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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimal Control of an Oscillating Surge Wave Energy Converter

During this project, we experimentally investigated the hydrodynamics and performance of a laboratory-scale oscillating surge wave energy converter (OSWEC).We looked at how flap buoyancy and driveline losses (primarily in the form of stiction) affected the dynamics and performance of the device. In addition, we assessed the influence of flap profile (rounded vs. square edges) on OSWEC hydrodynamics. Through this, we were able to develop a deeper understanding of OSWEC performance and provide guidance on strategies to counteract artifacts that may be present in laboratory models, but are absent in field-scale devices. To do this, we tested a laboratory-scale OSWEC in the Sea Wave Environmental Lab (SWEL) wave tank at the National Renewable Energy Laboratory (NREL). We ran several types of experiments to investigate the hydrodynamics and performance of the device. Overall, we achieved the overall goal of experimentally investigating the hydrodynamics and performance of this device. We discovered important and unexpected trends in performance, and collected time-resolved data to help us further investigate the underlying hydrodynamics responsible for these trends. In addition, we are currently using the time-resolved data from these experiments to build data-driven models of the dynamics, which can in turn be used to inform data-driven model predictive control of this device and address this objective in the future.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2022

Lattice dynamics of high-pressure hydrides studied by inelastic neutron scattering

Due to the small mass and anomalously large neutron scattering cross-section of proton (about 80 barns compared to a few barns for other nuclei), inelastic neutron scattering is considered as one of the most effective tools in studying optical vibrations of hydrogen atoms in metal hydrides. The current review is focused on the binary hydrides of 3d- and 4d-metals of groups VI–VIII, which were produced at high hydrogen pressures of several gigapascals in relatively large quantities of hundreds of mg, quenched to low temperature and studied by INS ex situ at ambient pressure with high statistical accuracy. One of the unusual effects revealed by INS is a strong increase in the strength of the metal-hydrogen interactions with decreasing atomic number of the d-metal accompanied by an increase in the Me-H distance. Based on the available experimental results, the spectra g(E) of the phonon density of states and temperature dependencies CV(T) of the heat capacity at constant volume at T up to 1000 K have been derived in this paper and presented both in the figures and in digital form. This provides the reference data for the theoretical investigations of the crystal structures and compositions of new practically important hydrides giving the opportunity to validate calculation methods by comparing the calculated g(E) and CV(T) with the accurate experimental dependencies for the binary hydrides. Recent INS studies showed [R.A. Klein et al., J. Alloy. Compd. 894 (2022) 162381] that the fingerprints of anomalously short H-H separations of 1.6 Å violating the “2 Å rule” can be easily and unambiguously identified in the complex INS spectra of quaternary hydrides (La,Ce)NiInH 1+x . This makes neutron spectroscopy an attractive means for obtaining valuable data in the search for novel hydrides with a record high hydrogen capacity.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗
DOE OSTI2025

Investigation of fuel film formation and soot emissions in a GDI engine during cold-start with Split-injection strategies

This study investigates the impact of fuel-film formation on engine-out soot emissions in a gasoline direct injection (GDI) engine under cold-start conditions. Split-injection strategies were applied by varying the number of injections, injection duration, and total fuel quantity to affect wall wetting and control the average in-cylinder equivalence ratio. A combined experimental and numerical approach was employed to analyze fuel-film deposition, combustion efficiency, and engine-out soot and unburnt hydrocarbons (UHC) emissions. In particular, fuel film distribution estimated by means of non-reacting, 3-D, computational fluid dynamics (CFD) simulations, together with experimentally measured soot data, were used to investigate fuel film formation and its role in soot generation. In the experiments, a skip-fired engine control strategy was applied to mimic the transient nature of engine cold-start operation. The results indicate that, under the same total number of injections, increasing the average in-cylinder equivalence ratio through longer injection durations improves combustion stability, as indicated by the decrease of the coefficient of variation of IMEP n (Net Indicated Mean Effective Pressure) from 6.0 % to 0.6 %. However, this strategy leads to higher soot emissions, which increased by nearly an order of magnitude, primarily due to enhanced wall-film formation. In contrast, increasing the number of injections while maintaining a constant equivalence ratio significantly impacts fuel-film deposition and, consequently, soot emissions, with a fivefold reduction of the measured engine-out soot, decreasing from 3.5 mg to 0.7 mg. A soot-film correlation was developed and achieved a high coefficient of determination (r 2 = 0.95) and was further extended to account for spark timing effects. These findings confirm the effectiveness of split-injection for avoiding wall film formation and soot emissions, and the critical role of fuel film in soot generation, supporting the hypothesis that pool fires play a crucial role in contributing to soot formation under these cold-start conditions. In conclusion, the study also indicates the value of a predictive soot-film correlation for developing cold-start emission control strategies.

Engine Cold-start↗
DOE OSTI2020

Looking through model medieval green glasses: From color to recipe

Abstract The colors of medieval stained glasses depend on their chemical composition. The redox interactions in glass between iron and copper and their influence on the color were investigated in two soda‐lime silicate model glasses. Both glasses have close iron and copper redox ratios and copper oxidizes iron when the iron content is smaller than the copper content. The redox speciation of iron and copper was determined using combining x‐ray and optical absorption spectroscopies in relation with the resulting color. The molar extinction coefficient of Cu 2+ was estimated from the combined analysis to be 32 mol/L/cm. The glass color change from green to blue‐green color is assigned to the decrease of the total iron content, confirming that ancient glassmakers could control the final color with the glass recipe. These results provide reference data for further spectroscopic investigations of medieval stained glasses. Comparison with ancient copper‐colored green glass compositions suggests that the higher ratio of copper to iron total contents was imposed by the more reducing melting conditions in medieval furnaces.

Hunault, Myrtille O. J. Y.↗
DOE OSTI2025

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stress localization investigation of additively manufactured GRCop-42 thin-wall structure

A full-field crystal plasticity (CP) framework is presented for the GRCop-42 alloy to study microscopic mechanical behavior and local stress heterogeneities. The microstructures of additively manufactured (AM) materials are often unique relative to conventionally processed materials, and the local thermal histories drive these differences during the build process. These thermal histories depend on the process parameters (laser power, scan speed, and scan strategy) and the part geometry. Prior research has shown that the mechanical properties of thin-walled structures can vary significantly with wall thickness due to changes in the thermal boundary conditions during manufacturing. It is, therefore, desirable to perform CP simulations based on the phenomenological constitutive model to predict the local mechanical responses induced by microstructural heterogeneities. This work generates representative microstructures based on experimentally collected grain information (i.e., texture) for grain scale stress analysis, and the material constitutive parameters are calibrated using the experimental mechanical testing data. Here, we specifically investigated the effect of crystallographic texture and grain morphologies on the size-dependent mechanical properties of AM GRCop-42. The selection of appropriate material properties for implementing an effective free surface boundary condition and the influence of adjacent buffer layers are also discussed. Analysis of local field results reveals a strong correlation between stress localization and the initial grain orientation. However, no significant relationship between the misorientation of the individual adjacent grains and the average misorientation is observed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones

Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4 USGS Landsat 4 TM Collection 1 Tier 1 TOA (top of atmosphere) Reflectance, Landsat 5 USGS Landsat 5 TM (thematic mapper) Collection 1 Tier 1 TOA Reflectance, and Landsat 8 USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Within Google Earth Engine, we selected the date and location (latitude and longitude), calculated NDVI, kNDVI, and EVI utilizing Landsat bands (see metadata_NGEE-tropics_cyclones), and extracted post- and pre-cyclone values for each case study to calculate cyclone-induced change in the vegetative index. Due to limited spatial resolution of Landsat remote sensing data, MODIS products were investigated next. First, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product was used to extract 250m EVI and NDVI (Didan, 2015) and then the MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m product product was used to extract LAI 500m (Myneni et al., 2015). Pre- and post-cyclone values, change in the vegetative index, and standard deviation for all values are included in the main csv (see case_study_data.csv) for all vegetative indices collected, including LAI 500m, EVI 250m, NDVI 250m, NDVI 90m, kNDVI 90m, EVI 90m, and NDVI 5km. Lastly, recovery values were calculated utilizing a standardization method (see metadata_NGEE-tropics_cyclones) and recovery values for MODIS (see MODIS_recovery.csv) and Landsat (Landsat_recovery.csv) data are included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗
DOE OSTI2026

Groundwater and river water elevations and temperature from 2017 to 2022 across Meander Z in the East River Watershed, Colorado

This dataset includes groundwater and river water elevations and temperature data collected in the East River watershed located in the Upper Colorado River Basin. The data were collected in order to investigate the coupling between hydrology and biogeochemical processes in the floodplain. Data was collected at ten groundwater locations in Meander Z (MZ), located just upstream of the confluence with Brush Creek and two river locations directly adjacent to Meander Z from 2017-2019. From 2019-2022, data was collected at five groundwater locations in Meander Z. Note that location names, not location identifiers (IDs), are used in the related publication Dewey et al. (2022). Both location IDs and names are included in data files. Files in this dataset include the main data files for each location zipped into a single folder (waterlevel_data.zip), an installation methods file describing sensor installation (InstallationMethods.csv), a file containing field metadata including GPS (Global Positioning System) coordinates and ground surface elevations (transducers_locations.csv). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. This dataset conforms to the ESS-DIVE hydrological reporting format. 2026-04-27 Update: The river water elevation data files (ER-MZR1.csv and ER-MZR2.csv) were corrected. The data for these two locations were inadvertently swapped in the original published data. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An Analysis of the Impact of Particle Growth on Transport Losses in Polymer-Electrolyte Fuel Cells

Voltage cycling causes catalyst nanoparticles in polymer-electrolyte fuel cells to grow. The concomitant loss of interfacial area results in larger kinetic, transport, and possibly ohmic overpotentials. This paper uses recently published experimental data and mathematical models to investigate the evolutions of transport and ohmic resistances to platinum nanoparticles located on the surface and inside the micropores of carbon black supports. Resistance to oxygen transport rises as surface area declines primarily because the flux to each remaining larger platinum particle increases. The path lengths governing oxygen diffusion to surface and buried platinum sites also increase as nanoparticles grow. Platinum nanoparticles on the surface become relatively less favorable as voltage cycling proceeds because they grow faster than platinum in micropores. Because voltage cycling causes total interfacial area to decline, and the fraction located inside micropores to increase, nanoscale ohmic losses increase as a catalyst layer decays. The practical importance of this effect is difficult to discern because proton conductivity in carbon micropores is not well characterized.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Using angular two-point correlations to self-calibrate the photometric redshift distributions of DECaLS DR9

Calibrating the redshift distributions of photometric galaxy samples is essential in weak lensing studies. The self-calibration method combines angular auto- and cross-correlations between galaxies in multiple photometric redshift (photo-z) bins to reconstruct the scattering rates matrix between redshift bins. In this paper, we test a recently proposed self-calibration algorithm using the DECaLS Data Release 9 and investigate to what extent the scattering rates are determined. We first mitigate the spurious angular correlations due to imaging systematics by a machine learning based method. We then improve the algorithm for χ 2 minimization and error estimation. Finally, we solve for the scattering matrices, carry out a series of consistency tests, and find reasonable agreements: (1) finer photo-z bins return a high-resolution scattering matrix, and it is broadly consistent with the low-resolution matrix from wider bins; (2) the scattering matrix from the Northern Galactic Cap is almost identical to that from Southern Galactic Cap; (3) the scattering matrices are in reasonable agreement with those constructed from the power spectrum and the weighted spectroscopic subsample. We also evaluate the impact of cosmic magnification. Although it changes little the diagonal elements of the scattering matrix, it affects the off-diagonals significantly. The scattering matrix also shows some dependence on scale cut of input correlations, which may be related to a known numerical degeneracy between certain scattering pairs. In conclusion, this work demonstrates the feasibility of the self-calibration method in real data and provides a practical alternative to calibrate the redshift distributions of photometric samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Determination of β -decay feeding patterns of 88 Rb and 88 Kr using the Modular Total Absorption Spectrometer at ORNL HRIBF

We report precise determination of ground-state feeding in the β decay of fission products is an important but challenging component in modeling reactor antineutrino flux and reactor decay heat. The Modular Total Absorption Spectrometer (MTAS) is a versatile NaI(Tl) detector array that determines the true β-decay pattern free from the pandemonium effect, including precise ground-state feeding intensities. In this paper, we report MTAS results of the β feeding intensities of 88 Rb and 88 Kr, fission products with large cumulative yields in nuclear reactors. By comparing MTAS results with previous measurements, 88 Rb provides a validation of MTAS's ability to determine ground-state feedings in β decays, while the precision of 88 Kr ground-state feeding is improved when compared with the Evaluated Nuclear Structure Data File (ENSDF). The investigation of sources that contribute to β feeding branching uncertainties in MTAS experiments is discussed in detail. Lastly, the deconvolution of 88 Rb decay spectra suggests that MTAS can distinguish an allowed β spectral shape from a first forbidden unique β spectral shape.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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↗