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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗
DOE OSTI2019

Performance and energy impact of OpenMP runtime configurations on power constrained systems

Energy consumption of an application has attained the same vitality as execution time in current HPC systems, primarily due to the massive energy consumption of these systems and consequently substantial energy bills. This fact forced software developers as well as hardware vendors to put an extensive focus on energy consumption. This focus becomes even more crucial in power constrained systems, where the amount of power dedicated to applications is limited. In such systems, finding the optimal balance between execution time and energy consumption has a significant impact on utilizing these systems efficiently. A programming model is a necessary layer where optimizations can be applied to find the optimal balance. OpenMP is a standard programming model for intra-node parallelism. In this paper, we give a detailed analysis of how to optimize the execution time and energy consumption of an OpenMP application using the OpenMP runtime environment at different power levels. We studied twenty-eight OpenMP parallel regions from five NAS Parallel Benchmark (NPB) applications. Based on the study, we show that for a given power level, a suitable selection of OpenMP runtime parameters can improve the execution time and energy consumption of a parallel region up to 67% and 72%, respectively. We also show that these fine-grained improvements resulted in up to 26% of execution time and 38% of energy consumption improvement for a given OpenMP application. We finally observed that optimizing the energy consumption behavior alone across all power levels regardless of execution time behavior can lead to an additional energy consumption improvement of up to 55%. However, this improvement comes at the expense of execution time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-T-48: A Multi-Institutional Study of Independent Dose Verification for Conventional, SRS and SBRT

Purpose: To show the results of a multi-institutional study of the independent dose verification for conventional, Stereotactic radiosurgery and body radiotherapy (SRS and SBRT) plans based on the action level of AAPM TG-114. Methods: This study was performed at 12 institutions in Japan. To eliminate the bias of independent dose verification program (Indp), all of the institutions used the same CT-based independent dose verification software (Simple MU Analysis, Triangle Products, JP) with the Clarkson-based algorithm. Eclipse (AAA, PBC), Pinnacle{sup 3} (Adaptive Convolve) and Xio (Superposition) were used as treatment planning system (TPS). The confidence limits (CL, Mean±2SD) for 18 sites (head, breast, lung, pelvis, etc.) were evaluated in comparison in dose between the TPS and the Indp. Results: A retrospective analysis of 6352 treatment fields was conducted. The CLs for conventional, SRS and SBRT were 1.0±3.7 %, 2.0±2.5 % and 6.2±4.4 %, respectively. In conventional plans, most of the sites showed within 5 % of TG-114 action level. However, there were the systematic difference (4.0±4.0 % and 2.5±5.8 % for breast and lung, respectively). In SRS plans, our results showed good agreement compared to the action level. In SBRT plans, the discrepancy between the Indp was variable depending on dose calculation algorithms of TPS. Conclusion: The impact of dose calculation algorithms for the TPS and the Indp affects the action level. It is effective to set the site-specific tolerances, especially for the site where inhomogeneous correction can affect dose distribution strongly.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

University Coalition for Fossil Fuel Energy Research

Following a nationwide open competition, the University Coalition for Fossil Energy Research (UCFER) was established in October 2015 through a cooperative agreement between Penn State and the Department of Energy (DOE) National Energy Technology Laboratory (NETL). Penn State lead UCFER with the objective of advancing basic and applied research for clean and low-carbon energy based on fossil fuels in support of the DOE’s mission. UCFER focused on research that improves the efficiency of production and use of fossil energy resources, while minimizing the environmental impacts and reducing greenhouse gas emissions. Penn State lead a team of nine universities (Massachusetts Institute of Technology, The Pennsylvania State University, Princeton University, Texas A&M University, University of Kentucky, University of Southern California, The University of Tulsa, University of Wyoming, and Virginia Polytechnic and State University) during the competition stage, adding seven more universities in 2017 (Carnegie Mellon University, Louisiana State University, The Ohio State University, University of North Dakota, University of Pittsburgh, University of Utah, and West Virginia University). This Coalition exhibited a wide geographical distribution across the U.S. bringing a wide variety of fossil energy expertise. This national university alliance was a major collaborative effort with NETL to address specific topics of R&D in NETL’s mission area, which involved one or more of NETL’s five core competencies (Geologic and Environmental Systems, Materials Engineering and Manufacturing, Energy Conversion Engineering, Systems Engineering and Analysis, and Computational Science and Engineering). The first five to six months of the project was the definitization stage. During this period, Penn State worked closely with NETL to finalize the Coalition organizational structure and By- Laws, prepare a statement of substantial involvement and a statement of project objectives, and develop operations and membership plans. A major component of this stage included preparing an execution plan to solicit research, evaluate proposals, recommend selected projects to NETL, and award projects. In addition, a plan was prepared to monitor projects, review projects, disseminate knowledge from research projects and develop an online system for Coalition research portfolio management. This included developing a website and several databases. The first of six rounds of solicitations started in mid-2016. Projects from the sixth solicitation started February 1, 2021, and ended January 31, 2023. Projects that were selected represented twelve technology lines. Approximately $16.6 million in funding was available for the six solicitations. Most of the funding was provided by DOE, Office of Fossil Energy (DOEFE) with the DOE Fuel Cells Technologies Office (DOE-FCTO) providing funding for a few projects. Coalition universities submitted 259 proposals in response to the solicitations, requesting approximately $67.0 million in funding, and forty-three projects were selected. However, one project withdrew after the principal investigator left the university. The management of the Coalition projects was a major activity by Penn State. Managing the Coalition projects consisted of monitoring the projects, reviewing the projects through annual technical review meetings, disseminating knowledge from the research projects, and developing an online system for Coalition research portfolio management. Penn State’s OMT monitored projects to ensure that all milestones (technical, schedule, budget) were met, expenditures were allowable, cost share (when applicable) were reported, and all technical reports were submitted. The OMT also posted the technical reports electronically on a secure members-only website for access and review by the Coalition members. Disseminating knowledge from the research projects was done through a website, newsletters, various meetings, conferences, journal articles, publicity/press releases, and project summaries that were prepared after each project was completed. Penn State kept NETL apprised of UCFER progress through quarterly reports (thirtyone were submitted by Penn State), verbal and written communications, yearly updates at the annual technical review meetings, and cost accrual reports. The UCFER project had a significant impacty. The UCFER program established the first national university alliance in fossil energy research with a major collaboration effort with DOE NETL that addressed specific topics in NETL’s research and development mission areas. It generated inter-university collaborations, which was another program interest. Twenty-two out of 259 proposals contained collaborations (≈8.5%) and three of forty-two funded projects involved inter-university collaborations (≈7.0%). The forty-two funded projects provided support at fourteen universities involving 269 personnel. Research was conducted by 106 faculty, 115 graduate and undergraduate students, forty-six research staff and post-doctoral scholars, and two visiting scholars. Students and post-doctoral scholars were also on-site at NETL through CRADAs. In addition, non-Coalition participants included six universities and sixteen companies and national laboratories. The non-Coalition participants were involved as subcontractors, providers of cost share, performed unpaid consultation and sample analysis, served as advisory board members, or were providers of samples and materials for testing. UCFER also produced visibility in that fifty-six refereed journal articles were published, fifty-seven conference papers and twenty-three posters were prepared, 190 presentations were given, eight patent applications were filed, two books/book chapters were written, and ten software codes were developed. Collaboration between NETL and the individual projects was a major requirement for all funded projects. This included NETL staff time to support collaboration, consultation, technical guidance, sample preparation and analysis, internships at NETL, on-site testing and equipment usage by Coalition participants at NETL, co-mentoring students, and coauthoring journal articles and conference papers. Collaboration was impacted by COVID-19 in that not all on-site activities could be performed. NETL personnel were coauthors on eight of the conference papers (fourteen percent of the conference papers that were prepared) and seventeen of the journal articles (thirty percent of the journal articles that were prepared). A website was developed for an online proposal solicitation and review process and to provide exposure to UCFER. A website analysis highlighted the large amount of member and general public interest in UCFER by interpreting access statistics from March 2016 through June 2023. Visitors to the site originated from many different organizations, businesses, and countries. The website provided a means to disseminate information to both the general public and the UCFER members and was successfully used for outreach activities. In addition, NETL required that RFP release, proposal submission, and proposal reviews all be performed online. Penn State successfully developed these capabilities in a secure section of the website, which were used throughout the UCFER project. It is recognized that each project had its technical successes. In addition, highlighted successes were compiled and summarized from the research projects. Information was requested from the PIs of completed projects. In addition, Penn State’s Operations Management Team reviewed subcontract reports to identify project successes. Examples of information requested from PIs included (not all-inclusive): new projects that have been funded as a result of UCFER funding; new commercial products; establishment of a new center; new patent; new software; best paper awards; highly-cited work; and graduate student successes. A total of forty-eight highlighted successes were reported.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE

Summary Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, “Artificial Intelligence Driven protein Estimation (AIDE)”, enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API), i.e. drop-in compatible with scikit-learn transformers and pipelines. Availability and implementation AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Reactivity Effects at Mayak Production Association January 2, 1958 Criticality Accident Using Serpent 2 and OpenFOAM

The process nuclear criticality accident that occurred at the Mayak Production Association (Chelyabinsk 40) on January 2, 1958 involving a vessel of uranyl nitrate solution claimed the lives of three workers and left a fourth worker with continuing health problems. There are a myriad of uncertain parameters involved with this accident: What was the molarity of the solution? How much solution was in the vessel at the time of the accident? In what position was the vessel and the solution when it went critical? How important was the impact of reflection due to the workers and/or the floor? These uncertain parameters have made this accident particularly difficult to analyze in the past. This work aims to lower the uncertainty on some of these parameters. A most-probable solution composition is determined by comparing literature on the physical properties of uranyl nitrate solutions to those presented in LA-13638, which describes the accident in question. Using this most-probable solution, the main contributions to the reactivity of the system and hence the eventual accident, are identified through Serpent 2 and OpenFOAM analyses. Serpent 2, a Monte Carlo software tool, is used to perform calculations of the reactivity effects of lowering the vessel toward the floor and the reactivity added by the close proximity of workers. OpenFOAM, a C++ partial differential equation solver toolkit, is used to simulate the fluid inside the vessel as the vessel is tipped. This is done by treating the solution and air inside the vessel as two incompressible, isothermal, and immiscible fluids using a volume of fluid (VoF) approach. The goal of this approach is simply to track the interface between the two fluids, and hence give an accurate description of the geometrical structure of the solution as the vessel is tipped. These two unique tools are then coupled to provide a time-dependent flow simulation to study the effect that the changing geometrical structure had on the criticality of the system, which is novel to the criticality safety field. This work provides a more accurate picture of the accident going forward.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-T-372: Evaluation and Comparison of Second-Check Monitor Unit Calculation Software with Pinnacle Treatment Planning System

Purpose: The purpose of this study was to evaluate and compare the accuracy of dose calculation algorithms in the second check software programs Radcalc, Diamond, IMSure, and MUcheck, against the Pinnacle3 treatment planning system (TPS). Methods: Baseline accuracy of the second check software was established by comparison against Pinnacle TPS data using open square fields of 5, 10, 20, 30 and 40cm in a SAD setup. 18 previously treated patients’ files were exported from the Pinnacle3 TPS to each of the four second check softwares, consisting of 146 step and shoot intensity modulated radiotherapy (IMRT) beams and 60 Smart Arcs. Monitor unit (MU) calculated in each of the software were compared with the TPS and the values were represented as a percent difference. Open fields were calculated as a baseline for each software’s accuracy using 5×5, 10×10, 20×20, 30×30, and 40×40 fields. Box plots, Pearson correlation, and Bland-Altman analysis were used for comparison of the results. Results: The baseline accuracy was established to within 0.6%, −1.4%, −0.2%, and −1.0% for Diamond, IMSure,MUcheck, and Radcalc, respectively. In the clinical data, the dose difference represented as mean ± 1 standard deviation were 0.7%±0.1%, −0.3%±0.1%, −1.5%±0.1%, and 0.4%±0.0% for Diamond, IMSure, MUcheck, and Radcalc, respectively Conclusion: The implementation of Clarkson algorithm for the dose calculation between each of the software in question can vary considerably. The currently used second check software, Radcalc has shown the best agreement on average, variance, and smallest percent range from Pinnacle3 TPS values. The closest in average percent difference from the TPS data was the IMSure software, but has significantly larger variance and percent range. The mean percent differences in Diamond and MUcheck were significantly larger than Radcalc and IMSure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-11: A New Optical Method to Register Patient External Motion

Purpose: To devise and implement a new system to measure and register the patient motion during radiotherapy treatments. Methods: The system can obtain the position of several points in the 3D-space, through their projections in the 2D-images recorded by two cameras. The algorithm needs a series of constants, that are obtained using the images of a calibrated phantom.To test the system, some adhesive labels were placed on the surface of an object. Two cameras recorded the moving object over time. An in-house developed software localized the labels in each image. In the first pair of images, the program used a first approximation given by the user. In the subsequent images, it used the last position as an approximate location. The final exact coordinates of the point were obtained in a two-step process using the contrast of the images. From the 2D-positions of the point in each frame, the 3D-trajectories of each of these marks were obtained.The system was tested with linear displacements, oscillations of a mechanical oscillator, circular trajectories of a rotating disk, and with respiratory motion of a volunteer. Results: Trajectories of several points were reproduced with sub-millimeter accuracy in the three directions of the space. The system was able to follow periodic motion with amplitudes lower than 0.5mm; and trajectories of rotating points at speeds up to 200mm/s. The software could also track accurately the respiration motion of a person. Conclusion: A new, inexpensive optical tracking system for patient motion has been demonstrated. The system detects motion with high accuracy. Installation and calibration of the system is simple and quick. Data collection is not expected to involve any discomfort for the patient, nor any delay for the treatment. The system could be also used as a method of warning for patient movements, and for gating. We acknowledge financial support from Fundacion Mutua Madrilena, Madrid, Spain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Coupled-cluster techniques for computational chemistry: The CFOUR program package

An up-to-date overview of the CFOUR program system is given. After providing a brief outline of the evolution of the program since its inception in 1989, a comprehensive presentation is given of its well-known capabilities for high-level coupled-cluster theory and its application to molecular properties. Subsequent to this generally well-known background information, much of the remaining content focuses on lesser-known capabilities of CFOUR, most of which have become available to the public only recently or will become available in the near future. Each of these new features is illustrated by a representative example, with additional discussion targeted to educating users as to classes of applications that are now enabled by these capabilities. Lastly, some speculation about future directions is given, and the mode of distribution and support for CFOUR are outlined in the appendix.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Dynamic analysis environment for nuclear forensic analyses

We report a Dynamic Analysis Environment (DAE) software package is introduced to facilitate group inclusion/exclusion method testing, evaluation and comparison for pre-detonation nuclear forensics applications. Employing DAE, the multivariate signatures of a questioned material can be compared to the signatures for different, known groups, enabling the linking of the questioned material to its potential process, location, or fabrication facility. Advantages of using DAE for group inclusion/exclusion include built-in query tools for retrieving data of interest from a database, the recording and documentation of all analysis steps, a clear visualization of the analysis steps intelligible to a non-expert, and the ability to integrate analysis tools developed in different programming languages. Two group inclusion/exclusion methods are implemented in DAE: principal component analysis, a parametric feature extraction method, and k nearest neighbors, a nonparametric pattern recognition method. Spent Fuel Isotopic Composition (SFCOMPO), an open source international database of isotopic compositions for spent nuclear fuels (SNF) from 14 reactors, is used to construct PCA and KNN models for known reactor groups, and 20 simulated SNF samples are utilized in evaluating the performance of these group inclusion/exclusion models. For all 20 simulated samples, PCA in conjunction with the Q statistic correctly excludes a large percentage of reactor groups and correctly includes the true reactor of origination. As a result, employing KNN, 14 of the 20 simulated samples are classified to their true reactor of origination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-G-TeP4-06: An Integrated Application for Radiation Therapy Treatment Plan Directives, Management, and Reporting

Purpose: With electronic medical records, patient information for the treatment planning process has become disseminated across multiple applications with limited quality control and many associated failure modes. We present the development of a single application with a centralized database to manage the planning process. Methods: The system was designed to replace current functionalities of (i) static directives representing the physician intent for the prescription and planning goals, localization information for delivery, and other information, (ii) planning objective reports, (iii) localization and image guidance documents and (iv) the official radiation therapy prescription in the medical record. Using the Eclipse Scripting Application Programming Interface, a plug-in script with an associated domain-specific SQL Server database was created to manage the information in (i)–(iv). The system’s user interface and database were designed by a team of physicians, clinical physicists, database experts, and software engineers to ensure usability and robustness for clinical use. Results: The resulting system has been fully integrated within the TPS via a custom script and database. Planning scenario templates, version control, approvals, and logic-based quality control allow this system to fully track and document the planning process as well as physician approval of tradeoffs while improving the consistency of the data. Multiple plans and prescriptions are supported along with non-traditional dose objectives and evaluation such as biologically corrected models, composite dose limits, and management of localization goals. User-specific custom views were developed for the attending physician review, physicist plan checks, treating therapists, and peer review in chart rounds. Conclusion: A method was developed to maintain cohesive information throughout the planning process within one integrated system by using a custom treatment planning management application that interfaces directly with the TPS. Future work includes quantifying the improvements in quality, safety and efficiency that are possible with the routine clinical use of this system. Supported in part by NIH-P01-CA-059827.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-486: A Simple Approach to Performing Light Versus Radiation Field Coincidence Quality Assurance Using An Electronic Portal Imaging Device (EPID)

Purpose: To develop an accurate, straightforward, and user-independent method for performing light versus radiation field coincidence quality assurance utilizing EPID images, a simple phantom made of readily-accessible materials, and a free software program. Methods: A simple phantom consisting of a blocking tray, graph paper, and high-density wire was constructed. The phantom was used to accurately set the size of a desired light field and imaged on the electronic portal imaging device (EPID). A macro written for use in ImageJ, a free image processing software, was then use to determine the radiation field size utilizing the high density wires on the phantom for a pixel to distance calibration. The macro also performs an analysis on the measured radiation field utilizing the tolerances recommended in the AAPM Task Group #142. To verify the accuracy of this method, radiochromic film was used to qualitatively demonstrate agreement between the film and EPID results, and an additional ImageJ macro was used to quantitatively compare the radiation field sizes measured both with the EPID and film images. Results: The results of this technique were benchmarked against film measurements, which have been the gold standard for testing light versus radiation field coincidence. The agreement between this method and film measurements were within 0.5 mm. Conclusion: Due to the operator dependency associated with tracing light fields and measuring radiation fields by hand when using film, this method allows for a more accurate comparison between the light and radiation fields with minimal operator dependency. Removing the need for radiographic or radiochromic film also eliminates a reoccurring cost and increases procedural efficiency.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources

In January 2019, the U.S. Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions for in situ data management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing system, cooperating toward a common objective. The workshop revealed two primary, interdependent motivations for processing and managing data in situ. The first motivation is that the in situ methodology enables scientific discovery from a broad range of data sources over a wide scale of computing platforms: leadership-class systems, clusters, clouds, workstations, and embedded devices at the edge. The successful development of ISDM capabilities will benefit real-time decision-making, design optimization, and data-driven scientific discovery. The second motivation is the need to decrease data volumes. ISDM can make critical contributions to managing large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency, often while simultaneously increasing scientific precision. A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery across the Office of Science mission space. Applications wanting to use the in situ capabilities include those where data analysis feeds back to the simulation, decisions are made autonomously, big data or machine learning is among the tasks to be coordinated, and computations need to be completed in real time. The workshop identified six priority research directions that highlight the components and capabilities needed for ISDM to be successful for the wide variety of applications discussed: making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗