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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Long-term stability-degradation analysis of DUNE SiPMs in Liquid Nitrogen

We developed a cryogenic photon readout system to monitor arrays of DUNE SiPMs operating at various over-voltages in liquid nitrogen (LN2) for over three months. Photoelectron signals were read out simultaneously via weak capacitive coupling to the micro-FEMB, a 32-channel charge sensitive readout board designed for 77 K to 300 K operation in liquid argon time projection chambers (LAr-TPCs). A waveform snippet acquisition scheme was implemented to capture waveform signals whenever they exceeded a predetermined trigger level; consequently, empty waveforms were discarded. Selected SiPM parameters were monitored to detect any deviations beyond statistical fluctuations. While some parameters exhibited ~1σ variations over the 3-month test period, there was no evidence of drift being enhanced when operating at 4 V or 5 V compared to a 3 V over-voltage. However, a discernible PDE drop was observed across all channels, which warrants further investigation. In addition, we demonstrated that our system can simultaneously read out single photons from a selected group of SiPMs at room temperature.

Tsang, Thomas [Brookhaven]↗
DOE OSTI2026

Investigating Interference Term in DUNE And SBND Neutrino Interactions with ACHILLES

Understanding neutrino-nucleus interactions is critical for conducting precise neutrino oscillation ex- periments. However, uncertainties in neutrino-nucleus cross section measurements and our incomplete understanding of nuclear effects remain a significant challenge in neutrino physics. In this paper, we investigate contributions from quantum interference between one-body and two-body interactions in charged-current quasi-elastic (CCQE) neutrino scattering amplitudes, using the ACHILLES event gen- erator. Simulations are performed using muon neutrino flux from Fermilab’s Booster Neutrino Beam (BNB), as well as Long-Baseline Neutrino Facility (LBNF) flux. We observe the neutrino flux interact- ing with Argon nuclei as detected by the Short Baseline Near Detector (SBND) and DUNE Near Detector (DUNE-ND), both Liquid Argon Time Projection Chambers (LArTPC). We focus on one muon and one proton final states with cascade interactions. We analyze multiple experimental observables including outgoing kinematic variables, energy-momentum transfer variables, and Transverse Kinematic Imbalance (TKI) variables. In this study, we also investigate the effects of PRISM (a method of sampling flux from multiple off-axis angles which creates different neutrino energy spectra). We find that the ratio of in- terference to quasi-elastic contributions gives a non-flat distribution across a 0-3.5 GeV energy range, indicating that effects of interference vary with the neutrino energy sampled. Our ultimate goal is to reduce systematic uncertainties from neutrino interactions when conducting oscillation experiments by working to disentangle the effects of quantum interference from the quasi-elastic signature.

Serumaga, Peera [Fermilab; UC, San Diego] (ORCID:0↗
DOE OSTI2020

The many-body expansion for aqueous systems revisited: 1. Water-water interactions

We revisit the Many-Body Expansion (MBE) for water-water interactions by examining the effects of the basis set, including those resulting from the Basis Set Superposition Error (BSSE) correction, on the various terms for selected sizes of water clusters up to n = 21. The analysis is performed at the second order Møller-Plesset (MP2) perturbation theory with the family of augmented correlation consistent basis sets up to five zeta quality for the (H2O)n, n = 7, 10, 13, 16 and 21 clusters, for which we report either the complete MBE (n = 7, 10) or the one through the 6-body (n = 13) and the 5-body terms (n = 16, 21). Our results suggest that any sizeable contributions to the total cluster binding energy arising from the 5-body and larger terms are solely an artifact of the size of the basis set. Indeed, all terms above the 4-body converge monotonically to practically zero at the Complete Basis Set (CBS) limit. The magnitudes of the 5-body and above terms are accurately reproduced even with the smaller basis set of the series (aug-cc-pVDZ) once the BSSE correction is taken into account. The same level of theory (MP2/aug-cc-pVDZ, BSSEcorrected) also accurately reproduces the MP2/CBS values of the 3- and 4-body terms. The contribution of electron correlation to the 3- and 4-body terms is quite small, so that neglecting the correlation contribution in all terms above the 3-body results in an error of ??(0.1%). The BSSE correction to the largest 2-body term in the MBE was accurately estimated from the function ?? [1 + erf(-?? ??)], which is proportional to the common (overlapping) area between two Gaussian distributions whose centers are separated by R, with the constants ??, ?? fitted to the calculated BSSE corrections for the individual 2-body terms of the clusters with each basis set. Our results unquestionably demonstrate that the MBE for water-water interactions converges to the 4-body term, since any finite terms above the 4-body are artifacts of the size of the basis set and can be safely truncated at the 4-body term when either a very large basis set is used or BSSE corrections are taken into account even with the aug-cc-pVDZ basis set. We expect these findings to have important consequences in the pursuit of accurate many-body molecular dynamics simulations for aqueous systems.

Heindel, Joseph↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

2020 Cost Analysis of Hydropower Options at Non-Powered Dams

This report provides a cost analysis of potential hydropower development for representative non-powered dam (NPD) sites in the United States. The overarching objective is to support the establishment of a basis for evaluating progress towards improving the competitiveness of new hydropower development for electricity generation. The representative sites, herein referred to as “reference sites”, were selected from sites with detailed water resource data to support the cost analysis. Facility designs, capital costs, plant generation and efficiency profiles, capacity factors, and levelized costs of energy (LCOE) are presented for the sites under baseline and near-term innovation cases. The baseline case represents facility configurations using technologies that are already widely applied in the hydropower industry, whereas near-term innovations are technologies likely to reach wide acceptance within the next 5 to 10 years. Cost savings in the near-term relative to the baseline are discussed to illustrate the potential benefits to the hydropower industry from research and development (R&D) and adoption of these innovations. Medium- to long-term innovation options expected from ongoing private and government R&D efforts are also highlighted but are not quantified in this report because of a current lack of complete information on these options. The key findings of the cost analysis in this study imply that near-term innovations could reduce the baseline costs of hydropower development at US NPD sites. Thus, if remaining R&D issues with these near-term innovations can be resolved within the next 5 to 10 years, industry-wide adoption of these technologies and improved competitiveness of NPD hydropower development can be expected. Specific key findings of the analysis include the following: 1) Potential installed capacity at the NPD sites evaluated in this study vary from 200 kW to 70 MW, representing two orders of magnitude variation across nearly 20 sites; 2) Capital costs, not including development costs, for the sites evaluated in this study vary from $2,200/kW to $34,000/kW but are below $12,000/kW for most sites; 3) Baseline LCOE estimates for lake dams are from $79/MWh to $790/MWh with most sites under $400/MWh, whereas estimates for lock dams are from $134/MWh to $289/MWh with most sites under $200/MWh; 4) Reductions in capital cost per kilowatt under the near-term innovation case are from −77% to −14% for lake dams and −21% to −2.1% for lock dams (excluding one site with a 14% increase in per-kilowatt capital costs); 5) Combined changes in capital costs and capacity factors in the near-term case relative to the baseline lead to reductions in LCOE of −71% to −14% for lake dams and −21% to −14% for lock dams; 6) Overall, LCOE reductions lead to 8 of the 18 sites under the near-term case having LCOE values below $150/MWh compared with only 2 of 18 sites in the baseline case.

13 HYDRO ENERGY↗
DOE OSTI2021

Redefining Net-Zero Water for Resilient Aquifers

Groundwater has long been a critical source of potable water, supplying one-third of all global water withdrawals and providing strategic reserves in times of drought (Konikow, 2012; Famiglietti, 2014). The risk in relying on groundwater is that it is often pumped at rates far greater than it can be naturally replenished. With increasing population and a changing climate, the depletion of potable water supplies has become an ever-growing concern (Srinivasan et al., 2012). Recently, the concept of “resilience” has been applied to groundwater, leaving many questions to be explored in terms of what resilience should mean for aquifers (Foster and MacDonald, 2014; Grönwall and Oduro-Kwarteng, 2017). The term is commonly used to indicate resilience to impacts of climate change specifically, but here we consider resilience in the context of more general water insecurity. Relatedly, the concept of “sustainable yield” has emerged in an attempt to determine a metric that can ensure the long-term resilience of groundwater systems, but the definition is fraught with inherent uncertainties and its quantification poses challenges (Rudestam and Langridge, 2014; Grönwall and Oduro-Kwarteng, 2017). At the heart of these concepts is a focus on sustainable management of aquifers. A sustainable approach to managing groundwater resources usually involves balancing long-term abstraction with the long-term recharge taking into account the needs for environmental flows and implementing drought-preparedness measures (Robins et al. 2006; Foster and MacDonald, 2014). At a high level, a sustainable balance of groundwater abstraction versus aquifer recharge may be considered “Net Zero” (NZ) or “Net Positive” (NP) in cases where recharge is equal to or in excess of abstraction, respectively. However, NZ/NP water management programs that address these concerns have varied definitions of compliance, which include considerations for the application at hand (ILFI, 2020; USGBC, 2020). Locations with high risk of low or poor water supply may be particularly interested in pursuing NZ/NP water. The AQUEDUCT Water Risk Atlas (World Resources Institute, 2019) indicates long-term availability, providing risk levels for quantity and quality of water at specific locations across the globe, for existing and predicted future conditions. This paper follows the U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) NZ water definition adopted by the U.S. Army for use at their installations, including Army Reserve Centers (ARCs). This definition also relates to the U.S. Army’s current focus on water resilience, which includes short-term and long-term availability of water resources.

net zero water, HUC12, Saipan, American Samoa, Mau↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

On the evolution of scalar iso-surface area density in a turbulent mixing layer

In this paper, the transport equation for scalar iso-surface area density (Σ) in a turbulent, temporally developing mixing layer is examined. Exploring the spatial and temporal evolution of the terms in the Σ transport equation is vital to improving our understanding of turbulent flows characterized by distinct interfaces, e.g. the flame surface or the turbulent/non-turbulent interface. Previous work reported by the authors identified that Σ exhibits self-similar behaviour consistent with the development of the temporal mixing layer. Accordingly, each of the terms in the Σ transport equation is found to behave in a self-similar manner, though there are notable differences in the self-similar behaviours for each term. Based on the results presented herein, it is suggested that the rate of change of Σ (∂Σ/∂t) and the advection term scale with hλ Φ /ΔU, where h is the width of the mixing layer, λ Φ is the scalar Taylor length scale and ΔU is the velocity difference. The production and destruction terms are found to scale with an additional factor (ReSc) 1/2 . In contrast, the molecular diffusion term is found to scale with a factor (ReSc) –1/2 compared to ∂Σ/∂t. Importantly, it is found that the difference between the production and destruction terms, or net surface ‘stretch’, scales with the same factor as ∂Σ/∂t and the advection term, which may have a significant impact on how the evolution of Σ is understood and modelled in turbulent flows.

42 ENGINEERING↗
DOE OSTI2020

Impacts of nitrogen addition on switchgrass root-associated diazotrophic community structure and function

ABSTRACT Cellulosic bioenergy crops, like switchgrass (Panicum virgatum), have potential for growth on lands unsuitable for food production coupled with potential for climate mitigation. Sustainability of these systems lies in identifying conditions that promote high biomass yields on marginal lands under low-input agricultural practices. Associative nitrogen fixation (ANF) is a potentially important nitrogen (N) source for these crops, yet ANF contributions to plant N, especially under fertilizer N addition are unclear. In this study, we assess structure (nifH) and function (ANF) of switchgrass root-associated diazotrophic communities to long-term and short-term N additions using soil from three marginal land sites. ANF rates were variable and often unexpectedly high, sometimes 10× greater than reported in the literature, and did not respond in repeatable ways to long-term or short-term N. We found few impacts of N addition on root-associated diazotrophic community structure or membership. Instead, we found a very consistent root-associated diazotrophic community even though switchgrass seeds were germinated in soil from field sites with distinct diazotrophic communities. Ultimately, this work demonstrates that root-associated diazotrophic communities have the potential to contribute to switchgrass N demands, independent of N addition, and this may be driven by selection of the diazotrophic community by switchgrass roots.

Microbiology↗
DOE OSTI2024

Effect of the volume fraction gradient on the phase interaction force model for disperse two-phase flows

In this work, the effects of the particle volume fraction gradient on fluid-particle interactions are studied. The phase interaction force is decomposed into three terms. For the first term, namely the symmetrized force density, we present theoretical reasoning and numerical evidence to assume that it is independent of the particle volume fraction gradient. The second term is the particle volume fraction gradient times a newly introduced diffusion stress. The third term is the divergence of the particle-fluid-particle (PFP) stress. If this assumption of independence of the particle volume fraction gradient for the first term can be verified, to the first order of the ratio of the mean distance between particles to the macroscopic lengthscale, all three terms can be studied and modeled in flows with uniform particle distributions. Models thus obtained are applicable to statistically inhomogeneous flows, with the second and third terms accounting for statistical inhomogeneity. To verify this assumption, numerical simulations of flows passing fixed arrays of particles are performed. Both uniform and nonuniform particle volume fractions are studied and compared for disperse multiphase flows with the particle Reynolds numbers ranging from 1 to 100, and particle volume fraction ranging from 1% to 26% in statistically steady states. It is found that the symmetrized force (first) term can be well approximated by the drag force obtained from studies of uniform flows. The diffusion stress is positive along the flow direction and negative in the directions perpendicular to the flow. In the case of moving particles, this stress could potentially cause particle aggregation in the flow direction and dispersion in the directions perpendicular to the flow. Finally, the diffusion stress is only important when there is a volume fraction gradient, while the PFP stress can be important in inhomogeneous flows with either nonuniform particle concentrations or nonuniform average relative velocities between the phases.

42 ENGINEERING↗
DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Inundation and Sediment Supply Predict Annual but Not Centennial Carbon Accumulation in Tidal Wetlands Across Land‐Use Types

Despite growing interest in the contributions of tidal wetlands to natural climate solutions, data remain scarce on how land use affects their carbon accumulation rates (CAR). Additionally, environmental factors driving the large observed variability in CAR among sites are poorly understood. To address these knowledge gaps, we measured short-term (∼1 year) and long-term (∼100 years) CAR using feldspar marker horizons, 210 Pb profiles, and surface elevation tables from 39 sites in seven estuaries in Oregon and Washington, USA. Sites varied in wetland type (tidal marsh, tidal swamp, nontidal pasture), land-use history (reference, restored, and disturbed), and salinity (fresh to polyhaline). Across both timescales, CAR was lowest in disturbed pastures and highest in restored marshes, reflecting patterns in sediment accretion rates. Short-term CAR exceeded long-term CAR by approximately 50% on average and was only weakly correlated with long-term CAR, with differences among hydrogeomorphic setting. Short-term CAR was well predicted by summer inundation frequency and relative sediment load, together explaining 60% of variance, whereas long-term CAR was poorly predicted by contemporary environmental conditions. These findings highlight the importance of aligning predictor and response timescales when modeling blue carbon dynamics, and reinforce the climate mitigation potential of tidal wetland restoration.

land use and land cover change↗
DOE OSTI2025

Exploring the interference between the atmospheric and solar neutrino oscillation subamplitudes

The interference between the atmospheric and solar neutrino oscillation subamplitudes is said to be responsible for C P violation () in neutrino appearance channels. More precisely, is generated by the interference between the parts of the neutrino oscillation amplitude that are C P even and C P odd: even or odd when the neutrino mixing matrix is replaced with its complex conjugate. This is the interference term, as it gives a contribution to the oscillation probability, the square of the amplitude, which is opposite in sign for neutrinos and antineutrinos and is unique. For this interference to be nonzero, at least two subamplitudes are required. There are, however, other interference terms, which are even under the above exchange, and these are the C P conserving () interference terms. In this paper, we explore in detail these interference terms and show that they cannot be uniquely defined, as one can move pieces of the amplitude from the atmospheric subamplitude to the solar subamplitude and vice versa. This freedom allows one to move the interference terms around, but does not let you eliminate them completely. We also show that there is a reasonable definition of the atmospheric and solar subamplitudes for the appearance channels such that in neutrino disappearance probability there is no atmospheric-solar interference term. However, with this choice, there is a interference term within the atmospheric sector. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Powering Post-COVID-19 Resilient Recovery with Green Stimulus

As the coronavirus disease (COVID-19) pandemic spread globally in early 2020, many governments enacted lockdown measures as an initial containment response. These lockdowns, while effective in slowing infection rates, have also had substantial economic consequences. A series of supply and demand shocks, workforce and supply chain disruptions, and other impacts of the pandemic and ensuing lockdowns have upended the livelihoods for much of the world's population. This quick read spotlights how green stimulus plans and other recovery measures can support near-term recovery and enable longer-term power system resilience against future threats. The term “green stimulus” encompasses fiscal measures (i.e., governmental tax and spending actions) that support short-term economic activity that enhances environmental and natural resource quality over a longer term. The deployment of energy efficiency (EE) and renewable energy (RE) technologies, as well as associated initiatives (e.g., grid modernization, electrification of transport, and so on) have been demonstrably effective in providing this kind of short-run economic boost while simultaneously strengthening the resilience of the power sector. Along with green stimulus packages, concurrent action on the regulatory side—for example, the development of green building codes, updating grid interconnection standards, creating programs and marketplaces for demand-side efficiency, and others—can be a force multiplier in ensuring that public spending achieves maximum impact in near-term recovery and long-term power sector transformation

29 ENERGY PLANNING, POLICY, AND ECONOMY↗