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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Investigating the Longevity of Hydraulic Power Take-Off Piston Seals in Wave Energy Converters

Wave energy converters (WECs) utilize hydraulic power take-off (PTO) systems. Hydraulic cylinder seals, which are commonly made from thermoplastic or elastomeric polymers, play a vital role in PTO system functionality as they are responsible for containing and isolating high-pressure fluids. However, manufacturers of WECs have identified a significant concern regarding the longevity of hydraulic piston seal wear due to a combination of stressors, such as high pressure, elevated temperature, chemical corrosion, and/or environmental conditions. Seal failure can result in a loss of tightness and fluid leakage, and consequently a reduction in the efficiency of WECs. Seal manufacturers do not typically provide durability data as the materials are not specifically designed for WEC applications, nor do they account for real-world conditions that the materials may be exposed to. Additionally, there is no comparative performance data available between different seal manufacturers, creating uncertainty in hydraulic PTO designs for WECs. This paper reviews the current state of available seal materials for WEC hydraulic PTO systems, seal selection criteria for WEC applications, potential seal degradation mechanisms, and also material properties of the seals, such as density, hardness, tensile strength, and operating temperature. In addition, this paper also examines current evaluation procedures for estimating the durability of WEC PTO seals. Lastly, additional research on estimating the lifespan of piston and rod polymer seals used in hydraulic PTO systems is proposed.

Elen, Muthu↗
DOE OSTI2025

Avenues for a number density interpretation of dihadron fragmentation functions

In this letter, we reassess the underlying physics of the number sum rule for dihadron fragmentation functions. We will argue that, currently, there are no settled constraints on what constitutes a valid number density interpretation for multihadron fragmentation functions. Imposing overly restrictive criteria might lead to misinterpretating the data. Most importantly, and on the basis of phenomenological analyses, the slightly varying definitions used in previous work are not excluded from possessing legitimate number density interpretations (up to the usual issues with ultraviolet divergences and renormalization), so long as they are paired with appropriate factorization theorems. We advocate for further theoretical analyses to be challenged with experimental data, available at JLab or at the future EIC.

First principle↗
DOE OSTI2020

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bioinformatic Teaching Resources – For Educators, by Educators – Using KBase, a Free, User-Friendly, Open Source Platform

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Hepatic transcriptome analyses of juvenile white bass ( Morone chrysops ) when fed diets where fish meal is partially or totally replaced by alternative protein sources

White bass (Morone chrysops) are a popular sportfish throughout the southern United States, and one parent of the commercially-successful hybrid striped bass (M. chrysops male x M. saxatilis female). Currently, white bass are cultured using diets formulated for other carnivorous fish, such as largemouth bass (Micropterus salmoides) or hybrid striped bass and contain a significant percentage of marine fish meal. Since there are no studies regarding the utilization of alternative proteins in this species, we evaluated the global gene expression of white bass fed diets in which fish meal was partially or totally replaced by various combinations of soybean meal, poultry by-product meal, canola meal, soy protein concentrate, wheat gluten, or a commercial protein blend (Pro Cision™). Six isonitrogenous (40% protein), isolipidic (11%), and isocaloric (17.1 kJ/g) diets were formulated to meet the known nutrient and energy requirements of largemouth bass and hybrid striped bass using nutrient availability data for most of the dietary ingredients. One of the test diets consisted exclusively of plant protein sources. Juvenile white bass (40.2 g initial weight) were stocked into a flow-through aquaculture system (three tanks/diet; 10 fish/tank) and fed the test diets twice daily to satiation for 60 days. RNA sequencing and bioinformatic analyses revealed significant differentially expressed genes between all test diets when compared to fish meal control. A total of 1,260 differentially expressed genes were identified, with major ontology relating to cell cycle and metabolic processes as well as immune gene functions. This data will be useful as a resource for future refinements to moronid diet formulation, as marine fish meal becomes limiting and plant ingredients are increasingly added as a reliable protein source

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantifying air quality co-benefits to industrial decarbonization: the local Air Emissions Tracking Atlas

Many decarbonization technologies have the added co-benefit of reducing short-lived climate pollutants, such as particulate matter (PM), nitrogen oxides (NO x ), and sulfur dioxide (SO 2 ), creating a unique opportunity for identifying strategies that promote both climate change solutions and opportunities for air quality improvement. However, stakeholders and decision-makers may struggle to quantify how these co-benefits will impact public health for the communities most affected by industrial air pollution. To address this problem, the LOCal Air Emissions Tracking Atlas (LOCAETA) fills a data availability and analysis gap by providing estimated air quality benefits from industrial decarbonization options, such as carbon capture and storage (CCS). These co-benefits are calculated using an algorithm that connects disparate datasets that separately report greenhouse gas emissions and other pollutants at U.S. industrial facilities. Version 1.0 of LOCAETA displays the estimated primary PM 2.5 emission reduction co-benefits from additional pretreatment equipment for CCS on industrial and power facilities across the state of Louisiana, as well as the potential for VOC and NH 3 generation. The emission reductions are presented in the tool alongside facility pollutant emissions information and relevant air quality, environmental, demographic, and public health datasets, such as air toxics cancer risk, satellite and in situ pollutant measurements, and population vulnerability metrics. LOCAETA enables regulators, policymakers, environmental justice communities, and industrial and commercial users to compare and contrast quantifiable public health benefits due to air quality impacts from various climate change mitigation strategies using a free and publicly-available tool. Additional pollutant reductions can be calculated using the same methodology and will be available in future versions of the tool.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo1−x−yO2 cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments “(LCA)” have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry’s shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction.

25 ENERGY STORAGE↗
DOE OSTI2024

A Comprehensive Analysis of PINNs for Power System Transient Stability

The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Experimentally Measured Thermal Masses of Adsorption Heat Exchangers

The thermal masses of components influence the performance of many adsorption heat pump systems. However, typically when experimental adsorption systems are reported, data on thermal mass are missing or incomplete. This work provides original measurements of the thermal masses for experimental sorption heat exchanger hardware. Much of this hardware was previously reported in the literature, but without detailed thermal mass data. The data reported in this work are the first values reported in the literature to thoroughly account for all thermal masses, including heat transfer fluid. The impact of thermal mass on system performance is also discussed, with detailed calculation left for future work. The degree to which heat transfer fluid contributes to overall effective thermal mass is also discussed, with detailed calculation left for future work. This work provides a framework for future reporting of experimental thermal masses. The utilization of this framework will enrich the data available for model validation and provide a more thorough accounting of adsorption heat pumps.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine-Guided Design of Oxidation-Resistant Superconductors for Quantum Information Applications

Decoherence in superconducting qubits has long been attributed to two-level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). The identity, thickness, and quality of the native surface oxide, is thought to play a major role, as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first computed this metric for known binary and ternary metal alloys using data available from the Materials Project and experimentally validated the strengths and limits of this metric through the preparation and controlled oxidation of eight known metal alloys. Then we trained a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allowed us to compute the metric for materials that are not present in the Materials Project, including a large selection of known superconductors, and, when combined with Tc, allowed us to identify new candidate superconductors for quantum information science and engineering (QISE) applications. We tested the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for the tailored and rapid selection of improved superconductors for QISE.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

One Health Approach to Tackle Microbial Contamination on Poultries—A Systematic Review

This study reports the search of available data published regarding microbial occupational exposure assessment in poultries, following the PRISMA methodology. Air collection through filtration was the most frequently used. The most commonly used passive sampling method was material collection such as dust, cages, soils, sediment, and wastewater. Regarding assays applied, the majority of studies comprised culture-based methods, but molecular tools were also frequently used. Screening for antimicrobial susceptibility was performed only for bacteria; cytotoxicity, virological and serological assays were also performed. Most of the selected studies focused on bacteria, although fungi, endotoxins, and β-glucans were also assessed. The only study concerning fungi and mycotoxins reported the carcinogenic mycotoxin AFB1. This study gives a comprehensive overview of microbial contamination in the poultry industry, emphasizing this setting as a potential reservoir of microbial pathogens threatening human, animal, and environmental health. Additionally, this research helps to provide a sampling and analysis protocol proposal to evaluate the microbiological contamination in these facilities. Few articles were found reporting fungal contamination in poultry farms worldwide. In addition, information concerning fungal resistance profile and mycotoxin contamination remain scarce. Overall, a One Health approach should be incorporated in exposure assessments and the knowledge gaps identified in this paper should be addressed in further research.

Gomes, Bianca (ORCID:0000000262646072)↗
DOE OSTI2023

Lepton–Nucleus Interactions within Microscopic Approaches

This review paper emphasizes the significance of microscopic calculations with quantified theoretical error estimates in studying lepton–nucleus interactions and their implications for electron scattering and accelerator neutrino oscillation measurements. We investigate two approaches: Green’s Function Monte Carlo and the extended factorization scheme, utilizing realistic nuclear target spectral functions. In our study, we include relativistic effects in Green’s Function Monte Carlo and validate the inclusive electron scattering cross section on carbon using available data. We compare the flux-folded cross sections for neutrino-carbon scattering with T2K and MINER$v$ A experiments, noting the substantial impact of relativistic effects in reducing the theoretical curve strength when compared to MINER$v$ A data. Additionally, we demonstrate that quantum Monte Carlo-based spectral functions accurately reproduce the quasi-elastic region in electron scattering data and T2K flux-folded cross sections. By comparing results from Green’s Function Monte Carlo and the spectral function approach, which share a similar initial target state description, we quantify errors associated with approximations in the factorization scheme and the relativistic treatment of kinematics in Green’s Function Monte Carlo.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

54 ENVIRONMENTAL SCIENCES↗