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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Influence of NOx chemistry on the prediction of natural gas end-gas autoignition in CFD engine simulations

Natural gas (NG) represents a promising low-cost/low-emission alternative to diesel fuel when used in high-efficiency internal combustion engines. Advanced combustion strategies utilizing high EGR rates and controlled end-gas autoignition can be implemented with NG to achieve diesel-like efficiencies; however, to support the design of these next-generation NG ICEs, computational tools, including single- and multi-dimensional simulation packages will need to account for the complex chemistry that can occur between the reactive species found in EGR (including NOx) and the fuel. Research has shown that NOx plays an important role in the promotion/inhibition of large hydrocarbon autoignition and when accounted for in CFD engine simulations, can significantly improve the prediction of end-gas autoignition for these fuels. However, reduced NOx-enabled NG mechanisms for use in CFD engine simulations are lacking, and as a result, the influence of NOx chemistry on NG engine operation remains unknown. Here, we analyze the effects of NOx chemistry on the prediction of NG/oxidizer/EGR autoignition and generate a reduced mechanism of a suitable size to be used in engine simulations. Results indicate that NG ignition is sensitive to NOx chemistry, where it was observed that the addition of EGR, which included NOx, promoted NG autoignition. The modified mechanism captured well all trends and closely matched experimentally measured ignition delay times for a wide range of EGR rates and NG compositions. Here, the importance of C2-C3 chemistry is noted, especially for wet NG compositions containing high fractions of ethane and propane. Finally, when utilized in CFD simulations of a Cooperative Fuels Research (CFR) engine, the new reduced mechanism was able to predict the knock onset crank angle (KOCA) to within one crank angle degree of experimental data, a significant improvement compared to previous simulations without NOx chemistry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Impacts of aluminum-cerium alloy deployment on different cerium commodities

An imbalance problem exists in the rare earth element (REE) market with cerium and lanthanum being more abundant and less valuable than REEs used in permanent magnets such as neodymium, praseodymium, and dysprosium. Research has been conducted on new applications for abundant REEs to improve mining economics. However, potential market impacts of these applications on individual REE commodities are unknown. A system dynamics model was built to investigate the impacts of a new aluminum-cerium (Al-Ce) alloy that can theoretically utilize three separate cerium forms: cerium carbonate, cerium oxide, or cerium metal. Cerium carbonate currently possesses the lowest price of the three available forms making it initially the most favorable choice. However, model results showed that cerium oxide could provide the best economics for future Al-Ce alloy deployment because its larger market size buffers major shocks to price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Model correction and updating of a stochastic degradation model for failure prognostics of miter gates

Understanding the degradation of the quoin block is vital for failure prognostics in miter gates. Due to the complicated degradation mechanism, degradation models based on simplifications and assumptions cannot accurately describe the damage evolution. It is observed that small errors in a simplified degradation model can lead to a large discrepancy in the remaining useful life estimation attributed to error accumulation over time. Aiming to address this issue in failure prognostics, this paper presents a dynamic model correction framework for a simplified degradation model using strain measurements. In the proposed framework, a polynomial chaos expansion (PCE) model is employed to compensate the missing physics in a simplified stochastic degradation model. Here, a maximum likelihood estimation method is developed to estimate the uncertain parameters of the simplified physics-based degradation model along with the unknown PCE model parameters using strain measurements as the observables. The updated damage degradation model is then applied to failure prognostics of a miter gate. Results of a case study show that the proposed approach can effectively improve the accuracy of failure prognostics in miter gates.

42 ENGINEERING↗
DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A radiative transfer model for solar induced fluorescence using spectral invariants theory

Solar Induced chlorophyll Fluorescence (SIF) shows promise as an approach for estimating gross primary production (GPP) remotely. However, sun-target-sensor geometry and within-canopy absorption of SIF can alter the relationship between measured SIF and GPP, because sensors can only retrieve some unknown fraction of the total emitted SIF. Radiative transfer models that allow for variation in canopy structure and sensor angles are therefore needed to properly interpret SIF measurements. Spectral invariants allow decoupling of the wavelength-independent canopy structure and the wavelength-dependent leaf and soil spectrum in the radiative transfer process. Here we develop a simple analytical Fluorescence Radiative Transfer model based on Escape and Recollision probability (FluorRTER) to investigate the impact of canopy structure and sun-target-sensor geometry on SIF emissions. SIF simulations using the FluorRTER model agreed well the one-dimensional Soil-Canopy Observation of Photochemistry and Energy balance (SCOPE) model and the three-dimensional Fluorescence model with Weighted Photon Spread (FluorWPS) model. The fractional vegetation cover (FVC) and clumping effect have a large influence the SIF emission of 3D discontinuous canopies. For a moderate solar zenith angle (30°) and a clumped canopy (FVC?=?0.6), the difference between the directional observed SIF of a 3D discontinuous canopy and a 1D homogeneous canopy was as large as 43.2% and 38.4% for Photosystem I?+?II fluorescence at 685?nm and at 740?nm, respectively. By bridging the gap between observed SIF and total emitted SIF over 3D heterogeneous vegetation canopies, the FluorRTER model can assist with the angular normalization of SIF measurements and enable the more robust interpretation of how variations in SIF from directional and hemispherical in-situ, airborne and satellite observations relate to leaf and whole-canopy physiological processes.

Zeng, Yelu↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2020

Role of clay-associated humic substances in catalyzing bioreduction of structural Fe(III) in nontronite by Shewanella putrefaciens CN32

Previous studies have shown that humic substances can serve as electron shuttle to catalyze bioreduction of structural Fe(III) in clay minerals, but it is unclear if clay-sorbed humic substances can serve the same function. It is unknown if the electron shuttling function is dependent on electron donor type and if humic substances undergo change as a result. Here, humic acid (HA) and fulvic acid (FA) were sorbed onto nontronite (NAu-2) surface. Structural Fe(III) in HA- and FA-coated NAu-2 samples was bioreduced by Shewanella putrefaciens CN32 using H 2 and lactate as electron donors. The results showed a contrasting effect of humic substances on bioreduction of structural Fe(III), depending on the electron donor type. With H 2 as electron donor, humic substances had little effect on bioreduction of Fe(III) (the reduction extent: 26.2%, 27.4%, 29.3% for HA-coated, FA-coated, and uncoated NAu-2, respectively). In contrast, these substances significantly enhanced bioreduction of Fe(III) with lactate as electron donor (the reduction extent: 20.2%, 20.7%, 11.5% for HA-coated, FA-coated, and uncoated NAu-2, respectively). This contrasting behavior is likely caused by the difference in reaction free energy and electron transport process between H 2 and lactate. When H 2 served as electron donor, more energy was released than when lactate served as electron donor. In addition, because of different cellular locations of lactate dehydrogenase (inner membrane) and H 2 hydrogenase (the periplasm), electrons generated by H 2 hydrogenase may pass through the electron transport chain more rapidly than those generated from lactate dehydrogenase. Through their functions as electron shuttle and/or carbon source, clay-sorbed HA/FA underwent partial transformation to amino acids and other compounds. The availability of external carbon source played an important role in the amount and type of secondary product generation. These results have important implications for coupled iron and carbon biogeochemical cycles in clay- and humic substance-rich environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Particulate plutonium released from the Fukushima Daiichi meltdowns

Traces of Pu have been detected in material released from the Fukushima Daiichi Nuclear Power Plant (FDNPP) in March of 2011; however, to date the physical and chemical form of the Pu have remained unknown. Here we report the discovery of particulate Pu associated with cesium-rich microparticles (CsMPs) that formed in and were released from the reactors during the FDNPP meltdowns. The Cs-pollucite-based CsMP contained discrete U(IV)O 2 nanoparticles, <~10 nm, one of which is enriched in Pu adjacent to fragments of Zr-cladding. The isotope ratios, 235 U/ 238 U, 240 Pu/ 239 Pu, and 242 Pu/ 239 Pu, of the CsMPs were determined to be ~0.0193, ~0.347, and ~0.065, respectively, which are consistent with the calculated isotopic ratios of irradiated-fuel fragments. Thus, considering the regional distribution of CsMPs, the long-distance dispersion of Pu from FNDPP is attributed to the transport by CsMPs that have incorporated nanoscale fuel fragments prior to their dispersion up to 230 km away from the Fukushima Daiichi reactor site.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A review of transformative strategies for climate mitigation by grasslands

Grasslands can significantly contribute to climate mitigation. However, recent trends indicate that human activities have switched their net cooling effect to a warming effect due to management intensification and land conversion. This indicates an urgent need for strategies directed to mitigate climate warming while enhancing productivity and efficiency in the use of land and natural (nutrients, water) resources. Here, in this work, we examine the potential of four innovative strategies to slow climate change including: 1) Adaptive multi-paddock grazing that consists of mimicking how ancestral herds roamed the Earth; 2) Agrivoltaics that consists of simultaneously producing food and energy from solar panels on the same land area; 3) Agroforestry with a reverse phenology tree species, Faidherbia (Acacia) albida, that has the unique trait of being photosynthetically active when intercropped herbaceous plants are dormant; and, 4) Enhanced Weathering, a negative emission technology that removes atmospheric CO 2 from the atmosphere. Further, we speculate about potential unknown consequences of these different management strategies and identify gaps in knowledge. We find that all these strategies could promote at least some of the following benefits of grasslands: CO 2 sequestration, non-CO 2 GHG mitigation, productivity, resilience to climate change, and an efficient use of natural resources. However, there are obstacles to be overcome. Mechanistic assessment of the ecological, environmental, and socio-economic consequences of adopting these strategies at large scale are urgently needed to fully assess the potential of grasslands to provide food, energy and environmental security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Elevated levels of radiocarbon in methane dissolved in seawater reveal likely local contamination from nuclear powered vessels

Measurements of the natural radiocarbon content of methane ( 14 C-CH 4 ) dissolved in seawater and freshwater have been used to investigate sources and dynamics of methane. However, during investigations along the Atlantic, Pacific, and Arctic Ocean Margins of the United States, as well as in the North American Great Lakes, some samples revealed highly elevated 14 C-CH 4 values, as much as 4–5 times above contemporary atmospheric 14 C-CH 4 levels. Natural production of the 14 CH 4 isotopologue is too low to cause these observations nor can it explain the variations in location and depth. Numerous lab and field validation tests and blanks, as well as the relatively small number of samples that display these elevated values, all suggest that these signals are not derived from an unknown procedural issue. Here, in this work, we suggest that the byproducts of nuclear power generation include localized discharges of the 14 CH4 isotopologue into marine and aquatic environments, severely altering the measured 14 C-CH 4 isotopic signals. Since several of our sample sites are distant from on-land nuclear powerplants, we conduct further calculations concluding that the most elevated anomalies in 14 C-CH 4 likely originate with discharge from nuclear-powered vessels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A novel probabilistic regression model for electrical peak demand estimate of commercial and manufacturing buildings

Due to the high cost of electricity in commercial and industrial sectors, demand forecast models have gained increasing attention. However, there are two unresolved issues: (1) Models are not adaptable when exposed to previously unknown data (2) The value of regression methods vs. state-of-the-art machine learning models has not been made apparent before. This study’s goal is to develop probabilistic demand estimation models. Herein, we propose a probabilistic Bayesian regression framework that can not only estimate future demands with high accuracy but also be updated once new information is available. By applying the proposed algorithm to two real-world case studies (commercial and manufacturing), we show a 40.3% and 30.8% improvement in terms of mean absolute error for the two cases. Moreover, the proposed technique outperforms powerful machine learning approaches, including support vector machine by 10.39%, random forest by 6.17%, and multilayer perceptron by 9.14% in terms of mean absolute percentage error.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Dynamic security assessment of systems powered only by grid-forming power plants with uncertain dispatch using polynomial vectors

A modern challenge in power engineering is to perform the dynamic security assessment (DSA) of grids that are 100% powered by inverter-based resources (IBRs). Addressing this challenge is difficult because: (i) the dispatch of IBRs can be uncertain as a result of the variability of renewable resources and (ii) they have hard current control limits that cannot be neglected, contrasting synchronous machines. To address this problem, this paper sets forth a framework to conduct DSA of bulk power systems that are 100% powered by grid-forming IBRs. Furthermore, the framework considers that IBR operational conditions are unknown but bounded by a zonotope which is also expressed as a polynomial vector for uncertainty propagation via Dormand–Prince integration. The framework is applied to modified versions of the WSCC 9-bus and IEEE 39-bus grids.

14 SOLAR ENERGY↗
DOE OSTI2022

Quantification of gas concentrations in NO/NO 2 /C 3 H 8 /NH 3 mixtures using machine learning

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO 2 , C 3 H 8 , and NH 3 , and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Of note, our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Soil microbial EPS resiliency is influenced by carbon source accessibility

The adaptability of soil microbial communities to prolonged periods of drought is influenced by their ability to produce extracellular polymeric substances (EPS) with sufficient water retention properties. Microbial EPSs as water reservoirs during drought have been extensively investigated, but it remains unknown how carbon substrate accessibility to soil microbial communities will affect the chemical properties of the EPS they generate, and whether this in turn will alter their water retention ability. In this work, we observed that the accessibility of carbon substrates influenced microbial community structure and, consequently, the chemical properties of EPS produced by the microbial communities. Further, our results demonstrated that an insoluble carbon substrate (i.e., chitin), stimulated microbial communities to produce EPS with better water retention properties in comparison to a soluble carbon substrate (i.e., N-acetylglucosamine; NAG). In all, this study demonstrates the importance of carbon substrate accessibility by soil microorganisms in regulating the community structure and consequently, the EPS carbon chemistry, which in turn can greatly influence the adaptability of soil microbial communities to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses

Nitrogen (N) fertilizer has often been generously applied to increase crop biomass yield. Although the influences of inorganic N fertilizer on soil microbial communities have been widely studied, the effect of N fertilizer on microbial co-occurrence networks and its metagenome is largely unknown. Further, seasonal changes in microbial community responses to N addition have rarely been reported. In this study, three N fertilizer rates (0, 56, 196 kg N/ha) were applied annually in switchgrass (Panicum virgatum L.) grown for bioenergy production in the upper Midwest, USA. The soil microbiome was affected by both fertilizer and low pH in the 7th year of fertilization treatments. The microbial community structures were relatively stable during the growing season for each N fertilizer rate. However, the excess N fertilizer (196N) increased the seasonal variation of bacterial and fungal communities. Network analysis showed that the microbial interactions at the 196N treatment were more intense, with decreased bacteria-fungal interactions compared to 56N and 0N. Furthermore, this suggests that the microbial community became more sensitive to environmental change under the influence of long-term excess N fertilizer. Metagenomic analysis showed that the long-term excess N fertilizer promoted many metabolic processes, especially carbohydrate and amino acid related metabolism and Archaea mediated ammonia oxidation. However, N fertilizer also reduced many other traits, especially N 2 fixation and signal transduction, the latter of which may contribute to the decreased interactions between bacteria and fungi.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil pH influences the structure of virus communities at local and global scales

The influence of biotic and abiotic factors on viral communities across environmental gradients in soil is relatively unknown. While soil pH strongly influences microbial community structure, it is unclear whether there is a similar influence on soil viruses. In this study, prokaryotic and viral communities were characterized in soils from a long-term pH-manipulated soil gradient (pH 4.5 and 7.5), and viral populations also compared to those of other soils ranging in pH (4.0–7.5). Viral communities were significantly influenced by pH at the local scale with 99% of viral operational taxonomic units restricted to pH 4.5 or 7.5 soil only. Analysis of viromes from six other European and North American soil systems demonstrated that a selection of viral clusters from acidic and neutral pH soils were more associated with those from the local gradient pH 4.5 or 7.5 soils, respectively. While direct pH effects on virion integrity and indirect selection via host composition were not distinguished, the results reveal that soil pH is a factor in structuring viral communities at local and global scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Disturbance legacies regulate coastal forest soil stability to changing salinity and inundation: A soil transplant experiment

Coastal forests worldwide are vulnerable to a dramatic transition from upland to wetland as sea-level rise accelerates and regimes of precipitation and storms change. However, the biogeochemical impacts of shifting salinity and inundation disturbance that foreshadow forest to wetland state transitions are largely unknown. This experiment used a natural salinity gradient in a tidal creek in eastern Maryland, U.S.A., to examine how soil respiration and chemistry may change under novel salinity and inundation disturbance regimes. Soil monoliths were transplanted in a reciprocal design among plots varying in seawater exposure and elevation above the creek. We monitored the monoliths’ carbon dioxide (CO 2 ) flux for two years and performed soil chemical analyses at the end of the experiment. Soil CO 2 flux was affected by changing disturbance regimes and responses were dependent upon the salinity and inundation legacies associated with each study location. Lowland soil CO 2 flux was resistant to changing salinity and inundation disturbance, with transplanted soil monolith chemistry composition in between that of its origin and destination. Conversely, upland soil CO 2 flux was sensitive to changing salinity and inundation disturbance and remained suppressed throughout the 2-year study when exposed to wetter, saline conditions. Additionally, transplanted upland soil chemistry composition was like that of its destination, rather than origin, with upland soils displaying higher pH, base saturation, and nutrient availability relative to lowland soils. Here, we hypothesize that reductions in soil respiration rates were driven by loss of soil nutrients and osmotic and redox stress on microbial communities following exposure to seawater. Together, our results suggest that disturbance legacies shape coastal forest soil responses to changing salinity and inundation disturbance regimes. However, fully understanding the dependence of system responses on disturbance legacies requires future study across a variety of systems and spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Edaphic controls on genome size and GC content of bacteria in soil microbial communities

Nutrient limitation has been shown to reduce bacterial genome size and influence nucleotide composition; however, much of this work has been conducted in marine systems and the factors which shape soil bacterial genomic traits remain largely unknown. Here, for this work, we determined average genome size, GC content, codon usage, and amino acid content from 398 soil metagenomes across a broad geographic range and used machine-learning to determine the environmental parameters that most strongly explain the distribution of these traits. We found that genomic trait averages were most related to pH, which we suggest is primarily due to the correlation of pH with several environmental parameters, particularly soil carbon content. Low pH soils had higher carbon to nitrogen ratios (C:N) and tended to have communities with lower GC content and larger genomes, potentially a response to increased physiological stress and a requirement for metabolic diversity. Conversely, communities in high pH and low soil C:N had smaller genomes and higher GC content—indicating potential resource driven selection against AT base pairs, which have a higher C:N than GC base pairs. Similarly, we found that nutrient conservation also applied to amino acid stoichiometry, where bacteria in soils with low C:N ratios tended to code for amino acids with lower C:N. Together, these relationships point towards fundamental mechanisms that underpin genome size, and nucleotide and amino acid selection in soil bacteria.

54 ENVIRONMENTAL SCIENCES↗