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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cesium Batch Contact Equilibrium Testing of Crystalline Silicotitanate (CST) Sieve Cuts in SRS Average Simulant and Examination of CST Samples Aged in Caustic and Simulant in Support of Tank Closure Cesium Removal 1A (TCCR-1A)

Batch contact testing to determine cesium equilibrium loading on Crystalline Silicotitanate ion exchange media in Savannah River Site Average Waste Simulant at 25 °C indicated that smaller diameter particles isolated by sieving pretreated CST media may load slightly higher (<10%) amounts of cesium, though the differences are within analytical uncertainty. In addition, ion exchange media sub-samples stored in 2-4 M NaOH and caustic simulant solutions for ~2.5 years were examined by optical microscopy and the 3 M NaOH sample was also analyzed to determine whether changes in the particle size distribution occurred during storage. No visual indications of particle attrition or agglomeration were observed for any sample. Particle size analysis indicated that a slight decrease occurred in the average particle diameter following contact with 3 M NaOH (541 µm average diameter versus 566 µm for the pretreated CST prior to contact). A small increase (from 0 to <0.5 wt. %) in the number of particles ranging from 271 and 322 µm was also observed for the CST sample contacted with 3 M NaOH relative to a sample of the original pretreated material. However, this small change could be due to sub-sampling differences or analytical uncertainty. It does not appear that small particles are formed to a significant degree during CST caustic contact or that small particles which do form (presumably from attrition of larger particles during pretreatment) load significantly more cesium than the bulk material. Minimal other negative consequences were observed associated with CST extended caustic or simulant contact, except for the tendency for more concentrated salt solutions to form some salt crystals which deposit on the media over time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

SU-E-J-85: The Effect of Different Imaging Modalities On the Delineation of the True Spinal Cord for Spinal Stereotactic Body Radiation Therapy

Purpose: SBRT allows the delivery of high dose radiation treatments to localized tumors while minimizing dose to surrounding tissues. Due to the large doses delivered, accurate contouring of organs at risk is essential. In this study, differences between the true spinal cord as seen using MRI and CT myelogram (CTM) have been assessed in patients with spinal metastases treated using SBRT. Methods: Ten patients were identified that have both a CTM and a MRI. Using rigid registration tools, the MRI was fused to the CTM. The thecal sac and true cord were contoured using each imaging modality. Images were exported and analyzed for similarity by computing the Dice similarity coefficient and the modified Hausdorff distance (greatest distance from a point in one set to the closest point in the other set). Results: The Dice coefficient was calculated for the thecal sac (0.81 ±0.06) and true cord (0.63 ±0.13). These two measures are correlated; however, some points show a low true cord overlap despite a high overlap for the thecal sac. The Hausdorff distance for structure comparisons was also calculated. For thecal sac structures, the average value, 1.6mm (±1.1), indicates good overlap. For true cord comparison, the average value, 0.3mm (±0.16), indicates very good overlap. The minimum Hausdorff distance between the true cord and thecal sac was on average 1.6mm (±0.9) Conclusion: The true cord position as seen in MRI and CTM is fairly constant, although care should be taken as large differences can be seen in individual patients. Avoidning the true cord in spine SBRT is critical, so the ability to visualize the true cord before performing SBRT to the vertebrae is essential. Here, CT myelogram appears an excellent, robust option, that can be obtained the day of treatment planning and is unaffected by uncertainties in image fusion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Predicted Radiation Output from Several Kilograms of Plutonium Oxide

Neutron and gamma signatures from 3.44 kg of plutonium oxide were predicted with MCNP6 in anticipation of a passive diagnostic measurement campaign. Calculated radiation fields are visualized through use of “FMESH” type tallies. This work is effectively an extension of previous efforts and application of MISC for the photon source term is the main advancement. Large sources of modeling uncertainty include physical can dimensions and thus internal volume and density estimates. Material age is unknown and will also affect the passive gamma signature. The measurement campaign will provide information for model refinement and subsequent simulations. The authors have requested passive data collection on the pair of cans with the largest and smallest mass and such data will be valuable for benchmarking purposes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗
DOE OSTI2021

Orbits and Masses of Binaries from Speckle Interferometry at SOAR

We present results from Speckle inteferometric observations of 15 visual binaries and one double-line spectroscopic binary, carried out with the HRCam Speckle camera of the SOAR 4.1 m telescope. These systems were observed as a part of an on-going survey to characterize the binary population in the solar vicinity, out to a distance of 250 pc. We obtained orbital elements and mass sums for our sample of visual binaries. The orbits were computed using a Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate their uncertainty. Their periods cover a range from 5 yr to more than 500 yr; and their spectral types go from early A to mid M, implying total system masses from slightly more than 4M{sub ⊙} down to 0.2M {sub ⊙}. They are located at distances between approximately 12 and 200 pc, mostly at low Galactic latitude. For the double-line spectroscopic binary YSC8, we present the first combined astrometric/radial-velocity orbit resulting from a self-consistent fit, leading to individual component masses of 0.897 ± 0.027 M {sub ⊙} and 0.857 ± 0.026 M {sub ⊙}; and an orbital parallax of 26.61 ± 0.29 mas, which compares very well with the Gaia DR2 trigonometric parallax (26.55 ± 0.27 mas). In combination with published photometry and trigonometric parallaxes, we place our objects on an H-R diagram and discuss their evolutionary status. We also present a thorough analysis of the precision and consistency of the photometry available for them.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Exploratory Nuclear Reactor Safety Analysis and Visualization via Integrated Topological and Geometric Techniques

A recent trend in the nuclear power engineering field is the implementation of heavily computational and time consuming algorithms and codes for both design and safety analysis. In particular, the new generation of system analysis codes aim to embrace several phenomena such as thermo-hydraulic, structural behavior, and system dynamics, as well as uncertainty quantification and sensitivity analyses. The use of dynamic probabilistic risk assessment (PRA) methodologies allows a systematic approach to uncertainty quantification. Dynamic methodologies in PRA account for possible coupling between triggered or stochastic events through explicit consideration of the time element in system evolution, often through the use of dynamic system models (simulators). They are usually needed when the system has more than one failure mode, control loops, and/or hardware/process/software/human interaction. Dynamic methodologies are also capable of modeling the consequences of epistemic and aleatory uncertainties. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. The major challenges in using MC and DET methodologies (as well as other dynamic methodologies) are the heavier computational and memory requirements compared to the classical ET analysis. This is due to the fact that each branch generated can contain time evolutions of a large number of variables (about 50,000 data channels are typically present in RELAP) and a large number of scenarios can be generated from a single initiating event (possibly on the order of hundreds or even thousands). Such large amounts of information are usually very difficult to organize in order to identify the main trends in scenario evolutions and the main risk contributors for each initiating event. This report aims to improve Dynamic PRA methodologies by tackling the two challenges mentioned above using: 1) adaptive sampling techniques to reduce computational cost of the analysis and 2) topology-based methodologies to interactively visualize multidimensional data and extract risk-informed insights. Regarding item 1) we employ learning algorithms that aim to infer/predict simulation outcome and decide the coordinate in the input space of the next sample that maximize the amount of information that can be gained from it. Such methodologies can be used to both explore and exploit the input space. The later one is especially used for safety analysis scopes to focus samples along the limit surface, i.e. the boundaries in the input space between system failure and system success. Regarding item 2) we present a software tool that is designed to analyze multi-dimensional data. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

MTUQ: a framework for estimating moment tensors, point forces, and their uncertainties

SUMMARY We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1-D and 3-D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface, accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: (1) a 2009 intraslab earthquake near Anchorage, Alaska; (2) an episode of the 2021 Barry Arm landslide in Alaska; and (3) the 2017 Democratic People’s Republic of Korea underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multimodal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

58 GEOSCIENCES↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2021

Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results.

East Fork Poplar Creek↗
DOE OSTI2017

Visual Exploration of Semantic Relationships in Neural Word Embeddings

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration

Purpose: Validating the usage of deformable image registration (DIR) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. The authors present a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy. Methods: The biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The voxels inside a given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as mass–spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifacts that may be caused by the biomechanical deformation process. Accuracy and stability of the model response were validated using ground-truth simulations representing soft tissue behavior under local and global deformations. Numerical accuracy of the HN deformations was analyzed by applying nonrigid skeletal transformations acquired from interfraction kVCT images to the model’s skeletal structures and comparing the subsequent soft tissue deformations of the model with the clinical anatomy. Results: The GPU based framework enabled the model deformation to be performed at 60 frames/s, facilitating simulations of posture changes and physiological regressions at interactive speeds. The soft tissue response was accurate with a R{sup 2} value of >0.98 when compared to ground-truth global and local force deformation analysis. The deformation of the HN anatomy by the model agreed with the clinically observed deformations with an average correlation coefficient of 0.956. For a clinically relevant range of posture and physiological changes, the model deformations stabilized with an uncertainty of less than 0.01 mm. Conclusions: Documenting dose delivery for HN radiotherapy is essential accounting for posture and physiological changes. The biomechanical model discussed in this paper was able to deform in real-time, allowing interactive simulations and visualization of such changes. The model would allow patient specific validations of the DIR method and has the potential to be a significant aid in adaptive radiotherapy techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Semi-inclusive Neutral Current Neutral Pion Production Selection at the NOvA (NuMI Off-axis Electron Neutrino Appearance) Near Detector Using Prong Level Convolutional Neural Networks

The NONA neutrino experiment based in Fermilab is designed to measure ν µ →ν e neutrino oscillations. This experiment will give us insight into the properties of massive neutrinos. Neutral current (NC) ν µ,e neutral pion production events can mimic the ν µ →ν e oscillation signal and therefore are an important background for NONA to understand. Neutral pions decay into two photons which can fake a single electron shower (νe appearance signal) in two ways: either the 2 photons can merge together or one of them may escape detection. In order to constrain this background, NONA utilizes the Near Detector to measure neutral current π 0 neutrino interactions. In this analysis, neutrino-Nucleus (ν µ →N) NC π 0 interactions with total pion energy greater then 0.3 GeV are studied by selecting two prong events with two final state photons as determined by prong based Convolutional Visual Networks (CVN). The analysis is performed on 3.54x10 21 Protons On Target (POT) of NONA Near Detector simulated data and compared to 8.09x10 20 POT of data. Optimization of the selection based on fractional cross-section uncertainty and an initial energy resolution study of the final sample are presented. The final 2 prong selection using prong based CVN gave a purity of 74%, selection efficiency of ~ 1.8%, and an expected (NC) ν µ,e neutral pion cross section of ~ 15.2%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian based informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Comparing Submillimeter Polarized Emission with Near-infrared Polarization of Background Stars for the Vela C Molecular Cloud

We present a large-scale combination of near-infrared (near-IR) interstellar polarization data from background starlight with polarized emission data at submillimeter wavelengths for the Vela C molecular cloud. The near-IR data consist of more than 6700 detections probing a range of visual extinctions between 2 and 20 mag in and around the cloud. The submillimeter data were collected in Antarctica by the Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry. This is the first direct combination of near-IR and submillimeter polarization data for a molecular cloud aimed at measuring the “polarization efficiency ratio” (R{sub eff}), a quantity that is expected to depend only on grain-intrinsic physical properties. It is defined as p{sub 500}/(p{sub I}/τ{sub V}), where p {sub 500} and p {sub I} are polarization fractions at 500 μm and the I band, respectively, and τ{sub V} is the optical depth. To ensure that the same column density of material is producing both polarization from emission and from extinction, we conducted a careful selection of near-background stars using 2MASS, Herschel, and Planck data. This selection excludes objects contaminated by the Galactic diffuse background material as well as objects located in the foreground. Accounting for statistical and systematic uncertainties, we estimate an average R{sub eff} value of 2.4 ± 0.8, which can be used to test the predictions of dust grain models designed for molecular clouds when such predictions become available. The ratio R{sub eff} appears to be relatively flat as a function of the cloud depth for the range of visual extinctions probed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Human Factors and Modeling Methods in the Development of Control Room Modernization Concepts

nuclear power plants. Although the nuclear industry has made steady improvement in outage optimization, each day of a refueling outage still represents an opportunity to save millions of dollars and each day an outage extends past its planned end date represents millions of dollars that may have been spent unnecessarily. Reducing planned outage duration or preventing outage extensions requires careful management of the outage schedule as well as constant oversight and monitoring of work completion during the outage execution. During a typical outage, there are typically more than 10,000 activities on the schedule that, if not managed efficiently, may cause expensive outage delays. Management of outages currently relies largely on paper-based resources and general-purpose office software. A typical tool currently used to monitor work performance is a burn-down curve, where total remaining activities are plotted against the baseline schedule to track bulk work completion progress. While these tools are useful, there is still considerable uncertainty during a typical outage that bulk work progress is adequate and therefore a lot of management time is spent analyzing the situation on a daily basis. This paper describes recent advances made in developing a framework for the design of visual outage information presentation, as well as an overview of the scientific principles that informed the development of the visualizations. To test the utility of advanced visual outage information presentation, an outage management dashboard software application was created as part of the Department of Energy’s Advanced Outage Control Center project. This dashboard is intended to present all the critical information an outage manager would need to understand the current status of a refueling outage. The dashboard presents the critical path, bulk work performance, key performance indicators, outage milestones and metrics relating current performance to historical performance. Additionally, the dashboard includes data analysis tools to allow outage managers to drill down into the underlying data to understand the drivers of the indicators.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

INTERSTELLAR ENVIRONMENTS AND DUST PROPERTIES TOWARD CYGNUS OB2 NO. 12: A REASSESSMENT

The B-type hypergiant Cygnus OB2 no. 12 is a popular target for studies of interstellar phenomena at visible-infrared wavelengths because of its exceptional brightness for a star dimmed by some 10 mag of visual extinction. A lack of detectable ice absorption has led investigators to regard the line of sight as a standard for studies of the “diffuse” interstellar medium (ISM), an assumption challenged both by observations of molecular gas toward the star and by uncertainties concerning the degree to which such a luminous object may affect its local environment. This paper presents a reassessment of the nature of the material responsible for extinction toward Cyg OB2 no. 12. The excess relative to other cluster members appears to occur in translucent clumps within an extensive network of clouds in the region. Attenuation of the ambient radiation field is sufficient in the cores of the clumps to support the presence of gas-phase molecules, but not to sustain detectable ice formation. In general, the optical properties of dust in the clumps are closely similar to those observed in typical diffuse interstellar material, with the notable exception of an unusually low value for the wavelength of maximum polarization. The implied enhancement of polarization by small grains is attributed to increased alignment efficiency in an enhanced magnetic field. This caveat apart, the results of the current paper provide reassurance that Cyg OB2 no. 12 is, indeed, an appropriate choice for studies that target diffuse and translucent phases of the ISM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-J-07: A Functional MR Protocol for the Pancreatic Tumor Delineation

Purpose: Pancreatic cancer is one of the cancers with the poorest survival prognosis. At the time of diagnosis most of pancreatic cancers are unresectable and those patients can be treated by radiotherapy. Radiotherapy for pancreatic cancer is limited due to uncertainties in CT-based delineations. MRI provides an excellent soft tissue contrast. Here, an MR protocol is developed to improve delineations for radiotherapy treatment of pancreatic cancer. In a later stage this protocol can also be used for on-line visualization of the pancreas during MRI guided treatments. Methods: Nine pancreatic cancer patients were included. The MR protocol included T2 weighted(T2w), T1 weighted(T1w), diffusion weighted(DWI) and dynamic contrast enhanced(DCE) techniques. The tumor was delineated on T2w and T1w MRI by an experienced radiation oncologist. Healthy pancreas or pancreatitis (assigned by the oncologist based on T2w) areas were also delineated. Apparent diffusion coefficient(ADC), and area under the curve(AUC)/time to peak(TTP) maps were obtained from DWI and DCE scans, respectively. Results: A clear demarcation of tumor area was visible on b800 DWI images in 5 patients. ADC maps of those patients characterized tumor as an area with restricted water diffusion. Tumor delineations based on solely DCE were possible in 7 patients. In 6 of those patients AUC maps demonstrated tumor heterogeneity: a hypointense area with a hyperintense ring. TTP values clearly discriminated the tumor and the healthy pancreas but could not distinguish tumor and the pancreatitis accurately. Conclusion: MR imaging results in a more pronounced tumor contrast than contrast enhanced CT. The addition of quantitative, functional MRI provides valuable, additional information to the radiation oncologist on the spatial tumor extent by discriminating tumor from the healthy pancreas(TTP, DWI) and characterizing the tumor(ADC). Our findings indicate that tumor delineation in pancreatic cancer can greatly benefit from the addition of MRI and especially functional MR techniques.

60 APPLIED LIFE SCIENCES↗