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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Carbon Life Cycle Analysis of CO 2 -EOR for Net Carbon Negative Oil (NCNO) Classification (Final Report)

This final report summarizes the work that was conducted to achieve the project’s general objective of developing a clear and repeatable methodology to determine whether the oil produced in a conventional CO 2 enhanced oil recovery (CO 2 -EOR) operation can be classified as Net Carbon Negative Oil (NCNO). The report also summarizes the reservoir mass accounting methodology and the monitoring, verification, and accounting (MVA) methodology, which are critical elements that support the general objective and were conceived as project goals. Because the purpose is to classify the EOR product (crude oil) and not the EOR project itself, and as the rate of crude oil production varies significantly with time, the methodology uses a novel dynamic carbon lifecycle analysis (d-LCA) that links instant energy demand and associated greenhouse gas (GHG) emissions to instant operational performance. The EOR operational performance is assessed through CO 2 utilization rates, which relate usage of CO 2 to oil production This dynamic method provides a better understanding of the evolution of the environmental impact (CO 2 emissions) and mitigation (geologic CO 2 storage) associated with an expanded carbon capture, utilization and storage (CCUS) system, from start to closure of operations. The dynamic interplay between operational and environmental performance forms the basis of our CCUS technology analysis. The LCA part of the work is meant to complement previous NETL LCA work, and so it focused on EOR/storage efficiency, its variability through time, and the impact of this variability on LCA results. The environmental impact of the CCUS system was assessed for 3 system boundaries: (1) gate-to-gate, (2) gate-to-grave, and (3) cradle-to-grave. The environmental impact was also assessed considering the electricity displacement from other generation facilities. Another goal of our study was to use results to make recommendations on EOR field development strategies that are conducive to producing NCNO. Because field operational strategies have a significant impact on reservoir engineering parameters that affect both CO 2 storage and oil production (e.g., sweep efficiency, flood conformance, fluid saturation distribution), we conducted a scenario analysis that assesses the operational and environmental performance of four common and novel CO 2 -EOR field development strategies. Results show that all CO 2 -EOR evaluated scenarios start operating with a negative carbon footprint and, years into the project, transition into operating with a positive carbon footprint. Having achieved a result that proves that all CO 2 -EOR operations produce NCNO during the first years of production is critical in the context of the urgency of climate change mitigation. Transition points are significantly different in each scenario. ii In a cradle-to-grave CCUS system, if the capture facility displaces a conventional power source and credits are added for displacement, the adverse impact caused by the system expansion not only is reversed but transformed into a significant environmental benefit. The effect of the displacement more than compensates the emissions associated with the electricity generation requirements for the production of CO 2 , as much more electricity will be displaced. Results provide an understanding of the evolution of the system’s net carbon balance of all four field development strategies studied. This information can be useful to CO 2 -EOR operators seeking value in storing more CO 2 through a carbon credit program (e.g., the 45Q federal carbon credit program). Most importantly, this study serves as confirmation that CO 2 -EOR can be operationally designed to both enhance oil production and reduce greenhouse gas emissions to the atmosphere.

02 PETROLEUM↗
DOE OSTI2026

Non-Contact Eddy Current Method for Assessing Proton Conductivity in Nafion Membranes

Accurate measurement of proton conductivity in Proton Exchange Membranes (PEMs) is vital for fuel cell performance and durability. This study explores eddy current testing as a non-destructive, non-contact and high-rate quality control (QC) method for measuring Nafion membrane proton conductivity, with potential for high-volume manufacturing applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Quantifying methane emissions from natural gas production in north-eastern Pennsylvania

Natural gas infrastructure releases methane (CH 4 ), a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of its greenhouse footprint. This study presents a new application of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in north-eastern Pennsylvania. An inventory of CH 4 emissions was compiled for major sources in Pennsylvania. This inventory served as input emission data for the Weather Research and Forecasting model with chemistry enabled (WRF-Chem), and atmospheric CH 4 mole fraction fields were generated at 3 km resolution. Simulated atmospheric CH 4 enhancements from WRF-Chem were compared to observations obtained from a 3-week flight campaign in May 2015. Modelled enhancements from sources not associated with upstream natural gas processes were assumed constant and known and therefore removed from the optimization procedure, creating a set of observed enhancements from natural gas only. Simulated emission rates from unconventional production were then adjusted to minimize the mismatch between aircraft observations and model-simulated mole fractions for 10 flights. To evaluate the method, an aircraft mass balance calculation was performed for four flights where conditions permitted its use. Using the model optimization approach, the weighted mean emission rate from unconventional natural gas production and gathering facilities in north-eastern Pennsylvania approach is found to be 0.36 % of total gas production, with a 2 σ confidence interval between 0.27 and 0.45 % of production. Similarly, the mean emission estimates using the aircraft mass balance approach are calculated to be 0.40 % of regional natural gas production, with a 2 σ confidence interval between 0.08 and 0.72 % of production. Furthermore these emission rates as a percent of production are lower than rates found in any other basin using a top-down methodology, and may be indicative of some characteristics of the basin that make sources from the north-eastern Marcellus region unique.

03 NATURAL GAS↗
DOE OSTI2023

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2022

This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2022 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2022. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: (1) HPCS EDG FTS failure probability, (2) EPS and HPCS EDG frequency of start demands (demands per reactor year), and (3) EPS and HPCS EDG frequency of FTLR demands. The following decreasing trends were identified for EDGs for the most recent 10-year period: (1) EPS EDG FTS failure probability, (2) EPS EDG FTLR failure probability, (3) EPS EDG unavailability, and (4) EPS and HPCS EDG frequency of FTLR events (failures per reactor year).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Machine learning reduces soft costs for residential solar photovoltaics

Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.

14 SOLAR ENERGY↗
DOE OSTI2023

Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater

Microbial electrolysis cells (MECs) have demonstrated high-rate H 2 production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H 2 production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. Here, we also provide perspectives on future development needed to transition MEC technology to the marketplace.

42 ENGINEERING↗
DOE OSTI2017

Recent Progress in Synthesis and Application of Low-Dimensional Silicon Based Anode Material for Lithium Ion Battery

Silicon is regarded as the next generation anode material for LIBs with its ultra-high theoretical capacity and abundance. Nevertheless, the severe capacity degradation resulting from the huge volume change and accumulative solid-electrolyte interphase (SEI) formation hinders the silicon based anode material for further practical applications. Hence, a variety of methods have been applied to enhance electrochemical performances in terms of the electrochemical stability and rate performance of the silicon anodes such as designing nanostructured Si, combining with carbonaceous material, exploring multifunctional polymer binders, and developing artificial SEI layers. Silicon anodes with low-dimensional structures (0D, 1D, and 2D), compared with bulky silicon anodes, are strongly believed to have several advanced characteristics including larger surface area, fast electron transfer, and shortened lithium diffusion pathway as well as better accommodation with volume changes, which leads to improved electrochemical behaviors. Finally, in this review, recent progress of silicon anode synthesis methodologies generating low-dimensional structures for lithium ion batteries (LIBs) applications is listed and discussed.

25 ENERGY STORAGE↗
DOE OSTI2026

Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Geometry-aware training of factorized layers in tensor Tucker format

Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during train and inference of large networks. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format and parameterizes it as the product of two rank-r matrices. However, this family of approaches often requires an initial full-model warm-up phase, prior knowledge of a feasible rank, and it is sensitive to parameter initialization.In this work, we introduce a novel approach to train the factors of a Tucker decomposition of the weight tensors. Our training proposal proves to be optimal in locally approximating the original unfactorized dynamics and stable for the initialization. Furthermore, the rank of each mode is dynamically updated during training.We provide a theoretical analysis of the algorithm, showing convergence, approximation and local descent guarantees. The method's performance is further illustrated through a variety of experiments, showing remarkable training compression rates and comparable or even better performance than the full baseline and alternative layer factorization strategies.

Zangrando, Emanuele [Gran Sasso Science Institute ↗
DOE OSTI2022

First-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Enhanced Component Performance Study: Emergency Diesel Generators 1998-2024

This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2024 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2024. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: • EPS and HPCS EDG frequency of start demands (demands per reactor year) • EPS and HPCS EDG frequency of FTLR demands • EPS and HPCS EDG frequency of run>1H hours. The following decreasing trends were identified for EDGs for the most recent 10-year period: • EPS EDG FTR>1H failure rate • EPS EDG unreliability • EPS and HPCS EDG frequency of FTR>1H events (failures per reactor year).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Evaluating the Performance of Island Kitchen Range Hoods

A key aspect of achieving acceptable indoor air quality is source control. Cooking has been recognized as a significant source of pollutants for health impacts (e.g., PM2.5 and NO2) as well as moisture and odour. A common method of controlling this pollutant source is by using a range (or cooker) hood that vents to outside. However, field and laboratory experiments have shown highly variable performance for these devices. We use the capture efficiency metric (the fraction of the pollutants that are exhausted to outside at steady state) to characterize the range hood performance. To address this issue and provide useful information for builders, contractors, designers and home occupants, a laboratory rating method for range hood capture efficiency has recently been developed by LBNL and ASTM. The test method uses standardized emitters to create a heated plume and seed it with tracer gas. The tracer gas measurements in the room, the range hood exhaust and in the ambient air are used to estimate capture efficiency. However, this test method only applies to wall-mounted range hoods. Some range hoods are not wall-mounted: island range hoods are designed to operate over a cooktop in the middle of a room rather than against a wall and downdraft hoods draw air from near the cooktop rather than overhead. This paper discusses the development of a new test apparatus for island and downdraft hoods and presents measured capture efficiency data from example hoods. The results of this work will be used in future revisions to the ASTM standard.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Systems and methods for quality control of a periodic structure

Quality control of a periodic structure is performed using the damping rate of acoustic waves generated in the periodic structure. In this technique, an excitation light beam illuminates the first layer in the periodic structure to excite an acoustic wave. Possible irregularities in the periodic structure can scatter the acoustic wave, thereby increasing the damping rate of the acoustic wave. A sequence of probe light beams illuminates the periodic structure to measure the acoustic wave as a function of time to generated a temporal signal representing the damping rate of the acoustic signal. The acquired damping rate is employed to evaluate the quality of the periodic structure.

Maznev, Alexei↗
DOE OSTI2017

High capacity Li-ion battery anodes: Impact of crystallite size, surface chemistry and PEG-coating

Battery electrodes are complex mesoscale systems comprised of an active material, conductive agent, current collector, and polymeric binder. Previous work showed that introduction of poly [3-(potassium-4-butanoate) thiophene] (PPBT) as a binder component coupled with a polyethylene glycol (PEG) surface coating on magnetite (Fe 3 O 4 ) nanoparticles enhanced electron and ion transport in the high capacity anode system. Here, the impact of Fe 3 O 4 crystallite size (10 nm vs. 20 nm) and surface chemistry were explored to evaluate their effects on interfacial interactions within the composite PEG/PPBT based electrodes and resultant battery performance. The Fe 3 O 4 synthesis methods inevitably lead to differences in surface chemistry. For instance, the Fe 3 O 4 particles synthesized using ammonium hydroxide appeared more dispersed, and afforded improved rate capability performance. Notably, chemical interactions between the active nanoparticles and PPBT binder were only seen with particles synthesized using triethylamine. Capacity retention and cycling performance were unaffected. Thus, this study provides fundamental insights into the significant impact of active material synthesis on the design and fabrication of composite battery electrodes.

25 ENERGY STORAGE↗
DOE OSTI2026

pyFLANK, a graph neural network based null distribution inference model for F ST outlier detection

Detecting genomic regions under selection is essential for understanding how populations adapt to different environments, yet it remains challenging due to the confounding effects of demographic history and linkage disequilibrium (LD). Fixation index (F ST ) is a widely used statistic to identify genomic regions under adaptation. However, identifying genes under selection by defining F ST outliers often remains challenging, owing to confounding effects of underlying demographic history. Traditional methods assume independence among loci and rely on simple demographic models, while newer models perform much better but are computationally expensive and not easily scalable. Here, we present pyFLANK, an open-source and automated Python implementation which detects F ST outliers using a null distribution inferred from quasi-independent loci. Our tool integrates three approaches to identify loci obeying a null distribution: graph neural network (GNN) inference, linkage disequilibrium (LD)-based inference, and user-defined input. Because pyFLANK uses GNN-based inference of quasi-independent loci, it yields a more accurate null model with less need for user parameter input. In simulation experiments, pyFLANK achieved lower false positive rates than current methods while maintaining comparable detection power, indicating that its refined null model better distinguishes true adaptive loci from background variation. The GNN-based model, in particular, detected additional loci associated with phenotypic variance that were not identified by existing methods. Assessments of simulation and real data from different species demonstrate that pyFLANK achieves lower false positive rates compared with other commonly used F ST outlier detectors, while maintaining comparable detection power and excellent computational performance, providing a robust and user-friendly tool for identifying loci under divergent selection. It extends existing F ST outlier frameworks by incorporating explicit LD-aware strategies for null model calibration. The method is intended as a practical and scalable complement to existing genome scan approaches.

FST↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Using Current Data to Detect Hardware Faults at Solar Plants

Monitoring amperage data is an effective means of detecting faults in PV plant data. Utilizing amperage data collected at the combiner box gives plant operators an up-to-date list of faulted equipment, allowing them to coordinate maintenance needs at much shorter intervals than previously. Shorter maintenance intervals will increase PV plant production levels, narrowing the gap between expected and actual PV plant performance. The amperage monitoring method performs at a high level, with a string-outage related fault detection True Positive Rate of 46% and False Positive Rate of 8%.

14 SOLAR ENERGY↗