendix shows that similar modes can be found in R / a = 4 tokamaks for certain q-profiles and β values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

GHG Emissions Inventory Report for LvivTeploEnergo (LTE)

PNNL prepared the greenhouse gas (GHG) emissions inventory for LTE to help gain international funding for modernization requirements necessary to decarbonize LTE’s DH network. The inventory itself provides a comprehensive account of total greenhouse gas emissions from (a) LTE’s transportation (commuting and product transport), (b) purchased electricity (for water heating, heat production, and other uses), and (c) heat production for the DH network. The greenhouse gases analyzed in the inventory are Carbon Dioxide (CO 2 ), Carbon Monoxide (CO), and Nitrogen Oxide (NO x ). All data gathered for the inventory came directly from previous years of LTE data, with total emissions of each activity calculated by using emission factors from several internationally reputable sources (e.g., United National Framework Convention on Climate Change (UNFCCC), the ECLIPSE v6 dataset from the International Institute for Applied Systems Analysis (IIASA), the European Modeling and Evaluation Programme (EMEP), and the World Resources Institute (WRI)). This report further explains the methodology and justification for which scopes of emissions were included in the GHG emissions inventory spreadsheet and a description of references from which the emission factors were taken.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2023, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, net value, and interconnection queue data.

analysis↗
DOE OSTI2020

Computational Analysis of Water Film Thickness During Rain Events for Assessing Hydroplaning Risk Part 2: Rough Road Surfaces

Hydroplaning occurs when a water film forms on a road during a rain event and vehicles are traveling at a speed that does not provide sufficient time for the tires to push the water film out of the tire path. Under these conditions, the tire loses contact with the road and the driver may lose control over the vehicle. The present study is a part of a multiyear research effort aimed at gaining a better understanding of the factors that contribute to hydroplaning risk in order to minimize the occurrence of hydroplaning accidents. Three-dimensional computational fluid dynamics (CFD) analysis was used to investigate the water film thickness on multilane roads, one of the key parameters in evaluating hydroplaning risk. In the first part of the study, water films forming on wide roadways, i.e. multilane highways with 2, 4, and 6 lanes per side, were analyzed, with varying cross slope, longitudinal slope, and rainfall rate. Roadways with and without curbs and drainage were included in the analysis. The analysis in the first part was limited to nearly smooth asphalt or concrete surfaces because the maximum roughness height that can be specified in a CFD model using wall functions to determine shear stress at the road surface boundary is one half the thickness of computational cell layer adjacent to the boundary and the need to resolve the vertical velocity distribution in a thin water film requires cell layers that are a fraction of a millimeter thick. In this part of the study, the analysis was extended to consider the influence of pavement roughness over a range extending up 3.6 mm on the development of water film thickness. Various methods of modeling roughness were investigated and are presented: the roughness height model, a meshed-out geometry of the macrotexture of the pavement, and a porous region model. The computational results were analyzed and compared with experimental measurements performed by Gallaway et al.

42 ENGINEERING↗
DOE OSTI2021

Grid Voltage Control Analysis for Heavy-Duty Electric Vehicle Charging Stations: Preprint

This paper presents an analysis of voltage control strategies for heavy duty electric vehicle (EV) charging stations. The performance of three voltage control approaches including power factor control, preset volt-var curve control, and customized volt-var curve control, have been investigated and evaluated for three different sizes of charging stations, including single-port small charging station, three-port middle size charging station, and 6-port travel center. The charging stations have been placed on various locations of four different types of distribution systems, to obtain a comprehensive performance analysis of the three voltage control approaches under different scenarios. To quantitively compare the performance of the three voltage control approaches, a series of metrics have been designed to quantify the performance and contribute to the comprehensive analysis summary.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Deletion analysis of the itaconic acid biosynthesis gene cluster components in Aspergillus pseudoterreus ATCC32359

The filamentous fungus Aspergillus terreus has been successfully used for industrial production of itaconic acid (IA) for many years. The IA biosynthesis pathway has recently been characterized at a molecular genetic level as an IA gene cluster (IA cluster) by a clone-based transcriptomic approach. The cluster consists of four genes, including genes for cis-aconitic acid decarboxylase (cadA), a predicted transcription factor (tf), mitochondrial organic acid transporter (mttA), and MFS (Major Facilitator Superfamily) type transporter (mfsA). In this research, we performed EST (Expressed Sequence Tag) analysis and systematic gene deletions to investigate the role of those genes in IA biosynthesis in A. pseudoterreus ATCC32359. EST analysis showed a similar expression pattern among those four genes that was distinct from neighbouring genes and further confirmed that they belong to the same biosynthesis cluster. Systematic gene deletion analysis demonstrated that tf, cadA, mttA, and mfsA genes in the cluster are essential for IA production. Deletion of any of them will either completely abolish the IA production or dramatically decrease amount of IA produced. More importantly, a significant amount of aconitic acid was detected in the cadA deletion strain but not in the other deletion strains. Therefore, the cadA deletion strain is a novel microorganism host for production of aconitic acid and other value-added chemicals from lignocellulosic biomass.

Deng, Shuang↗
DOE OSTI2026

Analysis of vapor diffusion Nb$^{3}$Sn coating at Fermilab: Minimizing impurities using TOF-SIMS

Nb₃Sn demonstrates steady advancements nowadays offering reduced power cost in superconducting radio-frequency cavities due to its high critical temperature, quality factor, and achieved accelerating gradient. However, theoretical estimates of its radio-frequency parameters have not been achieved due to several potentially limiting mechanisms: tin spots, patchy regions, defects, thermal impedance, and impurities. While some of these limitations have been intensively studied, impurity analysis in Nb$_3$Sn coatings have received less attention. We report an investigation of impurities in several vapor-diffused Nb₃Sn coated samples using time-of-flight secondary ion mass spectroscopy (TOF-SIMS) and show allowable impurity levels in view of superconducting cavity performance. Challenges and lessons learned in maintaining clean Nb$_3$Sn coatings are also discussed.

Tagdulang, Nikki [Fermilab]↗
DOE OSTI2025

Initial Uncertainty Analysis of Carbon Tetrachloride Contamination and Remediation in the Ringold A and Lower Mud Units at the Central Plateau

The long-term effectiveness of groundwater cleanup at the Hanford Site Central Plateau depends on predictive models that can capture key uncertainties in contaminant fate and transport. Carbon tetrachloride (CCl 4 ), a persistent and toxic compound, presents particular challenges due to variability in degradation rates, uncertainty in initial plume distribution, and subsurface heterogeneity. These uncertainties directly influence plume persistence, migration pathways, and remedy performance, and thus must be systematically evaluated to support long-term remediation planning. To address these gaps, a large-scale Monte Carlo analysis was conducted using the Plateau to River (P2R) model framework. The modeling approach parameterized three primary uncertainty factors: (1) degradation rate, (2) initial plume distribution, and (3) hydraulic conductivity. Degradation was represented as a first-order process, with half-lives ranging from 70 to 700 years. Initial plume distributions were created using a geostatistical simulation method (sgsim), which generates many equally plausible versions of how contaminants might be distributed underground. From this, 100 different scenarios were mapped onto the P2R grid. Variability in hydraulic conductivity was represented in a similar way, with 100 scenarios each for the Ringold Lower Mud and Ringold A units (layers 6 and 7), based on fitted exponential variograms and conditioned to well data. In total, more than 1000 realizations were simulated to assess plume behavior under uncertainty. Results demonstrate that degradation kinetics exert the strongest control over plume persistence: Shorter half-lives produced rapid mass reduction, while longer half-lives yielded persistent plumes with limited attenuation. A nonlinear response was observed, with steep mass reductions at half-lives greater than 200 years and near-linear declines beyond this threshold, reflecting interactions between degradation and pumping. The initial plume distribution strongly influenced early transport patterns, with broader sources generating larger plume footprints, although pump-and-treat operations constrained plume migration to managed areas. By comparison, hydraulic conductivity variability in the Ringold units had only a secondary influence, modifying spreading behavior without altering the dominant migration pathways governed by source configuration and hydraulic controls. Overall, the analysis highlights that uncertainty in degradation rate and initial plume configuration are the primary drivers of variability in plume predictions, while conductivity heterogeneity plays a limited role. These findings underscore the need for improved site-specific data on degradation processes and source characterization to enhance the reliability of long-term performance assessments and to better inform remedial decision-making at the Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Virulence and Genetic Diversity of Puccinia spp., Causal Agents of Rust on Switchgrass (Panicum virgatum L.) in the USA

Switchgrass (Panicum virgatum L.) is an important cellulosic biofuel grass native to North America. Rust, caused by Puccinia spp. is the most predominant disease of switchgrass and has the potential to impact biomass conversion. In this study, virulence patterns were determined on a set of 38 switchgrass genotypes for 14 single-spore rust isolates from 14 field samples collected in seven states. Single nucleotide polymorphism (SNP) variation was also assessed in 720 sequenced cloned amplicons representing 654 base pairs of the elongation factor 1-α gene from the field samples. Five major haplotypes were identified differing by 11 out of the 39 SNP positions identified. STRUCTURE, Principal Coordinate Analysis, and phylogenetic analyses divided the rust population into two genetic clusters. Virginia and Georgia had the highest and lowest rust genetic diversity, respectively. Only nine accessions showed a differential disease response between the 14 isolates, allowing the identification of eight races, differing by 1–3 virulence factors. Overall, the results suggested clonal reproduction of the pathogen and a North–South differentiation via local adaptation. However, similar haplotypes and races were also recovered from several states, suggesting migration events, and highlighting the need to further investigate the switchgrass rust population structure and evolution in the USA.

Bahri, Bochra A. (ORCID:0000000159055880)↗
DOE OSTI2020

Glycolate Analysis in Tank 22: Developing and Testing Analytical Methods for the Savannah River Site Liquid Waste System

Researchers developed and tested a range of techniques to support a low mg/L Limit of Quantitation (LOQ) of glycolate in radioactive Tank 22 waste solution by Ion Chromatography (IC) and by proton NMR. For the IC method, Dionex OnGuard II cartridges were tested as a means of removing alkali earth and transition metals that can interfere during ion chromatography applications especially when analyzing for low-concentration, chelating analytes. Additionally, concentrations of nitrate in the raw Tank 22 sample (5,000 to 10,000 mg/L) were managed by using reasonable levels of sample dilution. The resulting IC performance quality was enhanced by improving the baseline, increasing sensitivity [Limit of Quantitation (LOQ) of 12 mg/L and Limit of Detection (LOD) of 4.0 mg/L], and resolving analytes into well-defined Gaussian peaks when using the Dionex OnGuard II H⁺ cartridges to remove matrix interferences. High concentrations of nitrate limited the performance of the IC method. To achieve an acceptable baseline, samples with high nitrate content require more dilution, resulting in higher detection limits. Tests on samples with higher concentrations of nitrate (Tank 30 and 32 supernate with approximately 150000 mg/L nitrate) suggested that IC detection limits for glycolate in these samples would be > 500 mg/L. Thus, an LOQ of 12 mg/L is not feasible by this IC method on evaporator feed samples. An alternative method of glycolate analysis, using proton nuclear magnetic resonance (H NMR), was developed by the research team. In initial tests, the H NMR technique provided reasonable quantitation of glycolate in Tank 22 conditions by direct observation of the liquid. The H NMR method may provide improved detection limits for solutions with higher nitrate concentrations. We recommend further development of this analysis for high nitrate LWS samples such as evaporator feed and evaporator drop tank content. A method for pretreatment of samples using crystalline silicotitanate (CST) was developed and tested. The objective of the pretreatment was to facilitate analysis of samples with higher levels of radioactivity. The pretreatment did not influence the glycolate concentration in solution, and the pretreatment is projected to reduce Cs 137 activity in a sample by a factor of 16,200. The double strike CST pretreatment was demonstrated and would allow milliliters of higher activity samples to be transferred from the Shielded Cells and handled in a containment unit for glycolate analysis. The various studies validated IC and H NMR methods for glycolate analysis, defined the range of applicability, and demonstrated key supporting analytical protocols. Based on the results, high quality glycolate analysis of Tank 22 is feasible down to approximately 12 mg/L, with the potential for broader applicability of the methods to other conditions in the Savannah River Site Liquid Waste System (LWS).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The bioprocess TEA calculator: An online technoeconomic analysis tool to evaluate the commercial competitiveness of potential bioprocesses

Techno-economic analysis connects R&D, engineering, and business. By linking process parameters to financial metrics, it allows researchers to understand the factors controlling the potential success of their technologies. In particular, metabolic and bioprocess engineering, as disciplines, are aimed at engineering cells to synthesize products with an ultimate goal of commercial deployment. As a result it is critical to be able to understand the potential impact of strain engineering strategies and lab scale results on commercial potential. To date, while numerous techno-economic models have been developed for a wide variety of bioprocesses, they have either required process engineering expertise to adapt and/or use or do not directly connect financial outcomes to potential strain engineering results. Despite the clear value of techno-economic analysis, these challenges have made it inaccessible to many researchers. Furthermore, the tool, currently focused on aerobic fermentation processes, can be used to understand the impact of fermentation level metrics on the commercial potential of a bioprocess for the production of a wide variety of organic molecules. Using the calculator, I review the commercially relevant targets for an aerobic bioprocess for the production of diethyl malonate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transportation and Systems Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. An analyst using NGSAM has the ability to define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions on and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams had been working closely for several years before formalizing this collaboration. START provides the SNF routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM, as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Causal relationship between mitochondrial-associated proteins and cerebral aneurysms: a Mendelian randomization study

Background Cerebral aneurysm is a high-risk cerebrovascular disease with a poor prognosis, potentially linked to multiple factors. This study aims to explore the association between mitochondrial-associated proteins and the risk of cerebral aneurysms using Mendelian randomization (MR) methods. Methods We used GWAS summary statistics from the IEU Open GWAS project for mitochondrial-associated proteins and from the Finnish database for cerebral aneurysms (uIA, aSAH). The association between mitochondrial-associated exposures and cerebral aneurysms was evaluated using MR-Egger, weighted mode, IVW, simple mode and weighted median methods. Reverse MR assessed reverse causal relationship, while sensitivity analyses examined heterogeneity and pleiotropy in the instrumental variables. Significant causal relationship with cerebral aneurysms were confirmed using FDR correction. Results Through MR analysis, we identified six mitochondrial proteins associated with an increased risk of aSAH: AIF1 (OR: 1.394, 95% CI: 1.109–1.752, p = 0.0044), CCDC90B (OR: 1.318, 95% CI: 1.132–1.535, p = 0.0004), TIM14 (OR: 1.272, 95% CI: 1.041–1.553, p = 0.0186), NAGS (OR: 1.219, 95% CI: 1.008–1.475, p = 0.041), tRNA PusA (OR: 1.311, 95% CI: 1.096–1.569, p = 0.003), and MRM3 (OR: 1.097, 95% CI: 1.016–1.185, p = 0.0175). Among these, CCDC90B, tRNA PusA, and AIF1 demonstrated a significant causal relationship with an increased risk of aSAH (FDR q < 0.1). Three mitochondrial proteins were associated with an increased risk of uIA: CCDC90B (OR: 1.309, 95% CI: 1.05–1.632, p = 0.0165), tRNA PusA (OR: 1.306, 95% CI: 1.007–1.694, p = 0.0438), and MRM3 (OR: 1.13, 95% CI: 1.012–1.263, p = 0.0303). In the reverse MR study, only one mitochondrial protein, TIM14 (OR: 1.087, 95% CI: 1.004–1.177, p = 0.04), showed a causal relationship with aSAH. Sensitivity analysis did not reveal heterogeneity or pleiotropy. The results suggest that CCDC90B, tRNA PusA, and MRM3 may be common risk factors for cerebral aneurysms (ruptured and unruptured), while AIF1 and NAGS are specifically associated with an increased risk of aSAH, unrelated to uIA. TIM14 may interact with aSAH. Conclusion Our findings confirm a causal relationship between mitochondrial-associated proteins and cerebral aneurysms, offering new insights for future research into the pathogenesis and treatment of this condition.

Wang, Shuai↗
DOE OSTI2021

Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (N CCN ) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy's Southern Great Plains (SGP) site to illustrate the dependence of N CCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (N accu ; median value 805 cm -3 ) and relatively high aerosol hygroscopicity (κ; median value ~0.25) values that result in the higher CCN activation and relatively high N CCN (median value 258–1578 cm -3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher N accu (~35%), and N CCN (230–1721 cm -3 at a SS of 0.1%–1.0%) with the lowest κ (~0.17). In contrast, relatively high nucleation mode particles (N nucl ; ~20%) and low N CCN (128–1553 cm -3 at a SS of 0.1%–1.0%) with higher κ (~0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better N CCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the N CCN prediction uncertainty at lower SS.

54 ENVIRONMENTAL SCIENCES↗