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In developing a software system, we would like to estimate the total number of faults inserted in to the system, its residual fault content at any given time, and the efficacy of the testing activity in executing the code containing the newly inserted faults.
A well-known hazard associated with exposure to the space environment is the risk of failure due to an impact from a micrometeoroid and orbital debris (MMOD) particle. As NASA prepares to return astronauts to the moon with the Artemis program, the next generation of spacesuit is in development to support future extravehicular activities (EVAs.) An MMOD impact to the spacesuit is of great concern as a large leak could prevent an astronaut from safely reaching an airlock in time resulting in a loss of life. The exploration extravehicular mobility unit (xEMU) must meet MMOD requirements for multiple environments including those in low earth orbit (LEO) as well as the meteoroid and secondary lunar regolith ejecta environments found on the lunar surface. The subject of this paper is an internal xEMU configuration design developed by NASA Johnson Space Center (JSC) personnel. This paper will expand on the hypervelocity impact (HVI) testing and ballistic limit equation (BLE) definition work that was partially presented at the 2nd International Orbital De-bris (IOC-II) Conference held in Sugar Land, TX in December 2023. The xEMU shares similarities with the legacy Extravehicular Mobility Unit (EMU) spacesuit that is currently used for ISS EVAs, however differences in the layup (e.g., materials, thicknesses, and layers) of the fabric environmental protection garment (EPG), portable life support system (xPLSS) and helmet required an extensive test program to determine ballistic performance. Over 100 hypervelocity impact (HVI) tests were performed by the NASA/JSC HVIT and White Sands Test Facility (WSTF) teams on the xEMU EPG, xPLSS and helmet to generate ballistic limit equations (BLEs) for MMOD impacts. Additionally, over 50 low speed tests (< 1km/s) were performed by the NASA/JSC HVIT and Southwest Research Institute (SwRI) teams on the xEMU EPG, xPLSS and helmet to generate BLEs for lunar ejecta impacts. Post testing, ballistic limit equations used to define the performance of the various regions on the xEMU spacesuit were developed from a generic set of BLEs. The HVI and low speed testing was performed to establish a physical basis for the equations with the co-efficients and exponents of the generic BLEs adjusted to fit the test data. The xEMU BLEs were added to the NASA/JSC software application used for space-craft MMOD risk assessments (BUMPER-3). A finite element model (FEM) of the xEMU spacesuit, which defines the size and shape of the spacesuit as well as the locations of the various shielding configurations, was created based on a solid model provided by the xEMU program office. Using the FEM file and added xEMU BLEs, BUMPER-3 assessments of the xEMU spacesuit for probability of no penetration (PNP) were performed. For the LEO assessment of a typical ISS EVA, the orbital debris and meteoroids environments were defined using the latest engineering models, ORDEM 3.2 and MEM-3 respectively. The lunar sur-face assessment again used the MEM-3 engineering model to define the meteoroid environ-ment along with the current released lunar surface ejecta model, NASA SP-8013 (developed during the Apollo Program). The Space Team in the Natural Environments Branch at Mar-shall Space Flight Center (MSFC) will soon release the new Lunar Meteoroid Ejecta Engineering Model (LMEEM), at which time the xEMU lunar surface EVA will be reassessed. Assessment of the MMOD risk for an 8-hour, 2-person EVA in both LEO and on the lunar surface showed that the xEMU spacesuit meets the program technical requirement of 1 in 2500 failure odds. Similar to the legacy EMU spacesuit, the majority of the MMOD risk (96% of the LEO EVA risk and 99% of the lunar surface EVA risk) is concentrated in regions of xEMU that are comprised primarily of softgoods (arms, legs, and gloves) rather than the hardgoods (xPLSS, hard upper torso and helmet).
There are many instances throughout a project’s lifecycle where there arises a need for quick and accurate risk assessment of building designs. For example, an unexpected design change during construction may necessitate structural engineers to perform a seismic risk assessment on analytical models of the updated building design using high fidelity structural analysis software, such as ANSYS or Abaqus. However, the efficiency of such workflows often depends upon the interoperability of architectural design software and structural analysis software. When the quality of this interoperability is lacking or even non-existent, the efficiency of virtual engineering workflows is hampered, which increases project costs. A McGraw Hill industry survey of professional users of Building Information Modeling (BIM) technologies found that there is high demand for BIM interoperability for structural analysis, but that the value/difficulty ratio is currently too low for practical use. There have been efforts by the academic community to facilitate model data exchange between the architectural design and structural analysis domains, but such solutions have not been widely adopted by industry, face technical challenges, and oftentimes are limited in applicability for users of various BIM software. Therefore, INL is developing capabilities to improve, automate, and generalize model data exchange between architectural BIM software (e.g., Revit) and structural analysis software (e.g., SAP2000, ANSYS). The goal is to help expedite and automate as much of the pre-processing step for creating analytical models in finite element analysis software as reasonably as possible. Such a "BIM-to-FEA" conversion tool should provide direct benefit to end-users through accuracy, automation, quick turn-around, and wide applicability. To generalize the application of this BIM-to-FEA conversion tool and increase its useability among the many different commercial BIM software currently used by industry, the program is being developed with the concept of openBIM. OpenBIM is the application of non-proprietary, open data standards that allow for BIM model data exchange in a format that is accessible, retainable, and useable for all users. The most widely used open, non-proprietary data exchange format for BIM is the Industry Foundation Classes (IFC) schema. IFC is developed by buildingSMART international and is ISO certified (ISO 16739-1:2018). The BIM-to-FEA conversion tool is being developed for compatibility with typical commercial building designs of steel framed structures. The tool is currently capable of importing architectural BIM data of framed building structures, recognizing and extracting the aspects of the model that are required for structural analysis, adjusting the connectivity of frame members, and finally exporting to an analytical model stored in the IFC format. The exported IFC analytical model can then be imported into various openBIM compliant software, such as SAP2000. Such capabilities have already been tested on commercial software, as shown above, and continue to be improved. Work is underway to test the conversion on various commercial BIM software, develop a user-friendly interface, incorporate the program into the broader DeepLynx data warehouse project being developed by INL, and to eventually open-source the tool for the benefit of the community. Future development of the tool envisions the ability for efficient iterative risk assessment of generative building designs, all within a workflow utilizing open-source tools. One such open-source tool will be MOOSE, an advanced finite element analysis tool developed at INL. The conversion tool will also branch out from typical commercial building designs and will aim to incorporate nuclear construction. The aim will be to convert both structural and non-structural components of nuclear facilities, such as curved concrete containment structures and piping systems, respectively.
To maximize efficiency and flexibility in Mission Operations System (MOS) design, we are evolving principles from agile and lean methods for software, to the complete mission system. This allows for reduced operational risk at reduced cost, and achieves a more effective design through early integration of operations into mission system engineering and flight system design. The core principles are assessment of capability through demonstration, risk reduction through targeted experiments, early test and deployment, and maturation of processes and tools through use.
To perform dosimetry and risk assessment, NASA collects astronaut ionizing radiation exposure data from space flight, medical imaging and therapy, aviation training activities and prior occupational exposure histories. Career risk of exposure induced death (REID) from radiation is limited to 3 percent at a 95 percent confidence level. The Radiation Health Office at Johnson Space Center (JSC) is implementing a program to integrate the gathering, storage, analysis and reporting of astronaut ionizing radiation dose and risk data and records. This work has several motivations, including more efficient analyses and greater flexibility in testing and adopting new methods for evaluating risks. The foundation for these improvements is a set of software tools called the Astronaut Radiation Exposure Analysis System (AREAS). AREAS is a series of MATLAB(Registered TradeMark)-based dose and risk analysis modules that interface with an enterprise level SQL Server database by means of a secure web service. It communicates with other JSC medical and space weather databases to maintain data integrity and consistency across systems. AREAS is part of a larger NASA Space Medicine effort, the Mission Medical Integration Strategy, with the goal of collecting accurate, high-quality and detailed astronaut health data, and then securely, timely and reliably presenting it to medical support personnel. The modular approach to the AREAS design accommodates past, current, and future sources of data from active and passive detectors, space radiation transport algorithms, computational phantoms and cancer risk models. Revisions of the cancer risk model, new radiation detection equipment and improved anthropomorphic computational phantoms can be incorporated. Notable hardware updates include the Radiation Environment Monitor (which uses Medipix technology to report real-time, on-board dosimetry measurements), an updated Tissue-Equivalent Proportional Counter, and the Southwest Research Institute Radiation Assessment Detector. Also, the University of Florida hybrid phantoms, which are flexible in morphometry and positioning, are being explored as alternatives to the current NASA computational phantoms.
The development of advanced vehicles that will travel between planetary surfaces with atmospheres and space requires the availability of experimental and computational capabilities that accomplish the research leading to new technologies and the development, test, and evaluation (RDT&E) of new flight systems. The United States is at risk of not having the required RDT&E workforce – qualified and in sufficient numbers – in place and ready to meet the future market’s commercial and defense needs. Systemic challenges include uncertain Federal budgets that limit and interrupt government research and development, the projectized nature of new space access systems that drive boom/bust cycles, an aging aerospace workforce (compounded by a mid-age demographic gap), limited to declining investment in sustaining and advancing experimental and computational tools infrastructure, lack of standards for sharing (and leveraging) data, dramatically changing technologies, changing social norms, and potentially large increases in commercial market needs. Flight systems have mission-based trajectories to and from space and designers must ensure risk is properly assessed across the entire trajectory. Thus, physics questions must be answered at each stage of flight, which requires a suite of experimental and computational tools. The RDT&E workforce utilizing these tools include subject matter experts from the producers, interested in acquiring data and information on the product, and from the capabilities being utilized, interested in addressing the product customer’s needs (providing robust data collection techniques, data quality, timeliness – available when needed, efficient with cost management, and teaming on data analysis). This requires a range of skills – in addition to aerospace, other engineers, and software developers, a highly trained and certified craft and technician workforce is critical to future success. This paper will present a human resources construct that addresses the system of needs for people – including, but more than just technical skills and an application of that construct in the hypersonic Test and Evaluation (T&E) community. This is part of a larger AIAA effort to document challenges and associated best practices for the aerospace RDT&E workforce.
The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.
Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.
The satellite systems Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most is system complexity due to a need to establish a multi-dimensional structure across hardware, software and operations. This structure is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. These architecture, implementation and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (VV) is challenging. A breakout session at the 2012 NASA Independent Verification Validation (IVV) Annual Workshop titled VV of Fault Management: Challenges and Successes exposed these issues in terms of VV for a representative set of architectures. NASA's IVV is funded by NASA's Software Assurance Research Program (SARP) in partnership with NASA's Jet Propulsion Laboratory (JPL) to extend the work performed at the Workshop session. NASA IVV will extract FM architectures across the IVV portfolio and evaluate the data set for robustness, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This work focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures, visibility, and associated VVIVV techniques provides a data set that can enable higher assurance that a satellite system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the satellite community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research including identification of FM architectures, visibility observations, and methods utilized for VVIVV.
This report summarizes the Integrated Application of Active Controls (IAAC) Technology to an Advanced Subsonic Transport Project, established as one element of the NASA/Boeing Energy Efficient Transport Technology Program. The performance assessment showed that incorporating ACT into an airplane designed to fly approximately 200 passengers approximately 2,000 nmi could yield block fuel savings from 6 to 10 percent at the design range. The principal risks associated with incorporating these active control functions into a commercial airplane are those involved with the ACT system implementation. The Test and Evaluation phase of the IAAC Project focused on the design, fabrication, and test of a system that implemented pitch axis fly-by-wire, pitch axis augmentation, and wing load alleviation. The system was built to be flight worthy, and was planned to be experimentally flown on the 757. The system was installed in the Boeing Digital Avionics Flight Controls Laboratory (DAFCL), where open loop hardware and software tests, and a brief examination of a direct drive valve (DDV) actuation concept were accomplished. The IAAC Project has shown that ACT can be beneficially incorporated into a commercial transport airplane. Based on the results achieved during the testing phase, there appears to be no fundamental reason(s) that would preclude the commercial application of ACT, assuming an appropriate development effort is included.
Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most missions is system complexity due to a need to establish a multi-dimensional structure across hardware, software and spacecraft operations. FM is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. Generally, FM architecture, implementation, and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (V&V) is challenging. A breakout session at the 2012 NASA Independent Verification & Validation (IV&V) Annual Workshop titled "V&V of Fault Management: Challenges and Successes" exposed this issue in terms of V&V for a representative set of architectures. NASA's Software Assurance Research Program (SARP) has provided funds to NASA IV&V to extend the work performed at the Workshop session in partnership with NASA's Jet Propulsion Laboratory (JPL). NASA IV&V will extract FM architectures across the IV&V portfolio and evaluate the data set, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This SARP initiative focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures and associated V&V/IV&V techniques provides a data set that can enable improved assurance that a system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the space community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research.
The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.
Topics covered include: Calibration Test Set for a Phase-Comparison Digital Tracker; Wireless Acoustic Measurement System; Spiral Orbit Tribometer; Arrays of Miniature Microphones for Aeroacoustic Testing; Predicting Rocket or Jet Noise in Real Time; Computational Workbench for Multibody Dynamics; High-Power, High-Efficiency Ka-Band Space Traveling-Wave Tube; Gratings and Random Reflectors for Near-Infrared PIN Diodes; Optically Transparent Split-Ring Antennas for 1 to 10 GHz; Ice-Penetrating Robot for Scientific Exploration; Power-Amplifier Module for 145 to 165 GHz; Aerial Videography From Locally Launched Rockets; SiC Multi-Chip Power Modules as Power-System Building Blocks; Automated Design of Restraint Layer of an Inflatable Vessel; TMS for Instantiating a Knowledge Base With Incomplete Data; Simulating Flights of Future Launch Vehicles and Spacecraft; Control Code for Bearingless Switched- Reluctance Motor; Machine Aided Indexing and the NASA Thesaurus; Arbitrating Control of Control and Display Units; Web-Based Software for Managing Research; Driver Code for Adaptive Optics; Ceramic Paste for Patching High-Temperature Insulation; Fabrication of Polyimide-Matrix/Carbon and Boron-Fiber Tape; Protective Skins for Aerogel Monoliths; Code Assesses Risks Posed by Meteoroids and Orbital Debris; Asymmetric Bulkheads for Cylindrical Pressure Vessels; Self-Regulating Water-Separator System for Fuel Cells; Self-Advancing Step-Tap Drills; Array of Bolometers for Submillimeter- Wavelength Operation; Delta-Doped CCDs as Detector Arrays in Mass Spectrometers; Arrays of Bundles of Carbon Nanotubes as Field Emitters; Staggering Inflation To Stabilize Attitude of a Solar Sail; and Bare Conductive Tether for Decelerating a Spacecraft.
This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.
BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. During an extended simulation modeling phase, IMPACT generated analytical results for medical risks, and the medical resources and capabilities to address those risks, for every segment of every crewed Artemis mission. This presentation will highlight the reliability, consistency and validity of IMPACT’s computational modeling techniques and will showcase the library of analytical outcomes generated for the Artemis missions. OVERVIEW During the early stages of IMPACT’s design, architecture and technical requirements collection, “scenarios” (use cases) - achievement goals required for acceptance testing, were identified by stakeholders. IMPACT successfully completed the scenario testing requirements and undertook an extensive operational run phase utilizing a wide range of input combinations with a goal of delivering a cohesive, trustworthy, reliable, vast, and diverse body of evidence. The intent of these modeling runs was to validate consistency in output, ensure solidity of executable operations and to streamline processes by identifying areas requiring efficiency improvements. Using the many missions of Artemis, IMPACT ran variations of operational runs to assess the output for acceptable, as well as unusual characteristics. This rigorous long-term “shakedown” analysis was implemented to help build a collective body of evidence to aid in securing a high level of confidence, reliability, and validity in the output, whether from the applicational components of IMPACT, or the entirety of the operational process. ANTICIPATED ANALYSIS AND CONCLUSION This presentation will discuss the various categories of input criteria; the comparisons in the application of these input criteria to various Artemis missions; the preparation and collection of the body of evidence, and reliability of the computational modeling techniques. This paper serves as an initial analytical overview of IMPACT’s probabilistic risk assessment (PRA) medical risk outputs covering Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.
NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.
The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.
The SSME has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) Develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system. (2) Develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amounts of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. A high compression ratio can be achieved to allow the minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities. (3) Integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for a quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.