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DOE OSTI2021

The Effect of the Flux Separability Approximation on Multigroup Neutron Transport

The angular dependence of flux-weighted multigroup cross sections is commonly neglected when generating multigroup libraries. The error of this flux separability approximation is typically not isolated from other error sources due to a lack of availability of library generation and corresponding solvers that cannot relax this approximation. These errors can now be isolated and quantified with the availability of a multigroup Monte Carlo transport and multigroup library-generation capability in the OpenMC Monte Carlo transport code. This work will discuss relevant details of the OpenMC implementation, provide an example case useful for detailing the type of errors one can expect from making the flux separability approximation, and end with more realistic problems which show the impact of the approximation and highlight how it can strongly arise from an energy-dependent resonance absorption effect. Since the angle-dependence is intrinsically linked to the energy group structure, these examples also show that relaxing the flux separability approximation with angle-dependent cross sections could be used to reduce either the fine-tuning required to set a multigroup energy structure for a specific reactor type or the number of energy groups required to obtain a desired level of accuracy for a given problem. This trade-off could increase the costs of generating multigroup cross sections, and has the potential to require more memory for storing the multigroup library during the transport calculations, but it can significantly reduce the computational time required since the runtime of a discrete ordinates or method of characteristics neutron transport solver scales roughly linearly with the number of groups.

42 ENGINEERING↗

DOE OSTI2015

JHR Project: a future Material Testing Reactor working as an International user Facility: The key-role of instrumentation in support to the development of modern experimental capacity

Research and development on fuel and material behaviour under irradiation is a key issue for sustainable nuclear energy in order to meet specific needs by keeping the best level of safety. These needs mainly deal with a constant improvement of performances and safety in order to optimize the fuel cycle and hence to reach nuclear energy sustainable objectives. A sustainable nuclear energy requires a high level of performances in order to meet specific needs such as: - Pursuing improvement of the performances and safety of present and coming water cooled reactor technologies. This will require a continuous R and D support following a long-term trend driven by the plant life management, safety demonstration, flexibility and economics improvement. Experimental irradiations of structure materials are necessary to anticipate these material behaviours and will contribute to their optimisation. - Upgrading continuously nuclear fuel technology in present and future nuclear power plants to achieve better performances and to optimise the fuel cycle keeping the best level of safety. Fuel evolution for generation II, III and III+ is a key stake requiring developments, qualification tests and safety experiments to ensure the competitiveness and safety: experimental tests exploring the full range of fuel behaviour determine fuel stability limits and safety margins, as a major input for the fuel reliability analysis. To perform such accurate and innovative progress and developments, specific and ad hoc instrumentation, irradiation devices, measurement methods are necessary to be set up inside or beside the material testing reactor (MTR) core. These experiments require beforehand in situ and on line sophisticated measurements to accurately determine different key parameters such as thermal and fast neutron fluxes and nuclear heating in order to precisely monitor and control the conducted assays. The new Material Testing Reactor JHR (Jules Horowitz Reactor) currently under construction at CEA Cadarache research centre in the south of France will represent a major Research Infrastructure for scientific studies regarding material and fuel behavior under irradiation. It will also be devoted to medical isotopes production. Hence JHR will offer a real opportunity to perform R and D programs regarding needs above and hence will crucially contribute to the selection, optimization and qualification of these innovative materials and fuels. The JHR reactor objectives, principles and main characteristics associated to specific experimental devices associated to measurement techniques and methodology, their performances, their limitations and field of applications will be presented and discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

An MLCommons Scientific Benchmarks Ontology

Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology, climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate benchmarks and frameworks into a single taxonomy of scientific, application, and system-level benchmarks. New benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality benchmarks, enabling stakeholders to select benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/benchmark/

Hawks, Ben [Fermilab] (ORCID:0000000157000288)↗

DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗

DOE OSTI2015

DGDFT: A massively parallel method for large scale density functional theory calculations

We describe a massively parallel implementation of the recently developed discontinuous Galerkin density functional theory (DGDFT) method, for efficient large-scale Kohn-Sham DFT based electronic structure calculations. The DGDFT method uses adaptive local basis (ALB) functions generated on-the-fly during the self-consistent field iteration to represent the solution to the Kohn-Sham equations. The use of the ALB set provides a systematic way to improve the accuracy of the approximation. By using the pole expansion and selected inversion technique to compute electron density, energy, and atomic forces, we can make the computational complexity of DGDFT scale at most quadratically with respect to the number of electrons for both insulating and metallic systems. We show that for the two-dimensional (2D) phosphorene systems studied here, using 37 basis functions per atom allows us to reach an accuracy level of 1.3 × 10{sup −4} Hartree/atom in terms of the error of energy and 6.2 × 10{sup −4} Hartree/bohr in terms of the error of atomic force, respectively. DGDFT can achieve 80% parallel efficiency on 128,000 high performance computing cores when it is used to study the electronic structure of 2D phosphorene systems with 3500-14 000 atoms. This high parallel efficiency results from a two-level parallelization scheme that we will describe in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Advanced instrumentation and analysis methods for in-pile thermal and nuclear measurements: from out-of-pile studies to irradiation campaigns

Research and development on nuclear fuel behavior under irradiations and accelerated ageing of structure materials is a key issue for sustainable nuclear energy in order to meet specific needs by keeping the best level of safety. A new Material Testing Reactor (MTR), the Jules Horowitz Reactor (JHR) currently under construction in the South of France in the CEA Cadarache research centre will offer a real opportunity to perform R and D programs and hence will crucially contribute to the selection, optimization and qualification of innovative materials and fuels. To perform such programs advanced accurate and innovative experiments, irradiation devices that contain material and fuel samples are required to be set up inside or beside the reactor core. These experiments needs beforehand in situ and on line sophisticated measurements to accurately reach specific and determining parameters such as thermal and fast neutron fluxes, nuclear heating and temperature conditions to precisely monitor and control the conducted assays. Consequently, since 2009 CEA and Aix-Marseille University collaborate in order to design and develop a new multi-sensor device which will be dedicated to measuring profiles of such conditions inside the experimental channels of the JHR. These works are performed in the framework of two complementary joint research programs called MAHRI-BETHY and INCORE. These programs couple experimental studies carried out both out-of nuclear fluxes (in laboratory) and under irradiation conditions (in OSIRIS MTR reactor in France and MARIA MTR reactor in Poland) with numerical works realized by thermal simulations (CAST3M code) and Monte Carlo simulations (MCNP code). These programs deal with three main aims. The first one corresponds to the design and/or the test of new in-pile instrumentation. The second one concerns the development of advanced calibration procedures in particular in the case of one specific sensor: a differential calorimeter used to quantify nuclear heating. The last one consists in the development of accurate measurement and analysis methods. The paper will be dedicated to a complete review of the experimental and numerical works performed since 2009 thanks to two parts. The first part will detail a new thermal approach implemented to improve nuclear heating measurements by radiometric calorimeters. New experimental tools (calorimeter prototypes and set-ups such BETHY Bench) developed to perform preliminary out-of-pile studies under suitable conditions will be presented (temperature and velocity of the external cooling fluid, heat source localization and intensity inside the calorimetric cells). Then the response of two kinds of sensors, their calibrations curves and their thermal behaviors will be compared for various parameters. Finally validated numerical thermal and Monte Carlo works will be discussed to propose new improvements. The second parts of the paper will focus on works realized in order to design, develop and test the first prototype of the multi-sensor device called CARMEN [7-9]. The two mock-ups dedicated respectively to neutron measurements and photon measurements will be detailed. The results obtained during two irradiation campaigns inside the periphery of OSIRIS reactor will be shown. The new analysis method will be discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

SU-F-T-493: An Investigation Into the Feasibility of Using PipsPro Software with Film for Linac QA

Purpose: To determine the feasibility of using radiochromic and radiographic film with Pipspro software for quality assurance of linear accelerators with no on-board imaging. Methods: The linear accelerator being used is a Varian Clinac 21EX. All IGRT is performed using the BrainLab ExacTrac system. Because of the lack of on board imaging, certain monthly and annual TG-142 quality assurance tests are more difficult to perform and analyze to a high degree of accuracy. Pipspro was not designed to be used with hard film, and to our knowledge its use with film had not been investigated. The film used will be GafChromic EBT3 film and Kodak EDR2 film, scanned with an Epson V700 scanner. The following routine tests will be attempted: MLC picket fence, light vs. radiation field coincidence, starshots, and MLC transmission. Results: The only tests that gave accurate and reliable results were the couch, gantry, and collimator starshots. Typical MV and kV images are acquired with a much higher level of contrast between the irradiated and non-irradiated areas when compared to film. Pipspro relies on this level of contrast to be able to automatically detect the fiducial points from its phantom devices, leaf edges for picket fence and transmission tests, and jaw edges for light vs. radiation field tests. Because of this, certain tests gave erroneous results and others were not able to be performed in the software at all, with either type of film. The number of monitor units delivered to the film, the experimental setup, and the scan settings was not able to rectify the problem. Conclusion: For linear accelerators with no on-board imaging, it is not recommended to use hard film with PipsPro to perform TG-142 quality assurance tests. Other software or methods should instead be investigated.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

On the Sampling-Based Computation of Nash Equilibria Under Uncertainty via the Nikaido–Isoda Function

We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be L 0 -Lipschitz continuous and convex, given rival decisions with convex and closed player-specific feasibility sets. To address this problem, we consider minimizing a suitably defined value function defined using the Nikaido–Isoda function. Such an avenue does not necessitate either monotonicity properties of the concatenated gradient map or potentiality requirements on the game but does require a suitable regularity requirement under which a stationary point is a Nash equilibrium. We design and analyze a sampling-enabled projected-gradient-response method, reliant on inexact resolution of a player-level best-response subproblem. Here, by deriving suitable Lipschitzian guarantees on the value function, we derive both asymptotic guarantees for the sequence of generated iterates as well as rate and complexity guarantees for computing a stationary point by appropriate choices of the sampling rate and inexactness sequence.

Nikaido-Isoda function↗

DOE OSTI2017

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Lastly, our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗

DOE OSTI2022

Joint Determination of Reactor Antineutrino Spectra from U 235 and Pu 239 Fission by Daya Bay and PROSPECT

A joint determination of the reactor antineutrino spectra resulting from the fission of 235U and 239Pu has been carried out by the Daya Bay and PROSPECT collaborations. This Letter reports the level of consistency of 235U spectrum measurements from the two experiments and presents new results from a joint analysis of both data sets. The measurements are found to be consistent. The combined analysis reduces the degeneracy between the dominant 235U and 239Pu isotopes and improves the uncertainty of the 235U spectral shape to about 3%. The 235U and 239Pu antineutrino energy spectra are unfolded from the jointly deconvolved reactor spectra using the Wiener-SVD unfolding method, providing a data-based reference for other reactor antineutrino experiments and other applications. This is the first measurement of the 235U and 239Pu spectra based on the combination of experiments at low- and highly enriched uranium reactors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Heat Exchanger Design Optimization for Mitigation of Diesel Engine Exhaust Fouling

Abstract The design optimization of a diesel exhaust-coupled heat and mass exchanger that drives a 2.71 kW cooling capacity absorption heat pump is presented in this study. Fouling layer thermal resistance and pressure drops from single-tube experiments are used to develop a thermodynamic, heat transfer, and pressure drop model for the exhaust-coupled desorber. A parametric study is performed to select a desorber design that meets system performance while minimizing footprint. Experimental heat duties and pressure drops are within 10% and 3%, respectively, of the model predictions. Thus, large data sets from single-tube experiments with representative geometries are successful in accounting for fouling effects at the component level. Desorber design optimization based on this approach ensures continued heat pump performance after fouling. This study, along with the single-tube experiments, presents a systematic approach to design exhaust-coupled heat exchangers while considering the effects of fouling. These results are applicable for a wide range of waste-heat recovery applications, and this method can be extended to different geometries and operating conditions.

Energy & Fuels↗

DOE OSTI2014

Assessment of dedicated low-dose cardiac micro-CT reconstruction algorithms using the left ventricular volume of small rodents as a performance measure

Purpose: Phase-correlated microcomputed tomography (micro-CT) imaging plays an important role in the assessment of mouse models of cardiovascular diseases and the determination of functional parameters as the left ventricular volume. As the current gold standard, the phase-correlated Feldkamp reconstruction (PCF), shows poor performance in case of low dose scans, more sophisticated reconstruction algorithms have been proposed to enable low-dose imaging. In this study, the authors focus on the McKinnon-Bates (MKB) algorithm, the low dose phase-correlated (LDPC) reconstruction, and the high-dimensional total variation minimization reconstruction (HDTV) and investigate their potential to accurately determine the left ventricular volume at different dose levels from 50 to 500 mGy. The results were verified in phantom studies of a five-dimensional (5D) mathematical mouse phantom. Methods: Micro-CT data of eight mice, each administered with an x-ray dose of 500 mGy, were acquired, retrospectively gated for cardiac and respiratory motion and reconstructed using PCF, MKB, LDPC, and HDTV. Dose levels down to 50 mGy were simulated by using only a fraction of the projections. Contrast-to-noise ratio (CNR) was evaluated as a measure of image quality. Left ventricular volume was determined using different segmentation algorithms (Otsu, level sets, region growing). Forward projections of the 5D mouse phantom were performed to simulate a micro-CT scan. The simulated data were processed the same way as the real mouse data sets. Results: Compared to the conventional PCF reconstruction, the MKB, LDPC, and HDTV algorithm yield images of increased quality in terms of CNR. While the MKB reconstruction only provides small improvements, a significant increase of the CNR is observed in LDPC and HDTV reconstructions. The phantom studies demonstrate that left ventricular volumes can be determined accurately at 500 mGy. For lower dose levels which were simulated for real mouse data sets, the HDTV algorithm shows the best performance. At 50 mGy, the deviation from the reference obtained at 500 mGy were less than 4%. Also the LDPC algorithm provides reasonable results with deviation less than 10% at 50 mGy while PCF and MKB reconstruction show larger deviations even at higher dose levels. Conclusions: LDPC and HDTV increase CNR and allow for quantitative evaluations even at dose levels as low as 50 mGy. The left ventricular volumes exemplarily illustrate that cardiac parameters can be accurately estimated at lowest dose levels if sophisticated algorithms are used. This allows to reduce dose by a factor of 10 compared to today's gold standard and opens new options for longitudinal studies of the heart.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents, VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2018

Predictive Control of Residential Gas Storage Water Heaters [SWR-13-36]

Predictive Control of Residential Gas Storage Water Heaters provides an energy-efficient control method for residential gas storage water heaters. Predictive Control of Residential Gas Storage Water Heaters discovers users' hot water consumption patterns from historical data and automatically modulates the thermostat setpoints to conserve energy while maintaining thermal comfort. The starting time of hot water draw events is recorded and used for clustering analysis. Time intervals are generated from the clusters in which hot water draw events are more likely to occur. The setpoint is set at normal level to maintain thermal comfort, and decreased to a lower level outside these time intervals to reduce tank loss and thereby improving energy efficiency of the water heater.

Christensen, Dane↗

DOE OSTI2018

Single-Molecule Interfacial Electron Transfer

Interfacial electron transfer (ET) plays an important role in many chemical and biological processes. Specifically, interfacial ET in TiO 2 -based systems is important to solar energy technology, catalysis, and environmental remediation technology. However, the microscopic mechanism of interfacial ET is not well understood with regard to atomic surface structure, molecular structure, bonding, orientation, and motion. In this project, we used two complementary methodologies; single-molecule fluorescence spectroscopy, and scanning-tunneling microscopy and spectroscopy (STM and STS) to address this scientific need. The goal of this project was to integrate these techniques and measure the molecular dependence of ET between adsorbed molecules and TiO 2 semiconductor surfaces and the ET induced reactions such as the splitting of water. The scanning probe techniques, STM and STS, are capable of providing the highest spatial resolution but not easily time-resolved data. Single-molecule fluorescence spectroscopy is capable of good time resolution but requires further development to match the spatial resolution of the STM. The integrated approach involving Peter Lu at Bowling Green State University (BGSU) and Wilson Ho at the University of California, Irvine (UC Irvine) produced methods for time and spatially resolved chemical imaging of interfacial electron transfer dynamics and photocatalytic reactions. An integral aspect of the joint research was a significant exchange of graduate students to work at the two institutions. This project bridged complementary approaches to investigate a set of common problems by working with the same molecules on a variety of solid surfaces, but using appropriate techniques to probe under ambient (BGSU) and ultrahigh vacuum (UCI) conditions. The molecular level understanding of the fundamental interfacial electron transfer processes obtained in this joint project will be important for developing efficient light harvesting, solar energy conversion, and broadly applicable to problems in interface chemistry and surface physics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

A Scoping Study to Develop a Computational Fluid Dynamics Based Model to Predict Radiological Materials Packaging Temperatures within a Generic Staging Building

The objective of this work is to perform computational fluid dynamics simulations of a ventilated radiological-material-package staging building to determine the effect of including or excluding a variety of physical effects and computational methods on both the predicted package temperatures and required computational resources. The generic building contains 640 drum-packages containing heat-generating, radiological material supported on four racks that are eight levels tall. Additionally, there is a forced ventilation system, lighting, and insulated walls. Computational models were constructed that included or excluded (a) shelving, (b) effects of unsteadiness, and (c) radiation heat transfer. Simulations with each drum modeled separately were compared to simpler simulations with sets of four drums represented by an equivalent box-package. These models employed between 106 to 107 elements. Steady state simulations predicted package temperatures that were within 0.1°C of simulations that included transient effects and required only one-eighth the computational resources. Calculations that excluded shelving predicted temperatures within 0.6°C of simulations that included shelving and required one-fourth the computational resources. Excluding radiation heat transfer systematically increased temperatures by around 1.5°C but reduced computational resources by a factor of four. The box-package model reduced the computational resources by a factor of 3, but systematically predicted higher temperatures by around 1.1°C. These results will be used to develop an efficient computational fluid dynamics model to assess the ability of different staging building designs to prevent the temperature of package components from exceeding specified limits.

42 ENGINEERING↗

DOE OSTI2019

MY EXES and EDM for Automated Remediation Data Quality Assessments - 19053

MY Electronic data exchange and Evaluation System (EXES) is a new web-based technology providing external admin access and full control to the Web-based EXES tool developed by GD for EPA for automated environmental remediation data quality assessments. EXES replaces most manual assessment processes, reducing remediation data review time and costs and ensuring that data are of known and documented quality. This tool is available from EPA to federal agencies and remediation firms and has successfully been used to support over 190 site investigations, 105 Remedial investigations and over 20 compliance monitoring efforts. The new MY EXES functionality has been developed for project managers and decision makers to setup projects, track receipt of environmental remediation monitoring data, assess data quality, and manage data. It is designed to be flexible and configurable to meet any government agency's data review and data management needs. It is accessible 24/7 through a secure portal. MY EXES efficiently evaluates analytical data regardless of the underlying method and data reporting requirements. It is a centralized project and data assessment tool that is scalable, flexible and configurable; improving data quality while reducing cost through the following: - Provides users with the ability to create a project and to specify the analytical methods corresponding to the data that will be submitted for that project. A project can be modified and managed anytime during the project's life cycle. - Allows users to manage a library of analytical methods that can be made available for association to their projects. - Users can specify the automated tests and assessment that needs to be performed on the analytical data deliverables based on specific requirements in the project's Quality Assurance Project Plan (QAPP). - Users specify: - The format of the incoming data deliverables from the laboratory; - The type and level of review services to be performed; - The assessment deliverables to be generated - output spreadsheets and reports; - Project owner sets up system access and permissions for members of the project team. - A faster (600%) and less expensive (85%) process than the traditional data usability/validation. The EXES Data Manager application (EDM) provides a centralized data repository to view and retrieve generated reports, spreadsheets, and other data deliverables as defined by the client, eliminating the need for data transfer between project team members. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗