Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “team”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team (A-Team). The A-Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an ATeam study. In these studies, Concept Maturity Levels (CML) are used to group methods. These levels include idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for exploring the science objectives, feasibility, and scope will be described including use of a new technique for understanding the most compelling science, called a Science Return Diagram (SRD). In the process of developing the SRD, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid towards developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions and reveal the mission concept’s most compelling science. This paper will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.In June of 2011 a new collaborative engineering approach forearly concept formulation began in the JPL InnovationFoundry [1], six months later becoming the “A-Team” [2].Responding to a need for exploring mission architecturelevel trades [3], the A-Team precedes Team X [4,5] in asequence of concurrent engineering teams at JPL that can beused to mature a concept from a “cocktail napkin” level ideato a complete mission point design. The A-Team efficientlyexplores the science, implementation, and programmatictrade space in early concept formulation. Small, facilitatedgroups of experts generate innovative ideas, quantitativelyassess feasibility, and discover key sensitivities in the tradespace through collaborative analysis and use of advancedmethods and tools. The A-Team process builds off theexperience within JPL and other recent approaches to earlyconcept formulation [6] including best practices of the JPLInnovation Foundry, Project Systems Engineering &Formulation Section, Team Eureka and the Rapid MissionArchitecture Team[7].The A-Team is a focal point for innovative formulationapproaches and people within JPL. It relies on a largebackground of study resources, creative thinkers and “greybeard” scrutinizers, advanced tools, and subject matterexperts with both breadth and depth in experience andexpertise that are all available at JPL. The A-Team isdesigned to be a rapid and efficient process takingapproximately 6 weeks (the entire process can be as short asjust a few days or as long as up to three months) and costingthe equivalent of a work-month of a full-time employee orless. Studies begin with detailed planning and client reviewfollowed by study sessions, analysis work, and reporting.The staffing on each study is customized to the study goalsand objectives, and it is addressed early in the A-Teamprocess. Sessions are generally half-day or whole-day eventsand conducted over a series of days with focused agendas thatare moderated by a trained facilitator. Preliminary results andknowledge capture are available within hours of each session,and a final report is generally available two weeks later.One of the biggest challenges facing early conceptdevelopment is understanding the gradient in science returnversus various available mission scenarios and payload options. Often times, major areas of scientific inquiry havealready been prioritized by science groups, including throughthe National Research Council’s Decadal Studies inAstronomy, Planetary, and Earth Science. Yet science teamscontinue to struggle, especially in competitive missionsolicitations, to capture the right amount of scope that’sachievable within the cost constraints of the opportunity.Often the desire to completely and comprehensively study ascience area in just one mission (after all, true missionopportunities are rare) drives teams to take on too much,providing requirements that are unachievable within theresources of the opportunity without inducing unacceptableimplementation risk. Alternatively, science teams can seekto reduce risk by using an established instrument, but havenot thought through the traceability and key aspects of thescience question to justify its use. Both scenarios lead to badassumptions at the beginning of the concept development thatcan then ripple through implementation option choices,potentially preventing what would have been a good scienceinvestigation from being selected.The purpose of this paper is first to provide some additionalbackground and summary of the A-Team process, tools,people, and facilities. We then focus on the A-Teammethodology for overcoming the barriers of defining thescience scope well at the early concept development stage.This includes understanding the science story andtraceability, and then examining the gradient in science returnversus key characteristics of observables, developing theright payload and mission requirement specification throughexamining the science and implementation trade space.

Ziemer, John K.↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRSDate not supplied

Teams in Space: Knowledge Gained, but More to Explore

NASA’s Human Research Program oversees the Team Risk (i.e., Risk of Performance and Behavioral Health Decrements due to Inadequate Cooperation, Coordination, Communication and Psychosocial Adaptation within a Team). Research in this area informs all aspects of an astronaut’s career, from hiring to training to mission support, and works to address new challenges related to lunar and Mars missions. NASA’s astronaut selection process creates an astronaut corps of highly qualified, team-oriented individuals, which allows mission planners much flexibility in composing small crews for specific missions. These crews are further developed and supported through extensive training, including team skills training, and countermeasures available to the crew throughout the mission. However, in the high consequence environment of long-duration missions, team composition is complex and is not a one-time concern to be addressed pre-mission. Team factors such as team cohesion, dyadic relationships, and shared team cognition are likely to change dynamically in response to each interaction and event experienced by the individuals and the team as a whole. Thus, monitoring and optimizing team composition at a more micro level (e.g., per task) is one way to support team functioning and performance. Spaceflight teams research also includes the multi-team system of Mission Control and coordination between space-to-ground, adding another avenue in which risk might be introduced, particularly under exploration missions that experience significant communication delays. Spaceflight teams research has recently experienced a concentrated flurry of analog research over the past decade, shedding light on the many unique challenges and potential solutions to mitigate the team risk in long-duration exploration missions. However, questions still remain about how to, for example, create unobtrusive operational measures and how to advance interdisciplinary teams research and countermeasure development. We present an overview of the challenges facing teams in space, our current knowledge, and the next steps for research and spaceflight operations.

Lauren Blackwell Landon↗
NASA NTRS2010

Developing Expert Teams with a Strong Safety Culture

Would you like to lead a world renowned team that draws out all the talents and expertise of its members and consistently out performs all others in the industry? Ever wonder why so many organizations fail to truly learn from past mistakes only to repeat the same ones at a later date? Are you a program/project manager or team member in a high-risk organization where the decisions made often carry the highest of consequences? Leadership, communication, team building, critical decision-making and continuous team improvement skills and behaviors are mere talking points without the attitudes, commitment and strategies necessary to make them the very fabric of a team. Developing Expert Teams with a Strong Safety Culture, will provide you with proven knowledge and strategies to take your team soaring to heights you may have not thought possible. A myriad of teams have applied these strategies and techniques within their organization team environments: military and commercial aviation, astronaut flight crews, Shuttle flight controllers, members of the Space Shuttle Program Mission Management Team, air traffic controllers, nuclear power control teams, surgical teams, and the fire service report having spectacular success. Many industry leaders are beginning to realize that although the circumstances and environments of these teams may differ greatly to their own, the core elements, governing principles and dynamics involved in managing and building a stellar safety conscious team remain identical.

Rogers, David G.↗
NASA NTRSDate not supplied

The Interplay of Humor and Team Cohesion Over Time

Team bonding and interpersonal relationships are increasingly important as we continue understanding the effects of the pandemic on team dynamics. Specifically, team cohesion, defined as having a sense of belonging and commitment towards the group, has been found to be important for key team outcomes such as performance, relationships, and productivity (Beal, Cohen, Burke, & McLendon, 2003; Bell & Marentette, 2011). Research has stressed the benefits of positive humor on cohesion, with humor creating positive emotions and decreasing social distance (e.g., Robert & Wilbanks, 2012; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). However, research around the impact of negative humor on cohesion is scarce. This study aims to examine the impact of both positive and negative humor on cohesion for teams over time. The longitudinal design allows us to examine the interplay of humor and cohesion throughout a team’s lifecycle to guide relationship development and management (Romero & Pescosolido, 2008). Humor is defined as remarks recognized as amusing or funny (Martin, 2007). Positive humor can be categorized as good-natured aimed to create mutual agreement, while negative humor is defined as aggressive, hostile, and unreciprocated (Coan & Gottman, 2007). Research has concluded the many benefits of positive humor, showing it can increase open communication, social support, and create a pleasant environment (Martineau, 1972; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). The Humor Wheel model suggests that positive humor is contagious due to the positive affect produced and mimicked (Lehmann-Willenbrock & Allen 2014). Research has suggested positive humor would increase over time based on the norm of reciprocity; those treated positively are likely to respond similarly (Gouldner, 1960). Multiple parties’ involvement in humor exchanges highlights the interpersonal nature of humor. Thus, long-term consequences on relationships should be considered, such as cohesion (Robert & Wilbanks, 2012). It would be instrumental to understand how humor interactions could weaken or strengthen group cohesion through relational and emotional bonds over time (Martineau, 1972; Romero & Pescosolido, 2008). Positive humor should fundamentally increase team cohesion (Mesmer-Magnus, Glew, & Viswesvaran, 2012; Walter & Baruch, 2008) by promoting group harmony, team member attractiveness, and ingroup identity (Holmes, 2006). Thus, it is expected there is a positive relationship between positive humor and team cohesion over time (hypothesis 1). Humor is not always beneficial, especially when it elicits negative emotions (Dikkers, Doosje, & de Lange, 2011). Negative humor can be used to humiliate, tease, or insult (Coan & Gottman, 2007; Collinson, 1988; Romero & Cruthirds, 2006). It tends to cause distress and damage relationships, and in turn, decrease organizational performance and production (Avolio, Howell, & Sosik, 1999; Janes & Olson, 2000; Yerkes, 2001). Research concludes negative humor may create subgroups and decrease cooperation (Romero & Arendt, 2011). Research has also indicated positive implications for negative humor under specific circumstances. Negative humor could help to solidify ingroup identity and strengthen bonds when directed outside the team (Terrion & Ashforth, 2002; Thomae & Pina, 2015). For example, sport teams may use negative humor towards their opponent before a game, which emphasizes their differences and lowers the status and power of their opponent, ultimately promoting solidarity and cohesion inside the group. For this study, it is anticipated that negative humor toward other teammates will decrease team cohesion over time due to the damage to relationships and negative affect produced (Duncan, Smeltzer, & Leap, 1990; Maples, Dupey, Torres‐Rivera, Phan, Vereen, & Garrett, 2001). We also predicted that negative humor focused on something or someone outside the team will increase cohesion by enhancing ingroup ties and creating a common identity (Thomae & Pina, 2015). Thus, the relationship between negative humor and cohesion will be moderated by the target of the humor. The relationship will be negative when the target is within the team and positive when the target is outside (hypothesis 2).

Melissa M. Vazquez↗
NASA NTRS1998

Perceptions of Engineers Regarding Successful Engineering Team Design

The perceptions of engineers and scientists at NASA Langley Research Center toward engineering design teams were evaluated. A sample of 49 engineers and scientists rated 60 team behaviors in terms of their relative importance for team success. They also completed a profile of their own perceptions of their strengths and weaknesses as team members. Behaviors related to team success are discussed in terms of those involving the organizational culture and commitment to the team and those dealing with internal team dynamics. The latter behaviors focused on team issues occurring during the early stages of a team's existence. They included the level and extent of debate and discussion regarding methods for completing the team task and the efficient use of team time to explore and discuss methodologies critical to the problem. The discussion includes a comparison of engineering teams with the prototypical business team portrayed in the literature.

Nowaczyk, Ronald H.↗
NASA NTRS2012

Portraying the Contribution of Individual Behaviors to Team Cohesion and Performance

Behaviors of individuals in teams both contribute to and are molded by team dynamics. How they do so has been the subject of much research. A method of portraying individuals' behaviors in teams, the Team Diagramming Method (TDM) is presented. Behaviors are rated by other team members on three important dimensions: positivity/negativity, dominant/submissive, and task-orientedness/expressiveness. A study of 5-person teams engaging in a 3-day moon simulation task demonstrated that measures of these perceived behaviors as well as the variances of these behaviors correlated with cohesion measures and performance. The method shows strengths and weaknesses of particular teams and, by comparison with high-performing teams, suggests interventions based on individual as well as team behaviors. The primary goal of this study was to determine the extent to which these team level variables, derived from all team members' rated behaviors, were associated with previous methods of measuring cohesion and with performance. A secondary goal was to determine the stability of TDM measures over time by comparing team level variables based on ratings early and later in the team s work together.

Parke, Bonny↗
NASA NTRS2001

A Genuine TEAM Player

Qualtech Systems, Inc. developed a complete software system with capabilities of multisignal modeling, diagnostic analysis, run-time diagnostic operations, and intelligent interactive reasoners. Commercially available as the TEAMS (Testability Engineering and Maintenance System) tool set, the software can be used to reveal unanticipated system failures. The TEAMS software package is broken down into four companion tools: TEAMS-RT, TEAMATE, TEAMS-KB, and TEAMS-RDS. TEAMS-RT identifies good, bad, and suspect components in the system in real-time. It reports system health results from onboard tests, and detects and isolates failures within the system, allowing for rapid fault isolation. TEAMATE takes over from where TEAMS-RT left off by intelligently guiding the maintenance technician through the troubleshooting procedure, repair actions, and operational checkout. TEAMS-KB serves as a model management and collection tool. TEAMS-RDS (TEAMS-Remote Diagnostic Server) has the ability to continuously assess a system and isolate any failure in that system or its components, in real time. RDS incorporates TEAMS-RT, TEAMATE, and TEAMS-KB in a large-scale server architecture capable of providing advanced diagnostic and maintenance functions over a network, such as the Internet, with a web browser user interface.

Source record↗
NASA NTRS1998

Factors Related to Successful Engineering Team Design

The perceptions of a sample of 49 engineers and scientists from NASA Langley Research Center toward engineering design teams were evaluated. The respondents rated 60 team behaviors in terms of their relative importance for team success. They also completed a profile of their own perceptions of their strengths and weaknesses as team members. Behaviors related to team success are discussed in terms of those involving the organizational culture and commitment to the team and those dealing with internal team dynamics. The latter behaviors included the level and extent of debate and discussion regarding methods for completing the team task and the efficient use of team time to explore and discuss methodologies critical to the problem. Successful engineering teams may find their greatest challenges occurring during the early stages of their existence. In contrast to the prototypical business team, members on an engineering design share expertise and knowledge which allows them to deal with task issues sooner. However, discipline differences among team members can lead to conflicts regarding the best method or approach to solving the engineering problem.

Nowaczyk, Ronald H.↗
NASA NTRS2021

The Evolution of Team-X: 25 Years of Concurrent Engineering Design Experience

Established in 1995 in response to NASA’s “Faster, Better, Cheaper” era, Team-X was born from a need to perform rapid space mission design for principal investigator-led competed proposals. The success and sustainability of Team-X over the 25 years that have followed is directly attributable to the Team-X business model and its evolution over time. While dozens of organizations and institutions have emulated the Team-X design process, there are nuances to the Team-X business model that are unique to JPL, and explain why it is different than other concurrent design teams. One of the key components of a business model is the customer segments that are served. Team-X was founded to conduct the Pre-Phase A work necessary to formulate a portfolio of multiple planetary mission concepts, but has since expanded to include the capability to conduct studies for Earth science, astrophysics, and heliophysics missions as well as Human Exploration and Operations missions and space technology development. Team-X delivers value to its clients both in terms of speed and cost. Team-X has also added value by creating teams to enable the development of Instrument and SmallSat Concepts. Value has further been enhanced through a revision of its process for reviews and the addition of pre-design architecting capabilities. Other aspects of the Team-X infrastructure, in addition to study process, have enabled it to succeed for over a quarter century. From our most important resource, the people, our tools, especially for cost estimating, as well as our increasing capable IT infrastructure have contributed to our capability to meet the demands of our clients. The Team-X business model and its evolution over time, position it well for success in the decades to come.

Murphy, Jonathan↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗
NASA NTRS2015

Pre-Mission Input Requirements to Enable Successful Sample Collection by a Remote Field/EVA Team

This paper is intended to evaluate the sample collection process with respect to sample characterization and decision making. In some cases, it may be sufficient to know whether a given outcrop or hand sample is the same as or different from previous sampling localities or samples. In other cases, it may be important to have more in-depth characterization of the sample, such as basic composition, mineralogy, and petrology, in order to effectively identify the best sample. Contextual field observations, in situ/handheld analysis, and backroom evaluation may all play a role in understanding field lithologies and their importance for return. For example, whether a rock is a breccia or a clast-laden impact melt may be difficult based on a single sample, but becomes clear as exploration of a field site puts it into context. The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is a new activity focused on a science and exploration field based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We used the FINESSE field excursion to the West Clearwater Lake Impact structure (WCIS) as an opportunity to test factors related to sampling decisions. In contract to other technology-driven NASA analog studies, The FINESSE WCIS activity is science-focused, and moreover, is sampling-focused, with the explicit intent to return the best samples for geochronology studies in the laboratory. This specific objective effectively reduces the number of variables in the goals of the field test and enables a more controlled investigation of the role of the crewmember in selecting samples. We formulated one hypothesis to test: that providing details regarding the analytical fate of the samples (e.g. geochronology, XRF/XRD, etc.) to the crew prior to their traverse will result in samples that are more likely to meet specific analytical objectives than samples collected in the absence of this premission information. We conducted three tests of this hypothesis. Our investigation was designed to document processes, tools and procedures for crew sampling of planetary targets. This is not meant to be a blind, controlled test of crew efficacy, but rather an effort to recognize the relevant variables that enter into sampling protocol and to develop recommendations for crew and backroom training in future endeavors. Methods: One of the primary FINESSE field deployment objectives was to collect impact melt rocks and impact melt-bearing breccias from a number of locations around the WCIS structure to enable high precision geochronology of the crater to be performed [1]. We conducted three tests at WCIS after two full days of team participation in field site activities, including using remote sensing data and geologic maps, hiking overland to become familiar with the terrain, and examining previously-collected samples from other islands. In addition, the team members shared their projects and techniques with the entire team. We chose our "crew members" as volunteers from the team, all of whom had had moderate training in geologic fieldwork and became familiar with the general field setting. The first two tests were short, focused tests of our hypothesis. Test A was to obtain hydrothermal vugs; Test B was to obtain impact melt and intrusive rock as well as the contact between the two to check for contact metamorphism and age differences. In both cases, the test director had prior knowledge of the site geology and had developed a study-specific objective for sampling prior to deployment. Prior to the field deployment, the crewmember was briefed on the sampling objective and the laboratory techniques that would be used on the samples. At the field sites (Fig. 2), the crewmember was given 30 minutes to survey a small section of outcrop (10-15 m) and acquire a suite of three samples. The crewmember talked through his process and the test director kept track of the timeline in verbal cues to the crewmember. At the conclusion, the team member conducting the scientific study appraised the samples and train of thought. Test C was a 90-minute EVA simulation using two crewmembers working out of line-of-sight in communication with a science backroom. The science objectives were determined by the science backroom team in advance using a Gigapan image of the outcrop (Fig. 1). The science team formulated hypotheses for the outcrop units and created sampling objectives for impact-melt lithologies; the science team turned these into a science plan, which they communicated to the crew in camp prior to crew deployment. As part of the science plan, the science team also discussed their sample needs in depth with the crewmembers, including laboratory methods, objectives, and samples sizes needed. During the deployment, the two crewmembers relayed real-time information to the science backroom by radio with no time delay. Both the crew and science team re-evaluated their hypotheses and science plans in real-time. Discussion: Upon evaluation, we found that the focused tests (Tests A and B) were successful in meeting their scientific objectives. The crewmember used their knowledge of how the samples were to be used in further study (technique, sample size, and scientific need) to focus on the sampling task. The crewmember was comfortable spending minimal time describing and mapping the outcrop. The crewmember used all available time to get a good sample. The larger test was unsuccessful in meeting the sampling objectives. When the crewmembers began describing the lithologies, it was quickly apparent that the lithologies were not as the backroom expected and had communicated to the crew. When the outcrop wasn't as expected, the crew members instinctively switched to field characterization mode, taking significant time to characterize and map the outcrop. One crew member admitted that he "kind of lost track" of the sampling strategy as he focused on the basic outcrop characterization. This is the logical first step in a field geology campaign, that a significant amount of time must be spent by the crew and backroom to understand the outcrop and its significance. Basic field characterization of an outcrop is a focused activity that takes significant time and training [2, 3]. Sampling of representational lithologies can be added to this activity for little cost [4]. However, we have shown that identification of unusual or specific samples for laboratory study also takes significant time and knowledge. We suggest that sampling of this type be considered a separate activity from field characterization, and that crewmembers be trained in sampling needs for different kinds of studies (representative lithologies vs. specialized samples) to acquire a mindset for sampling similar to field mapping. Sampling activities should be given a significant amount of specifically allocated time in scheduling EVA activities; and in the better case, that sampling be done as a second activity to a previously studied outcrop where both crew and backroom are comfortable with its context and characteristics. Our hypothesis posited that crewmember knowledge of how the samples would be used upon return would aid them in choosing relevant samples. Our testing bore this hypothesis out to some extent. We therefore recommend that crewmember training should include exposure to the laboratory techniques and analyses that will be used on the samples to foster this knowledge. There is also the potential for increasing crewmember contextual knowledge real-time in the field through the introduction of in situ geochemical technologies such as field portable XRF. The presence of field portable geochemical technology could enable the astronauts to interrogate the samples for K abundance real-time, ensuring they could collect valuable and dateable samples [5]. Though simulations such as these can teach us a fair bit about decision making processes and timeline building, one EVA participant noted that when he wasn't collecting "real" samples, he wasn't at his best. This effect suggests that higher-fidelity studies involving truly remote participants conducting actual scientific studies merit further attention to capture lessons for application to future crew situations.

sample collection↗
NASA NTRS2013

Exploring Mission Concepts with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts called the A-Team. The A-Team combines innovative collaborative methods with subject matter expertise and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an A-Team study. Methods are grouped by Concept Maturity Level (CML), from 1 through 3, including idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for each CML are presented, and the key team roles are described from two points of view: innovative methods and technical expertise. A-Team roles for providing innovative methods include the facilitator, study lead, and assistant study lead. A-Team roles for providing technical expertise include the architect, lead systems engineer, and integration engineer. In addition to these key roles, each A-Team study is uniquely staffed to match the study topic and scope including subject matter experts, scientists, technologists, flight and instrument systems engineers, and program managers as needed. Advanced analysis and collaborative engineering tools (e.g. cost, science traceability, mission design, knowledge capture, study and analysis support infrastructure) are also under development for use in A-Team studies and will be discussed briefly. The A-Team facilities provide a constructive environment for innovative ideas from all aspects of mission formulation to eliminate isolated studies and come together early in the development cycle when they can provide the biggest impact. This paper provides an overview of the A-Team, its study processes, roles, methods, tools and facilities.

Team Eureka↗
NASA NTRS2003

Capabilities of the Materials Contamination Team at Marshall Space Flight Center

The Materials Contamination Team at the Marshall Space Flight Center (MSFC) has been recognized for its contributions supporting the National Aeronautics and Space Administration (NASA) spacecraft development programs. These programs include the Reusable Solid Rocket Motor (RSRM), Chandra X-Ray Observatory, and the International Space Station (ISS). The Environmental Effects Group, with the Materials Contamination Team and the Space Environmental Effects Team has been an integral part of NASA's success by the testing, evaluation, and qualification of materials, hardware, and processes. This paper focuses on the capabilities of the Materials Contamination Team. The Materials Contamination Team's realm of responsibility includes establishing contamination control during all phases of hardware development, including design, manufacturing, assembly, test, transportation, launch site processing, on-orbit exposure, return, and refurbishment. The team continues its mission of reducing the risk of equipment failure due to molecular or particulate contamination. Contamination is a concern in the Space Shuttle with sensitive bond-lines and reactive fluid (liquid oxygen) compatibility as well as for spacecraft with sensitive optics, such as Hubble Space Telescope and Chandra X-ray Observatory. The Materials Contamination Team has a variety of facilities and instrumentation capable of contaminant detection, identification, and monitoring. The team addresses material applications dealing with environments, including production facilities, clean rooms, and on-orbit exposure. The optically stimulated electron emission (OSEE) system, the Ultraviolet (UV) fluorescence (UVF) surface contamination detection, and the Surface Optics Corporation 400 (SOC 400) portable hand-held Fourier Transform Infrared (FTIR) spectrometer are state-of-the-art tools for in-process molecular contamination detection. The team of engineers and technicians also develop contamination calibration standards and evaluate new surface cleanliness inspection technologies. The team utilizes facilities for on-orbit simulation testing of materials for outgassing and molecular film deposition characteristics in the presence of space environmental effects, such as Atomic Oxygen (AO) and UV radiation exposure. The Materials Contamination Team maintains databases for process materials as well as outgassing and optical compatibility test results for specific environments.

Burns, Howard↗
NASA NTRS2004

Team Formation in Partially Observable Multi-Agent Systems

Sets of multi-agent teams often need to maximize a global utility rating the performance of the entire system where a team cannot fully observe other teams agents. Such limited observability hinders team-members trying to pursue their team utilities to take actions that also help maximize the global utility. In this article, we show how team utilities can be used in partially observable systems. Furthermore, we show how team sizes can be manipulated to provide the best compromise between having easy to learn team utilities and having them aligned with the global utility, The results show that optimally sized teams in a partially observable environments outperform one team in a fully observable environment, by up to 30%.

Agogino, Adrian K.↗
NASA NTRS2007

NASA's Decadal Planning Team Mars Mission Analysis Summary

In June 1999 the NASA Administrator chartered an internal NASA task force, termed the Decadal Planning Team, to create new integrated vision and strategy for space exploration. The efforts of the Decadal Planning Team evolved into the Agency-wide team known as the NASA Exploration Team (NEXT). This team was also instructed to identify technology roadmaps to enable the science-driven exploration vision, established a cross-Enterprise, cross-Center systems engineering team with emphasis focused on revolutionary not evolutionary approaches. The strategy of the DPT and NEXT teams was to "Go Anywhere, Anytime" by conquering key exploration hurdles of space transportation, crew health and safety, human/robotic partnerships, affordable abundant power, and advanced space systems performance. Early emphasis was placed on revolutionary exploration concepts such as rail gun and electromagnetic launchers, propellant depots, retrograde trajectories, nano structures, and gas core nuclear rockets to name a few. Many of these revolutionary concepts turned out to be either not feasible for human exploration missions or well beyond expected technology readiness for near-term implementation. During the DPT and NEXT study cycles, several architectures were analyzed including missions to the Earth-Sun Libration Point (L2), the Earth-Moon Gateway and L1, the lunar surface, Mars (both short and long stays), one-year round trip Mars, and near-Earth asteroids. Common emphasis of these studies included utilization of the Earth-Moon Libration Point (L1) as a staging point for exploration activities, current (Shuttle) and near-term launch capabilities (EELV), advanced propulsion, and robust space power. Although there was much emphasis placed on utilization of existing launch capabilities, the team concluded that missions in near-Earth space are only marginally feasible and human missions to Mars were not feasible without a heavy lift launch capability. In addition, the team concluded that missions in Earth s neighborhood, such as to the Moon, can serve as stepping-stones toward further deep-space missions in terms of proving systems, technologies, and operational concepts. The material contained in this presentation was compiled to capture the work performed by the Mars Sub-Team of the DPT NEXT efforts in the late 1999-2001 timeframe.

Drake, Bret G.↗
NASA NTRS2009

Enhancing Team Performance for Long-Duration Space Missions

Success of exploration missions will depend on skilled performance by a distributed team that includes both the astronauts in space and Mission Control personnel. Coordinated and collaborative teamwork will be required to cope with challenging complex problems in a hostile environment. While thorough preflight training and procedures will equip creW'S to address technical problems that can be anticipated, preparing them to solve novel problems is much more challenging. This presentation will review components of effective team performance, challenges to effective teamwork, and strategies for ensuring effective team performance. Teamwork skills essential for successful team performance include the behaviors involved in developing shared mental models, team situation awareness, collaborative decision making, adaptive coordination behaviors, effective team communication, and team cohesion. Challenges to teamwork include both chronic and acute stressors. Chronic stressors are associated with the isolated and confined environment and include monotony, noise, temperatures, weightlessness, poor sleep and circadian disruptions. Acute stressors include high workload, time pressure, imminent danger, and specific task-related stressors. Of particular concern are social and organizational stressors that can disrupt individual resilience and effective mission performance. Effective team performance can be developed by training teamwork skills, techniques for coping with team conflict, intracrew and intercrew communication, and working in a multicultural team; leadership and teamwork skills can be fostered through outdoor survival training exercises. The presentation will conclude with an evaluation of the special requirements associated with preparing crews to function autonomously in long-duration missions.

Orasanu, Judith M.↗