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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2016

A systems engineering analysis of three-point and four-point wind turbine drivetrain configurations

This study compares the impact of drivetrain configuration on the mass and capital cost of a series of wind turbines ranging from 1.5 MW to 5.0 MW power ratings for both land-based and offshore applications. The analysis is performed with a new physics-based drivetrain analysis and sizing tool, Drive Systems Engineering (DriveSE), which is part of the Wind-Plant Integrated System Design & Engineering Model. DriveSE uses physics-based relationships to size all major drivetrain components according to given rotor loads simulated based on International Electrotechnical Commission design load cases. The model's sensitivity to input loads that contain a high degree of variability was analyzed. Aeroelastic simulations are used to calculate the rotor forces and moments imposed on the drivetrain for each turbine design. DriveSE is then used to size all of the major drivetrain components for each turbine for both three-point and four-point configurations. The simulation results quantify the trade-offs in mass and component costs for the different configurations. On average, a 16.7% decrease in total nacelle mass can be achieved when using a three-point drivetrain configuration, resulting in a 3.5% reduction in turbine capital cost. This analysis is driven by extreme loads and does not consider fatigue. Thus, the effects of configuration choices on reliability and serviceability are not captured. Furthermore, a first order estimate of the sizing, dimensioning and costing of major drivetrain components are made which can be used in larger system studies which consider trade-offs between subsystems such as the rotor, drivetrain and tower.

17 WIND ENERGY↗
DOE OSTI2019

Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble

Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza–Varas, Hargreaves–Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature (T{sub max}), minimum temperature (T{sub min}), mean temperature (T{sub mean}), relative humidity (R{sub H}), and wind speed (U{sub 2}) were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures (T{sub max}, T{sub mean}, T{sub min}) and relative humidity are the dominant parameters. While temperature-based empirical models and MLR model could be employed to achieve reliable results, AI-based models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.

58 GEOSCIENCES↗
DOE OSTI2025

Incorporating geological structure into sensitivity analysis of subsurface contaminant transport

Simulating subsurface contaminant transport at the kilometer-scale often entails modeling reactive flow and transport within and through complex geologic structures. These structures are typically meshed by hand and as a result geologic structure is usually represented by one or a few deterministically generated geological models for uncertainty studies of flow and transport in the subsurface. Uncertainty in geologic structure can have a significant impact on contaminant transport. In this study, the impact of geologic structure on contaminant tracer transport in a shale formation is investigated for a simplified generic deep geologic repository for permanent disposal of spent nuclear fuel. An open-source modeling framework is used to perform a sensitivity analysis study on transport of two tracers from a generic spent nuclear fuel repository with uncertain location of the interfaces between the stratum of the geologic structure. The automated workflow uses sampled realizations of the geological structural model in addition to uncertain flow parameters in a nested sensitivity analysis. Concentration of the tracers at observation points within, in line with, and downstream of the repository are used as the quantities of interest for determining model sensitivity to input parameters and geological realization. Finally, the results of the study indicate that the location of strata interfaces in the geological structure has a first-order impact on tracer transport in the example shale formation, and that this impact may be greater than that of the uncertain flow parameters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Origin of the sensitivity in modeling the glide behaviour of dislocations

The sensitivity in predicting glide behaviour of dislocations has been a long-standing problem in the framework of the Peierls-Nabarro model. The predictions of both the model itself and the analytic formulas based on it are too sensitive to the input parameters. In order to reveal the origin of this important problem in materials science, a new empirical-parameter-free formulation is proposed in the same framework. Unlike previous formulations, it includes only a limited small set of parameters all of which can be determined by convergence tests. Under special conditions the new formulation is reduced to its classic counterpart. In the light of this formulation, new relationships between Peierls stresses and the input parameters are identified, where the sensitivity is greatly reduced or even removed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Snapshot multispectral imaging using a diffractive optical network

Abstract Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 λ m , where λ m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Equation of state at neutron-star densities and beyond from perturbative QCD

We explore the consequences of imposing robust thermodynamic constraints arising from perturbative quantum chromodynamics (QCD) when inferring the dense-matter equation-of-state (EOS). We find that the termination density, up to which the EOS modeling is performed in an inference setup, strongly affects the constraining power of the QCD input. This sensitivity in the constraining power arises from EOSs that have a specific form, with drastic softening immediately above the termination density followed by a strong stiffening. Here we also perform explicit modeling of the EOS down from perturbative-QCD densities to construct a new QCD likelihood function that incorporates additional perturbative-QCD calculations of the sound speed and is insensitive to the termination density, which we make publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

EvoProtGrad (Directed Evolution for Proteins with Gradients) [SWR-23-48]

A Python package for directed evolution on a protein sequence with gradient-based discrete Markov chain monte carlo (MCMC). Users are able to compose custom models that map sequence to function with pretrained models, including protein language models (PLMs), to guide and constrain search. Our package natively integrates with the HuggingFace platform and supports PLMs from transformers. Our MCMC sampler identifies promising amino acids to mutate via model gradients taken with respect to the input (i.e., sensitivity analysis). We allow users to compose their own custom target function for MCMC by leveraging the Product of Experts MCMC paradigm. Each model is an "expert" that contributes its own knowledge about the protein's fitness landscape to the overall target function. The sampler is designed to be more efficient and effective than brute force and random search while maintaining most of the generality and flexibility. Additional information can be found in the related publication: https://iopscience.iop.org/article/10.1088/2632-2153/accacd

Emami, Patrick↗
DOE OSTI2017

CEA SMAD 2016 Digitizer Evaluation.

Sandia National Laboratories has tested and evaluated an updated SMAD digitizer, developed by the French Alternative Energies and Atomic Energy Commission (CEA). The SMAD digitizers are intended to record sensor output for seismic and infrasound monitoring applications. The purpose of this digitizer evaluation is to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The SMAD digitizers have been updated since their last evaluation by Sandia to improve their performance when recording at a sample rate of 20 Hz for infrasound applications and 100 Hz for hydro-acoustic seismic stations. This evaluation focuses primarily on the 20 Hz and 100 Hz sample rates. The SMAD digitizers are being evaluated for potential use in the International Monitoring System (IMS) of the Comprehensive Nuclear Test- Ban-Treaty Organization (CTBTO).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Evaluation of Two Guralp Preamplifiers for GS13 Seismometer Application.

Sandia National Laboratories has tested and evaluated a new preamplifier, the Guralp Preamplifier for GS13, manufactured by Guralp. These preamplifiers are used to interface between Guralp digitizers and Geotech GS13 Seismometers. The purpose of the preamplifier evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Guralp GS13 Preamplifiers are being evaluated for potential use in the International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO).

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

2017 Guralp Affinity Digitizer Evaluation.

Sandia National Laboratories has tested and evaluated two Guralp Affinity digitizers. The Affinity digitizers are intended to record sensor output for seismic and infrasound monitoring applications. The purpose of this digitizer evaluation is to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self- noise, dynamic range, system noise, response, passband, and timing. The Affinity digitizers are being evaluated for potential use in the International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO).

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Next Generation Qualification: Guralp Systems Affinity Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Affinity, manufactured by Guralp Systems Ltd. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Affinity digitizer is Guralp's latest release in their digitizer product line. The Affinity is available with either 4 or 8 channels at 24 bit resolution. In addition to the 24 bit channels, 16 multiplexed low resolution channels are provided. Other features include the means to accept multiple types of timing sources (e.g. GPS, NTP and PTP) and a web page interface for command and control of the unit.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Next Generation Qualification: Quanterra Q330HR Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Q330HR, manufactured by Quanterra. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Q330HR is Quanterra’s improved Q330 datalogger with a 26 bits of resolution on channels 1-3 and a 24 bits of resolution on channels 4-6 (26 bit is optional). The Quanterra Q330HR is being evaluated for potential use U.S. Air Force seismic monitoring systems as part of their Next Generation Qualification effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Next Generation Qualification: Nanometrics Centaur Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Centaur, manufactured by Nanometrics. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Centaur digitizer is Nanometrics’ replacement for their Taurus digitizer and marks Nanometrics first 6 channel, 24 bit resolution system. Other improvements include LED status indicators on the top of the unit, providing basic status of the core systems of a seismic station (e.g. timing, sensor SOH, storage, etc), an optional wifi system allowing password protected access to the unit and a web interface for monitoring and configuration of the unit. The Nanometrics Centaur is being evaluated for potential use U.S. Air Force seismic monitoring systems as part of their Next Generation Qualification effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Next Generation Qualification: Geotech Instruments SMART24B Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a digitizer, the SMART24B, manufactured by Geotech Instruments, LLC. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The SMART24B is Geotech's datalogger intended for borehole deployment in their digitizer product line. The SMART24B is available with either 3 or 6 channels at 24 bit resolution. The digitizer is to be deployed in boreholes, therefore are a minimum number of connections required on the digitizer case as datalogger utilizes a distribution panel, mounted up-hole, serving to breakout power, GPS, serial communications and ethernet connections.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

2018 Nanometrics Centaur Digitizer Evaluation

Sandia National Laboratories has tested and evaluated two Nanometrics Centaur digitizers. The Centaur digitizers are intended to record sensor output for seismic and infrasound monitoring applications. The purpose of this digitizer evaluation is to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self- noise, dynamic range, system noise, response, passband, and timing. The Centaur digitizers are being evaluated for potential use in the International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO).

47 OTHER INSTRUMENTATION↗