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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Long-term increase in atmospheric stagnant conditions over northeast Asia and the role of greenhouse gases-driven warming

We report the recent trend in the air quality degradation of urban South Korea prompted the authors to examine the long-term changes in the near-surface wind speed and its link with the lower atmospheric circulation, using modern reanalysis datasets. Analyses based on JRA-55 data show a negative correlation between near-surface wind speed and static stability over the Yellow Sea and the western part of South Korea. Near-surface wind speed shows a clear declining trend accompanied by increased static stability, while its negative correlation strengthened recently. Further investigations using CMIP5 single-forcing experiments suggest that global warming because of anthropogenic greenhouse gases probably increased static stability in the lower troposphere, implying further atmospheric stagnant conditions in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment the leaching characteristics and long-term leaching behavior of some radionuclides from synthesized zeolite cement matrix

Highlights: • Synthesis and characterization of zeolite cement synthesized from fly ash • Leaching tests were used to assess the release rate of {sup 85}Sr and {sup 60}Co radionuclides. • Simple mathematical models were used to detect leaching mechanisms. • Long-term leaching behavior of {sup 85}Sr and {sup 60}Co from cementitious matrix was assessed. The development of cementitious materials remains a vital goal to produce valuable products with good mechanical, physical and chemical properties suitable for the safe disposal of concentrated radionuclides resulted from the treatment of contaminated solutions. In this paper, zeolite cement with good properties was laboratory synthesized from industrial by-products fly ash and characterized using various characterization techniques. The leaching characteristics of strontium and cobalt radionuclides from the synthesized zeolite cement were scrutinized conforming with the standard leaching methodology of International Atomic Energy Authority (IAEA). A mechanical strength assessment was executed to characterize the extent of immobilization process of the solidified matrices. The cumulative leaching fraction (φ) of the two studied radionuclides was found to be less than 5% in all examined conditions, which implies the applicability of the IAEA-recommended methodology for estimating the diffusion coefficient. The experimental leaching data were regressed nonlinearly to various mathematical kinetic models to assess the controlling leaching mechanism and to determine the leaching parameters. The regression results indicated that strontium ({sup 85}Sr) and cobalt ({sup 60}Co) leaching resulted from two succeeding mechanisms: as first order kinetic reaction, and then diffusion. The calculated values of leachability indices signify that the performance of each of the studied matrices is within an acceptable range. A simplified mathematical model, rooted in the first order reaction and diffusion mechanisms, was simulated to predict the radionuclides leaching rates from zeolite cement matrices. By comparing the synthesized zeolite cement with other cementitious materials, it can be concluded that the synthesized material can be classified as an efficient material suitable to immobilize {sup 85}Sr and {sup 60}Co from radioactive wastes. The acquired findings demonstrated that the studied immobilized waste matrices have acceptable mechanical effectiveness.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanochemical formation of highly stable amorphous calcium carbonate in calcium silicates: Potential long-term storage of CO 2 in cement

Here, we have investigated mechanochemical reactions with the calcium silicate wollastonite to probe potential mechanisms of sequestration and long-term storage of CO 2 as mineral carbonates in cement pastes. Wollastonite, CaSiO 3 , was milled under ambient and 13 C enriched CO 2 atmospheres. Milling induced a structure change from monoclinic wollastonite-2M to triclinic-1T, consistent with high pressure treatment. The results from solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy shows this process forms an extraordinarily stable amorphous calcium carbonate (ACC) phase that remains despite a high temperature dehydration treatment (130 °C) and persists in the same form after 3 yrs. of storage at ambient conditions. Thermogravimetric analysis (TGA) of these samples indicates that ACC is produced at the similar solid concentrations in both CO 2 -enriched and ambient atmospheres. The observation of meta-stable ACC due to wollastonite carbonation may play an important role in further exploration and optimization of mineral carbonation reactions for CO 2 capturing cement formulations.

13C solid-state NMR↗
DOE OSTI2023

Predictive model for real-time energy disaggregation using long short-term memory

To provide affordable energy-saving solutions for the small and medium-sized manufacturers (SMMs), we propose a unified framework for generating predictive models that support real-time disaggregation of power consumption from combined inputs, enabling automatic machine state identification simultaneously for joint analysis of energy usage patterns. Further, the proposed framework transforms raw power consumption into a time series with look-back and bootstrap capabilities for historical pattern detection, while a learning architecture utilizes the stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results demonstrate 93.65% minimum accuracy in ideal case of real-time energy usage and machine state prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

This paper proposes a new optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. The model optimizes both investment decisions (e.g., size, location, and time to install, retire and decommission facilities) and operation decisions (e.g., on/off status, operating capacity, and expected power output). It is also able to optimize reserve systems (or backup systems), as well as the main systems, to improve power systems reliability. An impact of operational strategies of generators (i.e., participating in electricity production vs. remaining as idle units during operation) on power systems reliability is considered. Probability of equipment failures and capacity failure states are used to rigorously estimate the power systems reliability depending on design and operation strategies. The optimization model is formulated with Generalized Disjunctive Programming (GDP), which is reformulated as a mixed-integer linear programming (MILP) model using the Hull relaxation. Two reliability-related penalties, such as downtime penalty and unmet demand penalty, are included in the objective function to maximize reliability while minimizing the total net present cost. Furthermore, a bilevel decomposition with tailored cuts is developed to reduce computational times of the multi-scale optimization model. The effectiveness of the proposed model is shown by comparing the results with the results obtained from the expansion planning models that do not explicitly consider reliability. In conclusion, we also show that the proposed bilevel decomposition is computationally efficient for solving large scale problems through 5-years and 10-years planning case studies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Technoeconomic analysis for near-term scale-up of bioprocesses

Growing the bioeconomy requires products and pathways that are cost-competitive. Technoeconomic analyses (TEAs) aim to predict the long-term economic viability and often use what are known as n th plant cost and performance parameters. However, as TEA is more widely adopted to inform everything from early-stage research to company and investor decision-making, the n th plant approach is inadequate and risks being misused to inform the early stages of scale-up. Some methods exist for conducting first-of-a-kind/pioneer plant cost analyses, but these receive less attention and have not been critically evaluated. This article explores TEA methods for early-stage scale-up, critically evaluates their applicability to biofuels and bioproducts, and recommends strategies for producing TEA results better suited to guiding prioritization and successful scale-up of bioprocesses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Long-term carbon intensity reduction potential of K-12 school buildings in the United States

School buildings have a great potential for carbon emission reduction since their annual emission is about 72 million metric tons. Currently, more than 30% of school buildings were built before 1960 and are underperforming. To effectively reduce carbon emissions via school building retrofits, it is critical for policymakers to understand the carbon intensity reduction potential of retrofitting school buildings in different regions. Hence, this study develops a method to comprehensively assess the long-term carbon intensity reduction potential of aggregated commercial buildings on a county-by-county basis in the continental U.S. We apply this method to the K-12 school buildings including primary and secondary school buildings. Here, this paper predicts the carbon intensity reduction potential of K-12 school buildings with eight building retrofit measures from 2022 to 2050 in the continental U.S. The results reveal several interesting findings: (1) In the approximately 3,000 counties of the U.S. from 2022 to 2050, the carbon intensity reduction potential of retrofitting K-12 school buildings in each county ranges from 0.41 kg/m 2 to 40.00 kg/m 2 . (2) Even in the same climate zone, the trends of carbon intensity reduction potential from 2022 to 2050 are different depending on their electricity sources. For example, in a hot and humid climate zone, the carbon intensity reduction potential in Florida will decrease from 2044 to 2048. However, in Mississippi, the carbon intensity reduction potential from 2044 to 2046 will increase due to the termination of the nuclear energy usage. (3) Generally, reducing lighting power density leads to more carbon intensity reduction in most states, but it might not be applicable for states with high clean energy penetration, such as Washington.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2020

Severe weather, utility spending, and the long-term reliability of the U.S. power system

There has been a limited amount of peer-reviewed literature on long-term trends in electricity reliability including the underlying factors that impact reliability across the United States. In this analysis, we considered up to 16 years of data from 203 U S. utilities—representing about 70% of electricity sales. Annual frequency of interruptions for an average customer—at the regional and U.S. national-level—has generally decreased over this timeframe. But we do not find that there is a statistically significant trend in the annual duration of interruptions for an average customer. We find that more explicit measures of severe weather are correlated with reliability. We are able to explain 7% and 16% of past variation in the reliability metrics system average interruption duration and frequency indices, respectively, is due to severe weather—a significant improvement over earlier studies. We find that current year spending by utilities is correlated with worse reliability and that cumulative spending over the preceding three years is correlated with better reliability. Finally, we demonstrate that using a statistical instrument to represent the annual frequency of interruptions for an average customer can greatly improve analysis of trends in the annual duration of interruptions for an average customer.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2021

Long-term cyclability of Li 4 Ti 5 O 12 /LiMn 2 O 4 cells using carbonate-based electrolytes for behind-the-meter storage applications

Li 4 Ti 5 O 12 /LiMn 2 O 4 (LTO/LMO) chemistry was evaluated as a potential candidate for behind-the-meter storage (BTMS) applications. Its long-term cycle performance at 45 °C was tested using ethylene carbonate (EC) and propylene carbonate (PC) solvent electrolytes. Over 1000 cycles, LTO/LMO cells exhibited ~80% capacity retention and Coulombic efficiency higher than 99.96%. Electrochemical test results showed the major degradation mode of LTO/LMO cells arises from continuous electrolyte decomposition at the LTO anode and loss of Li inventory. EC and PC electrolytes created distinct surface layers, where the EC reduction products were more effective in passivating the LTO electrode surface. Dissolution and migration of Mn from the cathode was probed as Mn 2+ species distributed throughout the surface layer at the anode. By utilizing a prelithiated LTO electrode, the LTO/LMO cell performance was significantly enhanced with EC electrolyte. On the other hand, PC electrolyte resulted in accelerated electrolyte decomposition at the lithiated LTO surface due to the lack of surface passivation. Thus, mitigating parasitic reactions at the LTO electrode is the key to developing successful LTO/LMO cells.

25 ENERGY STORAGE↗
DOE OSTI2024

The path to 2060: Saudi Arabia's long-term pathway for GHG emission reduction

Saudi Arabia, as part of its Saudi Green Initiative, has announced its goal to achieve net zero green-house gas emissions by 2060. This ambitious target underscores the nation's dedication to address-ing climate change. However, there is a significant gap in comprehensive analysis regarding the long-term effects of Saudi Arabia's climate policies and their collective contribution towards the net-zero objective. This study endeavors to bridge this gap through a detailed examination using the GCAM-KSA, a specialized version of the Global Change Analysis Model tailored for Saudi Arabia, employing a multi-sectoral methodology that integrates economic, energy, and land use systems within a coherent framework to assess the impact of climate policies on GHG emissions. Our anal-ysis reveals that reaching net-zero GHG emissions by 2060 is a complex challenge requiring con-certed efforts across all sectors of the economy. While transitioning to low-carbon electricity and improving energy efficiency offer considerable emission reductions, fully decarbonizing the indus-trial and transportation sectors poses a significant hurdle. Our findings suggest that Saudi Arabia must triple its emission reduction commitments in its next Nationally Determined Contributions (NDCs) update to align with its 2060 net-zero goal. Early action and increased ambition could avoid the chances of getting locked into the high emission assets and give enough time to transform the energy system. Furthermore, the adoption and integration of Carbon Dioxide Removal (CDR) tech-nologies are identified as crucial for offsetting residual emissions, especially in sectors that might continue to rely on fossil fuels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Effects of short-term over-discharge cycling on the performance of commercial 21,700 lithium-ion cells and the identification of degradation modes

In this paper, short-term over-discharge cycling was performed on commercially available 21,700 lithium-ion cells to investigate its effects on cell performance; the degradation modes were also identified and analyzed in detail using both in-situ and ex-situ methods. Results show that observable capacity loss occurs when the cell is over-discharged to 112.5% depth of discharge. Moreover, the negative impact of the depth of discharge on cell performance (or cell aging) is more serious than the number of cycles, and continuous over-discharge cycling will accumulate such impact. The loss of Li inventory related to the decomposition and reformation of solid electrolyte interphase film is an important cause of the irreversible capacity loss/degradation of Li-ion cells during over-discharge cycling testing. Furthermore, the dissolution of copper current collector can greatly reduce the mechanical stability of the anode, and further, the deposition of Cu, the exfoliation and the cracking of active material particles can influence the transfer of ions and lead to the loss of Li inventory and the loss of active material within Li-ion cells. Additionally, when the depth of discharge exceeds 108.3%, deposition of Cu is observed on the ceramic-covered side of separators.

25 ENERGY STORAGE↗
DOE OSTI2022

Uranium sorption to organic matter and long-term accumulation in a pristine alpine wetland

Understanding the controls on uranium (U) mobility in the environment is key to improve the management of sites contaminated by U mining activities. Previous research has shown that natural or engineered wetlands are particularly able to scavenge high amounts of U(VI) and U(IV) under noncrystalline forms. However, questions remain on the respective roles of sorption and reduction processes in the removal of U from running waters in wetlands, as well as on the long-term stability of U storage. Here, we performed a series of geochemical, isotopic ($δ$ 238 U, ( 234 U/ 238 U)), microscopic (SEM-EDXS, EPMA) and spectroscopic (µ-XRF, µ-XAS, XANES and EXAFS at the U L 3 and M 4 -edges and Fe K-edge) investigations to determine the modes of U accumulation and assess U mobility in a natural exceptionally U-enriched (up to 5000 µg/g) wetland on the shore of Lake Nègre (Mediterranean Alps, France). Uranium (VI) was largely dominant in the two studied soil cores, except a few samples containing as much as ~50 % U(IV). At the particle scale, U is associated to a variety of organic constituents of the soil matrix with a homogenous oxidation state. Bulk EXAFS spectroscopy at the U L 3 -edge shows that U is mostly mononuclear, with dominant monodentate binding to organic moieties (C neighbors at ~3.45 Å). An additional minor fraction of U under polymeric forms is inferred from wavelet (CCWT) analysis of the EXAFS data. These observations are reinforced by 1 M bicarbonate extractions that result in the dissolution of 82–96 % of total U, including putative polymeric species. At the wetland scale, similar or slightly fractionated isotopic ratios ($δ$ 238 U) between the wetland-feeding creek waters and the wetland soils are observed, supporting the idea that U(VI) sorption on organic matter is the primary U scavenging mechanism. Furthermore, it indicates that partial U(VI) reduction to U(IV) occurs as a second step, after sorption. Analysis of U decay chain disequilibria in the cores as a function of depth suggests that U accumulation in this wetland has lasted for several thousand years. In conclusion, we propose that the wetland acts as an active reactor where U has been massively accumulating for ~14500 years, especially as U(VI) forms associated to organic matter and is further partly exported to the lake through soil erosion.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Long-Term Toxicity and Health-Related Quality of Life After Adjuvant Chemoradiation Therapy or Radiation Therapy Alone for High-Risk Endometrial Cancer in the Randomized PORTEC-3 Trial

The survival results of the PORTEC-3 trial showed a significant improvement in both overall and failure-free survival with chemoradiation therapy versus pelvic radiation therapy alone. The present analysis was performed to compare long-term adverse events (AE) and health-related quality of life (HRQOL).

62 RADIOLOGY AND NUCLEAR MEDICINE↗