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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Thermal Analysis for Orbiter and ISS Plume Impingement on International Space Station

The NASA Reaction Control System (RCS) Plume Model (RPM) is an exhaust plume flow field and impingement heating code that has been updated and applied to components of the International Space Station (ISS). The objective of this study was to use this code to determine if plume environments from either Orbiter PRCS jets or ISS reboost and Attitude Control System (ACS) jets cause thermal issues on ISS component surfaces. This impingement analysis becomes increasingly important as the ISS is being assembled with its first permanent crew scheduled to arrive by the end of fall 2000. By early summer 2001 , the ISS will have a number of major components installed such as the Unity (Node 1), Destiny (Lab Module), Zarya (Functional Cargo Block), and Zvezda (Service Module) along with the P6 solar arrays and radiators and the Z-1 truss. Plume heating to these components has been analyzed with the RPM code as well as additional components for missions beyond Flight 6A such as the Propulsion Module (PM), Mobile Servicing System, Space Station Remote Manipulator System, Node 2, and the Cupola. For the past several years NASA/JSC has been developing the methodology to predict plume heating on ISS components. The RPM code is a modified source flow code with capabilities for scarfed nozzles and intersecting plumes that was developed for the 44 Orbiter RCS jets. This code has been validated by comparison with Shuttle Plume Impingement Flight Experiment (SPIFEX) heat flux and pressure data and with CFD and Method of Characteristics solutions. Previous analyses of plume heating predictions to the ISS using RPM have been reported, but did not consider thermal analysis for the components nor jet-firing histories as the Orbiter approaches the ISS docking ports. The RPM code has since been modified to analyze surface temperatures with a lumped mass approach and also uses jet-firing histories to produce pulsed heating rates. In addition, RPM was modified to include plume heating from ISS jets to ISS components where the jet coordinates are specified, together with the engine cant angle. These latter studies have been focused on the PM with plumes from its reboost and ACS jets impinging on various ISS components and also focused on the Japanese H2 Transfer Vehicle (HTV) with the plumes from its reboost engines impinging on the Cupola window. This paper will present plume heating and surface temperature results on a number of ISS components with and without jet-firing histories, evaluate post-flight data, and describe any potential thermal issues

Rochelle, William C.↗
NASA NTRS2017

Validation of Fatigue Modeling Predictions in Aviation Operations

Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

operational safety↗
NASA NTRSDate not supplied

Establishing Recommendations for the Development of Space Suit Integrated Food Systems and the Delivery of Nutrition Before, During, and After Lunar EVA

Extravehicular activity (EVA) operations are complex and challenging, and Artemis missions will include a higher tempo and frequency of EVAs than any previous space program. For this reason, surface EVAs greater than 4 h require additional nutrition support. The ability to provide this additional nutritional support is limited on Artemis missions given crew schedules and the current inability to provide nutrition to astronauts while suited. This project seeks to help define means for provision of nutritional support to Artemis astronauts during lunar EVAs.

E L Dillon↗
NASA NTRSDate not supplied

Composing Teams with TEAMSTaR Tool for Evaluating and Mitigating Space Team Risk

As NASA sets its sights on more Earth-independent missions, the mission to Mars as a prime example, a team’s composition becomes a critical issue for mitigating the risks of such endeavors. The purpose of this project is to develop and validate TEAMSTaR (Tool for Evaluating And Mitigating Space Team Risks), a team composition decision support system that can be used by mission stakeholders (e.g., mission schedulers, crew members) to predict how a hypothetical team’s social relationships are likely to evolve and influence crew performance over the course of a mission. TEAMSTaR will enable decision makers to evaluate composition scenarios for a set of teams, for single-member replacements, and/or for subsets of teams.

N S Contractor↗
NASA NTRSDate not supplied

A PI’s Guide to Analog Research

Congratulation on being awarded a grant to conduct research in a spaceflight analog! NASA funded research in a Human Research Program (HRP) managed analog environment is very different from research in a traditional laboratory. This presentation will provide a guide to help investigators successfully plan studies for integration and implementation in an HRP analog platform. There are many things to consider when planning a study for implementation in an analog facility and campaign or mission. How do you get from grant approval to data analysis? This is where HRP’s Research Operations and Integration (ROI) team comes in. The ROI team works with sponsoring HRP elements and PI teams to capture requirements and help the PI teams through the phases of research complement development, integration and implementation. A complement is comprised of a group of studies requiring a common platform and/or scenario that are able to be integrated on a noninterference basis for implementation. Properly defining and documenting requirements and study needs is crucial in successful integration and implementation. This presentation will outline and provide the information needed for PI teams to understand the integration process and the role of the ROI team in the successful integration and implementation of their study. Topics to be discussed will include but not be limited to science requirement definition and documentation, NASA IRB submissions, subject recruitment, screening and selection, study integration process, efficiency in data collection and data sharing, daily crew schedules, hardware and software shipping, receipt and checkout, biological sample collection, mission support, data management and receipt of data.

B. Caldwell↗
NASA NTRSDate not supplied

Optimized Trajectory Correction Burn Placement for the NASA Artemis II Mission

The NASA Artemis II mission represents the first time humans plan to return to the lunar vicinity in over 50 years with a crew traveling to the Moon in the Orion spacecraft on a free return trajectory. This first crewed mission of the Artemis program will evaluate human-rated elements of Orion in preparation to sending astronauts to the lunar surface. The selected free-return cislunar trajectory profile that is reminiscent of the Apollo 8 mission that nominally requires no additional translational burns following the trans-lunar injection (TLI) burn. Due to crew activity, maneuver execution errors, navigation uncertainty, orbit insertion errors, disturbance accelerations, and other system limitations; periodic trajectory corrections burns are necessary to ensure proper entry interface (EI) conditions are satisfied for a safe return to Earth. Robust trajectory optimization techniques are utilized to determine the optimized placements for the Artemis II trajectory correction burns that accounts for the crew schedule, both the primary and backup navigation systems, targeting strategies and burn plan configurations, spacecraft venting, thruster selection, and the integrated GN&C performance.

Linear Covariance Analysis↗
NASA NTRS2006

In-Space Crew-Collaborative Task Scheduling

As humans venture farther from Earth for longer durations, it will become essential for those on the journey to have significant control over the scheduling of their own activities as well as the activities of their companion systems and robots. However, the crew will not do all the scheduling; timelines will be the result of collaboration with ground personnel. Emerging technologies such as in-space message buses, delay-tolerant networks, and in-space internet will be the carriers on which the collaboration rides. Advances in scheduling technology, in the areas of task modeling, scheduling engines, and user interfaces will allow the crew to become virtual scheduling experts. New concepts of operations for producing the timeline will allow the crew and the ground support to collaborate while providing safeguards to ensure that the mission will be effectively accomplished without endangering the systems or personnel.

Jaap, John↗
NASA NTRS2018

Increasing Human Spaceflight Capabilities: Demonstration of Crew Autonomy Through Self-Scheduling Onboard International Space Station

For the first time in a spaceflight operational environment, our team enabled an ISS (International Space Station) crewmember to plan, reschedule, and execute their activities in real-time while abiding by flight and scheduling constraints. The Crew Autonomous Scheduling Test (CAST) investigated the novel concept of operations: allowing crew to manage their own timeline.

crew autonomy↗
NASA NTRS2006

In-Space Crew-Collaborative Task Scheduling

As humans venture farther from earth for longer durations, it will become essential for those on the journey to have significant control over the scheduling of their own activities as well as the activities of their companion systems and robots. However, there are many reasons why the crew will not do all the scheduling; timelines will be the result of collaboration with ground personnel. Emerging technologies such as in-space message buses, delay-tolerant networks, and in-space internet will be the carriers on which the collaboration rides. Advances in scheduling technology, in the areas of task modeling, scheduling engines, and user interfaces will allow the crew to become virtual scheduling experts. New concepts of operations for producing the timeline will allow the crew and the ground support to collaborate while providing safeguards to ensure that the mission will be effectively accomplished without endangering the systems or personnel.

Jaap, John↗
NASA NTRS2000

Scheduling and Estimating the Cost of Crew Time

In a previous paper, Theory and Application of the Equivalent System Mass Metric, Julie Levri, David Vaccari, and Alan Drysdale developed a method for computing the Equivalent System Mass (ESM) of crew time. ESM is an analog of cost. The suggested approach has been applied but seems to impose too high a cost for small additional requirements for crew time. The proposed method is based on the minimum average cost of crew time. In this work, the scheduling of crew time is examined in more detail, using suggested crew time allocations and daily work schedules. Crew tasks are typically assigned using priorities, which can also be used to construct a crew time demand curve mapping the value or cost per hour versus the total number of hours worked. The cost of additional crew time can be estimated by considering the intersection and shapes of the demand and supply curves. If e assume a mathematical form for the demand curve, a revised method can be developed for computing the cost or ESM of crew time. This method indicates a low cost per hour for small additional requirements for crew time and an increasing cost per hour for larger requirements.

Jones, Harry↗
NASA NTRS1980

A ranking algorithm for spacelab crew and experiment scheduling

The problem of obtaining an optimal or near optimal schedule for scientific experiments to be performed on Spacelab missions is addressed. The current capabilities in this regard are examined and a method of ranking experiments in order of difficulty is developed to support the existing software. Experimental data is obtained from applying this method to the sets of experiments corresponding to Spacelab mission 1, 2, and 3. Finally, suggestions are made concerning desirable modifications and features of second generation software being developed for this problem.

Grone, R. D.↗
NASA NTRS2018

Predicting Crew Time Allocations for Lunar Orbital Missions Based on Historical ISS Operational Activities

As the National Aeronautics and Space Administration continues to define candidate architectures for the planned lunar “Gateway”, it will be necessary to have a detailed understanding of how the crew will inhabit, operate, and maintain the spacecraft. The nature of the Gateway vehicle systems configuration and operations will have a direct impact on the scope of work activities required of the crew. Crew work schedules are sensitive to variations in spacecraft architecture, visiting vehicle activities, and logistics operations – particularly within short duration missions as initially planned for the lunar Gateway. These system and operational configurations must be taken into account when planning for crew time availability to conduct science activities on Gateway missions. This paper presents a methodology that is used to predict crew time distributions for lunar Gateway missions, as applied in NASA’s Exploration Crew Time Model (ECTM). The process utilized for evaluating crew time distributions is based on the categorization of all crew activities into a standardized ontology. Historical ISS daily crew timeline data from July 20, 2011 (post STS retirement) to present day was captured via the Operational Planning Timeline Integration System (OPTimIS) database and characterized according to the standardized ontology. This process enabled correlation and statistical analysis of the ISS data according to common mission parameters such as crew size, ECLSS system design, vehicle traffic operations, and logistics delivery operations. The results of the statistical analysis are a set of crew time distributions for each activity category. These distributions are then utilized within the ECTM to examine crew time allocations based on mission parameter inputs, which serve to characterize the Gateway mission configurations. Results for predicted crew time allocations for representative short duration Gateway missions are presented. These results can be used to evaluate crew schedule availability for science and utilization activities. Variations in expected mission architectures and mission operations are accounted for to correct crew time predictions. The analysis is being leveraged to plan utilization capability objectives that are achievable on the Gateway missions, as well as inform the viability of various mission architecture options.

Stromgren, Chel↗
NASA NTRS1987

Knowledge-based simulation

An architecture for a knowledge-based simulator is described. The task of scheduling represents an area in which such a tool might be applied. More specifically, scheduling for crew and ground support activities for the shuttle and space station would benefit from the application of knowledge-based simulation. The knowledge-based simulator would allow the crew and support personnel to schedule and reschedule activities in a timely and flexible manner in order to examine and test possible plans.

Newman, P. A.↗
NASA NTRSDate not supplied

Early Assessments of Crew Timelines for the Lunar Surface Habitat

As NASA progresses towards sustained crewed space missions, crew timelines will become increasingly important to achieving mission goals. While it is desirable to spend as much time as possible during crewed space missions on science activities and experiments, there are a large number of activities that crew members must perform each day in order to maintain both crew and vehicle health and safety. The time available for science activities in space is highly dependent on mandatory tasks required for crew and vehicle health and safety. The different crewed activities need to be planned accordingly long before the start of a mission in order to optimize crew time for science. To begin assessing the potential crew time for available for science, an understanding of the requirements to maintain crew and vehicle health and safety is needed. These additional activities may include sleep, exercise, vehicle maintenance, logistics handling, crew personal time, as well as many other tasks. The remaining time outside of these required tasks, within a reasonable crew work schedule, can be dedicated to science operations. This paper will detail a collaborative effort to analyzing crew times for sustained spaceflight missions and how the results of that analysis are applied to the crew timeline for the proposed Lunar Surface Habitat (SH).To determine the crew time for all of these required activities, an analysis was conducted utilizing defined agency requirements and historical crewed mission data. Predicted crew activity times were integrated into a daily schedule in order to optimize the crew’s time during the mission. This methodology was utilized to produce expected crew timelines for NASA’s proposed Artemis Base Camp (ABC) missions. The current plans for the ABC contain two different sustained habitats, the Pressurized Rover (PR) and the Surface Habitat (SH). While the crew are separated between the two habitats, the timelines for each element are dependent on the other element’s operations, so the two element timelines are formed in conjunction with one another. The results described in this paper, however, will focus solely on the crew timeline in the SH. This paper will explain the methodology behind predicting the required crew time spent in the SH for each activity, and the process of incorporating these predicted crew times into a coherent schedule.

Crew Time↗
NASA NTRS2000

ISS Expedition 1 Crew Interviews: William M. Shepherd

Live footage of a preflight interview with Commander Bill Shepherd is seen. The interview addresses many different questions including why Shepherd became interested in the space program, the events that led to his interest, the transition from the navy to his selection in the astronaut program. Other interesting information that this one-on-one interview discusses are the main goals of the first Expedition Crew, their scheduled docking with the International Space Station (ISS), making the ISS ready for human inhabitance, and all the specifics that will make his living arrangements difficult. Shepherd mentions his responsibilities during the much-anticipated two-day flight to the ISS, as well as the scheduled space-walk. Shepherd also discusses the crew's first tasks upon entrance including other scheduled tasks for the first week, docking from cargo ships, and spacecraft delivering equipment or performing Extra Vehicular Activities (EVA). He explains his interpretation of the meaning of mission success, and the implications of having human beings in space.

Source record↗
NASA NTRSDate not supplied

Crew Autonomy Through Self-Scheduling: Operational Characterization

NASA’s future long-duration exploration missions (LDEMs) will encounter increasing communication transmission delays as they move farther from Earth-based ground stations. This necessitates a new approach, as crews can no longer rely on real-time support from ground planners and must self-schedule their own operational timelines effectively and efficiently. To enable this transition, our team has developed Playbook, a mission planning and scheduling tool. Our research focuses on quantifying scheduling performance using Playbook to inform the design and development of future features to streamline timeline creation. We also aim to propose standards and guidelines for autonomous crews in LDEMs. In the past year, we have focused on further validating and quantifying the effects of countermeasure aids on self-scheduling performance. There are two software aids in Playbook (self-scheduling platform): Suggested Fixes, which propose an edit to resolve violations within a timeline, and No-Go Zones, which highlight where activities should not be scheduled on a timeline. We have made significant progress in HERA Campaign 7 (C7) data collection, increasing the number of days crew must self-schedule from 4 to 8. As a result, almost 20% of the mission is self-scheduled by the analog astronauts. We have also started data collection on a controlled lab experiment designed to quantify performance effects due to the countermeasures. We expect to present the preliminary results from both efforts. Finally, we have conducted an exploratory analysis of NASA’s HERA Campaign 6 (C6), investigating the mission-level impacts of self-scheduling. We derived basic patterns and descriptive statistics to better characterize crew autonomy through self-scheduling. We also assessed if there are any individual indicators of preference for self-scheduling, such as experience or predilection for autonomy. Preliminary analysis indicates that the HERA C6 crew self-scheduled one out of four flexible activities, indicating unprompted adoption of self-scheduling as a concept of operation for crew autonomy.

analog↗
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗