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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Carbon Anode Material from Biomass Pyrolysis Oil

Lithium-ion batteries (LIB) are an important component of electric vehicles and sodium-ion batteries (SIB) are an attractive alternative for grid electric storage. We are investigating a novel approach to synthesizing the carbon anode materials for these batteries from biomass pyrolysis oil as part of conversion processes to produce biofuels. Graphite is used as anode material in LIB and is exclusively produced from petroleum residue or mined mineral carbon. We have synthesized drop-in graphite by coking the heavy residual oil from the distillation of catalytic fast pyrolysis oil and by catalytic graphitizing raw pyrolysis oil. XRD and Raman spectroscopies were used to verify the production of high purity crystalline graphite. Figure 1 shows typical XRD spectra of the bio-graphite compared to commercial material as well as the charge/discharge cycle. Graphite particle sizes and morphologies were adjusted to improve performance using jet milling and carbon coating. The bio-graphite delivered a high capacity (335 mAh/g) when tested in a graphite half-cell. Full cell coin cell experiments were also conducted to verify the observations from half-cell testing. The high value of graphite (>$9/kg) can positively impact the economics of biofuels production and can be an important part of a future circular carbon economy. We have also investigated synthesizing hard carbon from pyrolysis oil for use as anode material in SIB. These batteries are not commercially mature but have great promise in grid storage and use earth abundant elements. We have demonstrated that pyrolysis oil can be used to synthesize hard carbon, which was subsequently be used in SIB experiments. This material achieved a sodium-ion capacity of 250 mAh/g and a first cycle efficiency of 78%, with opportunities to further optimize this performance from optimizing the composition of the pyrolysis oil to shaping the hard carbon particles. As with graphite, hard carbon is a high value (> $10/kg) material that could facilitate biofuels production.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2025

Assessing High Burnup U-19Pu-10Zr Fuel Performance against Historical and Modeled Behavior

Advancing the deployment of sodium-cooled fast reactors (SFRs) requires thorough testing of metallic fuel pins under accident conditions to establish safe operational limits of high burnup fuel. To conduct transient testing, a comprehensive understanding of steady-state fuel behavior obtained through both experimental characterization and accurate predictive capabilities is needed. This study comparatively assesses the steady-state irradiation performance of two high burnup U-19Pu-10Zr fuel pins, DP-36 and DP-40, irradiated under prototypic fast reactor conditions in preparation for planned safety testing at the Transient Reactor Test Facility. Since DP-40 was designated for use in the test and DP-36 serves as its sibling pin, non-destructive, engineering-scale post-irradiation examinations (PIE) were conducted on both pins while destructive examinations were performed exclusively on DP-36. The results were then assessed against historical performance data from similar fuel pins irradiated in the Experimental Breeder Reactor-II. Additionally, the steady-state irradiation of each pin was modeled using the BISON fuel performance code to assess the accuracy of current modeling capabilities in predicting the baseline irradiation behavior. Non-destructive examinations included neutron radiography to measure fuel column elongation, gamma scanning to verify pin integrity and fission product migration, and profilometry to assess dimensional changes. Benchmarking against existing PIE data revealed consistent patterns in axial fuel column growth and cladding diametral strain, though both pins exhibited longer low-density “fluff” structures, which can have implications for core reactivity and source term calculations. Destructive examinations on DP-36 included fission gas release analysis and sectioning for optical microscopy, which showed more complex constituent redistribution patterns than the traditionally accepted 3-ring model. The axial evolution of fractional areas and porosities of each of the redistributed zones were quantified and presented. Modeling comparisons showed agreement in fractional fission gas release but consistently overestimated axial and radial swelling and disagreed with measured axial porosity patterns. These conservative overpredictions suggested that the pins would appear closer to failure or operational limits at the start of transient tests, potentially leading to higher strain accumulation during the transient. While conservative estimates provide safety margins, they can negatively impact fuel economics. A review of the swelling models identified areas for improvement in the gaseous swelling, solid swelling, and fuel hot-pressing models when applied to ternary fuel. The results of this study highlight the critical importance of conducting pre-test characterization on both test and sibling pins to accurately capture steady-state fuel behavior, providing a precise baseline for post-test evaluations and essential inputs for transient modeling of the planned experiments. The analysis also revealed significant data gaps that require further investigation to enhance the understanding and prediction of fuel swelling and pore dynamics. Collecting comprehensive data across different irradiation conditions, burnup levels, and fuel compositions are essential for refining existing models and developing mechanistic models for both binary and ternary metallic fuels, ultimately improving the integration of modeling and experimental approaches in accident testing.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Hydropower expansion in eco-sensitive river basins under global energy-economic change

Vast hydropower resources remain untapped globally, the deployment of which could provide energy-economic benefits but impact riverine ecosystems. Across eco-sensitive river basins, it is unclear how drivers of hydropower expansion, such as rapid economic growth and a low-carbon energy transition, could interact with countervailing forces, such as increasingly cost-competitive variable renewable energy (VRE). Using an integrated energy-water-economy model, we explore the effects of these forces on long-term hydropower expansion in the world’s 20 most eco-sensitive basins, which have high ecological richness and untapped hydropower potential. Here, we find that a low-carbon transition exerts the strongest development pressure, causing deployment exceeding 80% of exploitable potential in more than 72% of eco-sensitive basins by 2050, most of which have limited deployment today. Rapid economic growth induces such extensive deployment in only 44% of eco-sensitive basins. Enhanced integration of VRE reduces deployment, alleviating the impacts of rapid economic growth but not the low-carbon transition.

13 HYDRO ENERGY↗
DOE OSTI2022

Electrification Futures Study

Through the Electrification Futures Study (EFS), NREL explored the impacts of widespread electrification in all U.S. economic sectors. For the multiyear study, NREL and its research partners - Electric Power Research Institute, Evolved Energy Research, Lawrence Berkeley National Laboratory, Northern Arizona University, and Oak Ridge National Laboratory - used multiple analytic tools and models to develop and assess electrification scenarios designed to quantify potential energy, economic, and environmental impacts to the U.S. power system and broader economy. This presentation summarizes the analysis and key findings from across the EFS publications, with a focus on the data and results that are most relevant - and of most interest - to the state of Montana.

EFS↗
DOE OSTI2025

Coordinated Thermal Safety Attack and Defense on EV Battery Management Systems

Battery temperature sensor and battery current sensor data which are key sensing inputs to the Battery Management Controllers in electric vehicles, are vulnerable to possible cyber/ physical manipulation due to known vulnerabilities inherited from CAN bus technology that is used for in-vehicle communications between electronic control units that transfer sensing and control data. In this paper, we first create a simulation that enables us to evaluate impact of cyber physical attacks on electric vehicle battery management system in a controlled environment that violates thermal safety. Specifically, we emulate a Level 3 - DC fast charging system with SAE J1772/CCS, integrated with standard charging controls and thermal safety controls on EVs, and various sensing data flows. Second, we propose a coordinated current and battery temperature attack that has crippling economic, and safety impacts. Third, we quantify the usability, economic and safety impacts of such attacks as a function of the extent of data manipulation. Finally, we propose a physics model driven detection technique to detect presence of such attacks.

25 ENERGY STORAGE↗
DOE OSTI2020

COVID-19 and the Power Sector in Southeast Asia: Impacts and Opportunities

The COVID-19 pandemic is unique among the range of threats encountered over the last century, and its specific impacts do not necessarily resemble those of other disasters. In the near term, the pandemic is causing demand shocks, workforce impacts, supply chain complications, and a global, economic contraction with long-term consequences as yet unknown. In contrast, with natural disasters the principal impacts are more acute. These include infrastructure destruction, displaced populations, and often localized economic impact; however, the long-term response to both of these threats–pandemics and destructive forces of nature–requires similar approaches to building short-term and long-term resilience that prepare power sectors for safe and reliable operation during times of disruption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Upcycling of post-consumer mixed polyolefin feedstock: An economic and technical evaluation

Here, this study reports techno-economic and life cycle analyses to evaluate the economic and environmental impacts of mechanically recycled PE/PP blends in the presence of rheology modifiers. Additionally, fiber-reinforced composites derived from the compatibilized blends were prepared and evaluated for their performance compared with virgin plastics. Results suggest that compatibilized PE/PP blends exhibit a 70% lower selling price compared to virgin PE. Furthermore, these blends achieved a 74% reduction in greenhouse gas emissions or climate change impact compared to the virgin counterpart. Fiber-reinforced composites from compatibilized PE/PP blends demonstrated improved or comparable mechanical properties relative to composites made from virgin PE/PP blends. Based on their favorable cost and environmental impact, along with performance comparable to virgin composites, compatibilized PP/PE composites made from post-consumer plastics can find applications in large-scale composite manufacturing.

Fiber reinforced plastics (FRP)↗
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

CLimate Impact: Determining Etiology thRough pAthways (CLDERA)

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗
DOE OSTI2024

Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy

Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.

Life Cycle Analysis↗
DOE OSTI2020

LID and LeTID Impacts to PV Module Performance and System Economics: Draft Analysis

This webinar is a technical review of the measurements and causes of BO LID, UV LID and LeTID, as well as modeling their impacts to PV project financial metrics. We will first review how Boron-Oxygen complexes form and detail the range of impacts to PV module power ratings over time and across climates. Similarly, we will also discuss the measurements and extent of UV LID over time and across climates. Then, we will demonstrate a methodology for translating these effects to pro forma cash flow models and their impacts to PV project financial returns and levelized cost of electricity (LCOE). We will conclude with estimates of the value of solutions to these degradation mechanisms.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Technoeconomic Analysis of a Novel Microwave Process to Produce Ethylene from Methane

Ethylene is a critical feedstock in industrial processes, serving as a raw material in the petrochemical industry to produce plastics and commodity chemicals. Conventional ethylene production routes are energy-intensive and contribute substantially to the carbon footprint of chemical manufacturing. In this study, an industrial-scale novel microwave process was simulated using ASPEN Plus to assess its economic viability. Technoeconomic analysis confirmed the economic competitiveness of the novel microwave process, with a levelized cost of ethylene of USD 0.51/kg compared to USD 0.56/kg for the conventional base case. Key economic drivers of the process were identified, and sensitivity analyses were conducted to evaluate their impact on project economics. Furthermore, 87.7% of the total utility consumption in the novel microwave process is electricity, highlighting its potential to contribute to the electrification of the chemical industry. The findings of this study confirm the economic feasibility of both industrial-scale microwave reactors and modular plant configurations for the production of ethylene from methane, offering a promising alternative to conventional processes.

electromagnetic radiation↗
DOE OSTI2022

Exploring Impacts to COVID-19 Herd Immunity Thresholds Under Demographic Heterogeneity that Lowers Vaccine Effectiveness

The COVID-19 pandemic has caused severe health, economic, and societal impacts across the globe. Although highly efficacious vaccines were developed at an unprecedented rate, the heterogeneity in vaccinated populations has reduced the ability to achieve herd immunity. Specifically, as of Spring 2022, the 0–4 year-old population is still unable to be vaccinated and vaccination rates across 5-11 year olds are low. Additionally, vaccine hesitancy for older populations has further stalled efforts to reach herd immunity thresholds. This heterogeneous vaccine landscape increases the challenge of anticipating disease spread in a population. We developed an age-structured Susceptible-InfectiousRecovered-type mathematical model to investigate the impacts of unvaccinated subpopulations on herd immunity. The model considers two types of undervaccination - age-related and behaviorrelated - by incorporating four age groups based on available FDA-approved vaccines. The model accounts for two different types of vaccines, mRNA (e.g., Pfizer, Moderna) and vector (e.g., Johnson and Johnson), as well as their effectiveness. Our goal is to analyze different scenarios to quantify which subpopulations and vaccine characteristics (e.g., rate or efficacy) most impact infection levels in the United States, using the state of New Mexico as an example.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Adapting the Technology Performance Level Integrated Assessment Framework to Low-TRL Technologies Within the Carbon Capture, Utilization, and Storage Industry, Part I

With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO 2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Adoption of perennial energy crops in the US Midwest: Causal and heterogeneous determinants

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm-scale. This study uses survey data to examine potential adoption decisions by farmers in the US Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal-relationship between farmers adoption decisions and the influencing factors. Here, we find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identify statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

09 BIOMASS FUELS↗
DOE OSTI2021

Data for Adoption of Perennial Energy Crops in the U.S. Midwest, Causal and Heterogeneous Determinants

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm scale. This study uses survey data to examine potential adoption decisions by farmers in the U.S. Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal relationship between farmers adoption decisions and the influencing factors. We find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identified statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

Modeling↗