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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Review of Online Monitoring within Used Nuclear Fuel Recycling Processes

The processing of used nuclear fuels and related materials is often complex and variable. The ability to quickly optimize conditions to the material being processed can aid in increasing efficiency and safety, but requires very quick determination of the conditions present in the feedstock, the process, and the product. Furthermore, accurate quantification of materials such as enriched uranium and plutonium aids in maintaining material accountancy and avoiding nuclear proliferation risks. Traditional analytical methods require process samples to be collected and analyzed in a laboratory, which often takes days to weeks. Online monitoring is suitable for collecting this information nearly instantaneously, enabling much faster optimization of the process or detection of material diversion. Online monitoring is also beneficial as it is typically based on robust and nondestructive analytical methods, so no material is removed as samples. This review examines online monitoring relevant to used nuclear fuel processing for the determination of both chemical and physical parameters. The chemical parameters include quantities such as concentration, isotopic composition, and speciation. These values are often well suited to spectroscopic or spectrometric measurements as they are fast, nondestructive, and easily implemented in an online manner. Physical quantities are often more varied and include temperature, pressure, tank fill levels, and others. Due to the specificity of these quantities, specialized instrumentation is often used. However, this instrumentation is often amendable to online monitoring.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Demystifying Piecewise and Localized Scatter Correction Methods

Multiplicative scatter is a common source of noise in near-infrared spectroscopy and other related instrumental techniques. A wide variety of methods are commonly used for the correction of multiplicative scatter. However, the majority of such methods assume that the parameters that describe the scatter are constant throughout the measured spectrum, which is often not the case. This work investigates a family of methods that perform scatter correction using local regions of neighboring wavelengths in order to better account for wavelength-dependent scattering. The methods in question are piecewise standard normal variate, localized standard normal variate, piecewise multiplicative scatter correction, and localized multiplicative scatter correction. This work describes the theoretical and algorithmic foundations of the family of local region-based scatter correction methods and compares their application and optimization at a qualitative and quantitative level using several datasets.

NIR spectroscopy↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic Profiling Through ASSIST Observations and TROPoe Retrievals

This report reviews the most relevant theoretical aspects of thermodynamic profiling techniques based on spectral observations from ASSIST-II infrared radiometers and TROPoe retrievals. The ASSIST+TROPoe system is a cutting-edge remote sensing technology deployed during the AWAKEN and WFIP3 field campaigns to estimate high-frequency profiles of temperature and humidity in the atmosphere. These profiles are highly valuable for characterizing atmospheric stratification, improving wind models, and understanding the impacts of wind plants on the climate. In this document, we discuss the operating principles of ASSIST, the physics of atmospheric infrared radiation, and the mathematical framework and capabilities of TROPoe. Sources of uncertainties in both the instrument and the retrieval method are also thoroughly addressed. This guide is designed to help users of ASSIST, TROPoe, and thermodynamic data in collecting, estimating, and applying thermodynamic profiles rigorously and scientifically.

17 WIND ENERGY↗
DOE OSTI2025

High-fidelity holographic beam shaping with optimal transport and phase diversity

A phase-only spatial light modulator (SLM) provides a powerful way to shape laser beams into arbitrary intensity patterns but at the cost of a hard computational problem of determining an appropriate SLM phase. Here, we show that optimal transport methods can generate approximate solutions to this problem that serve as excellent initializations for iterative phase retrieval algorithms, yielding vortex-free solutions with superior accuracy and efficiency. Additionally, we show that analogous algorithms can be used to measure the intensity and phase of the input beam incident upon the SLM via phase diversity imaging. These techniques furnish flexible and convenient solutions to the computational challenges of beam shaping with an SLM.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2025

Machine Learning-Based Technique for Automated Sensor Characterization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert s time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Zepeda, Cuevas [Chicago U., KICP]↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
DOE OSTI2025

Bias‐Dependent Quantum Efficiency Reveals Recombination Pathways in Thin Film Solar Cells

Identifying where recombination predominantly occurs—whether at the front interface, back interface, or throughout the bulk—is crucial for optimizing CdSeTe solar cells and many other photovoltaic device architectures. Here, a simple and effective diagnostic is demonstrated: measuring external quantum efficiency (QE) under varying forward biases. The drift–diffusion simulations reveal that each recombination pathway leaves a distinct bias-induced signature in the normalized QE: a progressive drop at long wavelengths for back-limited devices, a short-wavelength decline for front-limited devices, and a relatively uniform decrease across all wavelengths for bulk-limited devices. These predictions are validated with experiments on As-doped and Cu-doped CdSeTe devices, with and without passivation layers or different front buffers. In each case, the observed bias-dependent QE spectral changes align with the simulated recombination map. Because this method uses standard QE instrumentation, it offers a broadly accessible and cost-efficient means to diagnose recombination pathways—extending beyond CdSeTe to other thin-film and emerging solar cell technologies. By pinpointing recombination bottlenecks, researchers and manufacturers can strategically refine doping profiles, passivation schemes, and interface designs to further improve device performance.

CdTe↗
DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Evaluation of the response to electrons and pions in the scintillating fiber and lead calorimeter for the future electron-ion collider

The performance of the Baby Barrel Electromagnetic Calorimeter (Baby BCAL) — a small-scale lead-scintillating-fiber (Pb/ScFi) prototype of the GlueX Barrel Electromagnetic Calorimeter (BCAL) — was tested in a dedicated beam campaign at the Fermilab Test Beam Facility (FTBF). This study provides a benchmark for the Pb/ScFi component of the future Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron-Ion Collider (EIC). The detector response to electrons and pions was studied at beam energies between 4 and 10 GeV, extending previous GlueX tests to a higher energy regime. The calibrated detector exhibits good linearity within uncertainties, and its electron energy resolution meets EIC requirements. The data further constrain the constant term in the energy resolution to below 1.9%, improving upon previous constraints at lower energies. Simulations reproduce key features of the electron and pion data within the limitations of the collected dataset and the FTBF test environment. Electron-pion separation in the test beam setup was analyzed using multiple methods, incorporating varying degrees of beam-related effects. The inclusion of longitudinal shower profile information enhanced the separation performance, underscoring its relevance for the full-scale BIC in ePIC. These results provide essential benchmarks for the Pb/ScFi section of the future BIC, validating detector simulations and guiding optimization strategies for electron-pion discrimination.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Development of a bi-solvent liquid scintillator with slow light emission

One of the most promising approaches for the next generation of neutrino experiments is the realization of large hybrid Cherenkov/scintillation detectors made possible by recent innovations in photodetection technology and liquid scintillator chemistry. The development of a potentially suitable future detector liquid with particularly slow light emission is discussed in the present publication. This cocktail is compared with respect to its fundamental characteristics (scintillation efficiency, transparency, and time profile of light emission) with liquid scintillators currently used in large-scale neutrino detectors. In addition, the optimization of the admixture of wavelength shifters for a scintillator with particularly high light emission is presented. Furthermore, the pulse-shape discrimination capabilities of the novel medium was studied using a pulsed particle accelerator driven neutron source. Beyond that, purification methods based on column chromatography and fractional vacuum distillation for the co-solvent DIN (Diisopropylnaphthalene) are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Spin echo small-angle neutron scattering using superconducting magnetic Wollaston prisms

Here, we show the implementation of superconducting magnetic Wollaston prisms for spin echo small-angle neutron scattering. Two calibration methods for the spin echo length are presented: one utilizing spin echo modulated small-angle neutron scattering and the other based on the neutron refraction by quartz wedge crystals. Our experimental results with polystyrene nano-particle colloids showcase the system’s efficacy in measuring both dilute and concentrated colloidal systems. Additionally, investigations into the pore diameter and pitch of a nano-porous alumina membrane demonstrate its capability in analyzing nano-porous materials. Furthermore, we discuss potential optimizations to further extend the accessible spin echo length.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data

We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and our results, which can be expected given the approximate nature of the mocks, although we do identify discrepancies between the shot-noise properties of the DESI fiber assignment algorithm and the faster approximation (emulator) used in the mocks. Importantly, we find a close agreement (≤ 8% relative differences) in the projected errorbars for distance scale parameters for the baryon acoustic oscillation measurements. This confirms our method as an attractive alternative to simulation-based covariance matrices, especially for non-standard models or galaxy sample selections, making it particularly relevant to the broad current and future analyses of DESI data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Photonuclear Production of Radioxenon for Air Samples

Radioxenon plays an important role in ensuring compliance with the Comprehensive Nuclear-Test-Ban Treaty due to its ability to be transported through the atmosphere, as well as the fact that the half-lives of its isotopes provide a window long enough to be detected but not long enough that it could become background radiation. This allows it to be correlated with specific events that indicate the testing of nuclear weapons. Idaho National Laboratory supports this mission by providing spiked air samples for monitoring stations to enable instrument calibration and ensure measurement accuracy. We tested a photonuclear method of producing one of the main isotopes of radioxenon, Xe-135. We also tested the separation of Xe-135 from the parent isotope Xe-136 using the method of kinematic recoil. Aluminum coils were placed in quartz ampoules filled with enriched Xe-136 and were irradiated by a bremsstrahlung beam with an endpoint energy of 21 MeV. The results showed that we were not only successful in producing Xe-135 but also that the Xe-135 was deposited into the coil allowing it to be removed from the Xe-136 gas. The coil and the Xe-135 can then be chemically separated. At the time of counting the coil, it was calculated that there was about 18 Bq of activity from Xe-135 remaining in the coil. Future work includes determining the optimal material and geometry of the catcher to maximize the amount of Xe-135 captured.

07 ISOTOPE AND RADIATION SOURCES↗