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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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NASA NTRS2014

On Noise Assessment for Blended Wing Body Aircraft

A system noise study is presented for the blended-wing-body (BWB) aircraft configured with advanced technologies that are projected to be available in the 2025 timeframe of the NASA N+2 definition. This system noise assessment shows that the noise levels of the baseline configuration, measured by the cumulative Effective Perceived Noise Level (EPNL), have a large margin of 34 dB to the aircraft noise regulation of Stage 4. This confirms the acoustic benefits of the BWB shielding of engine noise, as well as other projected noise reduction technologies, but the noise margins are less than previously published assessments and are short of meeting the NASA N+2 noise goal. In establishing the relevance of the acoustic assessment framework, the design of the BWB configuration, the technical approach of the noise analysis, the databases and prediction tools used in the assessment are first described and discussed. The predicted noise levels and the component decomposition are then analyzed to identify the ranking order of importance of various noise components, revealing the prominence of airframe noise, which holds up the levels at all three noise certification locations and renders engine noise reduction technologies less effective. When projected airframe component noise reduction is added to the HWB configuration, it is shown that the cumulative noise margin to Stage 4 can reach 41.6 dB, nearly at the NASA goal. These results are compared with a previous NASA assessment with a different study framework. The approaches that yield projections of such low noise levels are discussed including aggressive assumptions on future technologies, assumptions on flight profile management, engine installation, and component noise reduction technologies. It is shown that reliable predictions of component noise also play an important role in the system noise assessment. The comparisons and discussions illustrate the importance of practical feasibilities and constraints in aircraft system noise studies, which include aerodynamic performance, propulsion efficiency, flight profile limitation and many other factors. For a future aircraft concept to achieve the NASA N+2 noise goal it will require a range of fully successful noise reduction technology developments.

Guo, Yueping↗
DOE OSTI2026

Structural and compositional complexities of hierarchical self-assembly: A hypergraph approach

Programmable self-assembly enables the construction of complex molecular, supramolecular, and crystalline architectures from well-designed building blocks. In this work, we introduce a hypergraph-based formalism, Blocks & Bonds (B&B), which generalizes classical chemical graph theory by incorporating directed and multicolored interactions, internal symmetries, and hierarchical organization. Within this framework, we develop the Structure Code (SC), a compact and versatile language for describing self-assembled architectures. We define a Kolmogorov-style structural complexity as the total information content of SC, obtained through its tokenization and Shannon information assignment. Complementing this encoding-based measure, we introduce a much simpler quantity, the compositional complexity, which depends only on the number and cumulative usage of block and bond types in the construction set. A central result of this work is a strong empirical correlation between the token-based structural complexity and the compositional complexity across all examined systems. Owing to this agreement, the compositional complexity emerges as the most practical and broadly applicable measure: it is easy to compute, requires no explicit encoding, and yet closely tracks the actual information content of structurally diverse architectures. Applications to molecular systems (ethylene glycol and glucose), DNA-origami lattices, and crystalline assemblies show that B&B hypergraphs provide a unified, scalable, and information-efficient representation of structural organization, naturally capturing symmetry, modularity, and stereochemistry. This framework establishes a quantitative foundation for complexity-aware classification and inverse design of programmable matter.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
DOE OSTI2024

A semi–automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves

Background and purpose: Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. Methods: An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). Results: The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. Conclusions: A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. Finally, the methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evaluating Acoustic vs. AI-Based Satellite Leak Detection in Aging US Water Infrastructure: A Cost and Energy Savings Analysis

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $\$4$–$\$4.8$ billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes.

acoustic leak detection↗
NASA NTRS1997

Gravity, light and plant form

Plants have evolved highly sensitive and selective mechanisms that detect and respond to various aspects of their environment. As a plant develops, it integrates the environmental information perceived by all of its sensory systems and adapts its growth to the prevailing environmental conditions. Light is of critical importance because plants depend on it for energy and, thus, survival. The quantity, quality and direction of light are perceived by several different photosensory systems that together regulate nearly all stages of plant development, presumably in order to maintain photosynthetic efficiency. Gravity provides an almost constant stimulus that is the source of critical spatial information about its surroundings and provides important cues for orientating plant growth. Gravity plays a particularly important role during the early stages of seedling growth by stimulating a negative gravitropic response in the primary shoot that orientates it towards the source of light, and a positive gravitropic response in the primary root that causes it to grow down into the soil, providing support and nutrient acquisition. Gravity also influences plant form during later stages of development through its effect on lateral organs and supporting structures. Thus, the final form of a plant depends on the cumulative effects of light, gravity and other environmental sensory inputs on endogenous developmental programs. This article is focused on developmental interactions modulated by light and gravity.

NASA Discipline Plant Biology↗
DOE OSTI2020

An anode-free Li metal cell with replenishable Li designed for long cycle life

Pit corrosion of Li during stripping is an important factor responsible for poor Li cycling efficiency, a metric that determines its cycling life. When excess Li is present, it has been observed that Li tends to strip in a non-uniform fashion, forming pits that extend well past the theoretical Li depth that inevitably lead to the formation of electronically isolated “dead” Li particles. In this work, a novel cell with replenishable Li is shown to inherently mitigate the formation of this “dead” Li, as a direct result of a design in which the intrinsically more homogenous stripping behavior of anode-free cells are combined with a replenishable limited Li reservoir. These novel cells (Li|Cu||LiFePO 4 ) exhibit 25% and 34% higher cumulative capacities than the conventional cells (Cu|Li||LiFePO 4 ) in carbonate and ether electrolytes, respectively, enabling a significant increase in cycle life without impacting energy density. This improvement strategy represents a new direction in Li metal battery improvement, in which improved cycling can be achieved regardless of electrolyte chemistry.

Long cycle life↗
DOE OSTI2024

Experimental Combustion and Flame Characterization of a Chemical Looping-Based Oxidative Dehydrogenation Byproduct Fuel Mixture Containing High CO2 Dilution

Abstract This study investigates the combustion performance of a CO2-rich fuel mixture containing ethane and methane as active species using a constant volume combustion chamber. This fuel is obtained as a byproduct of a chemical looping-based oxidative dehydrogenation (Cl-ODH) process ethylene production. The byproduct gas mixture has 40.79% CO2, 39.49% ethane, and 4.88% methane by weight with other minor compounds. Using this fuel for energy extraction would improve the process efficiency of the ethane to ethylene conversion. After initial combustion modeling, the gas fuel mixture was reduced to just the major species: CO2, ethane, and methane. The mixture was then tested for flammability limits and combustion performance under spark-ignition conditions. Effects of ambient conditions like temperatures between 300 and 400 K with initial pressures from 1 to 10 bar were tested. The effects of stoichiometry were tested to understand flame velocities and heat release. The fuel mixture showed an overall reduced flame velocity compared to gasoline. Instability in combustion was believed to be caused by the dissociation of ethane under elevated conditions. At higher pressures, the flame produces lower cumulative heat release. Simulations were also performed using a model tuned to replicate the operations of the combustion chamber used in the experiments. Heat release and unburnt fuel mass data were calculated to identify the discrepancies in the combustion completeness at elevated pressures. The effects of CO2 quenching the flame coupled with the increased dissociation of the fuel species can lead to up to more than 75% of the fuel mixture being unburnt. Data from this study were used to modify a small-scale spark-ignition engine to use this fuel and produce usable energy.

Energy & Fuels↗
DOE OSTI2021

A Gate-to-Gate Life Cycle Assessment for the CO 2 -EOR Operations at Farnsworth Unit (FWU)

Greenhouse gas (GHG) emissions related to the Farnsworth Unit’s (FWU) carbon dioxide enhanced oil recovery (CO 2 -EOR) operations were accounted for through a gate-to-gate life cycle assessment (LCA) for a period of about 10 years, since start of injection to 2020, and predictions of 18 additional years of the CO 2 -EOR operation were made. The CO 2 source for the FWU project has been 100% anthropogenically derived from the exhaust of an ethanol plant and a fertilizer plant. A cumulative amount of 5.25 × 106 tonnes of oil has been recovered through the injection of 1.64 × 106 tonnes of purchased CO 2 , of which 92% was stored during the 10-year period. An LCA analysis conducted on the various unit emissions of the FWU process yielded a net negative (positive storage) of 1.31 × 106 tonnes of CO 2 equivalent, representing 79% of purchased CO 2 . An optimized 18-year forecasted analysis estimated 86% storage of the forecasted 3.21 × 106 tonnes of purchased CO 2 with an equivalent 2.90 × 106 tonnes of crude oil produced by 2038. Major contributors to emissions were flaring/venting and energy usage for equipment. Improvements on the energy efficiency of equipment would reduce emissions further but this could be challenging. Improvement of injection capacity and elimination of venting/flaring or fugitive gas are methods more likely to be utilized for reducing net emissions and are the cases used for the optimized scenario in this work. This LCA illustrated the potential for the CO 2 -EOR operations in the FWU to store more CO 2 with minimal emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Non-equilibrium low-temperature plasma-assisted combustion of iso-octane: Perturbing pyrolysis and oxidation kinetics

Here, in this study, a plasma-coupled flow reactor facility is used to examine the effects of non-equilibrium low-temperature plasmas on perturbing the pyrolysis and oxidation kinetics of iso-octane. Experiments were performed in highly dilute reactive mixtures of nitrogen, at near isothermal conditions for temperatures ranging from 523 K to 1203 K. Experiments cumulatively demonstrated enhanced chemical reactivity with the plasma for temperatures below 900 K, and a lowering of the hot-ignition temperature. Detailed kinetic insight was derived from a 0D plasma-coupled kinetic model, utilizing a constructed mechanism that combined both plasma-specific chemistry and the neutral combustion chemistry. For pyrolysis conditions, the model displayed relatively good agreement with fuel consumption and the formation of most intermediates compared to the experimental data, demonstrating the model is able to accurately predict primary radical formation from the plasma directly interacting with the fuel. Enhanced reactivity was attributed to collisional quenching of excited-states of N 2 with fuel, which led to efficient fuel fragmentation and enhancement of the H-radical flux. For oxidation conditions, the model displayed satisfactory agreement with the experiments. Model predictions were able to accurately predict fuel consumption and most intermediate speciation data for T > 800 K, but most discrepancies were towards T < 800 K in particular with oxygenated intermediates. In the presence of oxygen, plasma effects were predominantly spent on efficient enhancement of O- and H-radical fluxes, leading to further fuel fragmentation and initiation of both the OH- and HO 2 -radical pools. Subsequent reactivity of iso-octane was then dictated by the response of the temperature-dependent neutral chemistry. At low-temperatures (T = 643 K), enhanced fuel radicals and O 2 -additon chemistry lead to the formation of oxygenated species, while at intermediate temperatures (T = 843 K) net decrease in OH-radical reactivity led to an increase in hydrocarbon speciation. Near the self-ignition threshold (T = 1163 K), radicals generated by high-temperature branching reactions dominate the oxidation process and effectively ignition. This study ultimately demonstrated that the enhancement of radicals afforded by the plasma causes a deviation in known understanding of iso-octane kinetics in some regards and warrants future studies to reconcile these discrepancies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Global Sensitivity Analysis of a Reactive Transport Model for Mineral Scale Formation During Hydraulic Fracturing

Injection of water-based hydraulic fracturing fluid (HFF) into tight shale gas/oil formations can increase formation permeability and enhance production rates, but this process frequently causes mineral scale formation that can occlude pore space and hinder flow. To identify the most important factors that control the formation of mineral scales, we applied a novel global sensitivity analysis method—distance-based generalized sensitivity analysis (DGSA)—to a reactive transport model (RTM) that was previously built and calibrated to simulate precipitation of barite [BaSO4] and iron (hydr)oxide [Fe(OH) 3 ] in shale matrices and on fracture surfaces. Reactive transport simulations were run with model parameters randomly sampled based on assigned uncertainties. Modeling results for barite and Fe(OH)3 formation were clustered using machine-learning algorithms. A list of ranked critical input parameters was obtained after statistical quantification of cumulative distribution functions of input parameters. We found that barite formation is most sensitive to the rate of sulfate ion generation, which is determined by the pyrite dissolution rate coefficient and oxidant availability. In addition, barite formation is sensitive to the initial amounts of barite in HFF and shale, followed by barite thermodynamics/kinetics. For Fe(OH) 3 formation, the ranked factors are Fe(OH)3 precipitation rate coefficients, initial HFF pH, initial Fe(OH) 3 amount in HFF, and oxidant availability. Overall, our results provide insights into managing mineral scale formation during hydraulic fracturing to enhance production. Meanwhile, this study serves as an example of global sensitivity analysis of RTMs using the efficient, straightforward, and open-source DGSA method.

58 GEOSCIENCES↗
DOE OSTI2024

An Updated Life Cycle Assessment of Utility-Scale Solar Photovoltaic Systems Installed in the United States

Given the high deployment targets for solar photovoltaics (PV) needed to meet U.S. decarbonization goals, and the limited carbon budget remaining to limit global temperature rise, accurate accounting of the energy-use and greenhouse-gas emissions over the life-cycle of PV systems is needed. In the United States, most PV systems are large utility-scale systems which use single-axis trackers and central inverters, which are not commonly examined in existing life-cycle assessment (LCA) literature. In this study, we present a cradle-to-grave LCA of a typical silicon U.S. utility PV (UPV) installation which is consistent with the utility system features documented in the annual NREL PV system cost benchmark reports. We analyze and present results for four main metrics: cumulative energy demand (CED), greenhouse gas (GHG) emissions, energy payback time (EPBT), and carbon payback time (CPBT). We consider six primary manufacturing options: three based on an imported PV module supply chain (comparing low-carbon imports, high-carbon imports, and average imports), and three based on a potential domestic PV module supply chain (comparing low-carbon U.S. regions, high-carbon U.S. regions, and average U.S. regions). These manufacturing options were then paired with installation locations to create six main cases: low-carbon options were installed in Phoenix (Arizona), high-carbon options were installed in Seattle (Washington), and average options were installed in Fredonia (Kansas). These locations were selected to represent a range of irradiance and grid mixes in the United States, in order to illustrate the likely range of EPBTs and CPBTs possible across the United States. For all six cases, a sensitivity analysis for end-of-life (EOL) handling was explored to capture current and future management options: landfilling, partial recycling, and high-quality recycling. For the purposes of this report, the benchmark system was defined to use an average imported supply chain with partial recycling, installed in Fredonia (Kansas). CED results show ratios at or below 0.1 MJ oil-eq /MJgenerated which demonstrates efficient use of primary energy resources (below a 1:1 ratio), and represents a slight improvement over previous results in literature. GHG emissions per kWh range from 10-36 g CO 2 e, which are consistent with or lower than previous results published by NREL and IEA-PVPS. We use a graphical approach for calculating EPBT and CPBT in this report, which improves upon methods typically used in literature by accounting for non-linearity and avoiding data quality issues associated with long-term projections. EPBT was determined to vary from 0.5 to 1.2 years, with a benchmark EPBT of 0.6 years; CPBT was shown to vary from 0.8 to 20 years, with benchmark CPBT of 2.1 years, which is lower than other estimates from recent literature (typically >2 years).

14 SOLAR ENERGY↗
DOE OSTI2025

An update to the Sandia method for creating Typical Meteorological Years from a limited pool of calendar years

Typical Meteorological Years (TMYs) are essential for the efficient evaluation of energy system performance. Ideally, 30 years of weather data are required to generate TMYs, but significantly fewer years are typically available due to practical limitations. To address this issue, an update to the Sandia method was developed, referred to as the Argonne method, to create TMYs from a limited number of years. Furthermore, this method enhances candidate diversity by systematically shifting original candidate months forward or backward by specific days, creating an expanded pool of candidates. The effectiveness of the Argonne method was validated through statistical testing, comparison of monthly average weather parameters, and numerical simulations. The results demonstrate a high probability of identifying at least one shifted month whose cumulative distribution functions of weather parameters closely align with long-term distributions. In 67 % of all comparisons, the monthly average weather parameters in TMYs generated using the Argonne method exhibit better agreement with long-term averages than TMY3. Moreover, in 74 % of the 318 building simulation cases, the Argonne method outperforms TMY3 in estimating long-term average building heating and cooling demands. Therefore, the Argonne method effectively diversifies the candidate pool and produces typical years that provide more accurate estimations of long-term averages compared to TMY3 when only a limited pool of calendar years (10 years or fewer) is available.

Building energy modeling↗
DOE OSTI2020

Technical Qualification of New Materials for High Efficiency Coal-Fired Boilers and Other Advanced FE Concepts: Haynes® 282® ASME Boiler and Pressure Vessel Code Case

This DOE Fossil Energy project, FEAA117, addressed materials issues relevant to qualifying and deploying a Ni-base alloy for a new application in an advanced ultra-supercritical coal-fired boiler. The goal was the deployment of Haynes International alloy Haynes® 282® for applications in superheaters, reheaters, and steam delivery pipes, by completing base metal, cross-weld and all-weld metal mechanical testing needed for an ASME Boiler and Pressure Vessel Code Case and the associated microstructural analyses needed for assurance of boiler-relevant lifetimes. The alloy also is of interest for other applications including supercritical CO₂ power generation cycles. Tensile testing was completed from 20°-927°C on the three base metal alloy 282 heats, two sets of cross-weld specimens and all weld metal specimens. A total of 148 creep tests with over 565,000 h of cumulative testing was completed on base metal, cross-weld and all weld metal specimens. This project was performed with cost share from Haynes International.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Thermodynamic and Kinetic Modeling of Co-utilization of Glucose and Xylose for 2,3-BDO Production by Zymomonas mobilis

Prior engineering of the ethanologen Zymomonas mobilis has enabled it to metabolize xylose and to produce 2,3-butanediol (2,3-BDO) as a dominant fermentation product. When co-fermenting with xylose, glucose is preferentially utilized, even though xylose metabolism generates ATP more efficiently during 2,3-BDO production on a BDO-mol basis. To gain a deeper understanding of Z. mobilis metabolism, we first estimated the kinetic parameters of the glucose facilitator protein of Z. mobilis by fitting a kinetic uptake model, which shows that the maximum transport capacity of glucose is seven times higher than that of xylose, and glucose is six times more affinitive to the transporter than xylose. With these estimated kinetic parameters, we further compared the thermodynamic driving force and enzyme protein cost of glucose and xylose metabolism. It is found that, although 20% more ATP can be yielded stoichiometrically during xylose utilization, glucose metabolism is thermodynamically more favorable with 6% greater cumulative Gibbs free energy change, more economical with 37% less enzyme cost required at the initial stage and sustains the advantage of the thermodynamic driving force and protein cost through the fermentation process until glucose is exhausted. Glucose-6-phosphate dehydrogenase (g6pdh), glyceraldehyde-3-phosphate dehydrogenase (gapdh) and phosphoglycerate mutase (pgm) are identified as thermodynamic bottlenecks in glucose utilization pathway, as well as two more enzymes of xylose isomerase and ribulose-5-phosphate epimerase in xylose metabolism. Acetolactate synthase is found as potential engineering target for optimized protein cost supporting unit metabolic flux. Pathway analysis was then extended to the core stoichiometric matrix of Z. mobilis metabolism. Growth was simulated by dynamic flux balance analysis and the model was validated showing good agreement with experimental data. Dynamic FBA simulations suggest that a high agitation is preferable to increase 2,3-BDO productivity while a moderate agitation will benefit the 2,3-BDO titer. Taken together, this work provides thermodynamic and kinetic insights of Z. mobilis metabolism on dual substrates, and guidance of bioengineering efforts to increase hydrocarbon fuel production.

23-butanediol↗
DOE OSTI2026

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗
DOE OSTI2023

Complete β-decay patterns of 142 Cs, 142 Ba, and 142 La determined using total absorption spectroscopy

Background: The β decays of fission products produced in nuclear fuel are important for nuclear energy applications and fundamental science of reactor antineutrinos. In particular, nuclear reactor safety is related to the decay modes of radioactive neutron-rich nuclei, primarily via the emission of γ rays, neutrons, and electrons. Additionally nuclear reactors are the most powerful man-made source of antineutrinos emitted during the β decay of fission products. These antineutrinos are used to inspect fundamental properties of leptons as well as informing reactor operation. However, the majority of data on complex decays of fission products collected in the evaluated nuclear data repositories like Evaluated Nuclear Structure Data File (ENSDF) and Evaluated Nuclear Data Files (ENDF) are based on low-efficiency and often incomplete measurements resulting in questionable reference reactor antineutrino flux predictions, see the analysis by [Nichols, J. Nucl. Sci. Technol. 52, 17 (2015)]. Various assessments like the one done under the auspices of the [Yoshida et al., Assessment of Fission Product Decay Data for Decay Heat Calculations: A report by the Working Party on International Evaluation Co-operation of the Nuclear Energy Agency Nuclear Science Committee (Nuclear Energy Agency, Organization for Economic Co-operation and Development, Paris, France, 2007), Vol. 25], as well as by [Sonzogni, Johnson, and McCutchan, Phys. Rev. C 91, 011301(R) (2015)] and [Dwyer and Langford, Phys. Rev. Lett. 114, 012502 (2015)], list the A = 142 isobars with high cumulative fission yield among the important nuclei where data for reactor decay heat and/or antineutrino production should be verified and/or improved. Purpose: Here, our goal is to improve the quality of β -decay measurements and evaluate the impact of modified decay schemes on reactor decay heat and antineutrino energy spectra, for fission products along the A = 142 isobaric chain. This work is an in depth follow-up on [Rasco et al., Phys. Rev. Lett. 117, 092501 (2016)]. which presented briefly the impact of the corrected decay scheme of 142 Cs . Here, we extend the data to full isobaric decay chain including the daughter nuclei, 142 Ba and 142 La, and present more details on the 142 Cs results. Method: The decays of neutron-rich isobars of mass A = 142 produced by means of proton-induced fission of 238 U were measured using the Modular Total Absorption Spectrometer (MTAS) array on-line at the mass separator and Tandem accelerator at Oak Ridge National Laboratory. Results: The β -decay schemes for 142 Cs and 142 La were modified with respect to the nuclear data repositories. A small β-delayed neutron branching ratio for 142 Cs emitter was remeasured as $0.10^{+5}_{–3}% %. Improved precision on the measured half-lives is reported. Small corrections to the low-energy decay of 142 Ba are made. The β-decay patterns for 142 La and 142 Cs are presented. The decay heat release and cross section for the detection of reactor antineutrinos are deduced and compared to earlier results. Conclusions: The β-feeding pattern for 142 Cs having decay energy value $Q_β$ of over 7 MeV was substantially modified with respect to the current ENSDF entry. Smaller changes were encountered for 142 La, but since this A = 142 isobar also has a large cumulative yield in fission, the changes influence both decay heat and the antineutrino spectra. The previously known β intensities for 142 Ba decay ($Q_β$ value of 2.2 MeV) were verified and slightly modified. Overall, increased decay heat values and lower flux of antineutrinos interacting with matter are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Anaerobic reductive bioleaching of manganese ores

The increasing demand of manganese in the industries and various hindrances in its production from low grade ores by conventional method has made it imperative for researchers around the world to develop a method of manganese extraction from low grade ores that is both environment-friendly and economical. Bioleaching has shown significant potential in manganese extraction and efficiencies of extraction have been found to be 70–98% with the help of various bacteria and fungi. Here, this study focuses on extraction of manganese with the help of mixed bacterial strains that have been collected from their natural anaerobic environment where manganese reducing activity was evident. The extraction of manganese from reagent grade manganese dioxide and high grade manganese ore has been studied over 180 days in an anaerobic environment at room temperature and pH around 5, without the addition of any mineral acids. Highest concentrations of dissolved manganese have been found to be 928.58 mg Mn/L for reagent grade manganese dioxide and 864.54 mg Mn/L for ore grade manganese, corresponding to 650 mg and 400 mg of cumulative manganese, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗