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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Experiments and simulations of isochorically heated warm dense carbon foam at the Texas Petawatt Laser

An experimental and simulation study of warm dense carbon foams at ambient density (n e ~10 21 cm –3 ) is presented. This study of isochorically heated foams is motivated by their potential application in carbon-atmosphere white-dwarf envelopes, where there are modeling uncertainties due to the equation of state. The foams are heated on an approximately picosecond time scale with a laser-accelerated proton beam. The cooling and expansion of the heated foams can be modeled with appropriately initialized radiation-hydrodynamics codes; xRAGE code is used in this work. The primary experimental diagnostic is the streaked optical pyrometer, which images a narrow band of radiation from the rear surface of the heated material. Presented are xRAGE modeling results for both solid aluminum targets and carbonized resorcinol-formaldehyde foam targets, showing that the foam appears to cool slowly on the pyrometer because of partial transparency. So that simulations of cooling foam are processed properly, it is necessary to account for finite optical depth in the photosphere calculation, and the methods for performing that calculation are presented in depth.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Radiation Monitoring for Volatilized Zinc Contamination Using Gamma-Ray Imaging and Spectroscopy

Gamma-ray imaging is a tool that has grown in importance in the applications of non-destructive assay (NDA) for radioactive survey and analysis of nuclear facilities. Imaging techniques have shown great promise in providing valuable information involving radioactive waste management and contamination prevention. For the application studied in this work, 65 Zn has been identified as a radioactive contaminant during tritium extraction. Due to the volatile nature of 65 Zn under the pressure and temperature changes during extraction operations, 65 Zn can easily travel through components of the extraction system as vapor, making it difficult to trap. Previous research involving the development of a filtration system showed that the 65 Zn can be trapped, mitigating product contamination. However, during the extraction process, direct analysis of the equipment to confirm that zinc contamination is trapped in the filter and has not spread to other components is impractical. Here, in this situation, the need to assay the location of the contamination with little-to-no interference with operations is vital. In this work, we demonstrate the use of a commercialized 3D position-sensitive CdZnTe (CZT) gamma-ray imaging spectrometer to provide analysis of the 65 Zn contamination. Onsite measurements during an extraction process are studied to assess the location and migration of the 65 Zn. The results obtained from real-time glovebox monitoring demonstrate the feasibility of gamma-ray imaging for localizing the contamination and providing a preliminary qualitative assessment that is intended to be used in future work quantifying the contamination build-up and activity over time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Special Section Guest Editorial: Active Spectroscopy

A special edition of the journal Optical Engineering invited several scientists to participate in the editorial board of this special issue. The eleven papers in this special section cover recent developments in active spectroscopy, including new phenomenology and applications, advances in systems and components, and new methods for data processing and exploitation.

42 ENGINEERING↗
DOE OSTI2020

A VIPA Spectrograph with Ultra-high Resolution and Wavelength Calibration for Astronomical Applications

A prototype spectrograph using a Virtually Imaged Phased Array (VIPA) as the main dispersion element is presented, and its performance is fully examined in our laboratory. The single-mode, fiber-fed spectrograph with simultaneous wavelength calibration possesses a spectral resolution well in excess of R≈1.12×10{sup 6} while the size of the VIPA is several orders of magnitude smaller than that of a conventional échelle with comparable resolution. In laboratory tests, the VIPA-based instrument with a homemade Yb:fiber ring laser frequency comb demonstrates a mode-to-mode tracking stability of 41 cm s{sup −1} over a period of 6 hr. The VIPA spectrograph has promising applications in various astronomical observations in which ultra-high resolution and calibration precision are imperative, such as solar physics research, exoplanet searching with the radial velocity method, and O{sub 2} detection in the atmosphere of Earth-like planets. Ultimately, feasible optimizations for night-sky observations under seeing limited conditions are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Production of High Specific Activity 155 Tb, 161 Tb and 203 Pb for Research and Clinical Applications: Effective Target Design, Target Material Recycling and Radioisotope Separation (Final Technical Report)

The overall objectives of this project were (1) to develop methods for the production and separation of a diagnostic and therapeutic or “theranostic” pair of radioisotopes, terbium-155 ( 155 Tb) and terbium-161 ( 161 Tb) and (2) to train graduate students and postdoctoral fellows in technologies and methods used in radionuclide production. Radionuclides can be incorporated into drugs called radiopharmaceuticals that target a specific disease (e.g., cancer). The need for theranostic radionuclides is escalating with the clinical translation of radiopharmaceuticals due to their implementation in personalized medicine, which has demonstrated enhanced patient treatments. High purity and high specific activity radionuclides are critical for theranostic agent development, for example to maintain diagnostic image quality, to minimize radiation dose to the patient, and to increase uptake in the targeted tissue (e.g., tumor), especially in the case of receptor- and antigen-targeted agents. The 155 Tb (diagnostic) and 161 Tb (therapeutic) radioisotopes that were generated through this project are a theranostic pair with demonstrated potential for the development and translation into individualized, targeted, and dosimetry-driven radiotherapies. However, the development of such radiotherapies has been hindered by the lack of a routine and reliable supply of these isotopes in the United States. Methods for the production, separation, and supply of 155 Tb and 161 Tb were investigated and developed in this project. Further, the strong emphasis throughout the project on the training of graduate students and postdoctoral fellows has helped to ensure and enhance the nuclear science workforce through the training of the next generation of highly qualified scientists in nuclear and radiochemistry. This grant also continued a collaboration between scientists at the University of Washington (UW), the University of Missouri (MU) and Brookhaven National Laboratory (BNL). All three institutions were involved in the project, but to different degrees on the various tasks through which the overall objectives were met.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Revealing the three-dimensional structure of microbunched plasma-wakefield-accelerated electron beams

Abstract Plasma wakefield accelerators use tabletop equipment to produce relativistic femtosecond electron bunches. Optical and X-ray diagnostics have established that their charge concentrates within a micrometre-sized volume, but its sub-micrometre internal distribution, which critically influences gain in free-electron lasers or particle yield in colliders, has proven elusive to characterize. Here, by simultaneously imaging different wavelengths of coherent optical transition radiation that a laser-wakefield-accelerated electron bunch generates when exiting a metal foil, we reveal the structure of the coherently radiating component of bunch charge. The key features of the images are shown to uniquely correlate with how plasma electrons injected into the wake: by a plasma-density discontinuity, by ionizing high- Z gas-target dopants or by uncontrolled laser–plasma dynamics. With additional input from the electron spectra, spatially averaged coherent optical transition radiation spectra and particle-in-cell simulations, we reconstruct coherent three-dimensional charge structures. The results demonstrate an essential metrology for next-generation compact X-ray free-electron lasers driven by plasma-based accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

PyPhase – a Python package for X-ray phase imaging

X-ray propagation-based imaging techniques are well established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase-retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and diffusion of such techniques difficult. Here, PyPhase, a free and open-source package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license, is presented. PyPhase implements some of the most popular phase-retrieval algorithms in a highly modular framework supporting its deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straightforward. Its capabilities and simplicity are presented by application to data acquired at the synchrotron source MAX IV (Lund, Sweden).

36 MATERIALS SCIENCE↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2023

Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network

The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm 3 ) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Radiation Mapping for an Unmanned Aerial Vehicle: Development and Simulated Testing of Algorithms for Source Mapping and Navigation Path Generation

Image reconstruction algorithms were developed for radiation source mapping and used for generating the search path of a moving radiation detector, such as one onboard an unmanned aerial vehicle. Simulations consisted of first assuming radioactive sources of varying complexity and estimating the radiation fields that would then be produced by that source distribution. Next, the "measurements" that would result from a pair of adjacent spatial locations were computed. A crude estimate of the source distribution likely to have produced such "measurements" was reconstructed based upon the limited measurements. Location of the next "measurement" was then determined as halfway between the location of the estimated source and the current "measurement." With each additional sample, improved source distribution reconstructions were made and used to inform the immediate direction of detector motion. Source reconstruction or mapping was formulated as an inverse problem solved with either maximum a posteriori or least squares (LS) regression deconvolution methods. Different amounts of noise were added to the simulated "measurements," allowing evaluation of the methods' performances as functions of signal-to-noise ratio of the measured map. As expected, methods that promote sparsity were better suited in reconstructing point sources. Reliable prior information of the source distribution also improved the reconstruction results, especially with distributed sources. With a non-negative least square algorithm and the suggested paths it generated, location of sources was successfully estimated to an accuracy of 0.014 m within nine iterations in a single-source scenario and 12 iterations in a two-source scenario, given a 10% error on the integrated counts and a Poisson distribution of the noise associated with the measured counts.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Image Alignment and Flat-Field Correction of Film and Computed Radiography Images on the LLNL Flash Testbed

Computed radiography (CR) imaging plates and film are used in HEAF firing tanks and in the NDE group. The imaging plates allow for the creation of high-resolution digital images with flash X-ray (FXR). A typical treatment of flash X-ray radiographs is flat-field correction, where the image from an experiment is normalized by a “flat-field” or “bright-field” image. This flat-field image is taken in an identical configuration to the experimental image, but without the object or experiment in the field of view (FOV). This treatment reduces spatial effects from the FXR spot size and detector misalignment, as each pixel value in the corrected image represents a ratio of collected radiation with the object in FOV to the collected radiation without the object in FOV. One challenge in the creation of a corrected image is the misalignment of the CR plate or film pack between capturing the flat-field image and the experimental image. Fiducial structures can remedy this issue. Small (3.18mm) stainless steel ball bearings serve as good fiducial structures due to their small size and high radiographic contrast. Spheres are view-agnostic geometry, always presenting a circular cross section no matter the orientation. This process was developed for images from the flash X-ray testbed. The flash testbed was used to compare the X-ray transmission at different thicknesses of aluminum and copper step wedges. Each step wedge section is a rectangular shape, so evaluation is made much simpler if the rectangles are not rotated with respect to the image. This alignment process aligns the object and flat-field images together and leaves the rectangles of each step aligned with the image.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Iterative Reconstruction for Multimodal Neutron Tomography

Here, we describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials.

Hydrogen scatter↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
DOE OSTI2022

Apparatus, system, and method for radiation hardened plastic and flexible elastomer scintillator

A scintillating material that is a radiation hardened plastic and flexible elastomer is disclosed. The material is useful in a wide range of high energy particle environments and can be used to create detectors. Such detectors can be used in physics experiments or in medical treatment or imaging. The scintillator can be radiation hardened so as to allow for an extended lifetime over other materials.

Akgun, Ugur↗
DOE OSTI2021

Early MRI Blood Volume Changes in Constrictor Muscles Correlate With Postradiation Dysphagia

Predicting individual patient sensitivity to radiation therapy (RT) for tumor control or normal tissue toxicity is necessary to individualize treatment planning. In head and neck cancer, radiation doses are limited by many nearby critical structures, including structures involved in swallowing. Previous efforts showed that imaging parameters correlate with RT dose; here, we investigate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) blood volume (BV) changes in predicting dysphagia.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗