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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 OSTI2024

Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysis

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. Here, the validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Monte Carlo Driven MDI Optimization at a Muon Collider

A Muon Collider represents a very interesting possibility for a future machine to explore the energy frontier in particle physics. However, to reach the needed luminosity, beam intensities of the order of 10⁹–10¹² muons per bunch are needed. In this context, the Beam-Induced Background must be taken into account for its effects on magnets and detector. Several mitigation strategies can however be conceived. In this view, it is of crucial importance to develop a flexible tool that allows to easily reconstruct the machine geometry in a Monte Carlo code, allowing to simulate in detail the interaction of muon decay products in the machine, while being able to change the machine optics itself to find the best configuration. In this contribution, a possible approach to such a purpose is presented, based on FLUKA for the Monte Carlo simulation and on LineBuilder for the geometry reconstruction. Results based on the 1.5 TeV machine optics developed by the MAP collaboration are discussed, as well as a first approach to possible mitigation strategies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Impact of Wildfires on Solar Generation, Reserves and Energy Prices

Wildfire seasons in the Western U.S. become more prolonged and intense in recent years bringing significant variability and uncertainty of solar generation. It greatly challenges the bulk power system and electricity market operation managed by California Independent System Operator (CAISO) as California leads both the wildfire records and the solar power integration. This study presents a screening-level analysis of the impact of wildfires on solar generation, operating reserve and energy prices applying historical real-world wildfire and market operation data. To the best of the authors knowledge, it is a first-of-its-kind study and will lay the foundation for market impact quantification and wildfire mitigation strategies design based on projected wildfire activities in future years.

electricity price↗
DOE OSTI2020

Evaluation of Detritiation Strategies for Mitigating Tritium Releases

Tritium confinement is performed using different barriers to minimize releases to the environment. Primary confinement is usually performed by process piping and components, secondary confinement typically by inerted gloveboxes connected to a continuously operating tritium stripper system. Some facility designs employ tertiary tritium confinement by using process room confinement with a tritium stripper system activated after an accident to further mitigate tritium releases to the environment. Secondary confinement of some tritium systems such as radiation-hardened or shielded-cell enclosures are not easily achieved using designs for inerted glovebox. These enclosures can be challenging to seal for secondary tritium confinement due to heating, ventilation, and air conditioning (HVAC) needs for temperature control, as well as feedthroughs for equipment and instrumentation, are potential pathways for tritium leaks or loses. Additional tritium leak paths can be created in secondary confinement enclosures due to a Design Basis Accident (DBA) seismic event. These new, larger tritium leak rates from the DBA event are usually not part of the stripper system design basis so all tritium released to the secondary confinement enclosure is assumed released to the environment. Detritiation strategies for shielded enclosures and/or gloveboxes with significant leak rates are necessary to minimize off-site radiological dose consequences. To address this, the relationship between the confinement system leak rate and stripper system (recirculating) flow rate was evaluated to meet prescribed maximum allowable environmental tritium emissions from the event. The simple analysis described assumes a constant leak rate from the confinement system (i.e. shielded enclosure or glovebox) while a recirculating stripper system strips tritium from the system– a competition between tritium release and tritium recovery. At a high level, the resulting expression summarizes the relationship between system leak rate (F L ) and stripper flow rate (F S ) relative to allowable tritium release (Q A ) and initial tritium release (Q 0 ): $\frac{F_L}{F_s}$ ≈ $\frac{Q_A}{Q_0}$. This report derives the relationship between these parameters, examines the impact of finite stripping times followed by purging of the stripped volume. The analysis provides example results for up to 30 gram tritium releases: the maximum tritium inventory of a Hazard Category III (Department of Energy) Nuclear Facility. The detritiation model presented represents a high-level analysis of tritium recovery versus losses for “leaky” confinement enclosures such as shielded cells after large tritium releases. Key parameters for doing the analyses are the system leak rate, stripper flow rate, initial tritium release, and allowable tritium release values. The analyses show a proportional decrease in releases with reduction in initial tritium release but a non-linear increase in tritium recovery with increased stripper flow rate or reduced leak-rate. The analyses also apply to glovebox confinement systems which could have significant increases in leak rates after a DBA. The analysis recognizes but does not include tritium absorption followed by re-emission from the walls of the confinement volume – an analysis which could be pursued in future studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Bioenergy Technologies in Long-Run Climate Change Mitigation: Results from the EMF-33 Study

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

bioenergy↗
DOE OSTI2025

Potential Effects of Climate Change and Solar Radiation Modification on Renewable Energy Resources

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Kumler, Andrew [National Renewable Energy Laborato↗
DOE OSTI2022

Oxygen Solubility in Aqueous Amine Solvents with Common Additives Used for CO 2 Chemical Absorption

Major sources of anthropogenic CO 2 are power generation and transportation industries where researcher continue to explore CO 2 emission mitigation strategies as applied to these CO 2 sources through carbon capture, utilization, and sequestration (CCUS). The most mature CO 2 capture technology is post-combustion carbon capture (PCCC) using aqueous amine solutions/solvents, however solvent degradation and regeneration costs are slowing the widespread adoption of PCCC. Solvent degradation of the aqueous amine solutions is mainly caused from the temperature gradient between the absorber and stripper columns and common flue gas components such as SOx, NOx, and oxygen (O 2 ). The two main classifications of solvent degradation are thermal degradation, occurring when the amine reacts with itself at elevated temperatures and anaerobic conditions and oxidative degradation. Oxidative degradation reactions can occur from oxygen mass transfer and free radical oxidation. Metals, such as iron from the corrosion of steel structures used in industrial applications such as PCCC, can help to catalyze these reactions. Various oxidative degradation studies have shown how O 2 concentrations in flue gas and, to a lesser extent, temperature influence the extent of oxidative degradation. Accurately measuring the O 2 solubility, commonly referred to as dissolved oxygen (DO), in a quick, continuous, and efficient manner in aqueous amine solvents should contribute to determine the effectiveness of mitigation strategies for oxidative degradation. Knowing that commercial PCCC amine solvents contain components beyond water, amine, carbonate species and CO 2 , this investigation was conducted to determine the oxygen solubility changes of common aqueous amines solutions with commonly used and published solvent additives. The impact of carbon loadings with and without the additives was also examined. A commercial dissolved oxygen probe was used to measure the DO concentrations and compared them against standard Winkler Method titration values. The results show that antifoam shows minimal change in [DO]. MBT yielded lower DO values, and NaVO 3 showed a higher DO concentration due to interferences. These results indicate that most common amine solvent additives should be expected to minimally impact oxygen solubility and amine oxidative degradation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Assessing the Long-Term Stability of Anion Exchange Membranes for Electrochemical CO 2 Reduction

Materials and cell components used in CO 2 electrolysis have largely been adapted from technologies initially developed for water electrolysis and fuel cells. However, electrochemical CO 2 reduction introduces distinct material challenges due to the unique chemical environment in this process. Here, in this study, we conducted ex-situ 1000 h stability tests on commonly used anion exchange membranes, exposing them exclusively to electrolytes and organic molecules used or produced during CO 2 electrolysis, at concentrations relevant to and compatible with postseparation processes. Notably, 15% w/w n-propanol and 5 M acetic acid caused complete dissolution or partial disintegration of the membranes unless cross-linking was present and remained stable throughout the test. When the membranes stayed physically intact, most of them exhibited excellent chemical stability in alkaline medium containing alcohols or formic acid, which was confirmed by vibrational spectroscopy and ion exchange capacity measurements. However, exposure to alcohol-and acid-containing solutions led to a substantial increase in swelling and water uptake, with potential implications for mechanical stability, ion/product crossover, and compression management of adjacent components. The potential effects of CO 2 electroreduction products on membrane stability, their subsequent impact on electrolyzer performance, and mitigation strategies are discussed.

CO2RR↗
DOE OSTI2021

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soil carbon sequestration potential and the identification of hotspots in the Eastern Corn Belt of the United States

Soil carbon sequestration is a significant CO 2 mitigation strategy but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic carbon (SOC) in response to soil, climate, land condition and management interactions. We assessed the SOC sequestration potential of the Eastern Corn Belt (ECB) in the USA (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the STATSGO, National Land Cover Database and a PRISM climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid-1800s. Complete adoption of no-tillage practices on Prime Cropland would potentially recover 147 Tg SOC over 20 years, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC. Sequestration hotspots (> 500 Gg increase in SOC) under no-tillage cover 2.3 Mha providing 28 Tg C over 20 years. The conversion of Marginal (non-Prime) agricultural lands to Forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of Minelands to Forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Here, opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An effective survey method for studying volant species activity and behavior at tall structures

The effects of anthropogenic modification of air space on wildlife, particularly volant species, is not fully understood. Thus, it is essential to understand wildlife-interactions with tall structures to implement effective mitigation strategies. Yet, we are currently lacking standard protocols for visual surveys of wildlife behavior at such heights. Our study sought to determine an effective, repeatable method using readily available night vision and thermal technology to survey wildlife at tall structures. Using bats as the taxonomic group of interest, we (1) created a key to identify bats and their behavior, (2) compared the effectiveness of 2 different technologies, and (3) assessed optimal equipment placement to visually capture bat activity and behavior in proximity to wind turbine towers. For the latter, we tested thermal cameras at four distances from the base of the tower. The results of our study revealed that thermal cameras captured ∼34% more flying animals than night vision at a 2 m distance. However, due to the heat signature of the turbine towers themselves, it was challenging to identify behaviors and interactions that occurred in close proximity to the towers. In contrast, it was difficult to identify bats approaching the towers using night vision, yet we were able to clearly observe interactions with the towers themselves. With regards to equipment placement, we visually captured more bats with the thermal cameras placed 2 m from the tower base compared to farther distances. From our findings, we recommend that when using either thermal or night vision technology at tall structures, they be placed 2 m from the base to effectively observe interactions along the length of these structures. In addition, we further recommend that consideration be given to the use of these two technology types together to effectively conduct such surveys. If these survey techniques are incorporated into standard protocols, future surveys at a variety of tall structures are likely to become comparable and repeatable, thereby more effectively informing any mitigation strategies that may be required.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Using remote sensing to quantify the additional climate benefits of California forest carbon offset projects

Abstract Nature‐based climate solutions are a vital component of many climate mitigation strategies, including California's, which aims to achieve carbon neutrality by 2045. Most carbon offsets in California's cap‐and‐trade program come from improved forest management (IFM) projects. Since 2012, various landowners have set up IFM projects following the California Air Resources Board's IFM protocol. As many of these projects approach their 10th year, we now have the opportunity to assess their effectiveness, identify best practices, and suggest improvements toward future protocol revisions. In this study, we used remote sensing‐based datasets to evaluate the carbon trends and harvest histories of 37 IFM projects in California. Despite some current limitations and biases, these datasets can be used to quantify carbon accumulation and harvest rates in offset project lands relative to nearby similar “control” lands before and after the projects began. Five lines of evidence suggest that the carbon accumulated in offset projects to date has generally not been additional to what might have otherwise occurred: (1) most forests in northwestern California have been accumulating carbon since at least the mid‐1980s and continue to accumulate carbon, whether enrolled in offset projects or not; (2) harvest rates were high in large timber company project lands before IFM initiation, suggesting they are earning carbon credits for forests in recovery; (3) projects are often located on lands with higher densities of low‐timber‐value species; (4) carbon accumulation rates have not yet increased on lands that enroll as offset projects, relative to their pre‐enrollment levels; and (5) harvest rates have not decreased on most project lands since offset project initiation. These patterns suggest that the current protocol should be improved to robustly measure and reward additionality. In general, our framework of geospatial analyses offers an important and independent means to evaluate the effectiveness of the carbon offsets program, especially as these data products continue improving and as offsets receive attention as a climate mitigation strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France’s ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Field-based AFDD for refrigerant undercharge in residential HVAC systems: enhancing reliability through false alarm mitigation

This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.

False Alarm↗
DOE OSTI2023

Inhibiting collective cation migration in Li-rich cathode materials as a strategy to mitigate voltage hysteresis

Lithium-rich cathodes are promising energy storage materials due to their high energy densities. However, voltage hysteresis, which is generally associated with transition metal migration, limits their energy efficiency and implementation in practical devices. Here we reveal that voltage hysteresis is related to the collective migration of metal ions, and that isolating the migration events from each other by creating partial disorder can create high-capacity reversible cathode materials, even when migrating transition metal ions are present. In this study, we demonstrate this on a layered Li-rich chromium manganese oxide that in its fully ordered state displays a substantial voltage hysteresis (>2.5 V) associated with collective transition metal migration into Li layers, but can be made to achieve high capacity (>360 mAh g –1 ) and energy density (>1,100 Wh kg –1 ) when the collective migration is perturbed by partial disorder. This study demonstrates that partially cation-disordered cathode materials can accommodate a high level of transition metal migration, which broadens our options for redox couples to those of mobile cations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum utility-scale error mitigation for quantum quench dynamics in Heisenberg spin chains

Here, we implement a quantum error mitigation method termed self-mitigation, which is comparable to zero-noise extrapolation, at large scales to achieve quantum utility on near-term, noisy quantum computers. We investigate the effectiveness of several quantum error mitigation strategies, including self-mitigation, by simulating quantum quench dynamics for Heisenberg spin chains with system sizes up to 104 qubits using IBM quantum processors. In particular, we discuss the limitations of zero-noise extrapolation and the advantages offered by self-mitigation at large scales. The self-mitigation method demonstrates stable accuracy with large systems of 104 qubits comprising more than 3,000 CNOT gates. Also, we combine the discussed quantum error mitigation methods with practical entanglement entropy measuring methods, and it shows a good agreement with the theoretical estimation. Our study illustrates the usefulness of near-term noisy quantum hardware in examining the quantum quench dynamics of many-body systems at large scales and lays the groundwork for surpassing classical simulations with quantum methods prior to the development of fault-tolerant quantum computers.

97 MATHEMATICS AND COMPUTING↗