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DOE OSTI2014

SU-C-BRD-02: A Team Focused Clinical Implementation and Failure Mode and Effects Analysis of HDR Skin Brachytherapy Using Valencia and Leipzig Surface Applicators

Purpose: and Leipzig applicators (VLAs) are single-channel brachytherapy surface applicators used to treat skin lesions up to 2cm diameter. Source dwell times can be calculated and entered manually after clinical set-up or ultrasound. This procedure differs dramatically from CT-based planning; the novelty and unfamiliarity could lead to severe errors. To build layers of safety and ensure quality, a multidisciplinary team created a protocol and applied Failure Modes and Effects Analysis (FMEA) to the clinical procedure for HDR VLA skin treatments. Methods: team including physicists, physicians, nurses, therapists, residents, and administration developed a clinical procedure for VLA treatment. The procedure was evaluated using FMEA. Failure modes were identified and scored by severity, occurrence, and detection. The clinical procedure was revised to address high-scoring process nodes. Results: Several key components were added to the clinical procedure to minimize risk probability numbers (RPN): -Treatments are reviewed at weekly QA rounds, where physicians discuss diagnosis, prescription, applicator selection, and set-up. Peer review reduces the likelihood of an inappropriate treatment regime. -A template for HDR skin treatments was established in the clinical EMR system to standardize treatment instructions. This reduces the chances of miscommunication between the physician and planning physicist, and increases the detectability of an error during the physics second check. -A screen check was implemented during the second check to increase detectability of an error. -To reduce error probability, the treatment plan worksheet was designed to display plan parameters in a format visually similar to the treatment console display. This facilitates data entry and verification. -VLAs are color-coded and labeled to match the EMR prescriptions, which simplifies in-room selection and verification. Conclusion: Multidisciplinary planning and FMEA increased delectability and reduced error probability during VLA HDR Brachytherapy. This clinical model may be useful to institutions implementing similar procedures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Cloud-Based Model Calibration Using OpenStudio: Preprint

OpenStudio is a free, open source Software Development Kit (SDK) and application suite for performing building energy modeling and analysis. The OpenStudio Parametric Analysis Tool has been extended to allow cloud-based simulation of multiple OpenStudio models parametrically related to a baseline model. This paper describes the new cloud-based simulation functionality and presents a model cali-bration case study. Calibration is initiated by entering actual monthly utility bill data into the baseline model. Multiple parameters are then varied over multiple iterations to reduce the difference between actual energy consumption and model simulation results, as calculated and visualized by billing period and by fuel type. Simulations are per-formed in parallel using the Amazon Elastic Cloud service. This paper highlights model parameterizations (measures) used for calibration, but the same multi-nodal computing architecture is available for other purposes, for example, recommending combinations of retrofit energy saving measures using the calibrated model as the new baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Theoretical approach for optical response in electrochemical systems: Application to electrode potential dependence of surface-enhanced Raman scattering

We propose a theoretical approach for optical response in electrochemical systems. The fundamental equation to be solved is based on a time-dependent density functional theory in real-time and real-space in combination with its finite temperature formula treating an electrode potential. Solvation effects are evaluated by a dielectric continuum theory. The approach allows us to treat optical response in electrochemical systems at the atomistic level of theory. We have applied the method to surface-enhanced Raman scattering (SERS) of 4-mercaptopyridine on an Ag electrode surface. It is shown that the SERS intensity has a peak as a function of the electrode potential. Furthermore, the real-space computational approach facilitates visualization of variation of the SERS intensity depending on an electrode potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Amphibian Response to Rotenone Treatment of Ten Alpine Lakes in Northwest Montana

Abstract The piscicide rotenone is an important and widely used management tool for native fish conservation; however, long-term empirical field studies investigating potential effects on nontarget species are lacking. We tested for effects of rotenone on four native amphibian species inhabiting 10 fish-bearing alpine lakes in northwest Montana. Visual encounter amphibian surveys were conducted for 2–4 years to establish baseline species-specific detection frequencies prior to piscicide treatment to eradicate nonnative trout. Species diversity and detection frequencies varied considerably among lakes, presumably due to differences in the amount of emergent vegetation. Comparisons between baseline detection frequencies and those established from posttreatment surveys consistently revealed no significant changes in amphibian detection frequency, suggesting resiliency of amphibian populations to these piscicide treatments. These results may be explained by one or more of the following: (1) timing the rotenone application when many amphibians had metamorphosed beyond the gill-breathing life stage; (2) application of rotenone at a concentration that was sublethal to amphibians; and (3) posttreatment immigration of adults from habitats not treated with rotenone. This study provides insight into the factors that contribute to amphibian population resiliency to alpine lake rotenone treatments within the context of a watershed-scale native trout restoration program.

Fried, Lynda M.↗

DOE OSTI2019

Tracking materials science data lineage to manage millions of materials experiments and analyses

Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

Soedarmadji, Edwin↗

DOE OSTI2022

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Single-molecule reaction mapping uncovers diverse behaviours of electrocatalytic surface Pd–H intermediates

Many vital electrocatalytic transformations hinge on reactive surface metal–hydrogen intermediates (M–H*), yet the low concentration and transient nature of such intermediates present formidable challenges to in-depth investigation. Here we use single-molecule super-resolution reaction imaging to directly probe surface palladium–hydrogen (Pd–H*) intermediates on individual palladium nanocubes during electrocatalytic hydrogen evolution. Our approach visualizes hydrogen spillover from palladium to the surrounding substrate surface over hundreds of nanometres away and dissects substantial inter- and intraparticle heterogeneity. Through Gaussian-broadening kinetic analysis, we reveal that ensemble-averaged measurements systematically overestimate the stability of Pd–H*. Moreover, we resolve three subpopulations of palladium nanocubes with distinct reactivity features, uncovering critical correlations between intermediate stability, hydrogenation reactivity and transition-state properties. Finally, our findings highlight the necessity of single-particle resolution for capturing the intrinsic complexity of electrocatalysts; our approach is also broadly applicable to interrogate surface-reactive intermediates across a wide array of electrocatalytic pathways.

electrocatalysis↗

DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World’s Largest Open Source Geographic Data Sets

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725. Copyright This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

analytics↗

DOE OSTI2016

EnergyIQ

EnergyIQ-the first "action-oriented" benchmarking tool for non-residential buildings-provides a standardized opportunity assessment based on benchmarking results. along with decision-support information to help refine action plans. EnergyIQ offers a wide array of benchmark metrics, with visuall as well as tabular display. These include energy, costs, greenhouse-gas emissions, and a large array of characteristics (e.g. building components or operational strategies). The tool supports cross-sectional benchmarking for comparing the user's building to it's peers at one point in time, as well as longitudinal benchmarking for tracking the performance of an individual building or enterprise portfolio over time. Based on user inputs, the tool generates a list of opportunities and recommended actions. Users can then explore the "Decision Support" module for helpful information on how to refine action plans, create design-intent documentation, and implement improvements. This includes information on best practices, links to other energy analysis tools and more. The variety of databases are available within EnergyIQ from which users can specify peer groups for comparison. Using the tool, this data can be visually browsed and used as a backdrop against which to view a variety of energy benchmarking metrics for the user's own building. User can save their project information and return at a later date to continue their exploration. The initial database is the CA Commercial End-Use Survey (CEUS), which provides details on energy use and characteristics for about 2800 buildings (and 62 building types). CEUS is likely the most thorough survey of its kind every conducted. The tool is built as a web service. The EnergyIQ web application is written in JSP with pervasive us of JavaScript and CSS2. EnergyIQ also supports a SOAP based web service to allow the flow of queries and data to occur with non-browser implementations. Data are stored in an Oracle 10g database. References: Mills, Mathew, Brook and Piette. 2008. "Action Oriented Benchmarking: Concepts and Tools." Energy Engineering, Vol.105, No. 4, pp 21-40. LBNL-358E; Mathew, Mills, Bourassa, Brook. 2008. "Action-Oriented Benchmarking: Using the CEUS Database to Benchmark Commercial Buildings in California." Energy Engineering, Vol 105, No. 5, pp 6-18. LBNL-502E.

MILLS, EVAN↗

DOE OSTI2025

Comparison of three measurement modalities for 3D characterization of manufactured features and process-induced porosity in titanium alloy additively manufactured parts

Nondestructive characterization of internal features and defects within complex components is vital for many industrial applications, particularly with the advent of additive manufacturing (AM) technologies. However, community understanding of the limitations of nondestructive methods such as X-ray Computed Tomography (CT) can be limited in certain industrial sectors as these may be emergent applications. In this paper, we investigate the limits of X-ray CT measurements and compare extracted data with mechanical polishing serial sectioning (MPSS) and confocal laser scanning microscopy (CLSM). The test object is an additively manufactured titanium alloy disk that contains both process-induced porosity and machined features, including focused ion beam milled features designed to probe the resolution limits of X-ray CT. Results show that each of these characterization techniques has advantages and disadvantages. We compare data acquisition times, spatial resolution, geometric measurement accuracy and defect visualization fidelity across these modalities to establish a practical framework.

Additive manufacturing↗

DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗

DOE OSTI2017

Direct atomic-level insight into the active sites of a high-performance PGM-free ORR catalyst

Platinum group metal–free (PGM-free) metal-nitrogen-carbon catalysts have emerged as a promising alternative to their costly platinum (Pt)–based counterparts in polymer electrolyte fuel cells (PEFCs) but still face some major challenges, including (i) the identification of the most relevant catalytic site for the oxygen reduction reaction (ORR) and (ii) demonstration of competitive PEFC performance under automotive-application conditions in the hydrogen (H 2 )–air fuel cell. We demonstrate H 2 -air performance gains achieved with an iron-nitrogen-carbon catalyst synthesized with two nitrogen precursors that developed hierarchical porosity. In current densities recorded in the kinetic region of cathode operation, at fuel cell voltages greater than ~0.75 V, were the same as those obtained with a Pt cathode at a loading of 0.1 milligram of Pt per centimeter squared. The catalytic active site we proposed, carbon-embedded nitrogen-coordinated iron (FeN 4 ), was directly visualized with aberration-corrected scanning transmission electron microscopy, and the contributions of these active sites associated with specific lattice-level carbon structures were explored computationally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Challenges of Achieving 2012 IECC Air Sealing Requirements in Multifamily Dwellings

While previous versions of the International Energy Conservation Code (IECC) have included provisions to improve the air tightness of dwellings, for the first time, the 2012 IECC mandates compliance verification through blower door testing. Simply completing the Air Barrier and Insulation Installation checklist through visual inspection is no longer sufficient by itself. In addition, the 2012 IECC mandates a significantly stricter air sealing requirement. In Climate Zones 3 through 8, air leakage may not exceed 3 ACH50, which is a significant reduction from the 2009 IECC requirement of 7 ACH50. This requirement is for all residential buildings, which includes low-rise multifamily dwellings. While this air leakage rate requirement is an important component to achieving an efficient building thermal envelope, currently, the code language doesn't explicitly address differences between single family and multifamily applications. In addition, the 2012 IECC does not provide an option to sample dwellings for larger multifamily buildings, so compliance would have to be verified on every unit. With compliance with the 2012 IECC air leakage requirements on the horizon, several of CARB's multifamily builder partners are evaluating how best to comply with this requirement. Builders are not sure whether it is more practical or beneficial to simply pay for guarded testing or to revise their air sealing strategies to improve compartmentalization to comply with code requirements based on unguarded blower door testing. This report summarizes CARB's research that was conducted to assess the feasibility of meeting the 2012 IECC air leakage requirements in 3 multifamily buildings.

&#8203↗

DOE OSTI2014

Challenges of Achieving 2012 IECC Air Sealing Requirements in Multifamily Dwellings

While previous versions of the International Energy Conservation Code (IECC) have included provisions to improve the air tightness of dwellings, for the first time, the 2012 IECC mandates compliance verification through blower door testing. Simply completing the Air Barrier and Insulation Installation checklist through visual inspection is no longer sufficient by itself. In addition, the 2012 IECC mandates a significantly stricter air sealing requirement. In Climate Zones 3 through 8, air leakage may not exceed 3 ACH50, which is a significant reduction from the 2009 IECC requirement of 7 ACH50. This requirement is for all residential buildings, which includes low-rise multifamily dwellings. While this air leakage rate requirement is an important component to achieving an efficient building thermal envelope, currently, the code language doesn't explicitly address differences between single family and multifamily applications. In addition, the 2012 IECC does not provide an option to sample dwellings for larger multifamily buildings, so compliance would have to be verified on every unit. With compliance with the 2012 IECC air leakage requirements on the horizon, several of Consortium for Advanced Residential Building's (CARB’s) multifamily builder partners are evaluating how best to comply with this requirement. Builders are not sure whether it is more practical or beneficial to simply pay for guarded testing or to revise their air sealing strategies to improve compartmentalization to comply with code requirements based on unguarded blower door testing. This report summarizes CARB's research that was conducted to assess the feasibility of meeting the 2012 IECC air leakage requirements in 3 multifamily buildings.

2012 IECC↗

DOE OSTI2014

Building America Case Study: Challenges of Achieving 2012 IECC Air Sealing Requirements in Multifamily Dwellings, Upstate New York (Fact Sheet)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Application of the Combined Judgmental and Randomly Placed Sampling Method

In the wake of the 2001 anthrax incidents in the United States, there has been considerable discussion about the role of judgmental and probabilistic sampling strategies, particularly in the context of clearance sampling. The Combined Judgmental and Random (CJR) methodology provides a statistical methodology that allows practitioners to leverage information from both judgmental and randomly selected sample locations and then quantify the confidence in negative results. The objective of the CJR sampling design is to demonstrate, with high probability, that a high percentage of the decision area (or population) is acceptable, given that none of the samples are unacceptable. The purpose of this document is to assist practitioners to appropriately use the CJR methodology and to implement this sampling design in Visual Sample Plan (VSP). We begin by defining some fundamental terms. A decision area is the geographical or spatial area (indoor or outdoor) about which practitioners wish to draw a conclusion on the basis of sampling and other relevant information. Decision areas typically include all or part of a building, but they may be any contaminated (or potentially contaminated) area for which characterization or clearance is required. In this report, a statistical sampling approach refers to any sampling approach based on a mathematical or statistical model that provides confidence or probability statements about the decision area. Statistical sampling approaches typically involve taking samples at random locations, or at the nodes of a randomly located grid or lattice that is superimposed on the decision area. The term probabilistic sampling is often used to describe statistical sampling designs that exclusively use randomly located samples. This terminology implies that probabilistic statements can only be made based upon the results of randomly placed samples. However, the CJR method utilizes a Bayesian model that gives rise to a formal probabilistic statement regarding the acceptability of the decision area based upon the information obtained from both judgmental and random samples. Judgmental samples play a vital role in the characterization and clearance process because they include the informed prior belief and expert knowledge of personnel involved in the sampling process.

97 MATHEMATICS AND COMPUTING↗