Chel Stromgren | Engineering Papers

NASA NTRS2024

Lunar Logistics Drivers and Needs

For human exploration missions, it is critical to provide items such as food, water, air, spare parts, and other similar products required to sustain life, maintain systems, and allow for productive science and utilization activities. Together, these types of goods are referred to as logistics items. This white paper breaks down the different types of logistics items, explains the mission and architecture drivers that determine logistics item needs, and identifies the types of logistics items that tend to dominate overall needs.

Chel Stromgren↗

NASA NTRS2023

Exploration Systems Development Mission Directorate (ESDMD) Moon-to-Mars Architecture Definition Document

There are many opportunities in executing the ambitious Moon-to-Mars efforts through an integrated architecture. NASA addresses this in its Moon-to-Mars Strategy and Objectives Development2 document (hereafter referred to as the M2M Strategy). Many of these opportunities involve establishing a system engineering framework that can support the breadth of necessary program and system contributions. By applying these needs to nearer-term lunar development, NASA will be instituting the process, procedures, and techniques needed to enable longer-term Mars goals and more. Some of the challenges being addressed in the M2M Strategy are associated with the architecture definition and include broad/changing goals, funding, and external pressures/influences. This document and the methodology outlined for architecture definition have been crafted to contend with these using an iterative and adaptable framework. The primary purpose of the Architecture Definition Document (ADD) is to capture the methodology, organization, and decomposition necessary to translate the broad objectives outlined in the M2M Strategy into functions and use cases that can be allocated to implementable programs and projects. Inherent in this process will be the need to communicate the long-term vision, maintain traceability to responsible parties, and iterate on the architectural implementation as innovations and solutions develop. This document will be updated and improved in conjunction with the Architecture Concept Review (ACR) which will be held annually to help unify the Agency and to get buy-in and input from across the Agency on the human exploration architecture. The annual nature of the process provides the opportunity to continually incorporate new developments in technologies and new partnerships, whether they be with industry, the U.S. Government, international entities, or academia.

Moon-to-Mars Strategy↗

NASA NTRS2022

Moon to Mars (M2M) Habitation Considerations: A Snap Shot As of January 2022

The following NASA Technical Memorandum (TM) is intended to provide a snapshot in time of NASA’s current considerations (ground rules and assumptions, functional allocations, logistics) for habitation systems for the lunar surface (non-roving) and Mars transits. As NASA continues to refine the reference designs to meet the needs of an evolving architecture, it is expected that this information will also be updated as a result. Where appropriate, relevant publicly released documents will be referenced to provide further detail. NASA’s human lunar exploration plan under the Artemis program calls for achieving the goal of sending the first woman and first person of color to the surface of the Moon in the mid- 2020s and working toward sustainable exploration by the end of the decade. Working with both commercial and international partners, NASA will establish a permanent human presence on the Moon to uncover new scientific discoveries and lay the foundation for private companies to build a lunar economy. Longer duration missions on the lunar surface and in lunar orbit will also serve as a test bed for technologies to support future Mars exploration campaigns. The agency will use what we learn on the Moon to prepare for humanity's next giant leap – sending astronauts to Mars. NASA intends to establish a sustained lunar presence with the development of the Artemis Base Camp to prove technologies and capabilities that will one day enable humans to live and work on Mars, beginning with core elements including the Lunar Terrain Vehicle (LTV), the Pressurized Rover (PR), the lunar Surface Habitat (SH), power systems, and in-situ resource utilization (ISRU) systems. For in space operations and eventual transport of humans to Mars, NASA will utilize a Mars Transit Habitat (TH). Following deployment, the TH will complete a series of longer duration missions and shakedown testing while docked at Gateway, leveraging Gateway’s habitation redundancy for safety measures. Proposed Gateway-TH missions will far exceed the longest duration cislunar human missions to date. They will be the first operational readiness tests of our long-duration deep space systems, and of the split crew (two crew on the surface, two crew in space) operations that are vital to the approach for the first human Mars mission. Both the SH and TH are major architectural elements of NASA’s Moon to Mars (M2M) approach, each with very different concepts of operation. The SH is intended for use on the lunar surface as a home for astronauts, surface operations base, science facility, hub for communications, extravehicular activity (EVA) equipment repair site, waste processing facility, and supply hub. It serves as an enabler for a sustained surface presence and preparation for partial gravity operations during Mars missions. The SH will be designed to be self-sufficient for operations on the lunar surface. The SH will independently provide several functions, including its own power generation, energy storage capability, sleep quarters, hygiene areas, work areas, and dining areas. It will be capable of communicating with surface assets, orbital assets, and directly with Earth ground stations. It is planned to operate with two crew in the habitat for ~28- day stays with crew swap-outs in which the PR crew of two trades places with the habitat crew. During the swap-out, the habitat will nominally support four crew for a short period of time. For contingency scenarios, the habitat must also be capable of supporting four crew for up to 7 days. The TH will be designed to be capable of up to ~1,200-day Mars missions with the ability to carry all food and supplies needed to support a crew of four for this duration. An assumed Mars mission profile for the TH is to carry crew and supplies for ~850-day roundtrips between Earth and Mars orbit that allows 30-day stays on the Martian surface. To test the systems for this long journey, the TH will be used to extend the duration of missions at Gateway, enabling the orbiting outpost to be used as a Mars analog. These analog missions will be accomplished by attaching the TH to Gateway and conducting lunar surface operations from the TH. The TH may also need to perform free-flying shakedown missions to test out all systems prior to leaving for Mars. The habitat provides many critical functions including: a contingency airlock, crew quarters, galley, hygiene areas, safe haven capability, and science equipment. It can receive docked items from either axial end or on a radial port.

Habitat↗

NASA NTRSDate not supplied

Launch Availability Analysis for the Artemis Program

On March 26, 2019, Vice President Pence stated that the policy of the Trump administration and the United States of America is to return American astronauts to the Moon within the next five years i.e., by 2024. Since that time, NASA has begun the process of developing concepts of operations and launch campaign options to achieve that goal as well as to provide a sustainable human presence on the Moon. Whereas the Apollo program utilized one Saturn V rocket to carry out a single lunar landing mission of short duration, NASA’s preliminary plans for the Artemis Program call for a combination of medium lift class rockets along with the heavy lift Space Launch System (SLS)to achieve a lunar landing by 2024 as well as subsequent missions. This paper describes how discrete event simulation is used to model the launch campaigns and provide metrics on launch availability and mission duration for each element being launched. Possible methods for improving launch availability are presented.

Grant Cates↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Current Environmental Control and Life Support System (ECLSS) development and test activities are not generating data fast enough to provide statistically-supportable precise Orbital Replacement Unit (ORU) failure rate estimates for future missions. 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 operations on board the International Space Station (ISS), a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass for future missions. A Bayesian failure rate estimation approach, such as the one currently implemented by the ISS Program, can help 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 failure rate 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, these subjective (and often inaccurate) prior estimates currently have a 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.

Supportability↗

NASA NTRSDate not supplied

ECLSS Does Not Exist in a Vacuum: Integrated Analysis is Necessary to Inform System Architecture Decisions

Environmental Control and Life Support System (ECLSS) architecture selection has profound implications for mission cost and mass extending far beyond the ECLSS itself. Similarly, other mission architecture decisions – particularly involving transportation systems – can influence optimal ECLSS architectures. Loop closure influences requirements for water, oxygen, and other consumables. System maintainability and reliability influences spares mass and risk. System, consumables, and spares mass interact with transportation system architectures and propellant demands and propulsion element sizing. All these interactions with other systems must be considered when evaluating ECLSS options. Analyses that focus only on maximizing ECLSS loop closure – or minimizing ECLSS mass, or minimizing ECLSS life cycle cost – may lead to sub-optimal or even counterproductive system architecture and investment decisions at the mission level. For example, an ECLSS architecture that minimizes ECLSS lifecycle cost but results in excessively high logistics mass could lead to significantly increased transportation system costs or make interplanetary transportation infeasible. Increased loop closure could result in higher development costs and higher mass if system/spares mass increases outweigh consumables reduction. In addition, systems mass and consumables mass are not directly comparable and have different impacts on propellant requirements, as consumables mass changes over the course of the mission. This paper presents an integrated analysis examining the overall impact of different ECLSS and transportation architectures on mass for a crewed Mars mission, including the habitat and transportation systems as well as consumables, spares, and propellant. Key observations are discussed, along with opportunities for further sensitivity analysis and model development. Overall, ECLSS development activities must consider their impacts at the mission level, as part of an integrated system, rather than in isolation.

Systems Analysis↗

NASA NTRSDate not supplied

Integrated Trajectory, Habitat, and Logistics Analysis and Trade Study for Human Mars Missions

Environmental Control and Life Support System (ECLSS) architecture selection has profound implications for mission cost and mass extending far beyond the ECLSS itself. Similarly, other mission architecture decisions – particularly involving transportation systems – can influence optimal ECLSS architectures. Loop closure influences requirements for water, oxygen, and other consumables. System maintainability and reliability influences spares mass and risk. System, consumables, and spares mass interact with transportation system architectures and propellant demands and propulsion element sizing. All these interactions with other systems must be considered when evaluating ECLSS options. Analyses that focus only on maximizing ECLSS loop closure – or minimizing ECLSS mass, or minimizing ECLSS life cycle cost – may lead to sub-optimal or even counterproductive system architecture and investment decisions at the mission level. For example, an ECLSS architecture that minimizes ECLSS life cycle cost but results in excessively high logistics mass could lead to significantly increased transportation system costs or make interplanetary transportation infeasible. Increased loop closure could result in higher development costs and higher mass if system/spares mass increases outweigh consumables reduction. In addition, systems mass and consumables mass are not directly comparable and have different impacts on propellant requirements, as consumables mass changes over the course of the mission. This paper presents an integrated analysis examining the overall impact of different ECLSS and transportation architectures on mass for a crewed Mars mission, including the habitat and transportation systems as well as consumables, spares, and propellant. Key observations are discussed, along with opportunities for further sensitivity analysis and model development. Overall, ECLSS development activities must consider their impacts at the mission level, as part of an integrated system, rather than in isolation.

Mars↗

NASA NTRSDate not supplied

Integrated Trajectory, Habitat, and Logistics Analysis and Trade Study for Human Mars Missions

Environmental Control and Life Support System (ECLSS) architecture selection has profound implications for mission cost and mass extending far beyond the ECLSS itself. Similarly, other mission architecture decisions – particularly involving transportation systems – can influence optimal ECLSS architectures. Loop closure influences requirements for water, oxygen, and other consumables. System maintainability and reliability influences spares mass and risk. System, consumables, and spares mass interact with transportation system architectures, including propellant demands and propulsion element sizing. All these interactions with other systems must be considered when evaluating ECLSS options. Analyses that focus only on maximizing ECLSS loop closure – or minimizing ECLSS mass, or minimizing ECLSS life cycle cost – may lead to sub-optimal or even counterproductive system architecture and investment decisions at the mission level. For example, an ECLSS architecture that minimizes ECLSS life cycle cost but results in excessively high logistics mass could lead to significantly increased transportation system costs or make interplanetary transportation infeasible. Increased loop closure could result in higher development costs and higher mass if system/spares mass increases outweigh consumables reduction. In addition, systems mass and consumables mass are not directly comparable and have different impacts on propellant requirements, as consumables mass changes over the course of the mission. This paper presents an integrated analysis examining the overall impact of different ECLSS and transportation architectures on Earth departure mass for a crewed Mars mission, including the habitat and transportation systems as well as consumables, spares, and propellant. Key observations are discussed, along with opportunities for further sensitivity analysis and model development. Overall, ECLSS development activities must consider their impacts at the mission level, as part of an integrated system, rather than in isolation.

Mars↗

NASA NTRSDate not supplied

Regenerative ECLSS and Logistics Analysis for Sustained Lunar Surface Missions

As NASA begins to develop concepts for sustained crew missions to the lunar surface, it will be essential to evaluate the logistics requirements needed to enable such missions. Lunar surface missions will involve crews of four astronauts living on the surface for periods of 30 days or longer. Astronauts will either live in a surface habitat or transfer between a habitat and a pressurized rover. The amount of logistics that must be delivered to the surface to support the crew, support the surface systems, and conduct science could be substantial. Because NASA plans to conduct these missions on an annual basis, the complexity and cost of logistics delivery will likely drive campaign sustainability. The amount of required logistics is partially a function of the ECLSS system in the habitable elements on the surface. The higher the capability of regenerative ECLSS that can be accommodated, the lower the logistics requirements. However, the increased regenerative ECLSS capabilities will also increase the initial delivery mass of elements and require additional maintenance and spares to maintain the systems. This paper describes an effort to evaluate lunar sustained logistics requirements, including sensitivity analysis for ECLSS architecture options and their impact on requirements. The authors determine logistics requirements using an integrated surface ECLSS model. The model considers different configurations of rover and habitat setups with multiple ECLSS options on both. The tool can also model gas and liquid transfer options between the habitat and the rover. The authors use the model to evaluate several different ECLSS configurations and their logistics requirements. The authors then complete a sensitivity analysis that compares logistic requirements and the initial delivery mass over increasing ECLSS capabilities. Finally, the authors make recommendations for an ECLSS architecture option that balances the tradeoffs between logistics requirements and ECLSS mass.

Environmental Control and Life Support↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

Integrated Crewed Mars Mission Analysis, Part II: The Trajectory Strikes Back

Crewed missions to Mars present a challenging problem involving strong interactions between a variety of spacecraft systems. Design and architecture decisions made by one system can have powerful implications for another. Siloed subsystem development and optimization efforts are unlikely to generate optimal results for the integrated system and may lead to counterproductive investments at the mission and system level. Mismatched assumptions between different subsystems can also negatively impact the final result. Integrated systems analysis, trade studies, and sensitivity analysis are a critical element of successful Mars mission design. Previous work examined the linkages between Environmental Control and Life Support System (ECLSS) loop closure, food water content, spares Probability of Sufficiency (POS), and other factors by performing an integrated mass and cost assessment of a variety of Mars habitat and transportation system designs. This paper expands upon that work by evaluating the impact of mission duration, trajectory, and propulsion technology alongside key habitat variables. Transportation system and trajectory design decisions have enormous impacts on overall system mass, and the impacts of those decisions are themselves sensitive to the mass of the habitat that is being transported. This paper examines a variety of mission cases and evaluates total departure mass for each case. An estimate of relative costs for specific cases is also provided. The results are discussed, with an emphasis on the sensitivity of mass, cost, and other figures of merit to key parameters. Interactions and dependencies between various decisions, particularly those that relate to technology investments, are also highlighted. Overall, the various elements, systems, and subsystems required to support human spaceflight should not be optimized and evaluated in isolation, but instead should be analyzed as part of an integrated system. This paper demonstrates how integrated analysis can improve system understanding and lead to more optimal and effective crewed exploration systems.

Human Spaceflight↗

NASA NTRSDate not supplied

Integrated Crewed Mars Mission Analysis, Part II: The Trajectory Strikes Back

Crewed missions to Mars present a challenging problem involving strong interactions between a variety of spacecraft systems. Design and architecture decisions made by one system can have powerful implications for another. Siloed subsystem development and optimization efforts are unlikely to generate optimal results for the integrated system and may lead to counterproductive investments at the mission and system level. Mismatched assumptions between different subsystems can also negatively impact the final result. Integrated systems analysis, trade studies, and sensitivity analysis are a critical element of successful Mars mission design. Previous work examined the linkages between Environmental Control and Life Support System (ECLSS) loop closure, food water content, spares Probability of Sufficiency (POS), and other factors by performing an integrated mass and cost assessment of a variety of Mars habitat and transportation system designs. This paper expands upon that work by evaluating the impact of mission duration, trajectory, and propulsion technology alongside key habitat variables. Transportation system and trajectory design decisions have enormous impacts on overall system mass, and the impacts of those decisions are themselves sensitive to the mass of the habitat that is being transported. This paper examines a variety of mission cases and evaluates total departure mass for each case. An estimate of relative costs for specific cases is also provided. The results are discussed, with an emphasis on the sensitivity of mass, cost, and other figures of merit to key parameters. Interactions and dependencies between various decisions, particularly those that relate to technology investments, are also highlighted. Overall, the various elements, systems, and subsystems required to support human spaceflight should not be optimized and evaluated in isolation, but instead should be analyzed as part of an integrated system. This paper demonstrates how integrated analysis can improve system understanding and lead to more optimal and effective crewed exploration systems.

Human Spaceflight↗

NASA NTRSDate not supplied

Regenerative ECLSS and Logistics Analysis for Sustained Lunar Surface Missions

As NASA develops concepts for sustained crew missions to the lunar surface, a crucial component of mission planning will be evaluating the required amount of logistics to support the crew, surface systems, and science operations. This amount could be substantial. Because NASA plans to conduct these missions on an annual basis, the complexity and cost of logistics delivery will likely drive campaign sustainability. Logistics quantity is partially a function of the regenerative Environmental Control and Life Support System (ECLSS) capability in habitable elements on the surface. The regenerative ECLSS recycles human waste to produce water and oxygen, reducing the consumables needed for a mission. Thus, an ECLSS with increased regenerative capability will require less logistics. However, an ECLSS with enhanced regenerative abilities will also increase the initial delivery mass of elements and require extra maintenance items and spares. This paper analyzes the tradeoff between initial delivery masses of different regenerative ECLSS options and the amount of logistics resupply required for each option.

Environmental Control and Life Support Systems↗

NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repairs Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, it will be necessary to understand how crew repair and maintenance timelines are impacted by mission operations and technology changes. Past work has been done using historical ISS data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available data sets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat, and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA International Space Station (ISS) maintenance and crew time data are logged into two central databases, the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS) respectively. Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. MDC provides a detailed catalog of failure events and an overview of the failure’s required maintenance and OPTimIS provides a description of crew activities and crew time durations dedicated to maintenance. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair times for each failure event. Data is also classified by the outcome of each repair event, whether the failed component was replaced or whether it was repaired in place. The entire maintenance activity dataset is then categorized based on the class of failed component to allow for a statistically significant sample size for each class and to provide accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time To Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗

NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗