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Stochastic Verification by Analysis for Autonomous Systems Management Architecture (ASMA)

The Gateway Vehicle Systems Manager (VSM) is the top-level of a distributed, hierarchical software control system. VSM is data-driven and will make decisions related to mission, fault, resource management and vehicle control. These attributes combined with a high degree of autonomy make it susceptible to emergent behavior. In order to achieve the high level of confidence needed in this critical system, the VSM team has developed a multifaceted verification strategy employing traditional verification techniques, simulation, model checking, and runtime verification. Individual algorithms are verified using conventional testing and model checking using assume-guarantee contracts. A discrete event-based simulation approach is being developed to verify timelines. This presentation describes an enhancement to the verification approach using analysis to enhance system robustness by detecting and resolving the potential for emergent behavior. The verification by analysis employs a Software in the Loop (SITL) environment with real flight software executing on emulated processors, simulations of vehicle subsystems, flight dynamics, and human inputs. Since the possible input space and configuration data set are too large for exhaustive testing, a Monte Carlo approach is used to cover feasible scenarios, augmented with corner cases and known higher-risk scenarios. A key problem in using Monte Carlo-based system verification is evaluating test results to ensure that system behavior is correct. The presentation describes the approach the VSM team uses to monitor behavior for compliance with predetermined boundaries and to identify anomalous behavior for further analysis. This presentation describes the multi-level systems approach to verification, and the simulation-based layer that covers the feasible state space: 1. Overview of the Gateway VSM 2. Special challenges due to heterogeneous, hierarchical architecture 3. Modeling and simulation environment using flight software and system simulations 4. Developing input sets to ensure state-space coverage 5. Developing model and data configuration sets to ensure model coverage 6. Interpreting results without predetermined outcomes 7. Lessons learned and future work

Verification and Validation↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗
DOE OSTI2022

Standard operating procedures for the comprehensive and reliable analysis of cannabis terpenes

Terpenes are the primary determinants of cannabis flower aroma, and ongoing research tests their potential for impacting the overall experience. Frustratingly, despite the importance of terpenes in cannabis physiology and commercial uses, literature reports vary widely regarding the major constituents of volatile blends and the concentrations of individual terpenes. Here, in this article, we provide detailed descriptions of complementary approaches that will allow researchers to determine the identity and quantity of cannabis terpenes unequivocally and reliably. These standard operating procedures will guide decisions about which method to employ to address specific analytical goals. We are including two application examples to illustrate the utility of different approaches for tackling the analysis of terpenes in cannabis flower samples.

Polito, Joshua↗
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗
DOE OSTI2019

Demonstration of Integrated Hazard Analysis for Digital Reactor Trip Systems

Digital instrumentation and control (I&C) upgrades are a vital research area for nuclear industry. Despite their performance benefits, deployment of digital I&C in nuclear power plants (NPPs) has been limited. Digital I&C systems exhibit complex failure modes including common cause failures (CCFs) which can be difficult to identify. This paper describes the development of a redundancy-guided application of the Systems-Theoretic Process Analysis (STPA) and Fault Tree Analysis (FTA) for the hazard analysis of digital I&C in advanced NPPs. The resulting Redundancy-guided System-theoretic Hazard Analysis (RESHA) is applied for the case study of a representative state-of-the-art digital reactor trip system. The analysis qualitatively and systematically identifies the most critical CCFs and other hazards of digital I&C systems. Ultimately, RESHA can help researchers make informed decisions for how, and to what degree, defensive measures such as redundancy, diversity, and defense-in-depth can be used to mitigate or eliminate the potential hazards of digital I&C systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Updates to a Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 20420

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The U.S. Department of Energy (DOE) was required to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980 including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the West Valley Site that evaluates potential future consequences for human health and the environment. This forms the core of the spatial domain of the West Valley PPA Model. The PPA Model, developed using the GoldSim system modeling software, is a tool intended to provide support for decision making that evaluates uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-grouped contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of high level radioactive waste resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The PPA Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife could be exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the PPA Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. PPA Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes. With this information, the decision makers can drive informed, defensible decisions regarding decommissioning of the Site. This paper includes an update of the information presented at WM2019 [1]. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗
NASA NTRS2018

More Efficient Precipitation Rate Data Acquisition with the STORM Swath-Based Analysis Tool

A multitude of locations exist for acquiring satellite-derived precipitation rate data. Because the volume of data exceeds what can reasonably be analyzed manually, online tools have been developed to help researchers make more informed data acquisition decisions. Some interfaces provide sample images generated from prioritized variables to enable efficient visual inspection, while others include the ability to subset geographically to reduce the volume of the data to the area of interest. Building upon such value-added services is the STORM (the NASA Precipitation Processing System (PPS) data product ordering system) Swath-Based Analysis Tool (https://storm.pps.eosdis.nasa.gov/storm/Analysis.jsp). With it, users can compare overflights of multiple precipitation satellites simultaneously and from this make determinations about which swaths will best serve their research. They can preview these overflights with two distinct visualization tools. Finally, they can then download geographically subset swaths that have precipitation rate characteristics matching their research needs. A user starts by making up to six selections. They then select their date range of interest going back to 1997. Finally, they select a geographical region either by inputting latitude-longitude corners of a rectangle or drawing that rectangle on an interactive map. The system processes each file, determining aggregate statistics about the precipitation rates within the selected region as observed by each instrument. These statistical values are added to a time series graph as a "time remaining" counter is continuously updated. The chart is fully interactive, with detailed values displayed upon mouseover, and numerous options available upon clicking each point. Since the user is likely to need the relevant files for their own research, they can generate an order from their request. All files are geographically subset to the region of interest, and the user can use the statistical values to limit which files are added to the order. This flexibility helps to ensure researchers are only downloading the files they need for their work, making for a more efficient process both from a time-consumption and a storage perspective. This paper will introduce the Swath-Based Analysis Tool and explore its numerous features. It will discuss several use cases for precipitation scientists.

remote sensing↗
NASA NTRS1982

Analysis of physical exercises and exercise protocols for space transportation system operation

A quantitative evaluation of the Thornton-Whitmore treadmill was made so that informed management decisions regarding the role of this treadmill in operational flight crew exercise programs could be made. Specific tasks to be completed were: The Thornton-Whitmore passive treadmill as an exercise device at one-g was evaluated. Hardware, harness and restraint systems for use with the Thornton-Whitmore treadmill in the laboratory and in Shuttle flights were established. The quantitative and qualitative performance of human subjects on the Thorton-Whitmore treadmill with forces in excess of one-g, was evaluated. The performance of human subjects on the Thornton-Whitmore treadmill in weightlessness (onboard Shuttle flights) was also determined.

Coleman, A. E.↗
DOE OSTI2020

BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAMLCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAMLCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Decarbonizing residential buildings in the United States: A comparative analysis of households and construction professionals

In this study, we present a comparative analysis of surveys distributed to home occupants and construction professionals in the U.S., focused on energy upgrades and electrification retrofits that support residential building decarbonization. The surveys were executed by separate research groups and combined for this study. The study examines the decision-making, sentiments, perceptions, experiences, and practices of both groups by analyzing data from three separate surveys. These surveys assess technologies, attitudes, awareness, motivations, barriers, and opportunities related to energy retrofits and electrification. The analysis highlights key differences in the perceptions and behaviors of households and construction professionals, revealing substantial barriers to achieving decarbonization goals. For example, households cite climate change and sustainability as key motivators for pursuing energy retrofits (89%), while construction industry professionals view these themes as less important for their clients (44%). This suggests an opportunity for the construction industry to align its messaging with the values that households prioritize, helping to advance residential decarbonization. Overall, the study identifies challenges faced by both groups, factors influencing the adoption of energy-efficient practices, and inconsistencies between occupant and construction industry professionals' views. These insights contribute to the development of targeted strategies and policies to accelerate the decarbonization of residential buildings in the U.S.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Development of a Technical, Economic, and Risk Assessment Tool for the Evaluation of Work Reduction Opportunities

Efficient and cost-effective operation of a nuclear power plant (NPP) is essential to ensuring long-term economical and safe operation. Multiple cost saving opportunities exist, referred to here as work reduction opportunities (WRO). These WROs reduce plant operating costs by employing various cost-effective strategies (e.g., implementation of modern technologies). Identifying and objectively screening WROs is an essential task to help reduce overall costs. However, there is no comprehensive framework for assessing WROs in the nuclear industry and evaluating their impact on plant operations. This report presents a novel framework for systematically evaluating WROs from a technical, economic, and risk perspective. As NPPs continue to add new technology and implement modernization strategies into their current processes, potential WROs are commonly identified. Although most WROs have the potential to reduce costs, not all opportunities will result in significant cost savings due to unforeseen risks, large implementation costs, or benefits that fall short of expectations. Examples of this can be the result of a technology that is not fully developed, uncertainty in the amount of cost reduction, or difficulties introducing a new process into an organization. These uncertainties can manifest several ways and can result in a WRO with limited cost savings or even a loss of investment. The framework developed emphasizes the importance of effectively screening the WROs from a holistic perspective to objectively identify inefficiencies and ensure a positive impact to the organization. This report presents the Technical, Economic, and Risk Assessment (TERA) as a key methodology for the screening and evaluation of potential WROs. The TERA framework begins with a screening phase where the process is examined through a hybrid combination of Lean Six Sigma and Integrated Operations for Nuclear (ION) guiding principles. This framework examines the current processes using the Lean Six Sigma SIPOC (Suppliers, Inputs, Process, Outputs, Consumers) methodology but retains the ION key elements of People, Technology, Process, and Governance as important factors to the nuclear decision-making process. By combining the principles of Lean Six Sigma and ION, the developed screening process is specific to the nuclear industry in that it systematically evaluates WROs in order to implement new technology that is comprehensively evaluated. The TERA begins by mapping current processes as they relate to WROs and examining the inefficiencies. Furthermore, the created process map can be used to identify and evaluate potential solutions. Using key performance indicators (KPIs), the TERA evaluates each area—technology, economics, and risk—for uncertainties and to perform cost-benefit analysis. The results of the TERA are important KPIs that allow for an evaluation of different processes and technology implementations. This assessment enables decision-makers to compare various WROs based on metrics and then make informed decisions for which opportunity to implement first. This research includes not only the creation of the TERA framework, but also the evaluation of its performance. A case study for screening potential WROs at Southern Nuclear Company is presented that utilizes the TERA methodology. Through the use of TERA, various WROs were screened, and the solutions evaluated for cost-benefit expectations. The report concludes by summarizing the overall effort and implications for utility modernization. The performance of the screening and TERA are discussed as well as the impact on the nuclear industry. The TERA process enables utilities to evaluate and inform investment decisions for WROs and mitigate any potential risks. Through this research, we provide utilities with a valuable framework to optimize operations, reduce costs, and drive continuous process improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability, along with improved convergence stability and solution quality through the selection of the appropriate optimization strategy based on the degree of inter-device noise heterogeneity. Overall, this framework establishes a general approach for precision- and resource-aware optimization in scalable, automated experimental platforms, demonstrated here on a representative multi-device 3D printing case study.

Schenk, Christina↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
DOE OSTI2019

Design, Analysis, and Operations Toolkit (DAO-Tk)

The Design, Analysis, and Operations Toolkit (DAO-Tk) is a software package that allows users to explore design optimization, operations decision, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The DAO-Tk software is the first comprehensive design tool of it's kind to incorporate all of these aspects while being deployed as open source.

Martinek, Janna↗
DOE OSTI2025

Hierarchical Estimation For Planetary Protection

The software uses Bayes' theorem to describe the probability of an event based on prior knowledge of conditions that might be related to the event. The purpose of Bayesian analysis is to determine posterior probabilities based on prior probabilities where new information can be used in the decision-making process as additional data is gathered. The software will be used in Probabilistic Risk Assessments (PRAs) related to the Europa Clipper mission, which is one of NASA’s top priorities. Ultimately, the mission entails sending the Europa Clipper spacecraft to Jupiter’s Europa moon to orbit the planet and collect data for research and development. Europa is the smallest of the four Galilean moons orbiting Jupiter and is believed by researchers to be the most promising place to look for present-day environments suitable for life. Europa is thought to have an iron core, a rocky mantle, and a salt-water ocean covered by an ice-layered surface.

Gribok, Andrei [Idaho National Laboratory (INL), I↗

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