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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Advanced Models for Nondestructive Evaluation of Aging Nuclear Power Plant Cables

Background: Nuclear power plant (NPP) cables must function correctly for proper power supply and control of the nuclear reactor. This is especially important for public and environmental safety at critical times such as during reactor shut-down following loss of coolant. Over extended periods of service, the cable insulation and jacket polymer materials suffer from degradation due to exposure to environmental stressors such as heat and ionizing radiation. The focus of this project has been the development of detailed descriptions of the way in which insulation polymers degrade, from a materials science perspective, and the development of new electrical techniques to monitor cable polymer condition nondestructively. Objectives: The objectives of this project have been to i) perform detailed characterization experiments upon cable insulation polymers that have experienced thermal and radiation exposure mimicking that received during NPP operation, including the exploration of novel cable nondestructive evaluation methods; capacitive, THz and infrared methods; ii) identify the most sensitive indicators of aging in these materials, and iii) develop advanced, validated models describing microstructural and chemical changes that are observed in cable insulation polymers due to thermal and radiation exposure. Methods: Accelerated aging was performed on the two most common United States NPP cable insulation polymers (cross-linked polyethylene (XLPE) and ethylene propylene rubber (EPR)) under various conditions of elevated temperature and gamma radiation dose. Aging mechanisms were elucidated through extensive thermal, mechanical and microscopic characterization of the aged samples, and materials modeling. Dielectric and IR spectra were measured on pristine and aged samples. Data was analyzed statistically, via multiple linear regression, partial least squares and probabilistic neural network approaches. Impact: Extant cable monitoring techniques do not deal successfully with all polymer types. This research has laid a foundation upon which new electrical nondestructive test methods for cable polymer condition monitoring may be developed, hence launching the development of new test methods for cable insulation monitoring based on measuring electrical rather than mechanical parameters. Collaboration: This project has benefitted from collaboration between experts in materials science and engineering, materials modeling, electrical engineering, and nondestructive evaluation. The majority of the characterization measurements were carried out at Iowa State University. Materials modeling was carried out primarily at Washington State University. Irradiation of samples took place at Pacific Northwest National Laboratory (PNNL), where a student intern from Iowa State was hosted for one summer, setting up the aging experiments. A research student from the University of Bologna, Italy, joined the project for their thesis research at Iowa State University with samples provided by PNNL. The project benefitted from regular involvement of senior engineers in US-based industry partners. Project investigators presented findings at various national and international conferences and workshops.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

ORSphere: Critical, Bare, HEU(93.2)-Metal Sphere

In the early 1970’s Dr. John T. Mihalczo (team leader), J.J. Lynn, and J.R. Taylor performed experiments at the Oak Ridge Critical Experiments Facility (ORCEF) with highly enriched uranium (HEU) metal (called Oak Ridge Alloy or ORALLOY) in an attempt to recreate GODIVA I results with greater accuracy than those performed at Los Alamos National Laboratory in the 1950s (HEU-MET-FAST-001). The purpose of the Oak Ridge ORALLOY Sphere (ORSphere) experiments was to estimate the unreflected and unmoderated critical mass of an idealized sphere of uranium metal corrected to a density, purity, and enrichment such that it could be compared with the GODIVA I experiments. “The very accurate description of this sphere, as assembled, establishes it as an ideal benchmark for calculational methods and cross-section data files.” (Reference 1) While performing the ORSphere experiments care was taken to accurately document component dimensions (±0. 0001 in. for non-spherical parts), masses (±0.01 g), and material data The experiment was also set up to minimize the amount of structural material in the sphere proximity. A three part sphere was initially assembled with an average radius of 3.4665 in. and was then machined down to an average radius of 3.4420 in. (3.4425 in. nominal). These two spherical configurations were evaluated and judged to be acceptable benchmark experiments; however, the two experiments are highly correlated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

A scintillator-based approach to monitor secondary neutron production during proton therapy

Here, the primary objective of this work is to measure the secondary neutron field produced by an uncollimated proton pencil beam impinging on different tissue-equivalent phantom materials using organic scintillation detectors. Additionally, the Monte Carlo code mcnpx-PoliMi was used to simulate the detector response for comparison to the measured data. Comparison of the measured and simulated data will validate this approach for monitoring secondary neutron dose during proton therapy. Proton beams of 155- and 200-MeV were used to irradiate a variety of phantom materials and secondary particles were detected using organic liquid scintillators. These detectors are sensitive to fast neutrons and gamma rays: pulse shape discrimination was used to classify each detected pulse as either a neutron or a gamma ray. The mcnpx-PoliMi code was used to simulate the secondary neutron field produced during proton irradiation of the same tissue-equivalent phantom materials. As a result, an experiment was performed at the Loma Linda University Medical Center proton therapy research beam line and corresponding models were created using the mcnpx-PoliMi code. The authors’ analysis showed agreement between the simulations and the measurements. The simulated detector response can be used to validate the simulations of neutron and gamma doses on a particular beam line with or without a phantom. In conclusion, the authors have demonstrated a method of monitoring the neutron component of the secondary radiation field produced by therapeutic protons. The method relies on direct detection of secondary neutrons and gamma rays using organic scintillation detectors. These detectors are sensitive over the full range of biologically relevant neutron energies above 0.5 MeV and allow effective discrimination between neutron and photon dose. Because the detector system is portable, the described system could be used in the future to evaluate secondary neutron and gamma doses on various clinical beam lines for commissioning and prospective data collection in pediatric patients treated with proton therapy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Plutonium Age Dating Interlaboratory Comparison Overview - Project Design, Sample Preparation, and Results [Slides]

Project Design – How do you validate measured Pu model age? There are no certified reference materials (CRMs) that are certified for Pu model age or purification dates. There are no Am isotopic CRMs - IRMM 0243 243 Am spike has 241 Am impurity that can be used; Community working toward "consensus ages." This project executed and evaluated results from a Pu age dating interlaboratory comparison with six IAEA NWAL participant laboratories. It also ran the first quality control evaluation of single-picogram to sub-picogram scale interlaboratory 241 Am assay measurements from cotton swipe matrices. Results are promising, with a majority of 241 Am/ 241 Pu model age results agree to within 1 to 4 years. The calculated 241 Am assay results show interlaboratory variability, expanded percent uncertainties on calculated consensus 241 Am concentrations were: PuAge-S1 = 3.1 %, PuAge-S2 = 23 %, and PuAge-S3 = 26 %. Data can be used to investigate laboratory methods and improve precision and accuracy of 241 Pu and 241 Am assay for future campaigns.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Achieving groundwater monitoring optimization at SRS: a core team process based on rigorous technical assessment - 15273

The optimization of groundwater monitoring at Savannah River Site (SRS) was achieved through a core team process based on the application of a comprehensive, technical evaluation of individual regulated groundwater units. Groundwater monitoring at SRS is required at dozens of waste sites and includes about 4,000 samples annually. The expected longevity of groundwater contamination and associated groundwater monitoring and reporting constitutes a significant long-term cost to the environmental management budget. The core team, consisting of representatives from the Department of Energy (DOE); U.S. Environmental Protection Agency (USEPA); and the South Carolina Department of Health and Environmental Control (SCDHEC), recognized that monitoring needs for individual plumes evolve as remediation work progresses. The technical evaluation plan was generally based on the availability of many years of monitoring data. In addition, it was recognized that the well network, sampling frequency, and analytical suite had been the same for years and warranted evaluation to determine if the existing monitoring was meeting the current objectives. In order to systematically evaluate monitoring at eighteen different groundwater units, SRS developed a decision-logic analysis using flow sheets to address five areas of optimization identified with core team agreement, including: (1) current monitoring vs regulatory requirements, (2) spatial optimization, (3) temporal assessment, (4) analyte assessment, and (5) reporting assessment. The evaluation resulted in SRS proposing changes in the spatial, temporal, analyte, and reporting aspects of groundwater monitoring for fifteen of the eighteen individual groundwater units evaluated. Overall, these changes have resulted in annual savings of $370 K to date, with an additional $200 K in annual savings pending. Early and frequent core team deliberations helped ensure the success of this optimization effort. The technical evaluation was conducted after receiving core team buy-in for the goals and methods of the project. Technical evaluations for each unit were proposed to the regulators in unit-specific regulatory submittals. The technical justifications and core team decisions were all documented and implemented in accordance within the framework of the SRS Federal Facility Agreement or Resource Conservation and Recovery Act (RCRA) Permit process. This optimization approach can be expected to be highly successful for sites with rich historical data sets and where the requirements in regulatory monitoring plans can be negotiated. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Comparing Thick Target Neutron Yield Calculations for Alphas on F-19 using MCNP6 1.1 Beta with TENDL 2012 Libraries

Because of the heritage of MCNPX, the first production release of MCNP6{sup TM} used physics models, instead of ACE (A Compact Evaluated Nuclear Data File) formatted libraries, for simulating the transport of alpha particles at all energies, as MCNPX could not yet read ACE formatted light ion libraries. For non-Coulombic interactions, the physics models have suggested minimums of ∼ 20-200 MeV. The first production release of MCNP6, MCNP6{sup TM} 1.0, was still capable of transporting alphas to low energies; however, the physics models resulted in poor prediction of inelastic reactions at energies below the suggested minimums. This poorer prediction at low energies resulted in orders of magnitude lower neutron yields for events resulting from light ion interactions at low energies. For example, F-19(α,n)Na-22 from U-234 decay in UF{sub 6} produced poor neutron production results. Modeling these types of interactions correctly is vital for signal analysis for nonproliferation safeguards and nuclear security as well as for correctly characterizing source terms for radiation protection and shielding calculations. The TALYS computer code was developed to simulate nuclear reactions involving neutrons, photons, protons, deuterons, tritons, alphas and He-3 particles, in the 1 keV - 200 MeV energy range. TENDL is a nuclear data library, which provides the output of the TALYS nuclear model code system for direct use in applications (i.e. ACE formatted cross sections). Since 2008, TENDL had publicly provided ACE formatted libraries for alpha transport; however, not until the release of MCNP6{sup TM} 1.1 beta, had MCNP6 publicly possessed the capability to read such libraries. At the Radiation Protection and Shielding Division topical meeting in 2014, initial testing of MCNP6 1.1 beta showed that MCNP6 using TENDL 2012 resulted in poor prediction when compared to the compilation of experiment data provided in R. Heaton et al. Further comparisons for PuBe sources, using the MCNP6 1.1 beta spontaneous decay alpha feature coupled with alpha transport, were also given in the winter American Nuclear Society meeting in 2014, where the results showed large discrepancies with measured data. For F-19(α,n)Na-22 interactions, the R. Heaton et al. paper is a conglomeration of several important references; and the data provided by R. Heaton et al. used a numerical fit of the excitation function provided by Norman et al.. Norman et al. has recently published their (α,n) thick target yield data for PbF{sub 2} in tabular form, backing out the thick target yields of F-19(α,n)Na-22 and computing F{sub 2} and UF{sub 6} thick target neutron yields (as supplements to the PbF{sub 2} data). Here we compared measured thick target yields of E. B. Norman et al. to MCNP6 1.1 beta using TENDL 2012 libraries. Deterministically backing out the thick target yield from measured values involves using the stopping power. MCNP uses either the SPAR code or an interpolation of the Spar code and Bethe-Boch formula for computing stopping powers. As a result one may believe that these separate methods will arrive at separate thick target yields independent of the production cross section. In our MCNP calculations, we simulated the alpha until the alpha completely stopped in the medium. The energy loss in the condensed history algorithm is not continuous but discrete and related to the stopping powers. Because the neutron production yield is energy dependent, the energy loss dictates the emission of secondary neutrons; and therefore the difference in stopping powers used may have some slight effect on the yield difference. For this study, we chose to use the default condensed history algorithm setup, while future studies should address tightening steps in the condensed history algorithm to prove whether or not choice of stopping power actually matters. The fact that all 5 evaluated isotopic compositions resulted in different thick target yield percent differences does warrant this type of investigation. A typical user may not be only interested in integral emission, taking 10 s to 100 s of processor hours to simulate, but rather a timely generation of the spectra. Such an application would require biasing the production of secondaries from alpha sources such that millions of secondaries could be produced, emulating a converged energy dependent spectra. Neutron production biasing from alpha sources does not currently exist; however, developing a biasing algorithm for neutron production from alpha sources should be investigated in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

The D and D of Nuclear Facilities: a Systemic Approach for Project Organization and Monitoring - 18099

The Decontamination and Decommissioning (D and D) of Licensed Nuclear facilities takes place under circumstances which make it a very complex field. On the one hand, more and more facilities of widely varying types are concerned, and many D and D projects are currently running or planned. A nuclear facility's life cycle is often very long and the building may be modified or impacted by incidents during its operation. These changes need to be known and taken into account for the D and D plans, in order to ensure safety at all times. In addition, the numerous stakeholders involved in a project express needs which impose many requirements. These encompass various points of view, and data from different detail levels must be managed to satisfactorily meet them. On the other hand, legacy data (including records, plans etc.) is a recurring issue for D and D projects. The quality and availability of relevant data regarding the different phases of the nuclear facility lifetime may be insufficient, for instance due to the lack of traceability practices in the past. The uncertainty level is not always specified and explained, old documents can be damaged and are not necessarily digitized, etc. It is therefore particularly important to define the relevant type of raw data to be collected and traced for D and D projects as early as possible. In this context, CEA (Commissariat a l'energie Atomique et aux energies Alternatives) has been analyzing past D and D projects for many years. The goal is to facilitate reproducibility by generalizing best practices and promoting efficient organizational rules and technical solutions. A method is currently being studied to help project managers to prepare specifications for future D and D operations and improve both the efficiency and the relevance of the decommissioning strategy throughout each project, depending on the expected quality of its end-state. This method first consists in formalizing and specifying the entire set of requirements to be taken into consideration, e.g. technical, operational, logistical, regulatory, financial, organizational, safety and security, human, ethical, or, naturally, environmental aspects. Second, based on these requirements, it will enable the project team to structure, check, and then to demonstrate the coherence and feasibility of the project from both the technological and organizational points of view. Lastly, the method will enable the evaluation of the various possible solutions for the management of dismantling products, for instance, regarding the waste outlets: direct disposal or interim storage. To conclude, the method should permit a constant re-evaluation of the D and D strategy depending on the possible evolution of the stakeholders' requirements, and thus it should optimize the monitoring of D and D projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Method for estimating the density of high-level nuclear waste glass

A database of over 1100 silicate glass compositions and densities was compiled and used to evaluate the efficacy of an algorithm for estimating the density of silicate glass compositions. We sought to develop a parsimonious algorithm based on the additivity of partial molar volumes of individual oxide components weighted by their mole fraction in a glass composition. Bound molar volumes were used for oxides in which the density of the oxide bound in a glass matrix was previously determined. The bound molar volumes were known for oxides covering 97.5 mole percent of the database compositional space. The measured glass densities were plotted against the estimated glass densities and a linear regression yielded an R 2 adj. = 0.95 and a slope and intercept of approximately one and zero, respectively. This regression suggests that glass densities estimated by the algorithm, within analysis uncertainty, are equal to the measured densities of the glasses. In addition to the development of the density estimation, we corroborated many of the referenced bound molar volume data used in the parameterization of the estimation algorithm via linear regression of the individual partial molar volumes versus the inverse measured densities (specific volumes) of the glasses in the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Depletion capabilities in the OpenMC Monte Carlo particle transport code

A depletion solver has been implemented in OpenMC and is described herein. The depletion solver is implemented in Python and interfaces with OpenMC’s transport solver through a C++ application programming interface, which enables an in-memory transport-depletion coupling. Multiple integration methods for advancing in time have been implemented and exhibit tradeoffs in cost, accuracy, and memory use. For all time integration methods, evaluation of the matrix exponential is performed by using the incomplete partial fraction form of the Chebyshev rational approximation method. Simulations of a pressurized water reactor (PWR) pincell and a sodium-cooled fast reactor (SFR) assembly were carried out with OpenMC and Serpent. For both problems, the use of a high-fidelity depletion chain results in predictions of k eff that agree within 20–30 pcm between OpenMC and Serpent. Predicted actinide concentrations were found to agree to a fraction of a percent, and most fission product concentrations were found to agree within 1%. Here, the few cases where larger differences were observed can be attributed either to differences in how the energy dependence of fission product yields is handled or deficiencies in the nuclear data used. OpenMC simulations of the PWR and SFR problems using a simplified 228-nuclide depletion chain demonstrate that it achieves accuracy close to that of the full, high-fidelity depletion chain with respect to the studied responses.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. The final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Automatic recognition system for document digitization in nuclear power plants

With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are required to accurately model and predict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. Here we evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focal plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Use of Percutaneous Aspiration Thrombectomy vs. Anticoagulation Therapy to Treat Acute Iliofemoral Venous Thrombosis: 1-year Follow-up Results of a Randomised, Clinical Trial

PurposeThe purpose of this study was to compare the efficacy of percutaneous aspiration thrombectomy (PAT) followed by standard anticoagulant therapy, with anticoagulation therapy alone, for the treatment of acute proximal lower extremity deep vein thrombosis.MethodsIn this randomised, prospective study, 42 patients with acute proximal iliofemoral deep vein thrombosis documented via Doppler ultrasound examination, were separated into an interventional treatment group (16 males, 5 females, average age 51 years) and a medical treatment group (13 males, 8 females, average age 59 years). In the interventional group, PAT with large-lumen 9-F diameter catheterisation was applied, after initiation of standard anticoagulant therapy. Balloon angioplasty (n 19) and stent implementation (n: 14) were used to treat patients with residual stenosis (>50 %) after PAT. Prophylactic IVC filters were placed in two patients. The thrombus clearance status of the venous system was evaluated by venography. In both the medical and interventional groups, venous patency rates and clinical symptom scores were evaluated at months 1, 3, and 12 after treatment.ResultsDeep venous systems became totally cleared of thrombi in 12 patients treated with PAT. The venous patency rates in month 12 were 57.1 and 4.76 % in the interventional and medical treatment groups, respectively. A statistically significant improvement was observed in clinical symptom scores of the interventional group (PAT) with or without stenting (4.23 ± 0.51 before treatment; 0.81 ± 0.92 at month 12) compared with the medical treatment group (4.00 ± 0.63 before treatment; 2.43 ± 0.67 at month 12). During follow-up, four patients in the medical treatment and one in the interventional group developed pulmonary embolisms.ConclusionsFor treatment of acute deep vein thrombosis, PAT with or without stenting is superior to anticoagulant therapy alone in terms of both ensuring venous patency and improving clinical symptoms. PAT is a safe, inexpensive, and easily performed method of endovascular treatment with a low rate of major complications. Our present findings and literature data suggest that PAT can be used as first-line treatment in proximal deep vein thrombosis patients, especially when thrombolytic treatment is contraindicated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

AN ESTIMATE OF SPENT NUCLEAR FUEL MECHANICAL LOADS IN THE GENERAL 30 CM PACKAGE DROP SCENARIO

The US Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is performing research to determine the mechanical loading conditions applied to spent nuclear fuel (SNF) during normal conditions of transport to inform mechanical tests of SNF and close an important knowledge gap related to the practical disposition of SNF in the US. Researchers at Pacific Northwest National Laboratory (PNNL) have completed an extensive finite element study to characterize and estimate the potential mechanical loads on SNF during a hypothetical 30 cm drop of an SNF transportation package. This modeling study is validated with test data collected by the SFWST program during a physical test campaign that included one-third scale package drop tests and full-scale single fuel assembly drop tests. The test campaign was led by Sandia National Laboratories (SNL) and included international collaboration with Equipos Nucleares S.A, S.M.E (ENSA) and Bundesanstalt für Materialforschung und -prüfung (BAM). The key contribution of the modeling study is to go beyond the limitations of the limited number of physical tests to estimate the impact response to variations in impact angle, initial gap conditions, fuel assembly design, burnup and other parameters that affect the mechanical loads. The methodology of this study included a classic parametric study to calculate the impact response of highly detailed fuel assemblies over many combinations of parameters. Models of a 17x17 pressurized water reactor fuel assembly and a generic 10x10 boiling water reactor fuel assembly were both used in this study to cover the major fuel assembly types in the US inventory. Over 2,000 impact responses were calculated. The results of the parametric study were evaluated using traditional methods and basic statistics. The results were also used to construct a damage model using multiple nonlinear regression techniques to predict the mechanical loads over the full range of all input parameters. The damage model was found to work very well for all impact angle cases where the cask came to rest on its side. It was concluded that end drop cases where the cask remained vertical (instead of tipping over onto its side) were not sufficiently characterized by the current set of parametric study cases to include in the damage model, but it was not a priority to fully investigate that range because the highest mechanical loads were observed in the broader range of side impact cases. This modeling work provides sufficient insight into the mechanical loads on SNF during a hypothetical 30 cm package drop that, when considered along with the physical test data collected by the SNL-led team, the SFSWT program can consider the knowledge gap closed.

Klymyshyn, Nicholas A.↗