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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Feasibility and Safety Assessment for Advanced Reactor Concepts Using Vented Fuel

Recent interest in fast reactor technology has led to renewed analysis of past reactor concepts such as Gas Fast Reactors and Sodium Fast Reactors. In an effort to make these reactors more economic, the fuel is required to stay in the reactor for extended periods of time; the longer the fuel stays within the core, the more fertile material is converted into usable fissile material. However, as burnup of the fuel-rod increases, so does the internal pressure buildup due to gaseous fission products. In order to reach the 30 year lifetime requirements of some reactor designs, the fuel pins must have a vented-type design to allow the buildup of fission products to escape. The present work aims to progress the understanding of the feasibility and safety issues related to gas reactors that incorporate vented fuel. The work was separated into three different work-scopes: 1. Quantitatively determine fission gas release from uranium carbide in a representative helium cooled fast reactor; 2. Model the fission gas behavior, transport, and collection in a Fission Product Vent System; and, 3. Perform a safety analysis of the Fission Product Vent System. Each task relied on results from the previous task, culminating in a limited scope Probabilistic Risk Assessment (PRA) of the Fission Product Vent System. Within each task, many key parameters lack the fidelity needed for comprehensive or accurate analysis. In the process of completing each task, the data or methods that were lacking were identified and compiled in a Gap Analysis included at the end of the report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Quantitative Assessment of Workload and Stressors in Clinical Radiation Oncology

Purpose: Workload level and sources of stressors have been implicated as sources of error in multiple settings. We assessed workload levels and sources of stressors among radiation oncology professionals. Furthermore, we explored the potential association between workload and the frequency of reported radiotherapy incidents by the World Health Organization (WHO). Methods and Materials: Data collection was aimed at various tasks performed by 21 study participants from different radiation oncology professional subgroups (simulation therapists, radiation therapists, physicists, dosimetrists, and physicians). Workload was assessed using National Aeronautics and Space Administration Task-Load Index (NASA TLX). Sources of stressors were quantified using observational methods and segregated using a standard taxonomy. Comparisons between professional subgroups and tasks were made using analysis of variance ANOVA, multivariate ANOVA, and Duncan test. An association between workload levels (NASA TLX) and the frequency of radiotherapy incidents (WHO incidents) was explored (Pearson correlation test). Results: A total of 173 workload assessments were obtained. Overall, simulation therapists had relatively low workloads (NASA TLX range, 30-36), and physicists had relatively high workloads (NASA TLX range, 51-63). NASA TLX scores for physicians, radiation therapists, and dosimetrists ranged from 40-52. There was marked intertask/professional subgroup variation (P<.0001). Mental demand (P<.001), physical demand (P=.001), and effort (P=.006) significantly differed among professional subgroups. Typically, there were 3-5 stressors per cycle of analyzed tasks with the following distribution: interruptions (41.4%), time factors (17%), technical factors (13.6%), teamwork issues (11.6%), patient factors (9.0%), and environmental factors (7.4%). A positive association between workload and frequency of reported radiotherapy incidents by the WHO was found (r = 0.87, P value=.045). Conclusions: Workload level and sources of stressors vary among professional subgroups. Understanding the factors that influence these findings can guide adjustments to the workflow procedures, physical layout, and/or communication protocols to enhance safety. Additional evaluations are needed in order to better understand if these findings are systemic.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1988

The Environment for Application Software Integration and Execution (EASIE), version 1.0. Volume 3: Program execution guide

The Environment for Application Software Integration and Execution, EASIE, provides a methodology and a set of software utility programs to ease the task of coordinating engineering design and analysis codes. EASIE was designed to meet the needs of conceptual design engineers that face the task of integrating the results of many stand-alone engineering analysis programs. EASIE provides access to these programs via a quick, uniform, user-friendly interface. In addition, EASIE provides utilities which aid in the execution of the following tasks: selection of application programs, modification and review of program data, automatic definition and coordination of data files during program execution and a logging of steps executed throughout a design study. Volume 3, the Program Execution Guide, describes the executive capabilities provided by EASIE and defines the command language and menus available under Version 1.0. EASIE provides users with two basic modes of operation. One is the Application-Derived Executive (ADE) which provides users with sufficient guidance to quickly review data, select menu action items, and execute application programs. The second is the Complete Control Executive (CCE), which provides a full executive interface allowing users in-depth control of the design process.

Schwing, James L.↗
NASA NTRS1988

The Environment for Application Software Integration and Execution (EASIE), version 1.0. Volume 2: Program integration guide

The Environment for Application Software Integration and Execution, EASIE, provides a methodology and a set of software utility programs to ease the task of coordinating engineering design and analysis codes. EASIE was designed to meet the needs of conceptual design engineers that face the task of integrating many stand-alone engineering analysis programs. Using EASIE, programs are integrated through a relational data base management system. In volume 2, the use of a SYSTEM LIBRARY PROCESSOR is used to construct a DATA DICTIONARY describing all relations defined in the data base, and a TEMPLATE LIBRARY. A TEMPLATE is a description of all subsets of relations (including conditional selection criteria and sorting specifications) to be accessed as input or output for a given application. Together, these form the SYSTEM LIBRARY which is used to automatically produce the data base schema, FORTRAN subroutines to retrieve/store data from/to the data base, and instructions to a generic REVIEWER program providing review/modification of data for a given template. Automation of these functions eliminates much of the tedious, error prone work required by the usual approach to data base integration.

Jones, Kennie H.↗
NASA NTRS1989

The Environment for Application Software Integration and Execution (EASIE) version 1.0. Volume 1: Executive overview

The Environment for Application Software Integration and Execution (EASIE) provides a methodology and a set of software utility programs to ease the task of coordinating engineering design and analysis codes. EASIE was designed to meet the needs of conceptual design engineers that face the task of integrating many stand-alone engineering analysis programs. Using EASIE, programs are integrated through a relational database management system. Volume 1, Executive Overview, gives an overview of the functions provided by EASIE and describes their use. Three operational design systems based upon the EASIE software are briefly described.

Rowell, Lawrence F.↗
DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗
DOE OSTI2024

Role of the likelihood for elastic scattering uncertainty quantification

In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Rapid Quadrupole-Time-of-Flight Mass Spectrometry Method Quantifies Oxygen-Rich Lignin Compound in Complex Mixtures

Complex mixture analysis is a costly and time-consuming task facing researchers with foci as varied as food science and fuel analysis. When faced with the task of quantifying oxygen-rich bio-oil molecules in a complex diesel mixture, we asked whether complex mixtures could be qualitatively and quantitatively analyzed on a single mass spectrometer with mid-range resolving power without the use of lengthy separations. To answer this question, we developed and evaluated a quantitation method that eliminated chromatography steps and expanded the use of quadrupole-time-of-flight mass spectrometry from primarily qualitative to quantitative as well. To account for mixture complexity, the method employed an ionization dopant, targeted tandem mass spectrometry, and an internal standard. This combination of three techniques achieved reliable quantitation of oxygen-rich eugenol in diesel from 300 to 2500 ng/mL with sufficient linearity (R{sup 2} = 0.97 ± 0.01) and excellent accuracy (percent error = 0% ± 5). To understand the limitations of the method, it was compared to quantitation attained on a triple quadrupole mass spectrometer, the gold standard for quantitation. The triple quadrupole quantified eugenol from 50 to 2500 ng/mL with stronger linearity (R{sup 2} = 0.996 ± 0.003) than the quadrupole-time-of-flight and comparable accuracy (percent error = 4% ± 5). This demonstrates that a quadrupole-time-of-flight can be used for not only qualitative analysis but also targeted quantitation of oxygen-rich lignin molecules in complex mixtures without extensive sample preparation. The rapid and cost-effective method presented here offers new possibilities for bio-oil research, including: (1) allowing for bio-oil studies that demand repetitive analysis as process parameters are changed and (2) making this research accessible to more laboratories. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Bayesian Adaptive Polynomial Chaos Expansions

Polynomial chaos expansions (PCEs) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare—especially with implementations in R. Motivated by the success of adaptive Bayesian machine learning models such as BART, BASS and BPPR, we develop a new fully Bayesian adaptive PCE method with an efficient and accessible R implementation: khaos. Our approach includes a novel proposal distribution that enables data-driven interaction selection and supports a modified g-prior tailored to PCE structure. Through simulation studies and real-world UQ applications, we demonstrate that the Bayesian adaptive PCE provides competitive performance for surrogate modeling, global sensitivity analysis and ordinal regression tasks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Natural Language Processing Methods for Air Traffic Management Text and Speech Data

This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.

Natural Language Processing↗
NASA NTRS1987

Overview of Austrian Airborne Imaging Spectrometer (AIS) programme and first results

Airborne Imaging Spectrometer (AIS) data collected from eight test areas in Austria were evaluated for their usefulness in forest damage assessment, geobotany, alpine vegetation mapping, and land use classification. Difficulties encountered in installing the SPAM spectral analysis software for use on the image display system and the necessity to adapt existing programs for this task impeded and delayed the analysis of the AIS data. Spectral reflectance curves obtained from a geobotanical test site show a marked increase in reflectance across most of the measured spectrum for metal stressed spruce trees compared with nonstressed spruce trees.

Banninger, C.↗
DOE OSTI2013

Scalability Analysis of Gleipnir: A Memory Tracing and Profiling Tool, on Titan

Application performance is hindered by a variety of factors but most notably driven by the well know CPU-memory speed gap (also known as the memory wall). Understanding application s memory behavior is key if we are trying to optimize performance. Understanding application performance properties is facilitated with various performance profiling tools. The scope of profiling tools varies in complexity, ease of deployment, profiling performance, and the detail of profiled information. Specifically, using profiling tools for performance analysis is a common task when optimizing and understanding scientific applications on complex and large scale systems such as Cray s XK7. This paper describes the performance characteristics of using Gleipnir, a memory tracing tool, on the Titan Cray XK7 system when instrumenting large applications such as the Community Earth System Model. Gleipnir is a memory tracing tool built as a plug-in tool for the Valgrind instrumentation framework. The goal of Gleipnir is to provide fine-grained trace information. The generated traces are a stream of executed memory transactions mapped to internal structures per process, thread, function, and finally the data structure or variable. Our focus was to expose tool performance characteristics when using Gleipnir with a combination of an external tools such as a cache simulator, Gl CSim, to characterize the tool s overall performance. In this paper we describe our experience with deploying Gleipnir on the Titan Cray XK7 system, report on the tool s ease-of-use, and analyze run-time performance characteristics under various workloads. While all performance aspects are important we mainly focus on I/O characteristics analysis due to the emphasis on the tools output which are trace-files. Moreover, the tool is dependent on the run-time system to provide the necessary infrastructure to expose low level system detail; therefore, we also discuss any theoretical benefits that can be achieved if such modules were present.

Janjusic, Tommy↗
DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗
DOE OSTI2011

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

Purpose: This work applies a cascaded systems model for cone-beam CT imaging performance to the design and optimization of a system for musculoskeletal extremity imaging. The model provides a quantitative guide to the selection of system geometry, source and detector components, acquisition techniques, and reconstruction parameters. Methods: The model is based on cascaded systems analysis of the 3D noise-power spectrum (NPS) and noise-equivalent quanta (NEQ) combined with factors of system geometry (magnification, focal spot size, and scatter-to-primary ratio) and anatomical background clutter. The model was extended to task-based analysis of detectability index (d') for tasks ranging in contrast and frequency content, and d' was computed as a function of system magnification, detector pixel size, focal spot size, kVp, dose, electronic noise, voxel size, and reconstruction filter to examine trade-offs and optima among such factors in multivariate analysis. The model was tested quantitatively versus the measured NPS and qualitatively in cadaver images as a function of kVp, dose, pixel size, and reconstruction filter under conditions corresponding to the proposed scanner. Results: The analysis quantified trade-offs among factors of spatial resolution, noise, and dose. System magnification (M) was a critical design parameter with strong effect on spatial resolution, dose, and x-ray scatter, and a fairly robust optimum was identified at M {approx} 1.3 for the imaging tasks considered. The results suggested kVp selection in the range of {approx}65-90 kVp, the lower end (65 kVp) maximizing subject contrast and the upper end maximizing NEQ (90 kVp). The analysis quantified fairly intuitive results--e.g., {approx}0.1-0.2 mm pixel size (and a sharp reconstruction filter) optimal for high-frequency tasks (bone detail) compared to {approx}0.4 mm pixel size (and a smooth reconstruction filter) for low-frequency (soft-tissue) tasks. This result suggests a specific protocol for 1 x 1 (full-resolution) projection data acquisition followed by full-resolution reconstruction with a sharp filter for high-frequency tasks along with 2 x 2 binning reconstruction with a smooth filter for low-frequency tasks. The analysis guided selection of specific source and detector components implemented on the proposed scanner. The analysis also quantified the potential benefits and points of diminishing return in focal spot size, reduced electronic noise, finer detector pixels, and low-dose limits of detectability. Theoretical results agreed quantitatively with the measured NPS and qualitatively with evaluation of cadaver images by a musculoskeletal radiologist. Conclusions: A fairly comprehensive model for 3D imaging performance in cone-beam CT combines factors of quantum noise, system geometry, anatomical background, and imaging task. The analysis provided a valuable, quantitative guide to design, optimization, and technique selection for a musculoskeletal extremities imaging system under development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Development of A Crew Health and Performance System Probabilistic Risk Assessment Tool: Proof-of-Concept Approach

The crew health and performance (CHP) system represents the span of technological interventions and tested processes and procedures that in combination address the human risk to space flight. The Human Research Program (HRP) mental model of the CHP system breaks the capabilities needed to meet NASA human flight systems standards into specific categories (i.e., countermeasures, behavioral health, medical intervention). These categories are further broken down into specific sub-groups generally associated with the human system risks that these capabilities seek to mitigate. Like the approach used to develop the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Analysis Tool (MEDPRAT), HRP tasked NASA GRC’s Cross-Cutting Computational Modeling Project with developing a CHP probabilistic risk assessment tool, the CHP-PRA. The CHP-PRA model seeks to quantify and relatively assess the human risk state within the crew health and performance domain, using a combination of knowledge about human system risks and technology and practices likely to be applied during space flight missions. This modeling system will incorporate customer and stakeholder feedback and be flexible enough to address multiple different questions about important low-level mission-specific parameters. This presentation will introduce the initial concept and development timeline for this tool and demonstrate proof-of-concept through an application addressing a specific human risk question posed within the Artemis program.

Risk analysis↗
DOE OSTI2018

ASC CSSE Level 2 Milestone #6362: Resilient Asynchronous Many Task Programming Model

This report is an outcome of the ASC CSSE Level 2 Milestone 6362: Analysis of Re- silient Asynchronous Many-Task (AMT) Programming Model. It comprises a summary and in-depth analysis of resilience schemes adapted to the AMT programming model. Herein, performance trade-offs of a resilient-AMT prograrnming model are assessed through two ap- proaches: (1) an analytical model realized by discrete event simulations and (2) empirical evaluation of benchmark programs representing regular and irregular workloads of explicit partial differential equation solvers. As part of this effort, an AMT execution simulator and a prototype resilient-AMT programming framework have been developed. The former permits us to hypothesize the performance behavior of a resilient-AMT model, and has undergone a verification and validation (V&V) process. The latter allows empirical evaluation of the perfor- mance of resilience schemes under emulated program failures and enabled the aforementioned V&V process. The outcome indicates that (1) resilience techniques implemented within an AMT framework allow efficient and scalable recovery under frequent failures, that (2) the abstraction of task and data instances in the AMT programming model enables readily us- able Application Program Interfaces (APIs) for resilience, and that (3) this abstraction enables predicting the performance of resilient-AMT applications with a simple simulation infrastruc- ture. This outcome will provide guidance for the design of the AMT programming model and runtime systems, user-level resilience support, and application development for ASC's next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Quantitative Analysis Guidance in Safety Software Validation

Quantitative analysis in software development has been a challenging topic over the years in safety software. While qualitative processes for vigorous safety software verification and validation (V and V) have been provided through standard such as IEEE 1012, quantitative analysis for V and V tasks such as correctness, completeness or accuracy evaluation have not been formally defined. In practice, there are quantitative analyses in terms of using software metrics. Many of these software metrics are useful in analyzing the test results. Some of them are used for providing guidance in code evaluation activities e.g. numbers of line of codes not exceeding certain limits, recursive calls are not allowed, etc. However, few can be used for providing quantitative analysis guidance for the V and V tasks such as completeness evaluation. AREVA Federal Services (AFS) has provided services for many nuclear projects in US. Through compliance with IEEE 1012, AFS V and V processes have provided vigorous qualitative processes ensuring quality level of the software products used in the project meeting the required nuclear software level. In addition, quantitative analysis is developed to provide structured validation framework ensuring 'zero defect' in the software products. This paper describes some aspects of the quantitative approaches using a sample safety application. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗