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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Patterns, drivers, and a predictive model of dam removal cost in the United States

Given the burgeoning dam removal movement and the large number of dams approaching obsolescence in the United States, cost estimating data and tools are needed for dam removal prioritization, planning, and execution. We used the list of removed dams compiled by American Rivers to search for publicly available reported costs for dam removal projects. Total cost information could include component costs related to project planning, dam deconstruction, monitoring, and several categories of mitigation activities. We compiled reported costs from 455 unique sources for 668 dams removed in the United States from 1965 to 2020. The dam removals occurred within 571 unique projects involving 1–18 dams. When adjusted for inflation into 2020 USD, cost of these projects totaled $\$1.522$ billion, with per-dam costs ranging from $\$1$ thousand (k) to $\$268.8$ million (M). The median cost for dam removals was $\$157$k, $\$823$k, and $\$6.2$M for dams that were< 5 m, between 5–10 m, and > 10 m in height, respectively. Geographic differences in total costs showed that northern states in general, and the Pacific Northwest in particular, spent the most on dam removal. The Midwest and the Northeast spent proportionally more on removal of dams less than 5 m in height, whereas the Northwest and Southwest spent the most on larger dam removals > 10 m tall. We used stochastic gradient boosting with quantile regression to model dam removal cost against potential predictor variables including dam characteristics (dam height and material), hydrography (average annual discharge and drainage area), project complexity (inferred from construction and sediment management, mitigation, and post-removal cost drivers), and geographic region. Dam height, annual average discharge at the dam site, and project complexity were the predominant drivers of removal cost. The final model had an R 2 of 57% and when applied to a test dataset model predictions had a root mean squared error of $\$5.09$M and a mean absolute error of $\$1.45$M, indicating its potential utility to predict estimated costs of dam removal. We developed a R shiny application for estimating dam removal costs using customized model inputs for exploratory analyses and potential dam removal planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗
DOE OSTI2024

GALIC: hybrid multi-qubitwise pauli grouping for quantum computing measurement

Abstract Observable estimation is a core primitive in NISQ-era algorithms targeting quantum chemistry applications. To reduce the state preparation overhead required for accurate estimation, recent works have proposed various simultaneous measurement schemes to lower estimator variance. Two primary grouping schemes have been proposed: full commutativity (FC) and qubit-wise commutativity (QWC), with no compelling means of interpolation. In this work we propose a generalized framework for designing and analyzing context-aware hybrid FC/QWC commutativity relations. We use our framework to propose a noise-and-connectivity aware grouping strategy: Generalized backend-Aware pauLI Commutation (GALIC). We demonstrate how GALIC interpolates between FC and QWC, maintaining estimator accuracy in Hamiltonian estimation while lowering variance by an average of 20% compared to QWC. We also explore the design space of near-term quantum devices using the GALIC framework, specifically comparing device noise levels and connectivity. We find that error suppression has a more than 10 × larger impact on device-aware estimator variance than qubit connectivity with even larger correlation differences in estimator biases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗
DOE OSTI2020

Prediction of annual soil respiration from its flux at mean annual temperature

Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO2) across time and space is important to constrain and understand ecosystem to global scale SR, a major terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SRannual) from the flux measured at mean soil temperature (SRMAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SRMAST to predict SRannual. SRMAST estimated using a variety of methods all showed clear relationships with annual SRannual. Two single-rate methods (i.e., using the single SR rate most close to mean annual soil temperature, or the single SR rate most close to mean annual air temperature) showed the most pronounced divergence from the true SRannual, but errors significantly decreased when using multiple SR rates within 1 ? of the mean annual soil temperature to estimate SRMAST. SRannual was most closely correlated with SRMAST estimated via a Q10 relationship, but this method has a potential autocorrelation issue that we explore and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SRMAAT) can be used to predict SRannual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SRannual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions.

Soil respiration, temperature, modeling↗
DOE OSTI2025

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗
DOE OSTI2025

Mobility and threshold voltage extraction in transistors with gate-voltage-dependent contact resistance

The mobility of emerging (e.g., two-dimensional, oxide, organic) semiconductors is commonly estimated from transistor current-voltage measurements. However, such devices often experience contact gating, i.e., electric fields from the gate modulate the contact resistance during measurements, which can lead conventional extraction techniques to estimate mobility incorrectly even by a factor >2. Although this error can be minimized by measuring transistors at high gate-source bias |V gs |, this regime is often inaccessible in emerging devices that suffer from high contact resistance or early gate dielectric breakdown. Here, we propose a method of extracting mobility in transistors with gate-dependent contact resistance that does not require operation at high |V gs |, enabling accurate mobility extraction even in emerging transistors with strong contact gating. Our approach relies on updating the transfer length method (TLM) and can achieve <10% error even in regimes where conventional techniques overestimate mobility by >2×

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of machine-learning-enhanced Bayesian quantum state estimation

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Model-Based Detection Scheme for Spoofed Sensor Data in Grid-Connected Inverters

In this article, a novel method to detect spoofed sensor data (SSD) for grid-connected inverters is proposed. First, a Kalman filter is used to estimate the inverter status in the proposed method. Then, the impact of SSD on the residual between estimation and control reference is analyzed. By leveraging the standard error of residual, an adaptive cumulative sum (CUSUM) chart is developed to achieve fast SSD detection and improve robustness. In addition, the proposed SSD detection method is verified by the hardware experiment. A comparative experiment with a conventional method also demonstrates the rapidity and robustness of the proposed method. Lastly, the performance of the proposed method under different noise covariance matrices is analyzed. Comprehensive analysis shows that the proposed method meets the detection requirement in IEEE 1547 Standard.

42 ENGINEERING↗
DOE OSTI2022

Shear Measurement with Poorly Resolved Images

Weak lensing studies typically require excellent seeing conditions for the purpose of maximizing the number density of well-resolved galaxy images. It is interesting to ask to what extent the seeing size limits the usefulness of the astronomical images in weak lensing. In this work, we study this issue with the data of the DECam Legacy Survey, which is a part of the target selection program for the Dark Energy Spectroscopic Instrument. Using the Fourier_Quad shear measurement pipeline, we demonstrate that images with relatively poor seeing conditions (~1$^"_.$5) can still yield accurate shear estimators. We do not find any correlation between systematic shear error and the image resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Updating PV and Battery Bill Savings Calculations for Net Billing: New Best Practices for Input Data and Uncertainty

Jurisdictions are increasingly adopting compensation structures for distributed PV and PV-battery systems that price exported energy lower than energy consumed onsite (net billing rates). Standard methods for calculating the bill savings from PV and PV-battery systems were developed for net metering structures, and applying these same methods to net billing structures (such as using Typical Meteorological Year weather with actual year load) introduces bias errors that underestimate PV-battery system bill savings by between 1.5\% and 9\%, depending on the utility rate. We assess the magnitude of these errors and compare them to other sources of uncertainty when estimating the bill savings from PV and PV-battery systems under more complex utility rates.

14 SOLAR ENERGY↗
DOE OSTI2020

Performance Evaluation of High-Temperature Ultrasonic Communication System

Ultrasonic communication is a technique that allows transmitting information through a solid material such as tubes or bars using high-frequency elastic waves. The communication channels often extend into a high-temperature range in the field of power, oil, or nuclear industry. When the temperature is taken into consideration, the ultrasonic wave propagation through the solid channel is complex and unpredictable. It is necessary to study the influence of temperature on the ultrasonic guided wave to improve ultrasonic communication. In this study, a temperature control system is assembled to adjust the temperature of a steel pipe channel ranging from room temperature to 150 degrees C. The temperature performance evaluation is conducted to estimate the signal-to-noise ratio (SNR) and bit error rate (BER) of transferring the ultrasonic signal. The work aims to develop a reliable ultrasonic communication system in a high-temperature environment. The system temperature can be maintained at 150 degrees C and the LiNbO3 transducers are used as transmitters and receivers. No power amplifier is used in the communication system. The high-temperature ultrasonic communication system (HT-UCS) is feasible at 150 degrees C and can achieve a bit rate of 10 kbps without any error.

HT-UCS↗
DOE OSTI2021

On the estimation of boundary layer heights: a machine learning approach

Abstract. The planetary boundary layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum, and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity, etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as random forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018 and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates were observed using the RF model, with a 50 % improvement in mean absolute error and an R2 of greater than 85 % compared to the Tucker method zi. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Batch VUV4 characterization for the SBC-LAr10 scintillating bubble chamber

The Scintillating Bubble Chamber (SBC) collaboration purchased 32 Hamamatsu VUV4 silicon photomultipliers (SiPMs) for use in SBC-LAr10, a bubble chamber containing 10 kg of liquid argon. A dark-count characterization technique, which avoids the use of a single-photon source, was used at two temperatures to measure the VUV4 SiPMs breakdown voltage (V BD ), the SiPM gain (g SiPM ), the rate of change of g SiPM with respect to voltage (m), the dark count rate (DCR), and the probability of a correlated avalanche (P CA ) as well as the temperature coefficients of these parameters. A Peltier-based chilled vacuum chamber was developed at Queen's University to cool down the Quads to 233.15 ± 0.2 K and 255.15 ± 0.2 K with average stability of ±20 mK. An analysis framework was developed to estimate V BD to tens of mV precision and DCR close to Poissonian error. The temperature dependence of V BD was found to be 56 ± 2 mV K -1 , and m on average across all Quads was found to be (459 ± 3(stat.)±23(sys.))× 10 3 e- PE -1 V -1 . The average DCR temperature coefficient was estimated to be 0.099 ± 0.008 K -1 corresponding to a reduction factor of 7 for every 20 K drop in temperature. The average temperature dependence of P CA was estimated to be 4000 ± 1000 ppm K -1 . P CA estimated from the average across all SiPMs is a better estimator than the P CA calculated from individual SiPMs, for all of the other parameters, the opposite is true. All the estimated parameters were measured to the precision required for SBC-LAr10, and the Quads will be used in conditions to optimize the signal-to-noise ratio.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Hawthorne Nevada Deep Direct-Use Feasibility Study - Data Used for Geothermal Resource Conceptual Modeling and Power Capacity Estimates

This data submission includes several data components that were used to develop a conceptual model and power capacity-estimates of two low-temperature geothermal resources that define geothermal prospect A at Hawthorne, Nevada. Data are sourced from a combination of legacy publicly-available data and more recent data acquisition conducted by the US Navy Geothermal Program Office (2008-2013) and the Great Basin Center for Geothermal Energy at the University of Nevada, Reno (2008-2010). Data sets include compiled fluid geochemistry data, down-hole temperature logs for wells in the vicinity of prospect A, 2 meter temperature survey data, temperature-spinner logs acquired in well HWAAD-2A, fracture picks from image log data acquired in wells HWAAD-2 and HWAAD-3, and X-Ray Diffraction (XRD) analyses on cuttings from wells HWAAD-2A and HWAAD-3. These data have been reviewed for errors and inconsistencies, but it is possible that few errors could still remain. The resource conceptual model and power capacity estimates are included in the final report to the US Department of Energy, and are presented in a manuscript by Ayling and Hinz. A link to the manuscript published in Geothermics is linked below in this submission.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement

This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.

Bayesian inverse problem↗