Adjoint-Based Uncertainty Quantification for Radiation Transport Calculations with Uncertain Cross Section Data.
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We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.
The interactions of a He atom with a heavier atom are examined for 26 different elements, which are consecutive members selected from three rows (Li - Ne, Na - Ar, and K,Ca, Ga - Kr) and column 12 (Zn,Cd) of the periodic table. Interaction energies are determined wing high-quality ab initio calculations for the states of the molecule that would be formed from each pair of atoms in their ground states. Potential energies are tabulated for a broad range of Interatomic separation distances. The results show, for example, that the energy of an alkali interaction at small separations is nearly the same as that of a rare-gas interaction with the same electron configuration for the dosed shells. Furthermore, the repulsive-range parameter for this region is very short compared to its length for the repulsion dominated by the alkali-valence electron at large separations (beyond about 3-4 a(sub 0)). The potential energies in the region of the van der Waals minimum agree well with the most accurate results available. The ab initio energies are applied to calculate scattering cross sections and obtain the collision integrals that are needed to determine transport properties to second order. The theoretical values of Li-He total scattering cross sections and the rare-gas atom-He transport properties agree well (to within about 1%) with the corresponding measured data. Effective potential energies are constructed from the ab initio energies; the results have been shown to reproduce known transport data and can be readily applied to predict unknown transport properties for like-atom interactions.
Simulations of the Lawrence Livermore Pulsed Spheres with MAVRIC/Monaco and TRIPOLI-4{sup R} are presented. Both codes use ENDF/B-VII.1 cross section data, but TRIPOLI-4{sup R} also uses cross section data based on JEFF-3.1.1. The MAVRIC/Monaco simulations were performed with the SCALE 6.2.2 release while the TRIPOLI-4{sup R} simulations used an internal development version of the code. For the most part, the simulated and measured neutron counts agree within 30%. Simulated gamma flux data is compared, as well. There are no measured gamma data. The agreement between the simulated gamma flux spectra is often within 10% or less, but there some notable differences, especially when comparing simulations using ENDF/B-VII.1 and JEFF 3.1.1 based cross section data. The comparison of photon simulations suggests that improvements are needed in the cross-section data, cross section processing codes, and/or transport codes and gamma measurement data is needed for experimental validation. (authors)
In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.
A study of the transport and loss of ions in the earth's quiet time ring current, in which the standard radial diffusion model developed for the high-energy radiation belt particles is compared with the measurements of the lower-energy ring current ions, is presented. The data set provides ionic composition information in an energy range that includes the bulk of the ring current energy density, 1-300 keV/e. Protons are found to dominate the quiet time energy density at all altitudes, peaking near L of about 4 at 60 keV/cu cm, with much smaller contributions from O(+) (1-10 percent), He(+) (1-5 percent), and He(2+) (less than 1 percent). A minimization procedure is used to fit the amplitudes of the standard electric radial diffusion coefficient, yielding 5.8 x 10 exp -11 R(E-squared)/s. Fluctuation ionospheric electric fields are suggested as the source of the additional diffusion detected.
The inverse problem of parameterizing intermolecular potentials given macroscopic transport and thermodynamic data is addressed. Procedures are developed to create arbitrary-precision algorithms for transport collision integrals, using the Lennard-Jones (12–6) potential as an example. Interpolation formulas are produced that compute these collision integrals to four-digit accuracy over the reduced-temperature range 0.3≤T{sup ⁎}≤400, allowing very fast computation. Lennard-Jones parameters for neon, argon, and krypton are determined by simultaneously fitting the observed temperature dependences of their viscosities and second virial coefficients—one of the first times that a thermodynamic and a dynamic property have been used simultaneously for Lennard-Jones parameterization. In addition to matching viscosities and second virial coefficients within the bounds of experimental error, the determined Lennard-Jones parameters are also found to predict the thermal conductivity and self-diffusion coefficient accurately, supporting the value of the Lennard-Jones (12–6) potential for noble-gas transport-property correlation.
This viewgraph presentation reviews the experiences of the Mars Reconnaissance Orbiter (MRO). It includes an overview of the MRO mission; the challenges of data Management and Transport; a representation of the end-to-end flow for the telemetry data; a review of the data issues, and Coherent File Distribution Protocol (CFDP) uses and issues.
This report documents the evaluation of information and data available on the unclassified source term and radionuclide (RN) contamination for Central and Western Pahute Mesa (PM): Corrective Action Units (CAUs) 101 and 102. The Central and Western PM CAUs constitute two areas of the Nevada National Security Site (NNSS) used for underground nuclear testing (Figure 1-1). Eighty-five underground nuclear tests were conducted at the PM CAUs between 1965 and 1992 (NNSA/NFO, 2015b). Underground nuclear tests deposit radioactive elements in the subsurface, resulting in groundwater contamination in the vicinity of the underground test locations. At the PM CAUs, the testing resulted in more than 2.68 × 10 7 curies (Ci) of radioactivity (decay-corrected to September 30, 2012) remaining underground (Finnegan et al., 2016). Of the 85 underground nuclear tests conducted at the PM CAUs, three were shallow tests detonated within 200 meters (m) of the surface and referred to as the “near-surface tests”; the remaining 82 were detonated deeper than 200 m below ground surface (bgs) and are referred to as the “deep underground tests” in this document.
Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.
Thermodynamic data are essential for understanding and evaluating geochemical processes, as by speciation-solubility calculations, reaction-path modeling, or reactive transport simulation. These data are required to evaluate both equilibrium states and the kinetic approach to such states (via the affinity term or its equivalent in commonly used rate laws). These types of calculations and the data needed to carry them out are a central feature of geochemistry in many applications, including water-rock interactions in natural systems at low and high temperatures. Such calculations are also made in engineering studies, for example studies of interactions involving man-made materials such as metal alloys and concrete. They are used in a fairly broad spectrum of repository studies where interactions take place among water, rock, and man-made materials (e.g., usage on YMP and WIPP). Waste form degradation, engineered barrier system performance, and near-field and far-field transport typically incorporate some level of thermodynamic modeling, requiring the relevant supporting data.
Despite an abundance of online databases providing access to chemical data, there is increasing demand for highquality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency’s (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifer (ID) mapping and downloading of multiple data streams in several diferent formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identifcation of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA’s DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user trafc over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a fexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists.
The potential uses of remotely sensed data as applied to the transportation planning process are presented. By utilizing the remote sensing technology developed by the National Aeronautics and Space Administration in the various space programs, it is hoped that both the expense and errors inherent in the conventional data collection techniques can be avoided. Additional bonuses derived from the use of remotely sensed data are those of the permanent record nature of the data and the traffic engineering data simultaneously made available. The major mathematical modeling phases and the role remotely sensed data might play in replacing conventionally collected data are discussed. Typical surveys undertaken in the overall planning process determine the nature and extent of travel desires, land uses, transportation facilities and socio-economic characteristics. Except for the socio-economic data, data collected in the other surveys mentioned can be taken from photographs in sufficient detail to be useful in the modeling procedures.
With electricity demand on the rise, an increase in the use of nuclear energy is needed in order to meet future energy demands. The use of nuclear energy will not only begin to positively address this demand, but will also mitigate the negative impacts of carbon-induced climate change and provide energy security to the United States. An appropriate option to address the increasing energy demand is the use of advanced reactor systems like microreactors. Microreactors, often referred to as special-purpose reactors or vSMRs, are neutronically simple, factory manufacturable, easily transportable, and designed to produce less than 20 MWt (for classification as Hazard Category 2 per 10 CFR 830, DOE STD 1027). These reactors are decentralized energy sources that have the ability to provide sustainable and affordable power to remote communities and to industrial users, while having self-contained geometry that requires very low maintenance care. In order for microreactors to be approved for operation, they must abide by and successfully complete a rigorous regulatory and licensing process monitored by the Nuclear Regulatory Commission (NRC), the Department of Energy (DOE), or the Department of Defense (DoD). Because microreactors are a relatively new and disruptive technology, there is no clear licensing path for them to follow. Current licensing frameworks are tailored specifically to large, traditional light water reactors (LWRs) that have been developed and understood by the nuclear industry for many years. Unlike LWRs, microreactors have major design differences, specifically in the materials, coolant, reflectors and potential technology applications used. They also have higher in-core operating temperatures, which have the potential to push the materials used past their allowable limits. Because they are much smaller, safety margins, the amount of space required for assembly, and the distances required to meet dose-based regulatory criteria can technically be altered to better fit their new size. The following strategies and recommendations are intended to assure that key licensing issues do not influence the path to microreactor development, demonstration, and commercialization: Nuclear Regulatory Commission • The NRC should address the lack of knowledge and support of risk informed decision-making concepts from staff members and continue to advance and communicate risk initiatives using their Risk Informed Steering Committee • More research should be done into whether or not microreactors, which are small Class 103 facilities, can be licensed using Class 104-type guidance (e.g., NUREG 1537) • The NRC should use frequency-consequence curves to incrementally reduce regulatory uncertainty and risk to facilitate future construction and operation of advanced reactor designs Department of Energy • The DOE should continue to implement their multi-step Phenomena Identification and Ranking Table process in order to help detect and recommend the major design and technology work needed for licensing future reactor systems • The DOE should consider a joint licensing approach with the NRC where microreactors first go through the DOE process, then finish with the NRC process, providing an advantage for both applicants and NRC staff Department of Defense • The DoD should invest in personnel with sufficient expertise, incorporate their military reactors into tactical power systems, and self-regulate using Section 91b of the Atomic Energy Act Transportation • Licensees should convert enriched uranium hexafluoride into metal or oxide at an enrichment facility and then transport them to a fabrication facility • To avoid transportation issues altogether, licensees should consider positioning the enrichment and fabrication facilities on the same site • The DOE should provide their support in developing new shipping packages for safe transport, criticality benchmark data needed to license high-assay low-enriched uranium fuel facilities and transport packages, and provide financial and technical help to the Department of Transportation; and, • The NRC should help transportation efforts by finalizing and developing guidance for implementing 10 CFR 73 and 10 CFR 74 specifically for Category 2 Special Nuclear Material.
Essential supporting data to the short haul transportation study are presented. The specific appendices are arena characteristics, aerospace transportation analysis computer program, economics, model calibration, STOLport siting and services path selection, STOL schedule definition, tabulated California corridor results, and tabulated Midwest arena results.
Recent development in chemistry transport models (CTM) and in data assimilation systems (DAS) indicate impressive predictive capability for the movement of airparcels and the chemistry that goes on within these. This project was aimed at exploring the use of this capability to achieve improved retrieval of geophysical parameters from remote sensing data. The specific goal was to improve retrieval of the CLAES CH4 data obtained during the active north high latitude dynamics event of 18 to 25 February 1992. The model capabilities would be used: (1) rather than climatology to improve on the first guess and the a-priori fields, and (2) to provide horizontal gradients to include in the retrieval forward model. The retrieval would be implemented with the first forward DAS prediction. The results would feed back to the DAS and a second DAS prediction for first guess, a-priori and gradients would feed to the retrieval. The process would repeat to convergence and then proceed to the next day.
This report provides a compendium of NASA aircraft data that are available from NASA's Global Tropospheric Experiment's (GTE) Transport and Chemical Evolution over the Pacific (TRACE-P) Mission. The broad goal of TRACE-P was to characterize the transit and evolution of the Asian outflow over the western Pacific. Conducted from February 24 through April 10, 2001, TRACE-P integrated airborne, satellite- and ground-based observations, as well as forecasts from aerosol and chemistry models. The format of this compendium utilizes data plots (time series) of selected data acquired aboard the NASA/Dryden DC-8 (vol. 1) and NASA/Wallops P-3B (vol. 2) aircraft during TRACE-P. The purpose of this document is to provide a representation of aircraft data that are available in archived format via NASA Langley s Distributed Active Archive Center (DAAC) and through the GTE Project Office archive. The data format is not intended to support original research/analyses, but to assist the reader in identifying data that are of interest.
In this paper we consider the effects of the isotopic composition of the primary galactic cosmic rays (GCR), nuclear fragmentation cross-sections, and isotopic-grid on the solution to transport models used for shielding studies. Satellite measurements are used to describe the isotopic composition of the GCR. For the nuclear interaction data-base and transport solution, we use the quantum multiple-scattering theory of nuclear fragmentation (QMSFRG) and high-charge and energy (HZETRN) transport code, respectively. The QMSFRG model is shown to accurately describe existing fragmentation data including proper description of the odd-even effects as function of the iso-spin dependence on the projectile nucleus. The principle finding of this study is that large errors (+/-100%) will occur in the mass-fluence spectra when comparing transport models that use a complete isotopic-grid (approx.170 ions) to ones that use a reduced isotopic-grid, for example the 59 ion-grid used in the HZETRN code in the past, however less significant errors (<+/-20%) occur in the elemental-fluence spectra. Because a complete isotopic-grid is readily handled on small computer workstations and is needed for several applications studying GCR propagation and scattering, it is recommended that they be used for future GCR studies.