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NASA NTRS2013

Near-Earth Phase Risk Comparison of Human Mars Campaign Architectures

A risk analysis of the launch, orbital assembly, and Earth-departure phases of human Mars exploration campaign architectures was completed as an extension of a probabilistic risk assessment (PRA) originally carried out under the NASA Constellation Program Ares V Project. The objective of the updated analysis was to study the sensitivity of loss-of-campaign risk to such architectural factors as composition of the propellant delivery portion of the launch vehicle fleet (Ares V heavy-lift launch vehicle vs. smaller/cheaper commercial launchers) and the degree of launcher or Mars-bound spacecraft element sparing. Both a static PRA analysis and a dynamic, event-based Monte Carlo simulation were developed and used to evaluate the probability of loss of campaign under different sparing options. Results showed that with no sparing, loss-of-campaign risk is strongly driven by launcher count and on-orbit loiter duration, favoring an all-Ares V launch approach. Further, the reliability of the all-Ares V architecture showed significant improvement with the addition of a single spare launcher/payload. Among architectures utilizing a mix of Ares V and commercial launchers, those that minimized the on-orbit loiter duration of Mars-bound elements were found to exceed the reliability of no spare all-Ares V campaign if unlimited commercial vehicle sparing was assumed

Manning, Ted A.↗
NASA NTRS2014

An Example of Risk Informed Design

NASA Engineering requested a Probabilistic Risk Assessment (PRA) to compare the difference in the risk of Loss of Crew (LOC) and Loss of Mission (LOM) between different designs of a fluid assembly. They were concerned that the configuration favored by the design team was more susceptible to leakage than a second proposed design, but realized that a quantitative analysis to compare the risks between the two designs might strengthen their argument. The analysis showed that while the second design did help improve the probability of LOC, it did not help from a probability of LOM perspective. This drove the analysis team to propose a minor design change that would drive the probability of LOM down considerably. The analysis also demonstrated that there was another major risk driver that was not immediately obvious from a typical engineering study of the design and was therefore unexpected. None of the proposed alternatives were addressing this risk. This type of trade study demonstrates the importance of performing a PRA in order to completely understand a system's design. It allows managers to use risk as another one of the commodities (e.g., mass, cost, schedule, fault tolerance) that can be traded early in the design of a new system.

Banke, Rick↗
NASA NTRS2014

International Space Station End-of-Life Probabilistic Risk Assessment

Although there are ongoing efforts to extend the ISS life cycle through 2028, the International Space Station (ISS) end-of-life (EOL) cycle is currently scheduled for 2020. The EOL for the ISS will require de-orbiting the ISS. This will be the largest manmade object ever to be de-orbited, therefore safely de-orbiting the station will be a very complex problem. This process is being planned by NASA and its international partners. Numerous factors will need to be considered to accomplish this such as target corridors, orbits, altitude, drag, maneuvering capabilities, debris mapping etc. The ISS EOL Probabilistic Risk Assessment (PRA) will play a part in this process by estimating the reliability of the hardware supplying the maneuvering capabilities. The PRA will model the probability of failure of the systems supplying and controlling the thrust needed to aid in the de-orbit maneuvering.

Duncan, Gary↗
NASA NTRS2015

Dynamic Modeling of Ascent Abort Scenarios for Crewed Launches

For the last 30 years, the United States's human space program has been focused on low Earth orbit exploration and operations with the Space Shuttle and International Space Station programs. After nearly 50 years, the U.S. is again working to return humans beyond Earth orbit. To do so, NASA is developing a new launch vehicle and spacecraft to provide this capability. The launch vehicle is referred to as the Space Launch System (SLS) and the spacecraft is called Orion. The new launch system is being developed with an abort system that will enable the crew to escape launch failures that would otherwise be catastrophic as well as probabilistic design requirements set for probability of loss of crew (LOC) and loss of mission (LOM). In order to optimize the risk associated with designing this new launch system, as well as verifying the associated requirements, NASA has developed a comprehensive Probabilistic Risk Assessment (PRA) of the integrated ascent phase of the mission that includes the launch vehicle, spacecraft and ground launch facilities. Given the dynamic nature of rocket launches and the potential for things to go wrong, developing a PRA to assess the risk can be a very challenging effort. Prior to launch and after the crew has boarded the spacecraft, the risk exposure time can be on the order of three hours. During this time, events may initiate from either of the spacecraft, the launch vehicle, or the ground systems, thus requiring an emergency egress from the spacecraft to a safe ground location or a pad abort via the spacecraft's launch abort system. Following launch, again either the spacecraft or the launch vehicle can initiate the need for the crew to abort the mission and return to the home. Obviously, there are thousands of scenarios whose outcome depends on when the abort is initiated during ascent as to how the abort is performed. This includes modeling the risk associated with explosions and benign system failures that require aborting a spacecraft under very dynamic conditions, particularly in the lower atmosphere, and returning the crew home safely. This paper will provide an overview of the PRA model that has been developed of this new launch system, including some of the challenges that are associated with this effort. Key Words: PRA, space launches, human space program, ascent abort, spacecraft, launch vehicles

Bigler, Mark↗
NASA NTRS2014

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

The theory of System Health Management (SHM) and of its operational subset Fault Management (FM) states that FM is implemented as a "meta" control loop, known as an FM Control Loop (FMCL). The FMCL detects that all or part of a system is now failed, or in the future will fail (that is, cannot be controlled within acceptable limits to achieve its objectives), and takes a control action (a response) to return the system to a controllable state. In terms of control theory, the effectiveness of each FMCL is estimated based on its ability to correctly estimate the system state, and on the speed of its response to the current or impending failure effects. This paper describes how this theory has been successfully applied on the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program to quantitatively estimate the effectiveness of proposed abort triggers so as to select the most effective suite to protect the astronauts from catastrophic failure of the SLS. The premise behind this process is to be able to quantitatively provide the value versus risk trade‐off for any given abort trigger, allowing decision makers to make more informed decisions. All current and planned crewed launch vehicles have some form of vehicle health management system integrated with an emergency launch abort system to ensure crew safety. While the design can vary, the underlying principle is the same: detect imminent catastrophic vehicle failure, initiate launch abort, and extract the crew to safety. Abort triggers are the detection mechanisms that identify that a catastrophic launch vehicle failure is occurring or is imminent and cause the initiation of a notification to the crew vehicle that the escape system must be activated. While ensuring that the abort triggers provide this function, designers must also ensure that the abort triggers do not signal that a catastrophic failure is imminent when in fact the launch vehicle can successfully achieve orbit. That is, the abort triggers must have low false negative rates to be sure that real crew‐threatening failures are detected, and also low false positive rates to ensure that the crew does not abort from non‐crew‐threatening launch vehicle behaviors. The analysis process described in this paper is a compilation of over six years of lessons learned and refinements from experiences developing abort triggers for NASA's Constellation Program (Ares I Project) and the SLS Program, as well as the simultaneous development of SHM/FM theory. The paper will describe the abort analysis concepts and process, developed in conjunction with SLS Safety and Mission Assurance (S&MA) to define a common set of mission phase, failure scenario, and Loss of Mission Environment (LOME) combinations upon which the SLS Loss of Mission (LOM) Probabilistic Risk Assessment (PRA) models are built. This abort analysis also requires strong coordination with the Multi‐Purpose Crew Vehicle (MPCV) and SLS Structures and Environments (STE) to formulate a series of abortability tables that encapsulate explosion dynamics over the ascent mission phase. The design and assessment of abort conditions and triggers to estimate their Loss of Crew (LOC) Benefits also requires in‐depth integration with other groups, including Avionics, Guidance, Navigation and Control(GN&C), the Crew Office, Mission Operations, and Ground Systems. The outputs of this analysis are a critical input to SLS S&MA's LOC PRA models. The process described here may well be the first full quantitative application of SHM/FM theory to the selection of a sensor suite for any aerospace system.

Lo, Yunnhon↗
NASA NTRS2014

International Space Station End-of-Life Probabilistic Risk Assessment

The International Space Station (ISS) end‐of‐life (EOL) cycle is currently scheduled for 2020, although there are ongoing efforts to extend ISS life cycle through 2028. The EOL for the ISS will require deorbiting the ISS. This will be the largest manmade object ever to be de‐orbited therefore safely deorbiting the station will be a very complex problem. This process is being planned by NASA and its international partners. Numerous factors will need to be considered to accomplish this such as target corridors, orbits, altitude, drag, maneuvering capabilities etc. The ISS EOL Probabilistic Risk Assessment (PRA) will play a part in this process by estimating the reliability of the hardware supplying the maneuvering capabilities. The PRA will model the probability of failure of the systems supplying and controlling the thrust needed to aid in the de‐orbit maneuvering.

Duncan, Gary W.↗
NASA NTRS2015

Dynamic Modeling of Ascent Abort Scenarios for Crewed Launches

For the last 30 years, the United States' human space program has been focused on low Earth orbit exploration and operations with the Space Shuttle and International Space Station programs. After over 40 years, the U.S. is again working to return humans beyond Earth orbit. To do so, NASA is developing a new launch vehicle and spacecraft to provide this capability. The launch vehicle is referred to as the Space Launch System (SLS) and the spacecraft is called Orion. The new launch system is being developed with an abort system that will enable the crew to escape launch failures that would otherwise be catastrophic as well as probabilistic design requirements set for probability of loss of crew (LOC) and loss of mission (LOM). In order to optimize the risk associated with designing this new launch system, as well as verifying the associated requirements, NASA has developed a comprehensive Probabilistic Risk Assessment (PRA) of the integrated ascent phase of the mission that includes the launch vehicle, spacecraft and ground launch facilities. Given the dynamic nature of rocket launches and the potential for things to go wrong, developing a PRA to assess the risk can be a very challenging effort. Prior to launch and after the crew has boarded the spacecraft, the risk exposure time can be on the order of three hours. During this time, events may initiate from either the spacecraft, the launch vehicle, or the ground systems, thus requiring an emergency egress from the spacecraft to a safe ground location or a pad abort via the spacecraft's launch abort system. Following launch, again either the spacecraft or the launch vehicle can initiate the need for the crew to abort the mission and return home. Obviously, there are thousands of scenarios whose outcome depends on when the abort is initiated during ascent and how the abort is performed. This includes modeling the risk associated with explosions and benign system failures that require aborting a spacecraft under very dynamic conditions, particularly in the lower atmosphere, and returning the crew home safely. This paper will provide an overview of the PRA model that has been developed of this new launch system, including some of the challenges that are associated with this effort.

Bigler, Mark↗
NASA NTRS2011

Reliability Analysis and Standardization of Spacecraft Command Generation Processes

center dot In order to reduce commanding errors that are caused by humans, we create an approach and corresponding artifacts for standardizing the command generation process and conducting risk management during the design and assurance of such processes. center dot The literature review conducted during the standardization process revealed that very few atomic level human activities are associated with even a broad set of missions. center dot Applicable human reliability metrics for performing these atomic level tasks are available. center dot The process for building a "Periodic Table" of Command and Control Functions as well as Probabilistic Risk Assessment (PRA) models is demonstrated. center dot The PRA models are executed using data from human reliability data banks. center dot The Periodic Table is related to the PRA models via Fault Links.

human errors.↗
NASA NTRS2009

Probabilistic Risk Assessment for Decision Making During Spacecraft Operations

Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.

dynamic fault trees↗
NASA NTRS2015

Common Cause Failure Modeling

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFS are a set of dependent type of failures that can be caused by: system environments; manufacturing; transportation; storage; maintenance; and assembly, as examples. Since there are many factors that contribute to CCFs, the effects can be reduced, but they are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and CCF (dependent) failure and data is limited, especially for launch vehicles. The Probabilistic Risk Assessment (PRA) of NASA's Safety and Mission Assurance Directorate at Marshall Space Flight Center (MFSC) is using generic data from the Nuclear Regulatory Commission's database of common cause failures at nuclear power plants to estimate CCF due to the lack of a more appropriate data source. There remains uncertainty in the actual magnitude of the common cause risk estimates for different systems at this stage of the design. Given the limited data about launch vehicle CCF and that launch vehicles are a highly redundant system by design, it is important to make design decisions to account for a range of values for independent and CCFs. When investigating the design of the one-out-of-two component redundant system for launch vehicles, a response surface was constructed to represent the impact of the independent failure rate versus a common cause beta factor effect on a system's failure probability. This presentation will define a CCF and review estimation calculations. It gives a summary of reduction methodologies and a review of examples of historical CCFs. Finally, it presents the response surface and discusses the results of the different CCFs on the reliability of a one-out-of-two system.

Hark, Frank↗
NASA NTRS2016

[MaRS Project]

The Space Exploration Division of the Safety and Mission Assurances Directorate is responsible for reducing the risk to Human Space Flight Programs by providing system safety, reliability, and risk analysis. The Risk & Reliability Analysis branch plays a part in this by utilizing Probabilistic Risk Assessment (PRA) and Reliability and Maintainability (R&M) tools to identify possible types of failure and effective solutions. A continuous effort of this branch is MaRS, or Mass and Reliability System, a tool that was the focus of this internship. Future long duration space missions will have to find a balance between the mass and reliability of their spare parts. They will be unable take spares of everything and will have to determine what is most likely to require maintenance and spares. Currently there is no database that combines mass and reliability data of low level space-grade components. MaRS aims to be the first database to do this. The data in MaRS will be based on the hardware flown on the International Space Stations (ISS). The components on the ISS have a long history and are well documented, making them the perfect source. Currently, MaRS is a functioning excel workbook database; the backend is complete and only requires optimization. MaRS has been populated with all the assemblies and their components that are used on the ISS; the failures of these components are updated regularly. This project was a continuation on the efforts of previous intern groups. Once complete, R&M engineers working on future space flight missions will be able to quickly access failure and mass data on assemblies and components, allowing them to make important decisions and tradeoffs.

Aruljothi, Arunvenkatesh↗
NASA NTRS2016

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↗
NASA NTRS2016

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk, and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results, and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods, such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty, are rendered obsolete, since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods. This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper describes how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Novack, Steven D.↗
NASA NTRS2016

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty are rendered obsolete since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods.This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper shows how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Novack, Steven D.↗
NASA NTRS2016

Common Cause Failure Modeling

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFS are a set of dependent type of failures that can be caused by: system environments; manufacturing; transportation; storage; maintenance; and assembly, as examples. Since there are many factors that contribute to CCFs, the effects can be reduced, but they are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and CCF (dependent) failure and data is limited, especially for launch vehicles. The Probabilistic Risk Assessment (PRA) of NASA's Safety and Mission Assurance Directorate at Marshal Space Flight Center (MFSC) is using generic data from the Nuclear Regulatory Commission's database of common cause failures at nuclear power plants to estimate CCF due to the lack of a more appropriate data source. There remains uncertainty in the actual magnitude of the common cause risk estimates for different systems at this stage of the design. Given the limited data about launch vehicle CCF and that launch vehicles are a highly redundant system by design, it is important to make design decisions to account for a range of values for independent and CCFs. When investigating the design of the one-out-of-two component redundant system for launch vehicles, a response surface was constructed to represent the impact of the independent failure rate versus a common cause beta factor effect on a system's failure probability. This presentation will define a CCF and review estimation calculations. It gives a summary of reduction methodologies and a review of examples of historical CCFs. Finally, it presents the response surface and discusses the results of the different CCFs on the reliability of a one-out-of-two system.

Hark, Frank↗
NASA NTRS2015

Common Cause Failure Modeling in Space Launch Vehicles

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFs are a set of dependent type of failures that can be caused for example by system environments, manufacturing, transportation, storage, maintenance, and assembly. Since there are many factors that contribute to CCFs, they can be reduced, but are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and dependent CCF. Because common cause failure data is limited in the aerospace industry, the Probabilistic Risk Assessment (PRA) Team at Bastion Technology Inc. is estimating CCF risk using generic data collected by the Nuclear Regulatory Commission (NRC). Consequently, common cause risk estimates based on this database, when applied to other industry applications, are highly uncertain. Therefore, it is important to account for a range of values for independent and CCF risk and to communicate the uncertainty to decision makers. There is an existing methodology for reducing CCF risk during design, which includes a checklist of 40+ factors grouped into eight categories. Using this checklist, an approach to produce a beta factor estimate is being investigated that quantitatively relates these factors. In this example, the checklist will be tailored to space launch vehicles, a quantitative approach will be described, and an example of the method will be presented.

Hark, Frank↗
NASA NTRS2017

And the Humans Save the Day or Maybe They Ruin It: The Importance of Humans in the Loop

Flying a mission in space requires a massive commitment of resources, and without the talent and commitment of the people involved in this effort we would never leave the atmosphere of Earth. When we use the phrase "humans in the loop", it could apply to almost any endeavor since everything starts with humans developing a concept, completing the design process, building or implementing a product and using the product to achieve a goal or purpose. Narrowing the focus to spaceflights, there are a variety of individuals involved throughout the preparations for flight and the flight itself. All of the humans involved add value and support for program success. The purpose of this paper focuses on how a Probabilistic Risk Assessment (PRA) accounts for the human in the loop for potential missions using a technique called Human Reliability Analysis (HRA). Human actions can increase or decrease the overall risk via initiating events or mitigating them, thus removing the human from the loop doesn't always lower the risk.

DeMott, Diana↗
NASA NTRS2016

Estimating Orion Heat Shield Failure Due To Ablator Cracking During The EFT-1 Mission

The Orion EFT-1 heatshield suffered from two major certification challenges: First, the mechanical properties used in design were not evident in the flight hardware and second, the flight article itself cracked during fabrication. The combination of these events motivated the Orion Program to pursue an engineering-level Probabilistic Risk Assessment (PRA) as part of heatshield certification rationale. The PRA provided loss of Mission (LOM) likelihoods considering the probability of a crack occurring during the mission and the likelihood of subsequent structure over-temperature. The methods and input data for the PRA are presented along with a discussion of the test data used to anchor the results. The Orion program accepted an EFT-1 Loss of Vehicle (LOV) risk of 1-in-160,000 due to in-mission Avcoat cracking based on the results of this analysis. Conservatisms in the result, along with future considerations for Exploration Missions (EM) are also addressed.

Avcoat↗