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At least 289 records · Page 16

Brace For Impact – It’s Coming, Do You Know What to Do With It?

Long-duration human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for providing in-mission medical care. Compared with the International Space Station, the greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and significant limitations on the evacuation of ill or injured crew. Spacecraft mass, volume, and power will be curtailed while higher demands will be placed on the crew’s knowledge, skills, and abilities. In this higher risk environment, it is important to define and quantitatively estimate human system risk attributable to medical conditions. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a probabilistic risk assessment (PRA) and medical trade space analysis tool being developed by NASA to advance medical system design for exploration missions. IMPACT has made a number of enhancements on the Integrated Medical Model, the PRA tool currently used by NASA. These updates include a novel medical evidence base baselined to a long duration, deep space exploration environment; an expanded medical condition list; and a trade space analysis capability (e.g. comparing risk profiles and mass/volume constraints for medical capabilities and resources). IMPACT core functionality includes quantification of a medical capability set and assisting with identification of specific medical resources/hardware for exploration missions. In addition, IMPACT provides sophisticated trade space capabilities to estimate how human system risk varies with changes to the mission architecture or medical capability set. This presentation will provide a brief overview of the IMPACT tool, discuss representative use cases, and show example results.

Benjamin Easter↗

CHP-PRA: Sensorimotor Countermeasures Proof of Concept

The capabilities included in Crew Health and Performance (CHP) systems are designed to keep the crew healthy, happy, and productive. In doing so, the CHP system allows reductions in human related risks to be realized. All human missions, though particularly future Artemis and Mars missions, have constraints on the mass and volume allocated to the CHP system. Thus, trades must be made on how to best buy down risk while still meeting other requirements. Understanding how different CHP capabilities influence medical, performance, and long-term health risks is key to making informed choices among these trades. To address this, an integrated CHP Probabilistic Risk Assessment (PRA) model is being developed. Much like how IMPACT is designed to allow medical resource trades informed by medical risks, CHP PRA will enable analogous trades in human system risks across all CHP functions and capabilities. There are 29 Human System Risk Board (HSRB) risk areas to consider for model implementation. This effort focused solely on the implementation of countermeasures associated with sensorimotor risk. The framework and lessons learned from this proof of concept will benefit the implementation of other risks and countermeasures. Astronauts experience sensorimotor changes when entering or exiting a microgravity environment. While most sensorimotor issues are resolved within a few days, they pose a serious risk to crew health and performance during the adaptation period. This adaptation period aligns with gravity transitions and key mission phases where the crew members may be required to complete challenging tasks ideally with an undisturbed sensorimotor system. Countermeasures such as training or pharmaceuticals can be used to mitigate risk. This effort began by identifying physiological changes to the sensorimotor system that could lead to functional performance decrements or medical conditions and potential countermeasures, so that the overall effect of sensorimotor disturbance on human risk could be captured. We then selected example countermeasures, mapped relationships between countermeasures and outcomes, and used results from existing sensorimotor investigations to define the relationship between a sensorimotor countermeasure and subsequent performance of a sensorimotor-affected task. Specifically, using shuttle landing performance data, we compared the use or absence of inflight training capabilities to yield a relative performance change that can be applied to the prediction of the risk associated with manual control performance in future missions, as depicted in the figure. This sensorimotor proof of concept demonstrates how HRP research information can be transformed into quantifiable relationships to describe changes in HSRB medical and performance risks.

Caroline R Austin↗

Accounting for Point Estimate Uncertainty in Space Systems Reliability and Risk Analysis

Understanding and accounting for uncertainty in risk analysis is a critical step in the management and communication of risk in engineered systems. The component and system-level analysis to determine the probability of a negative outcome and its consequence is often quantified by a point estimate. Many Program and Enterprise decisions involving technical concerns and issues rely on reliability engineering activities to produce quantified risk analysis to inform the decision making process. At NASA, it is common to use a Probabilistic Risk Analysis (PRA) to inform the overall risk to Loss of Mission or Loss of Crew that involves integration across all spacecraft subsystem fault trees to produce an overall probability of mission failure. The point estimate is an estimate of this overall probability and is an immediate result of a fault tree model. It is the result of a model where the probability of each event is taken to be equal to its mean. The value provides an approximation of the overall mean without running any uncertainty calculations (e.g., no sampling). Using only the point estimate can lead to a false sense of precision and the point estimate may not match the resulting mean when uncertainty is taken into consideration. This paper will explore five conditions that can cause the PRA model mean to diverge from the point estimate and will provide engineers and managers insight into the importance of understanding uncertainty in the elements of PRA models.

Paul J Collier↗

Mass and Reliability System (MaRS)

The Safety and Mission Assurance (S&MA) Directorate is responsible for mitigating risk, providing system safety, and lowering risk for space programs from ground to space. The S&MA is divided into 4 divisions: The Space Exploration Division (NC), the International Space Station Division (NE), the Safety & Test Operations Division (NS), and the Quality and Flight Equipment Division (NT). The interns, myself and Arun Aruljothi, will be working with the Risk & Reliability Analysis Branch under the NC Division's. The mission of this division is to identify, characterize, diminish, and communicate risk by implementing an efficient and effective assurance model. The team utilizes Reliability and Maintainability (R&M) and Probabilistic Risk Assessment (PRA) to ensure decisions concerning risks are informed, vehicles are safe and reliable, and program/project requirements are realistic and realized. This project pertains to the Orion mission, so it is geared toward a long duration Human Space Flight Program(s). For space missions, payload is a critical concept; balancing what hardware can be replaced by components verse by Orbital Replacement Units (ORU) or subassemblies is key. For this effort a database was created that combines mass and reliability data, called Mass and Reliability System or MaRS. The U.S. International Space Station (ISS) components are used as reference parts in the MaRS database. Using ISS components as a platform is beneficial because of the historical context and the environment similarities to a space flight mission. MaRS uses a combination of systems: International Space Station PART for failure data, Vehicle Master Database (VMDB) for ORU & components, Maintenance & Analysis Data Set (MADS) for operation hours and other pertinent data, & Hardware History Retrieval System (HHRS) for unit weights. MaRS is populated using a Visual Basic Application. Once populated, the excel spreadsheet is comprised of information on ISS components including: operation hours, random/nonrandom failures, software/hardware failures, quantity, orbital replaceable units (ORU), date of placement, unit weight, frequency of part, etc. The motivation for creating such a database will be the development of a mass/reliability parametric model to estimate mass required for replacement parts. Once complete, engineers working on future space flight missions will have access a mean time to failures and on parts along with their mass, this will be used to make proper decisions for long duration space flight missions

Barnes, Sarah↗

Modular High Temperature Gas-Cooled Reactor: Accident Analysis

Licensing Modernization Project (LMP) Risk Informed Approach Selection of Licensing Basis Events (LBEs) Frequency-Consequence Target LBE Cumulative Risk Targets Structures, Systems, and Components (SSC) Safety Categories Classification Evaluation of Defense in Depth (DID) Use of Probabilistic Risk Assessment (PRA) in LMP Process PRA Policy Statement American Nuclear Society (ANS) Non-Light-Water Reactor (non-LWR) PRA Standard

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Impact-Identified Medical Capabilities with Largest Effect on Medical Risk for an Extended Duration Artemis Mission

Historically, identifying resources to include in a medical system has been based on heuristically guided clinical subject matter expert assessment. Probabilistic risk assessment (PRA) and tradespace analysis have the power to simplify and increase the fidelity of this traditional approach by providing initial risk estimates and system design solutions that fit within the specified constraints. This will be especially important as the increased mission complexity, distance from Earth, and duration of LDEMs is likely to drive an increase in mission medical risk. NASA’s Informing Mission Planning via Analysis of Complex Tradespaces tool (IMPACT) is designed to do just that. IMPACT uses an evidence based medical database of conditions likely to affect LDEM outcomes and a PRA computational engine to estimate how medical conditions and included medical capabilities affect mission outcomes. We identified the 10 medical conditions with the largest effect on medical risks and determined what medical system capabilities affected risk reduction the greatest.

Anderson A↗

Performance Risk Model Validation with Operationally Relevant Tasks

Human Research Program aims to develop methods to support astronauts’ health and productivity during spaceflight. The Crew Health and Performance Probabilistic Risk Assessment (CHP-PRA) team uses powerful computational methods to predict mission risk in both domains: medical and performance. Here, we show how CHP-PRA uses the Performance Risk Model (PRisM) to quantify the performance risk and show an application of the model on operationally relevant tasks. There are various metrics adopted across performance researchers that PRisM can accommodate. For data analysis, interpretation, and integration, we use a method of unifying data from multiple sources by converting each to a single metric. We consult subject matter experts prior to integrating the converted data into PRisM. The method we use is inspired by the Cooper-Harper rating scale [1]. Using this unified metric, we can easily combine data from various tests and lab groups. We explain our conversion method in detail and show how it pertains to the process of testing and validation of PRisM on operational tasks. We conducted an initial validation in collaboration with the Behavioral Health and Performance (BHP) lab. We test PRisM using data on their operationally relevant task ROBoT-r, a track-and-capture task for grappling incoming resupply vehicles [2]. Several other labs at NASA Johnson Space Center worked together to design 7 Functional Task Tests (FTTs) in pursuit of simulating the tasks required after landing on a planetary surface and after return to Earth [3]. Here we use the results from both ROBoT-r and the 7 FTTs and compare their experiment data to PRisM’s computational output to demonstrate how PRisM can support operations by predicting crew performance on future missions.

performance modeling↗

MEDPRAT-SEL: Medical Resource Set Selector Searching High-Dimensionality Space

NASA Human Research Program's (HRP) next generation risk model closes the loop on medical risk and resource evaluation, providing researchers with a tool to assess and choose capabilities scientifically. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) simulator (SIM) evaluates resources in terms of medical benefit, such as reduced crew quality time lost (QTL), vs. cost, in mass or volume. The MEDPRAT resource set selector (SEL) searches the space of resource benefits v. cost, closing a feedback loop around the simulator, thus selecting resource sets with best benefit for cost. We discuss the design and results of the resource set selector. NASA HRP's emphasis on scientific assessment of risk, PRA, has led to the Cross-cutting Computational Modeling Project (CCMP).

Leinweber, Lawrence↗

Performance Risk Model (PRisM) Proof-of-Concept: An Operational Decision Support Tool to Predict Crew Performance in Space from Available Performance Tests

The Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) team at NASA Glenn Research Center has developed a range of tools to evaluate astronaut health during spaceflight and to optimize the medical set required for missions. Among these, the Performance Risk Model (PRisM) represents a novel advancement, extending CHP-PRA’s focus beyond medical systems into the domain of human performance. Such tool could be pivotal in optimizing astronaut capabilities during space travel, thereby enhancing overall mission success. PRisM leverages data from well-established performance assessments conducted during previous crewed space missions and Earth analogs to strategically predict outcomes for planned tasks, even when direct performance testing has not been conducted for those specific tasks. To evaluate performance, PRisM references the various metrics outlined in NASA-STD-3001 and employs a methodology to integrate different performance scales. This framework analyzes the contribution of various human system task categories (HSTCs) to task execution and compares these contributions to the HSTCs’ involvement in other known tests. The model further employs a Monte Carlo simulation to sample performance scores from their distribution in operationally relevant tests such as those in Mulavara et al. (2018) and, by leveraging similarities in HSTC involvement, transfers this knowledge to actual mission tasks, such as those outlined in the "Human Exploration of Mars: Preliminary List of Crew Tasks”. The current PRisM proof-of-concept includes analyses of the impact of exercise and specific medications on astronauts’ performance, with provisions to incorporate additional countermeasures as data becomes available. Furthermore, the tool is customizable to include any system necessary to fully encompass the domain of human systems and can be adapted to evaluate performance for any spaceflight activities as requested by operational stakeholders. PRisM has the potential to assist the Human Research Program in exploring the capabilities trade space for optimized crew performance.

performance modeling↗

NSTS Orbiter auxiliary power unit turbine wheel cracking risk assessment

The present investigation of turbine-wheel cracking problems in the hydrazine-fueled APU turbine wheel of the Space Shuttle Orbiter's Main Engines has indicated the efficacy of systematic probabilistic risk assessment in flight certification and safety resolution. Nevertheless, real crack-initiation and propagation problems do not lend themselves to purely analytical studies. The high-cycle fatigue problem is noted to generally be unsuited to probabilistic modeling, due to its extremely high degree of intrinsic scatter. In the case treated, the cracks appear to trend toward crack arrest in a low cycle fatigue mode, due to a detuning of the resonance model.

Cruse, T. A.↗

Event-modeled Risk Assessment Using Linked Diagrams

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is a software tool developed at INL for researching the capabilities of dynamic PRA (Probabilistic Risk Assessment). In order to promote the effective use of dynamic PRA by the general community, EMRALD focuses on the following key aspects: Simplifying the modeling process by providing a structure that corresponds to traditional PRA modeling methods Providing a user interface (UI) that makes it easy for the user to model and visualize complex interactions Allowing the user to couple with other analysis applications such as physics based simulations. This includes one-way communication for most applications and two-way loose coupling for customizable applications Providing the sequence and timing of events that lead to the specified outcomes when calculating results Traditional aspects of components with basic events, fault trees, and event trees are all captured in a dynamic framework of state diagrams, which are displayed.

Prescott, SteveR↗

NASA System Safety Handbook: System Safety Framework and Concepts for Implementation - Volume 1

System safety assessment is defined in NPR 8715.3C, NASA General Safety Program Requirements as a disciplined, systematic approach to the analysis of risks resulting from hazards that can affect humans, the environment, and mission assets. Achievement of the highest practicable degree of system safety is one of NASA's highest priorities. Traditionally, system safety assessment at NASA and elsewhere has focused on the application of a set of safety analysis tools to identify safety risks and formulate effective controls.1 Familiar tools used for this purpose include various forms of hazard analyses, failure modes and effects analyses, and probabilistic safety assessment (commonly also referred to as probabilistic risk assessment (PRA)). In the past, it has been assumed that to show that a system is safe, it is sufficient to provide assurance that the process for identifying the hazards has been as comprehensive as possible and that each identified hazard has one or more associated controls. The NASA Aerospace Safety Advisory Panel (ASAP) has made several statements in its annual reports supporting a more holistic approach. In 2006, it recommended that "... a comprehensive risk assessment, communication and acceptance process be implemented to ensure that overall launch risk is considered in an integrated and consistent manner." In 2009, it advocated for "... a process for using a risk-informed design approach to produce a design that is optimally and sufficiently safe." As a rationale for the latter advocacy, it stated that "... the ASAP applauds switching to a performance-based approach because it emphasizes early risk identification to guide designs, thus enabling creative design approaches that might be more efficient, safer, or both." For purposes of this preface, it is worth mentioning three areas where the handbook emphasizes a more holistic type of thinking. First, the handbook takes the position that it is important to not just focus on risk on an individual basis but to consider measures of aggregate safety risk and to ensure wherever possible that there be quantitative measures for evaluating how effective the controls are in reducing these aggregate risks. The term aggregate risk, when used in this handbook, refers to the accumulation of risks from individual scenarios that lead to a shortfall in safety performance at a high level: e.g., an excessively high probability of loss of crew, loss of mission, planetary contamination, etc. Without aggregated quantitative measures such as these, it is not reasonable to expect that safety has been optimized with respect to other technical and programmatic objectives. At the same time, it is fully recognized that not all sources of risk are amenable to precise quantitative analysis and that the use of qualitative approaches and bounding estimates may be appropriate for those risk sources. Second, the handbook stresses the necessity of developing confidence that the controls derived for the purpose of achieving system safety not only handle risks that have been identified and properly characterized but also provide a general, more holistic means for protecting against unidentified or uncharacterized risks. For example, while it is not possible to be assured that all credible causes of risk have been identified, there are defenses that can provide protection against broad categories of risks and thereby increase the chances that individual causes are contained. Third, the handbook strives at all times to treat uncertainties as an integral aspect of risk and as a part of making decisions. The term "uncertainty" here does not refer to an actuarial type of data analysis, but rather to a characterization of our state of knowledge regarding results from logical and physical models that approximate reality. Uncertainty analysis finds how the output parameters of the models are related to plausible variations in the input parameters and in the modeling assumptions. The evaluation of unrtainties represents a method of probabilistic thinking wherein the analyst and decision makers recognize possible outcomes other than the outcome perceived to be "most likely." Without this type of analysis, it is not possible to determine the worth of an analysis product as a basis for making decisions related to safety and mission success. In line with these considerations the handbook does not take a hazard-analysis-centric approach to system safety. Hazard analysis remains a useful tool to facilitate brainstorming but does not substitute for a more holistic approach geared to a comprehensive identification and understanding of individual risk issues and their contributions to aggregate safety risks. The handbook strives to emphasize the importance of identifying the most critical scenarios that contribute to the risk of not meeting the agreed-upon safety objectives and requirements using all appropriate tools (including but not limited to hazard analysis). Thereafter, emphasis shifts to identifying the risk drivers that cause these scenarios to be critical and ensuring that there are controls directed toward preventing or mitigating the risk drivers. To address these and other areas, the handbook advocates a proactive, analytic-deliberative, risk-informed approach to system safety, enabling the integration of system safety activities with systems engineering and risk management processes. It emphasizes how one can systematically provide the necessary evidence to substantiate the claim that a system is safe to within an acceptable risk tolerance, and that safety has been achieved in a cost-effective manner. The methodology discussed in this handbook is part of a systems engineering process and is intended to be integral to the system safety practices being conducted by the NASA safety and mission assurance and systems engineering organizations. The handbook posits that to conclude that a system is adequately safe, it is necessary to consider a set of safety claims that derive from the safety objectives of the organization. The safety claims are developed from a hierarchy of safety objectives and are therefore hierarchical themselves. Assurance that all the claims are true within acceptable risk tolerance limits implies that all of the safety objectives have been satisfied, and therefore that the system is safe. The acceptable risk tolerance limits are provided by the authority who must make the decision whether or not to proceed to the next step in the life cycle. These tolerances are therefore referred to as the decision maker's risk tolerances. In general, the safety claims address two fundamental facets of safety: 1) whether required safety thresholds or goals have been achieved, and 2) whether the safety risk is as low as possible within reasonable impacts on cost, schedule, and performance. The latter facet includes consideration of controls that are collective in nature (i.e., apply generically to broad categories of risks) and thereby provide protection against unidentified or uncharacterized risks.

Dezfuli, Homayoon↗

Industry Level Integrated Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)

The software Fire Risk Investigation in 3D (FRI3D) has been developed over the last 2 years to integrate 3D spatial modeling with existing fire probabilistic risk assessment (PRA) models and fire simulation codes. The goal of this research and development is to automate many of the fire analysis manual tasks to reduce industry efforts in the initial fire modeling and operational costs for the model maintenance and evaluations required during normal plant operations. The tasks for Fiscal Year (FY) 2021 include first testing the FRI3D modeling capabilities by importing an industry fire model into FRI3D and making a 3D model of a complex/high-risk significant area. (For this work, the switchgear room was chosen.) Then, the second task of FY 2021 is to develop a dynamic fire PRA process that can help optimize traditional fire PRA models. The switchgear room model will be used for the dynamic fire PRA work. This report describes the work and insights learned when using FRI3D software to model both a Nuclear Regulatory Report (NUREG) example models and a full industry switchgear room.

97 MATHEMATICS AND COMPUTING↗

Light Water Reactor Sustainability Program: Upgrade of EMRALD to a Modern JavaScript-based Framework

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is a software tool developed at Idaho National Laboratory for researching the capabilities of dynamic probabilistic risk assessment. It provides a simple interface to represent complex interactions often seen when developing dynamic models. EMRALD can also interface with other applications by modifying inputs, running, and using their results within EMRALD for dynamic and integrated assessment. This report goes over the work performed as part of the Risk-Informed Systems Analysis Pathway under the Light Water Reactor Sustainability program to upgrade the EMRALD software.

97 MATHEMATICS AND COMPUTING↗

EMRALD Technology Advancements for Commercial Grade Dedication Readiness

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is a software tool developed at Idaho National Laboratory for researching the capabilities of dynamic probabilistic risk assessment. It provides a simple interface to represent complex interactions often seen when developing dynamic models. EMRALD can also interface with other applications by modifying inputs as well as running and using their results within EMRALD for dynamic and integrated assessment. To enable wider industry use cases, collaborative work between Idaho National Laboratory and FPoliSolutions was performed through the technology commercialization fund TCF-20-21448. This report covers the additional features and capabilities developed under this work.

97 MATHEMATICS AND COMPUTING↗

Risks from Solar Particle Events for Long Duration Space Missions Outside Low Earth Orbit

The Integrated Medical Model (IMM) simulates the medical occurrences and mission outcomes for various mission profiles using probabilistic risk assessment techniques. As part of the work with the Integrated Medical Model (IMM), this project focuses on radiation risks from acute events during extended human missions outside low Earth orbit (LEO). Of primary importance in acute risk assessment are solar particle events (SPEs), which are low probability, high consequence events that could adversely affect mission outcomes through acute radiation damage to astronauts. SPEs can be further classified into coronal mass ejections (CMEs) and solar flares/impulsive events (Fig. 1). CMEs are an eruption of solar material and have shock enhancements that contribute to make these types of events higher in total fluence than impulsive events.

health↗

Application of Risk Assessment Tools in the Continuous Risk Management (CRM) Process

Marshall Space Flight Center (MSFC) of the National Aeronautics and Space Administration (NASA) is currently implementing the Continuous Risk Management (CRM) Program developed by the Carnegie Mellon University and recommended by NASA as the Risk Management (RM) implementation approach. The four most frequently used risk assessment tools in the center are: (a) Failure Modes and Effects Analysis (FMEA), Hazard Analysis (HA), Fault Tree Analysis (FTA), and Probabilistic Risk Analysis (PRA). There are some guidelines for selecting the type of risk assessment tools during the project formulation phase of a project, but there is not enough guidance as to how to apply these tools in the Continuous Risk Management process (CRM). But the ways the safety and risk assessment tools are used make a significant difference in the effectiveness in the risk management function. Decisions regarding, what events are to be included in the analysis, to what level of details should the analysis be continued, make significant difference in the effectiveness of risk management program. Tools of risk analysis also depends on the phase of a project e.g. at the initial phase of a project, when not much data are available on hardware, standard FMEA cannot be applied; instead a functional FMEA may be appropriate. This study attempted to provide some directives to alleviate the difficulty in applying FTA, PRA, and FMEA in the CRM process. Hazard Analysis was not included in the scope of the study due to the short duration of the summer research project.

Paul S. Ray↗

IMPACT - Information for the Development of Human Health and Performance Systems

IMPACT is a suite of computational and systems engineering tools. Currently being developed for NASA Exploration Medical Capabilities (ExMC) and could be extended to other Human Research Program (HRP) elements. The purpose is to provide a data-driven means to inform human health and performance risk mitigation during exploration missions considering resource constraints in the medical system. IMPACT enables systematic trade studies to evaluate options. IMPACT uses Probabilistic Risk Assessment (PRA) as a systematic methodology to evaluate risks. IMPACT is currently under development to inform decision-makers the risks involved with trade-options that have likelihoods and consequences backed by scientific research in a medical evidence library. IMPACT supports multi-segment analysis to determine probabilities and risks in different mission segments. There are currently 122 medical conditions and 900 resources in the IMPACT Medical Database. IMPACT modeling is using mission segments and multiple vehicles for the NASA Artemis campaign and could support future complex missions in deep space.

IMPACT PRA↗