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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MACAW v1.0

The ability to embed molecules in a numeric space is essential in order to build mathematical and machine-learning models describing molecular properties or other processes affected by molecules. MACAW is a cheminformatic tool that allows embedding small molecules into a multidimensional numeric space. In the embedding, each molecule is assigned a numeric vector that captures information of the molecule in relation to other molecules, and that vector can be used as input to mathematical models. Molecules that are more similar to each other are embedded closer in this numeric space, whereas molecules that are more different are embedded further away. One advantage of the MACAW embedding technology compared to established alterantives is that it is fast and does not require extensive computational resources or expertise. In particular, MACAW embeddings can be used as input to mathematical models without the need for variable cleaning or feature selection, saving time and simplifying their use. On the other hand, MACAW also contains methods to generate new molecules and to recommend new molecules satisfying a desired molecular property. The generation of new molecules can be biased based on an input set of molecules, effectively generating molecular diversity around it. In its turn, MACAW's molecular recommendation tool is a novel method for evolving molecules in silico towards a desired molecular specification. In this method, the biased molecular generator tool is applied iteratively in combination with a molecular selection step. As a result, in each iteration the molecules selected by the software are increasingly closer to the desired specification. Both the molecular generation and the molecular recommendation tools are very fast, efficient, and intuitive to use.

Roger, VincentBlay↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Swept-Lookback Deflectometry for High Performance Concentrating Solar Power Optical Metrology

This report describes an initial investigation into a proposed solution to the important problem of performing a detailed evaluation of heliostat optical performance, in situ in a heliostat field. Our approach is to place digital cameras in a position near the receiver where they look back toward the heliostat mirrors. The pixels of each camera sensor identify a set of small cells on the mirror surface, each corresponding to a “mixel.” By either passing reflected sunbeam over the camera or passing the camera through the reflected sunbeam, the cameras intercept sunlight reflected from each mixel. We then analyze the recorded video data to determine times when each mixel transitions from dark to light, and then back to dark. We then use these transitions to construct vectors from the camera to the mixel, and then from the mixel to the edge of the Sun at that moment. We then compute the surface normal at the mixel, which bisects the angle between these vectors. Performing this analysis for all mixels in the mirror yields a high-resolution map of slope across the mirror surface. We have implemented most of this process, successfully collecting data for an example heliostat facet and computing a preliminary estimated slope map. However, more work remains to complete this calculation, since certain factors and transformations are not yet included. Our observations so far support our hypothesis that such a system is possible, but we have not yet completed our quantitative evaluation of the concept.

14 SOLAR ENERGY↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reversible hydrogen control of antiferromagnetic anisotropy in α-Fe 2 O 3

Antiferromagnetic insulators are a ubiquitous class of magnetic materials, holding the promise of low-dissipation spin-based computing devices that can display ultra-fast switching and are robust against stray fields. However, their imperviousness to magnetic fields also makes them difficult to control in a reversible and scalable manner. Here we demonstrate a novel proof-of-principle ionic approach to control the spin reorientation (Morin) transition reversibly in the common antiferromagnetic insulator α-Fe 2 O 3 (haematite) - now an emerging spintronic material that hosts topological antiferromagnetic spin-textures and long magnon-diffusion lengths. We use a low-temperature catalytic-spillover process involving the post-growth incorporation or removal of hydrogen from α-Fe 2 O 3 thin films. Hydrogenation drives pronounced changes in its magnetic anisotropy, Néel vector orientation and canted magnetism via electron injection and local distortions. We explain these effects with a detailed magnetic anisotropy model and first-principles calculations. Tailoring our work for future applications, we demonstrate reversible control of the room-temperature spin-state by doping/expelling hydrogen in Rh-substituted α-Fe 2 O 3 .

36 MATERIALS SCIENCE↗
DOE OSTI2024

Wavefront distortion correction in scanning tunneling microscope image

Here, we report an algorithm to identify and correct distorted wavefronts in atomic resolution scanning tunneling microscope images. This algorithm can be used to correct nonlinear in-plane distortions without prior knowledge of the physical scanning parameters, the characteristics of the piezoelectric actuator, or individual atom positions. The 2D image is first defined as a sum of sinusoidal plane waves, where a nonlinear distortion renders a curve for an otherwise ideal linear wavefront. Using the Fourier transforms of local areas of the image, the algorithm generates a wavefront vector field. The identified wavefronts are subsequently linearized for each plane wave without changing lattice orders, giving rise to distortion corrections. Our algorithm is complementary to conventional post-processing algorithms that require prior detection of real space features, which can also be used to correct nonlinear distortions in 2D images acquired by other microscopy techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Here we propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing a scalable parameter in each layer (layer-wise) and for every neuron (neuron-wise) separately, and then optimizing it using a variant of stochastic gradient descent algorithm. In order to further increase the training speed, an activation slope-based slope recovery term is added in the loss function, which further accelerates convergence, thereby reducing the training cost. On the theoretical side, we prove that in the proposed method, the gradient descent algorithms are not attracted to sub-optimal critical points or local minima under practical conditions on the initialization and learning rate, and that the gradient dynamics of the proposed method is not achievable by base methods with any (adaptive) learning rates. We further show that the adaptive activation methods accelerate the convergence by implicitly multiplying conditioning matrices to the gradient of the base method without any explicit computation of the conditioning matrix and the matrix–vector product. The different adaptive activation functions are shown to induce different implicit conditioning matrices. Furthermore, the proposed methods with the slope recovery are shown to accelerate the training process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A dynamic microbial sulfur cycle in a serpentinizing continental ophiolite

Summary Serpentinization is the hydration and oxidation of ultramafic rock, which occurs as oceanic lithosphere is emplaced onto continental margins (ophiolites), and along the seafloor as faulting exposes this mantle‐derived material to circulating hydrothermal fluids. This process leads to distinctive fluid chemistries as molecular hydrogen (H 2 ) and hydroxyl ions (OH − ) are produced and reduced carbon compounds are mobilized. Serpentinizing ophiolites also serve as a vector to transport sulfur compounds from the seafloor onto the continents. We investigated hyperalkaline, sulfur‐rich, brackish groundwater in a serpentinizing continental ophiolite to elucidate the role of sulfur compounds in fuelling in situ microbial activities. Here we illustrate that key sulfur‐cycling taxa, including Dethiobacter , Desulfitispora and ‘ Desulforudis ’, persist throughout this extreme environment. Biologically catalysed redox reactions involving sulfate, sulfide and intermediate sulfur compounds are thermodynamically favourable in the groundwater, which indicates they may be vital to sustaining life in these characteristically oxidant‐ and energy‐limited systems. Furthermore, metagenomic and metatranscriptomic analyses reveal a complex network involving sulfate reduction, sulfide oxidation and thiosulfate reactions. Our findings highlight the importance of the complete inorganic sulfur cycle in serpentinizing fluids and suggest sulfur biogeochemistry provides a key link between terrestrial serpentinizing ecosystems and their submarine heritage.

Sabuda, Mary C.↗
DOE OSTI2025

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the τ τ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A New Vehicle-to-Vehicle Communication Technology Based on Binary Light Code

The proliferation of Connected and Autonomous Vehicles (CAVs) has necessitated the development of efficient, reliable communication methods between vehicles and their surroundings. This study presents a novel methodology, the VECTOR system, that addresses this need by converting dynamic vehicle data into a binary code and displaying it on an LED panel. The system involves a three-step process of data collection, polynomial fitting of velocity data, and encoding the polynomial parameters into binary form using a Cyclic Redundancy Check (CRC). The study also explores the practical application of this system by conducting experiments with modified Lincoln MKZ hybrids, demonstrating the feasibility and efficiency of this approach. The paper presents both the methodology and results of these experiments, further expanding upon the potential applications and implications for CAV technology. By comparing the final accuracy, we find this approach can achieve 80% of the original velocity data.

Ma, Ke↗
DOE OSTI2021

Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T 2 ), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at $\sqrt{s} = 13$ TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton–proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015–2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across these generations, and the nonzero neutrino masses. In this report, the results of searches targeting vectorlike quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
DOE OSTI2019

Theoretical Analysis of the EPR Spectrum Saturation Effect with Account for Spectral Diffusion in a System with a Gaussian Distribution of Spin Resonance Frequencies

We have analyzed the effect of saturation of the EPR spectrum for the model situation when a set of resonance frequencies of spin packets is described by a Gaussian distribution and spectral diffusion, which is a random process without correlation, exists. We have obtained the form of the spectrum for arbitrary values of magnetic–resonant parameters: relaxation times for the longitudinal and transverse components of the magnetization vector of the system, the spectral diffusion rate, the dispersion of the Gaussian distribution of resonance frequency, and the magnetic induction of the microwave field. We have derived an analytic expression for the saturation curve of the EPR spectrum. Detailed analysis of the dependence of the spectrum saturation curve on the spin system parameters is performed for the optimal case when the microwave field frequency coincides with the average frequency of the Gaussian distribution for resonance frequencies. The value of the microwave field induction at which the peak of the saturation curve is attained has been determined for arbitrary values of the paramagnetic relaxation time, spectral diffusion rate, and dispersion of the Gaussian spin frequency distribution. We have formulated the algorithm for determining the spin–lattice relaxation time using the experimentally determined microwave field induction for which the saturation curve passes through the peak. It should be noted that numerical calculations have been performed for parameters typical of EPR spectroscopy. The general analytic expression derived for the shape of the spectrum in the saturation conditions and qualitative conclusions concerning the effect of spectral diffusion on the saturation of stationary spectra obtained in this study can also be used in other fields of spectroscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Median bed-material sediment particle size across rivers in the contiguous US

Abstract. Bed-material sediment particle size data, particularly the median sediment particle size (D50), are critical for understanding and modeling riverine sediment transport. However, sediment particle size observations are primarily available at individual sites. Large-scale modeling and assessment of riverine sediment transport are limited by the lack of continuous regional maps of bed-material sediment particle size. We hence present a map of D50 over the contiguous US in a vector format that corresponds to approximately 2.7 million river segments (i.e., flowlines) in the National Hydrography Dataset Plus (NHDPlus) dataset. We develop the map in four steps: (1) collect and process the observed D50 data from 2577 U.S. Geological Survey stations or U.S. Army Corps of Engineers sampling locations; (2) collocate these data with the NHDPlus flowlines based on their geographic locations, resulting in 1691 flowlines with collocated D50 values; (3) develop a predictive model using the eXtreme Gradient Boosting (XGBoost) machine learning method based on the observed D50 data and the corresponding climate, hydrology, geology, and other attributes retrieved from the NHDPlus dataset; and (4) estimate the D50 values for flowlines without observations using the XGBoost predictive model. We expect this map to be useful for various purposes, such as research in large-scale river sediment transport using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc. The map is available at https://doi.org/10.5281/zenodo.4921987 (Li et al., 2021a).

54 ENVIRONMENTAL SCIENCES↗