Nicole Piontek Exit Presentation Spring 2020
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Internship↗
Engineering topics
Nicole Piontek
Publications and source records attributed to Nicole Piontek.
More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction
The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.
Reliability↗
Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability
Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.
Testing↗
More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction
The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.
Reliability↗
Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability
Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.
Testing↗
Overview of Crew Operations for Transit to Mars
Crewed Mars missions are estimated to be 700-1,200 days in length which is two to three times longer than any continuous human spaceflight mission to date. When architecting a Mars mission there are numerous resources that must be considered, evaluated, and planned for, including—but not limited to—mass, cost, performance, and risk. Crew time is a limited resource that will need to be appropriately allocated during future Mars missions. NASA’s “Moon to Mars Objectives” specifically recognizes as Recurring Tenets the need to return crews safely to Earth while mitigating adverse impacts to crew health and maximizing crew time available for science and engineering activities within planned mission durations. Crew operations and the crew time allocation for a Mars missions will likely be different than current operational planning aboard the ISS due to communication delays, crew health and performance needs, transportation system needs, potential vehicle dormancy, and mass ejection. Crew will need to operate much more Earth independently and potentially be responsible for more operations since traditional Earth ground support will be delayed. Incidents requiring immediate crew action will therefore either be the responsibility of the crew or an automated feature of the transit vehicle. This paper discusses the operational challenges of a Mars transit mission and the associated activities that will need to take place during each operational phase of transit to and from Mars.
Crew Time↗
Overview of Crew Operations for Transit to Mars
Crewed Mars missions are estimated to be 700-1,200 days in length which is two to three times longer than any continuous human spaceflight mission to date. When architecting a Mars mission there are numerous resources that must be considered, evaluated, and planned for, including—but not limited to—mass, cost, performance, and risk. Crew time is a limited resource that will need to be appropriately allocated during future Mars missions. NASA’s “Moon to Mars Objectives” specifically recognizes as Recurring Tenets the need to return crews safely to Earth while mitigating adverse impacts to crew health and maximizing crew time available for science and engineering activities within planned mission durations. Crew operations and the crew time allocation for a Mars missions will likely be different than current operational planning aboard the ISS due to communication delays, crew health and performance needs, transportation system needs, potential vehicle dormancy, and mass ejection. Crew will need to operate much more Earth independently and potentially be responsible for more operations since traditional Earth ground support will be delayed. Incidents requiring immediate crew action will therefore either be the responsibility of the crew or an automated feature of the transit vehicle. This paper discusses the operational challenges of a Mars transit mission and the associated activities that will need to take place during each operational phase of transit to and from Mars.
Crew Time↗
Supportability Concepts for Crewed Deep Space Exploration
Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.
Supportability↗
Supportability Concepts for Crewed Deep Space Exploration
Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.
Supportability↗
A Historical Review of Logistics Mass and Crew Time Demands for ISS Operations
Following over 20 years of continuously crewed operations on the International Space Station (ISS), NASA is planning to return to the Moon and eventually send humans to Mars. ISS operations provide vital data to inform mission analysts as NASA prepares for longer and more complex missions with increased mission endurance. Endurance, defined as crewed operating time between cargo deliveries (or crew launch and return to Earth), is an important metric when analyzing mission needs. NASA is developing architectures to support sustained deep-space habitats in cislunar space, the lunar surface, Mars transit, and the surface of Mars. Unlike the ISS, these systems will not be continuously crewed, and unlike the Space Shuttle, these systems will not return to Earth for regular refurbishment between missions. Lunar systems will routinely go through long uncrewed periods between crewed missions. The systems on board will need to survive these dormancy periods with no crew present to provide maintenance. Mars systems will experience significantly longer endurance than past experience. Additionally, the inability to have quick aborts to return to Earth increases the need for system reliability, redundancy, and maintainability, as well as plans for contingency operations. This paper examines the historical logistics and crew time demand for ISS operations and mission objectives and provides an overview of missions to the ISS over its operating history, the mass and items delivered with the missions, and the crew time spent during missions. These parameters provide insight and valuable data to inform logistics and crew time estimates for future long-endurance crewed exploration missions.
Crew Logistics↗