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DOE OSTI2026

Drone Flight Data Logs

This dataset represents the open-air tests for the drones when testing different flight scenarios. For some flights we created and tested with a set of onboard sensors. For others we used the native logs for the drones. We recorded relevant conditions for each of the flights to examine environmental issues and weight impacts. We also looked at segmentations of flights to investigate the energy used in each type of flight.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Geothermal Play Fairway Analysis, Part 2: GIS methodology

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Sentiment analysis of the United States public support of nuclear power on social media using large language models

This study utilized large language models (LLMs) to analyze public sentiment in the United States (US) regarding nuclear power on social media, focusing on X/Twitter, considering climate change challenges and advancements in nuclear power technology. Approximately, 1.26 million nuclear tweets from 2008–2023 were examined to fine-tune LLMs for sentiment classification. We found the crucial role of accurate data labeling for model performance, with potential implications for a 15% improvement, achieved through high-confidence labels. LLMs demonstrated better performance compared to traditional machine learning classifiers, with reduced susceptibility to overfitting and up to 96% classification accuracy. LLMs are used to segment the US public tweets into policy and energy-related categories, revealing that 68% are politically themed. Policy tweets tended to convey negative sentiment, often reflecting opposing political perspectives and focusing on nuclear deals and international relations. Energy-related tweets covered diverse topics with predominantly neutral to positive sentiment, indicating broad support for nuclear power in 48 out of 50 US states. The US public positive sentiments toward nuclear power stemmed from its high power density, reliability regardless of weather conditions, environmental benefits, application versatility, and recent innovations and advancements in both fission and fusion technologies. Negative sentiments primarily focused on waste management, high capital costs, and safety concerns. The neutral campaign highlighted global nuclear facts and advancements, with varying tones leaning towards positivity or negativity. An interesting neutral theme was the advocacy for the combined use of renewable and nuclear energy to attain net-zero goals.

Energy & Fuels↗

DOE OSTI2026

Raman and Terahertz Spectroscopy of Low-Frequency Chiral Phonons in Amino Acids

Chiral phonons are mirror-symmetric vibrations with nonzero angular momenta that correspond to twisting and rotational motions of multiple atoms. In chiral crystals, these include low-energy terahertz (THz)-range vibrations of the molecular segments involving dozens of atoms with energies sensitive to molecular chirality. Here, we present spectral signatures of chiral phonons in circularly polarized Raman optical activity (ROA) spectra from enantiomers of amino acid crystals. Along with complementary THz circular dichroism (TCD) measurements, our ROA data reveal several vibrational bands in enantiomers of valine, alanine, tyrosine, and proline between 30 and 150 cm –1 (∼1–4.5 THz) that exhibit opposite intensities. Density functional theory calculations confirm their assignment to twisting and shearing molecular motions. The simultaneous registration of chiral phonon modes by ROA and TCD demonstrates the necessity of these complementary techniques to identify complex mirror-asymmetric vibrational modes and offers new insights into their interactions with circularly polarized light.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Three-dimensional structure of hybrid magnetic skyrmions determined by neutron scattering

Magnetic skyrmions are topologically protected chiral spin textures which present opportunities for next-generation magnetic data storage and logic information technologies. The topology of these structures originates in the geometric configuration of the magnetic spins, more generally described as the structure. While the skyrmion structure is most often depicted using a two-dimensional projection of the three-dimensional (3D) structure, recent works have emphasized the role of all three dimensions in determining the topology and their response to external stimuli. Here, grazing-incidence small-angle neutron scattering and polarized neutron reflectometry are used to determine the 3D structure of hybrid skyrmions. The structure of the hybrid skyrmions, which includes a combination of Néel-like and Bloch-like components along their length, is expected to significantly contribute to their notable stability, which includes ambient conditions. To interpret the neutron scattering data, micromagnetic simulations of the hybrid skyrmions were performed, and the corresponding diffraction patterns were determined using a Born approximation transformation. The converged magnetic profile reveals the magnetic structure along with the skyrmion depth profile, including the thickness of the Bloch and Néel segments and the diameter of the core.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Highly Segmented Silicon Strip Detectors for Radiation Detection (LDRD Final Report)

Large area highly segmented silicon strip detectors were investigated for use in applied radiation detection applications. Two ASIC-based readout electronics solutions were tested for compatibility. During the analysis, it was determined that significant computational resources and highly specialized firmware are required to reconstruct the data into a meaningful data stream for generalized spectroscopic performance. Future investment in the technology and careful choice of readout ASIC is expected to demonstrate relevance in measuring high activity samples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Use of Graph Theory and Neural Networks for Microstructural Classification

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Modifications in segmentation also show potential in improving the accuracy of our current pyCHIP classifier. Replacing previous encoders trained on ImageNet with those trained on microscopy images like MicroNet has initially shown better performance at classifying images of irradiated samples. Additionally, different normalization approaches were tested to show no discernable effect on classification. The Louvain method for community detection is analyzed on a set of irradiated samples with different parameters to determine which proved beneficial under what circumstances. We suggest that microscopy experiments be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The large-area hybrid-optics CLAS12 RICH: First years of data-taking

The CLAS12 deep-inelastic scattering experiment at the upgraded 12 GeV continuous electron beam accelerator facility of Jefferson Lab conjugates luminosity and wide acceptance to study the 3D nucleon structure in the yet poorly explored valence region, and to perform precision measurements in hadron spectroscopy. A large area ring-imaging Cherenkov detector has been designed to achieve the required hadron identification in the momentum range from 3 GeV/c to 8 GeV/c, with the kaon rate about one order of magnitude lower than the rate of pions and protons. The adopted solution comprises aerogel radiator and composite mirrors in a novel hybrid optics design, where either direct or reflected light could be imaged in a high-packed and high-segmented photon detector. The first RICH module was assembled during the second half of 2017 and installed at the beginning of January 2018, in time for the start of the experiment. The second RICH module, planned with the goal to be ready for the beginning of the operation with polarized targets, has been timely built despite the complications caused by the pandemic crisis and successfully installed in June 2022. Finally, the detector performance is here discussed with emphasis on the operation and stability during the data-taking, calibration and alignment procedures, reconstruction and pattern recognition algorithms, and particle identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Nondestructive In Operando Imaging of Thin Film Composite Membrane Compaction Enhanced by AI-Based Segmentation

Reverse osmosis (RO) membranes are essential for desalination and water reuse, yet their permeability declines in high-pressure applications due to membrane compaction. This study investigates the structural and functional responses of commercial brackish, seawater, and high-pressure RO membranes at applied pressures up to 120 bar using a multiscale, nondestructive in operando scanning electron microscopy (iSEM) imaging platform. The iSEM technique reveals progressive densification across the composite membrane structure, which correlates with observed declines in water and solute permeance. To quantify these structural changes with greater fidelity, we combined X-ray computed tomography with AI-based segmentation enabling precise analysis of pore size distribution and thickness of the polysulfone support layer. Compared to traditional thresholding, AI segmentation accurately delineates material phases and void spaces, enhancing the reproducibility and resolution of morphological assessments. The results demonstrate that compaction-induced reductions in porosity and thickness strongly impact membrane transport properties. These findings provide mechanistic insights into the compaction behavior of RO membranes and underscore the potential for advanced imaging and AI-driven data analysis to guide the design of next-generation membranes with improved mechanical resilience and operational longevity.

13 HYDRO ENERGY↗

DOE OSTI2020

Machine learning for endoleak detection after endovascular aortic repair

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Time Alignment of the CMS Hadron Calorimeter

The Hadron Calorimeter (HCAL) in the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) was recently upgraded for Run 3 (2022-2025) to introduce depth segmentation and online timing measurements. With increased segmentation and readout channels, the HCAL provides new timing capabilities for jets and hadronic tau decays with nearly 4π coverage and sensitivity to highly displaced decays within the calorimeter volume. Recent HCAL timing scans provide a valuable look at artificially delayed jets in collision data and are crucial to improving the detector’s performance. Online timing is utilized for detector alignment based on positioning the pulse rising edge, achieving an alignment accuracy of 0.5 ns, considerably higher than previous energy-weighting based approaches. Using precision arrival time measurements, significant advances have been made in understanding the propagation of hadronic showers throughout the calorimeter.

Kopp, Gillian [Princeton Univ., NJ (United States)↗

DOE OSTI2023

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

42 ENGINEERING↗

DOE OSTI2025

Effects of Internal and External Heat Sources on Cladding Microstructure and Rupture Performance

In the event of a Loss of Coolant Accident (LOCA), the primary supply of cooling water for a nuclear reactor is lost, leading to a significant pressure differential across the cladding wall. Without adequate cooling, the fuel rods continue to heat as a result of fission reactions. Research at Oak Ridge National Laboratory’s (ORNL’s) Severe Accident Test Station (SATS) is currently focused on evaluating fuel cladding performance using an external infrared lamp as a heat source, whereas legacy testing primarily utilized an internal heating approach. While external heating may better simulate the effect of neighboring fuel rods heating a central rod, it may not accurately represent the internal heat absorption from the fuel during accident transients. The heating dynamics depend greatly on the fuel rod's position within the bundle and the reactor. To fully understand the implications of a LOCA event and assess the influence of internal heating on cladding performance, combined internal heating and pressurization capability was developed at ORNL. Tests were conducted to compare cladding segments heated internally (representing heat from the fuel within the rod) and externally (representing heat from adjacent fuel rods). The findings indicate that both internal and external heating result in comparable rupture temperatures during 5°C/s laboratory LOCA tests and also agree with legacy test data. Axial temperature gradients and internal heat source dispersal were found to significantly impact cladding deformation and rupture geometry. Clear modifications were outlined to further improve the capability with heating rates above 5°C/s.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Effects of Internal and External Heat Sources on Cladding Microstructure and Rupture Performance

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗

DOE OSTI2024

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems [SWR-24-13]

This software provides a data-driven approach for efficiently identifying high impedance faults (HIFs) in distribution systems. To tackle the nonlinearity of the voltage current trajectory of HIFs, we first formulate linear least squares problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations.

Zhou, Yuqi↗

DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

PELICAN Status Report for Year 2021

The Pressure Drop Experimental Loop for Investigations of Core Assemblies in Advanced Nuclear Reactors, PELICAN, is a novel experimental facility designed and constructed to recreate the prototypic full scale hydraulic flow conditions for the Versatile Test Reactor (VTR) currently under development by the U.S. Department of Energy. To support VTR design efforts, PELICAN is used to replicate VTR’s primary heat transport system (PHTS) in the core assembly region. The measurement objectives for data generated from this test facility are to mimic the prototypic VTR assembly and to meet the validation needs for code calculations and simulations of the reference design for the VTR core. These objectives focus on the need to validate pressure drop results across the various segments of the fuel assembly, including the axial reflectors, fuel, and transition regions, as they relate directly to the pumping power and safety behavior of the reactor. In the following sections, the major milestones for the experimental work performed on PELICAN over the calendar year (CY) 2021 are presented. In the next section, we discuss the assembly and shakedown testing of the facility. In Section 3, an analysis of the errors in the major measurements is performed. In Section 4 and 5 we present the results from the first set of test articles placed inside the loop. The design of the remaining test articles as part of the full assembly are shown in Section 6 and the conclusion and path forward for the following year, CY22 is given in the final section.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗