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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Direct time-resolved detection and quantification of key reactive intermediates in diethyl ether oxidation at T = 450–600 K

High-pressure multiplexed photoionization mass spectrometry (MPIMS) with tunable vacuum ultraviolet (VUV) ionization radiation from the Lawrence Berkeley Labs Advanced Light Source is used to investigate the oxidation of diethyl ether (DEE). Kinetics and photoionization (PI) spectra are simultaneously measured for the species formed. Several stable products from DEE oxidation are identified and quantified using reference PI cross-sections. In addition, we directly detect and quantify three key chemical intermediates: peroxy (ROO˙), hydroperoxyalkyl peroxy (˙OOQOOH), and ketohydroperoxide (HOOP[double bond, length as m-dash]O, KHP). These intermediates undergo dissociative ionization (DI) into smaller fragments, making their identification by mass spectrometry challenging. Furthermore, with the aid of quantum chemical calculations, we identify the DI channels of these key chemical species and quantify their time-resolved concentrations from the overall carbon atom balance at T = 450 K and P = 7500 torr. This allows the determination of the absolute PI cross-sections of ROO˙, ˙OOQOOH, and KHP into each DI channel directly from experiment. The PI cross-sections in turn enable the quantification of ROO˙, ˙OOQOOH, and KHP from DEE oxidation over a range of experimental conditions that reveal the effects of pressure, O 2 concentration, and temperature on the competition among radical decomposition and second O 2 addition pathways.

42 ENGINEERING↗
DOE OSTI2020

Quantification of defects engineered in single layer MoS 2

Atomic defects are controllably introduced in suspended single layer molybdenum disulfide (1L MoS 2 ) using helium ion beam. Vacancies exhibit one missing atom of molybdenum and a few atoms of sulfur. Quantification was done using a Scanning Transmission Electron Microscope (STEM) with an annular detector. Experimentally accessible inter-defect distance was employed to measure the degree of crystallinity in 1L MoS 2 . A correlation between the appearance of an acoustic phonon mode in the Raman spectra and the inter-defect distance was established, which introduces a new methodology for quantifying defects in two-dimensional materials such as MoS 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analytical comparisons of handheld LIBS and XRF devices for rapid quantification of gallium in a plutonium surrogate matrix

This work compares a portable laser-induced breakdown spectroscopy (LIBS) analyzer to a portable X-ray fluorescence (XRF) device for quantification of gallium (Ga) in a plutonium surrogate matrix of cerium (Ce) for the first time. Calibration methods are developed with spectra of Ce–Ga samples from both devices. Here, metrics such as limit of detection (LoD) and mean average percent error (MAPE) are examined to evaluate calibration performance. While the portable LIBS device can yield a nearly instantaneous analytical measurement, its accuracy is hampered by self-absorption. By employing a self-absorption correction and increasing gating delay, LIBS calibrations with errors in the low single percents and LoDs of 0.1% Ga were constructed. The XRF device produces calibrations with superlative sensitivity, yielding LoDs for gallium in the low tens of parts-per-million (ppm), two orders of magnitude lower than the corrected LIBS models. However, a clear trade-off of measurement fidelity is established between the instantaneous analysis of the LIBS device and the minutes-long XRF measurement yielding superior detection limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simultaneous quantification of uranium( VI ), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring

In this work, laser-induced fluorescence spectroscopy (LIFS), Raman spectroscopy, and a stacked regression ensemble was developed for near real-time quantification of uranium(VI) (1–100 μg mL –1 ), samarium (0–200 μg mL –1 ) and nitric acid (0.1–4 M) with varying temperature (20 °C–45 °C). LIFS applications range from fundamental lab-scale studies to real-time process monitoring at industrial levels, such as nuclear reprocessing applications, provided the phenomena affecting the fluorescence spectrum are accounted for (e.g., absorption, quenching, complexation). Multiple chemometric models were examined and compared to a more traditional multivariate regression approach called partial least squares (PLS). Results obtained on synthetic samples selected using D-optimal experimental design indicated that a stacked regression method, which included ridge regression, random forest, PLS, and an eXtreme gradient boost algorithm, successfully measured uranium(VI) concentrations directly in nitric acid without measuring luminescence lifetimes or standard addition. The top model resulted in percent root-mean-square error of prediction values of 5.2, 1.9, 3.0, and 2.3% for U(VI), Sm 3+ , HNO 3 , and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (e.g., Sm 3+ ) to provide information on burnup of irradiated nuclear fuel. This novel framework reinforces the applicability of LIFS for real-time applications in nuclear fuel cycle applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Optimization and quantification of silver( II ) for mediated electrochemical oxidation applications

Mediated electrochemical oxidation (MEO) is a low-temperature, low-pressure, aqueous mineralization process used to treat organic waste. A powerful metal oxidant is used as a mediator in an acidic solution. Although Ce and Co are thoroughly studied mediators, Ag is a preferred choice because of the higher efficiency rates of mineralization observed with this system. Importantly, the quantification methodology and spectroscopic characteristics of the Ag(II) ion must be obtained. In this study, we determined molar extinction coefficients of the primary absorption band associated with the Ag(II) ion in 2–9 M HNO 3 solution. The optimization of Ag(II) electrooxidation was also determined by altering parameters such as HNO 3 concentration, mediator concentration, and temperature. The optimization studies and extinction coefficient data provide parameters for implementation of Ag as a suitable mediator for MEO processing of organic waste.

Schrage, Briana R. [Oak Ridge National Laboratory ↗
DOE OSTI2026

Quantification of trace iodine using laser-induced breakdown spectroscopy for real-time monitoring of nuclear off-gas streams

This study evaluated the potential of laser-induced breakdown spectroscopy (LIBS) for real-time monitoring of trace gas-phase iodine, which is an element of high significance in nuclear applications due to its long radioactive half-life (as iodine-129), volatility, and biological impact. In anticipation of iodine evolving into off-gas systems in molten salt reactor and nuclear fuel recycling applications, this research aimed to assess LIBS performance in flowing argon and helium matrices; optimize measurement parameters using a multichannel spectrometer; and perform calibrations to assess predictive capabilities and limits of detection (LODs). Experimental results successfully measured gas-phase iodine in flowing argon and helium; however, trace iodine was not detected in air. Optimal delay times were determined to be 10 µs for argon and 1 µs for helium, which are consistent with the expected shorter plasma lifetime in helium relative to argon. An emission line survey was provided with the 206.16, 804.37, 902.24, and 905.83 nm peaks, which were identified as the strongest emission peaks. Calibration models were successfully built in both helium and argon, achieving LODs down to 3 ppm in helium and 5 ppm in argon. The iodine emission at 905.83 nm emerged as the most robust for calibration and was subsequently applied to a time series dataset in argon. The predictive trace confirmed the feasibility of employing LIBS for continuous, online quantification of trace iodine in flowing gas systems.

Andrews, Hunter B. [Oak Ridge National Laboratory ↗
DOE OSTI2026

Experimental uncertainty quantification using templates of expected measurement uncertainties for fast neutron-induced total, capture, and scattering cross sections

Careful experimental uncertainty quantification (UQ) is key for developing trustworthy evaluated nuclear data. Templates to account for missing or under-reported experimental uncertainties were recently developed by the covariance committee of Cross Section Evaluation Working Group (CSEWG). In this work, we illustrate the practical application and limitations of these templates for selected neutron-induced reactions, including (n, tot), (n, γ), and (n, xn) in the fast energy range, to illustrate their use in data analyses for nuclear data evaluations. We show that while the templates provide consistent framework, proper implementation still requires detailed knowledge of experimental conditions and careful treatment of nonlinear effects in cross section derivation. Case studies highlight how template-assisted UQ improves consistency with previous evaluations such as ENDF/B and reveals open challenges in propagating uncertainties across different energy regimes. The main contribution of this paper is to connect formal template recommendations with their use in practical evaluation workflows, clarifying both their benefits and current limitations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Uncertainty Quantification for a Multi-phase Carbon Equation of State Model

Many physics models have tunable parameters that are calibrated by matching the model output to experimental or calculated data. However, given that calibration data often contain uncertainty and that different model parameter sets might result in a very similar simulated output for a finite calibration data set, it is advantageous to provide an ensemble of parameter sets that are consistent with the calibration data. Uncertainty quantification (UQ) provides a means to generate such an ensemble in a statistically rigorous fashion. In this work, we perform UQ for a multi-phase equation of state (EOS) model for carbon containing the diamond, graphite, and liquid phases. We use a Bayesian framework for the UQ and introduce a novel strategy for including phase diagram information in the calibration. The method is highly general and accurately reproduces the calibration data without any material-specific prior knowledge of the EOS model parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncertainty quantification for high explosive reactant and product equations of state

Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Quantification and assessment of the atmospheric boundary layer height measured during the AWAKEN experiment by a scanning LiDAR

The atmospheric boundary layer (ABL) height plays a key role in many atmospheric processes as one of the dominant flow length scales. However, a systematic quantification of the ABL height over the entire range of scales (i.e., with periods ranging from one minute to one year) is still lacking in literature. In this work, the ABL height is quantified based on high-resolution measurements collected by a scanning pulsed Doppler LiDAR during the recent American WAKE experimeNt (AWAKEN) campaign. The high availability of ABL height estimates (≈2200 collected over one year and each of them based on 10-min averaged statistics) allows to robustly assess five different ABL height models, i.e., one for convective thermal conditions and four for stable conditions. Thermal condition is quantified by a stability parameter spanning three orders of magnitude and probed by near-ground 3D sonic anemometry. The free-atmosphere stability, quantified by the Brunt–Väisälä frequency, is both calculated from simultaneous radiosonde measurements and obtained from the best fit of two of the chosen ABL height models. Good agreement is found between the data and three of the chosen models, quantified by mean absolute errors on the ABL height between 281 and 585 m. Furthermore, the seasonal variability of the convective ABL height model parameters (−15% to +23% with respect to the year baseline) agrees with the variability of buoyancy-generated turbulence caused by the variation in solar radiation throughout the year.

17 WIND ENERGY↗
DOE OSTI2024

Quantification and visualization of uncertainties in reconstructed penumbral images of implosions at Omega

Penumbral imaging is a technique used in plasma diagnostics in which a radiation source shines through one or more large apertures onto a detector. To interpret a penumbral image, one must reconstruct it to recover the original source. The inferred source always has some error due to noise in the image and uncertainty in the instrument geometry. Interpreting the inferred source thus requires quantification of that inference’s uncertainty. Markov chain Monte Carlo algorithms have been used to quantify uncertainty for similar problems but have never been used for the inference of the shape of an image. Because of this, there are no commonly accepted ways of visualizing uncertainty in two-dimensional data. This paper demonstrates the application of the Hamiltonian Monte Carlo algorithm to the reconstruction of penumbral images of fusion implosions and presents ways to visualize the uncertainty in the reconstructed source. This methodology enables more rigorous analysis of penumbral images than has been done in the past.

Instruments & Instrumentation↗
DOE OSTI2025

Calibrating a finite-strain phase-field model of fracture for bonded granular materials with uncertainty quantification

To study the mechanical behavior of mock high explosives, an experimental and simulation program was developed to calibrate, with quantified uncertainty, a material model of the bonded granular material Idoxuridine and nitroplasticized Estane-5703. This paper reports on the efficacy of such a framework as a generalizable methodology for calibrating material models against experimental data with uncertainty quantification. Additionally, this paper studies the effect of two manufacturing temperatures and three initial granular configurations on the unconfined compressive behavior of the resulting bonded granular materials. In each of these cases, the same calibration framework was used; in that, hundreds of high-fidelity direct numerical simulations using a new, graphics processing unit-enabled, high-performance finite element method software, Ratel, were run to calibrate a finite-strain phase-field fracture model against experimental data. It was found that manufacturing temperature influenced the elastic response of the mock high explosives, with higher temperatures yielding a stiffer response. By contrast, it was found that the initial configuration of the grains had a negligible impact on the overall behavior of the mock high explosives though it remains possible that local damage accumulation within the specimens could be altered by the initial configurations. Overall, the calibration framework was successful at creating well-calibrated models, showing its usefulness as an engineering and scientific tool.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantification of air leakage paths: a comparison of airflow and acoustic measurements

Today, the fan pressurization method is the most frequently used method to evaluate a building's airtightness. However, the localization and quantification of leaks remain difficult. In this paper, an acoustic method is introduced to estimate the leakage size of single leaks. Acoustic and airflow measurements were conducted and compared in laboratory tests within the same boundary conditions. This work aims to investigate if various leak sizes can be predicted using acoustic measurement methods. The test apparatus consists of two chambers, separated by a test wall. This wall represents a single characteristic air leakage path. Various types of wall structures with different slit geometries, wall thicknesses, and insulation materials were investigated. The acoustic measurements were performed with a sound source placed in one chamber and ultrasonic microphones located in both chambers. These results were compared to measured airflows through the test wall to provide estimates of uncertainty in the acoustic approach, which indicate a linear trend. Finally, these laboratory measurements were compared to the same measurements at a real office building. Finally, although the acoustic measurement uncertainty is still significant (greater than +/- 50%), the acoustic method has the potential to give an order of magnitude of single leak sizes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km 2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ~1250 km 2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ~65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗