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At least 343 records · Page 19

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DOE OSTI2025

Quantifying the impact of precision errors on quantum approximate optimization algorithms

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that fixed precision implementations of QAOA circuits are subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters at the cost of increasing circuit depth.

quantum algorithms↗

DOE OSTI2024

Wildfires increasingly threaten oil and gas wells in the western United States with disproportionate impacts on marginalized populations

The western United States is home to most of the nation’s oil and gas production and, increasingly, wildfires. Here, we examined historical threats of wildfires for oil and gas wells, the extent to which wildfires are projected to threaten wells as climate change progresses, and exposure of human populations to these wells. From 1984 to 2019, we found that, cumulatively, 102,882 wells were located in wildfire burn areas, and 348,853 people were exposed (resided within ≤ 1 km). During this period, we observed a 5-fold increase in the number of wells in wildfire burn areas and a doubling of the population within 1 km of these wells. These trends are projected to increase by late century, likely threatening human health. Approximately 2.9 million people reside within 1 km of wells in areas with high wildfire risk, and Black, Hispanic, and Native American people have disproportionately high exposure to wildfire-threatened wells.

02 PETROLEUM↗

DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗

NASA NTRS1999

The Impact of Buoyancy and Flame Structure on Soot, Radiation and NOx Emissions from a Turbulent Diffusion Flame

It is hypothesized that the spatial structure of a turbulent diffusion flame plays an important role in determining the emissions of radiative energy, soot and NO, from a combustor. This structure, manifested in the two point statistics, is influenced by buoyancy. Radiation, soot and NOx emissions are the cumulative result of processes that occur throughout a flame. For example, radiation fluxes along a line of sight can be found from summing up the contributions from sources in individual pockets of hot soot that emit, and from sinks in cold soot that absorb. Soot and NOx are both the results of slow chemistry and are not equilibrium products. The time that is available for production and burnout is crucial in determining the eventual emissions of these pollutants. Turbulence models generally rely on a single point closure of the appropriate time averaged equations. Hence, spatial information is lost and needs to be modeled using solution variables such as turbulence kinetic energy and dissipation rate, often with the assumption of isotropy. However, buoyancy can affect the physical structure of turbulent flames and can change the spatial extent of soot bearing regions. Theoretical comparisons with models are best done in the limit of infinite Froude number because the inclusion of buoyancy in flow models introduces significant uncertainties. Hence, LII measurements of soot, measurements of radiation fluxes from soot, Particle Imaging Velocimetry (PIV) of the flow field and measurements of post flame NOX will be carried out on the NASA Lewis 2.2 sec drop tower and eventually on the parabolic flight aircraft. The drop rig will be a modified version of a unit that has been successfully used at Lewis in the past.

Kennedy, I. M.↗

DOE OSTI2023

Interspecific oral rabies vaccine bait competition in the Southeast United States

Here, the United States Department of Agriculture’s National Rabies Management Program (NRMP) has coordinated the use of oral rabies vaccination (ORV) to control the spread of raccoon rabies virus variant west of the Appalachian Mountains since 1997. Working with state and local partners, the NRMP deploys ORV baits containing a rabies vaccine, primarily targeting raccoon populations (Procyon lotor). Bait competition between raccoons and non-target species may limit the effectiveness of ORV programs, but the extent of bait competition remains poorly quantified, particularly in the southeastern United States. We placed placebo ORV baits in bottomland hardwood (n = 637 baits) and upland pine (n = 681 baits) habitats in South Carolina, USA during August-December 2019 and used remote cameras to examine bait competition between raccoons and non-target species. The estimated proportion of bait consumed by raccoons was 18.8 ± 2.1% in bottomland hardwood and 11.6 ± 2.1% in upland pine habitats. Vertebrate competition appeared to have a minimal effect on raccoon uptake as estimated consumption did not exceed 5% for any species or 8% of bait uptake events cumulatively. We estimated that raccoons were the primary consumer of baits in bottomland hardwood, whereas invertebrates were the primary consumer in upland pine (26.7 ± 1.3% of baits). Our results indicate a need to closely consider the effects of invertebrates on bait consumption to minimize their potential impact on ORV bait uptake by target species. Uptake probabilities by raccoons were relatively low but not primarily driven by competition with vertebrates. As such, strategies to increase the specificity of raccoon uptake may be needed to enhance the effectiveness of ORV baiting programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Legacy Effects of Intercropping and Nitrogen Fertilization on Soil N Cycling, Nitrous Oxide Emissions, and the Soil Microbial Community in Tropical Maize Production

Maize-forage grasses intercropping systems have been increasingly adopted by farmers because of their capacity to recycle nutrients, provide mulch, and add C to soil. However, grasses have been shown to increase nitrous oxide (N 2 O) emissions. Some tropical grasses cause biological nitrification inhibition (BNI) which could mitigate N 2 O emissions in the maize cycle but the reactions of the N cycle and the microbial changes that explain the N 2 O emissions are little known in such intercropping systems. With this in mind, we explored intercropping of forage grasses ( Brachiaria brizantha and Brachiaria humidicola ) with distinct BNI and yield potential to increase N cycling in no-till maize production systems compared to monocrop with two N rates (0 and 150 kg ha −1 ) applied during the maize season. These grasses did not strongly compete with maize during the period of maize cycle and did not have a negative effect on grain yield. We observed a legacy of these grasses on N mineralization and nitrification through the soil microbiome during maize growth. We observed that B. humidicola , genotype with higher BNI potential, increased net N mineralization by 0.4 mg N kg −1 day −1 and potential nitrification rates by 1.86 mg NO 3 -N kg −1 day −1 , while B. brizantha increased the soil moisture, fungi diversity, mycorrhizal fungi, and bacterial nitrifiers, and reduced saprotrophs prior to maize growth. Their legacy on soil moisture and cumulative organic inputs (i.e., grass biomass) was strongly associated with enhanced mineralization and nitrification rates at early maize season. These effects contributed to increase cumulative N 2 O emission by 12.8 and 4.8 mg N 2 O-N m −2 for maize growing after B. brizantha and B. humidicola , respectively, regardless of the N fertilization rate. Thus, the nitrification inhibition potential of tropical grasses can be outweighed by their impacts on soil moisture, N recycling, and the soil microbiome that together dictate soil N 2 O fluxes.

Canisares, Lucas P.↗

NASA NTRS2018

Improvement of Stratospheric Aerosol Extinction Retrieval from OMPS/LP Using a New Aerosol Model

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) has been flying on the Suomi National Polar-orbiting Partnership (S-NPP) satellite since October 2011. It is designed to produce ozone and aerosol vertical profiles at 2-kilometer vertical resolution over the entire sunlit globe. Aerosol extinction profiles are computed with Mie theory using radiances measured at 675 nanometers. The operational Version 1.0 (V1.0) aerosol extinction retrieval algorithm assumes a bimodal lognormal aerosol size distribution (ASD) whose parameters were derived by combining an in situ measurement of aerosol microphysics with the Stratospheric Aerosol and Gas Experiment (SAGE II) aerosol extinction climatology. Internal analysis indicates that this bimodal lognormal ASD does not sufficiently explain the spectral dependence of LP-measured radiances. In this paper we describe the derivation of an improved aerosol size distribution, designated Version 1.5 (V1.5), for the LP retrieval algorithm. The new ASD uses a gamma function distribution that is derived from Community Aerosol and Radiation Model for Atmospheres (CARMA)-calculated results. A cumulative distribution fit derived from the gamma function ASD gives better agreement with CARMA results at small particle radii than bimodal or unimodal functions. The new ASD also explains the spectral dependence of LP-measured radiances better than the V1.0 ASD. We find that the impact of our choice of ASD on the retrieved extinctions varies strongly with the underlying reflectivity of the scene. Initial comparisons with collocated extinction profiles retrieved at 676 nanometers from the SAGE III instrument on the International Space Station (ISS) show a significant improvement in agreement for the LP V1.5 retrievals. Zonal mean extinction profiles agree to within 10 percent between 19 and 29 kilometers, and regression fits of collocated samples show improved correlation and reduced scatter compared to the V1.0 product. This improved agreement will motivate development of more sophisticated ASDs from CARMA results that incorporate latitude, altitude and seasonal variations in aerosol properties.

Ozone Mapping Profiler Suite Limb Profiler (OMPS/L↗

DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Comparing first- and second-generation bioethanol by-products from sugarcane: Impact on soil carbon and nitrogen dynamics

The first- and second-generation bioethanol by-products (bagasse and fermentation by-product, respectively) have different biochemical characteristics relating to carbon (C) and nitrogen (N) due to their production processes. To examine the impacts of fermentation by-product and bagasse on soil C and N dynamics, a 120 day laboratory incubation experiment was conducted by applying these amendments in soil at an equal carbon application rate (2.2 g C kg -1 soil). There was a significant increase in overall cumulative CO 2 -C production from amended soil compared to the control soil, though the loss was greatest in bagasse amended soil. However, δ13C-CO 2 measurements suggested that fermentation by-product addition suppressed native soil C mineralization and prompted strong negative C priming (-57%). In contrast, bagasse amendment maintained similar native soil C mineralization to control soil. Decreased microbial biomass turnover time appears to be the major driving force for increased CO 2 -C production and soil C loss following bioethanol by-product amendments. Increased mineral N production indicated net N mineralization after fermentation by-product addition, while addition of bagasse resulted in lower N availability and N immobilization. Overall, results suggested that fermentation by-product application is advantageous in maintaining native soil C stock and N mineralization compared to bagasse. Thus, in a sustainable second-generation bioethanol production system, bagasse may be used for second-generation bioethanol production and the cogenerated by-product could be used as an amendment to improve soil C stock and N availability.

09 BIOMASS FUELS↗

DOE OSTI2023

How Today's Hydropower Impacts Tomorrow's Grid: Counterfactual Scenarios Showing Grid Impacts if Hydropower Goes Away

Amidst ongoing discussions about hydropower removals, retirements, and reduced availability due to drought and other environmental considerations, it is important to understand the long-term effects of reduced hydropower resources on the U.S. electric grid. This analysis uses the Regional Energy Deployment System (ReEDS™) grid planning model to compare several representative scenarios of retiring hydropower and pumped storage hydropower (PSH) capacity over time and explore the overall implications on the U.S. grid from present day through 2050. Reduced hydropower capacity and generation is replaced by a mix of both fossil and non-fossil resources, including natural gas, wind, solar, and battery technologies. Additional natural gas usage leads to an increase in cumulative national electric sector carbon dioxide and criteria pollutant operating emissions of less than 1% in many scenarios but up to 4-5.3% in some cases. Total electric sector costs also increase by <1% in many scenarios but up to 3.6% in the most extreme scenarios where nearly all the hydropower and PSH fleet retires, equating to $\$$340 billion in undiscounted costs. National results indicate that absent additional interventions, retiring hydropower and PSH capacity could increase electric sector emissions and direct capital and operating costs. However, more focused analysis is required to evaluate specific asset-level, local, and regional implications, and a broader scope is necessary to weigh these electric sector impacts alongside economic, ecological, water management, and other cross-sectoral effects that could be either negative or positive.

14 SOLAR ENERGY↗

DOE OSTI2024

Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization

Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO 2 emissions. For the SSP1‐1.9 decarbonization scenario there is >99% probability that mean global warming exceeds 1.5°C, approximately even odds that it reaches 2°C, and ∼90% probability that the hottest year globally exceeds 2023 by at least 0.5°C. Further, for the SSP2‐4.5 decarbonization scenario, there is >90% probability that the hottest annual global temperature anomaly is twice the 2023 anomaly. That our framework makes highly accurate out‐of‐sample predictions of the hottest historical year provides confidence in the predicted future probabilities, suggesting substantial risks from the extreme local conditions that are likely to result from globally hot years during rapid decarbonization.

Diffenbaugh, Noah S. [Doerr School of Sustainabili↗

DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗

NASA NTRS1975

Size distribution of fragment debris produced by simulated meteoroid impact of spacecraft wall

A laboratory experimental program was conducted to determine the amount of fragment debris produced and its size distribution when simulated spacecraft walls are penetrated by hypervelocity projectiles. Two tests were made in which a spacecraft wall was struck with a small steel and an aluminum projectile, respectively. Results show that most of the fragment debris were irregularly shaped flat plates. The fragment data follow a cumulative number distribution law of the form N = am(exp b) where m is fragment mass and a and b are constants. Orbital lifetimes of most of the fragments were very short, being on the order of a few days.

T Dale Bess↗

DOE OSTI2020

Link Between CO 2 –Induced Wettability and Pore Architecture Alteration

Changes in pore (throat) size, surface roughness and mineralogy induced by supercritical-CO 2 -water-rock reactions impact petrophysical properties such as porosity, permeability, and especially wettability. Herein, we show that these changes directly impact relative permeability and capillary pressure curves, a fact rarely studied in the literature. In this work, we show that CO 2 contact angle changes emerge after Madison Limestone samples were soaked for 400 hours in CO 2 -enriched brine. Coreflooding results show that the water production rate and cumulative water production increased after the rock was exposed to carbonic acid. Moreover, the mercury capillary pressure decreased in meso- and macro-pores, indicating the increase of size in these pores due to reactions. This compounded wettability and pore network alteration can directly affect CO 2 injectivity, migration and storage capacity. Furthermore, this fundamental insight into CO 2 geological storage processes should aid practitioners to reduce uncertainties in forecasting CO 2 distribution via injection simulation.

58 GEOSCIENCES↗

DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Some correlation of rock exposure ages and regolith dynamics

Exposure age information on lunar rocks and regolith turnover rates are correlated. If plotted in a cumulative fashion, the distribution of spallogenic noble-gas exposure ages is remarkably parallel to the rate at which various fractions of the regolith surface are cratered and/or excavated. It appears that the rate at which lunar rocks are excavated from within the regolith is strongly controlled by the impact environment. Some suggestions for future refinement of regolith dynamics are presented.

Horz, F.↗

NASA NTRS1980

Rocks of the early lunar crust

Data are summarized which suggest a model for the early evolution of the lunar crust. According to the model, during the final stages of accretion, the outer part of the moon melted to form a magma ocean approximately 300 km deep. This ocean fractionated to form mafic and ultramafic cumulates at depth and an overlying anorthositic crust made up of ferroan anorthosites. Subsequent partial melting in the primitive mantle underlying the crystallized magma ocean produced melts which segregated, moved upward, intruded the primordial crust, and crystallized to form layered plutons consisting of Mg-rich plutonic rocks. Intense impact bombardment at the lunar surface mixed and melted the rocks of the two suites to form a thick layer of granulated debris, granulitic breccias, and impact-melt rocks.

James, O. B.↗