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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Thermal Performance of the Mars Science Laboratory Rover During Mars Surface Operations

On November 26, 2011, NASA launched a large (900 kg) rover as part of the Mars Science Laboratory (MSL) mission to Mars. Eight months later, on August 5, 2012, the MSL rover (Curiosity) successfully touched down on the surface of Mars. As of the writing of this paper, the rover had completed over 200 Sols of Mars surface operations in the Gale Crater landing site (4.5 degrees South latitude). This paper describes the thermal performance of the MSL Rover during the early part of its two Earth-0.year (670 Sols) prime surface mission. Curiosity landed in Gale Crater during early Spring (Solar longitude=151) in the Southern Hemisphere of Mars. This paper discusses the thermal performance of the rover from landing day (Sol 0) through Summer Solstice (Sol 197) and out to Sol 204. The rover surface thermal design performance was very close to pre-landing predictions. The very successful thermal design allowed a high level of operational power dissipation immediately after landing without overheating and required a minimal amount of survival heating. Early morning operations of cameras and actuators were aided by successful heating activities. MSL rover surface operations thermal experiences are discussed in this paper. Conclusions about the rover surface operations thermal performance are also presented.

thermal performance↗
NASA NTRS2015

The ISECG* Global Exploration Roadmap as Context for Robotic and Human Exploration Operations

The International Space Exploration Coordination Group (ISECG) Global Exploration Roadmap (GER) provides a broad international context for understanding how robotic missions and robotic assets can enable future human exploration of multiple destinations. This presentation will provide a brief high-level review of the GER with a focus on key robotic missions and robotic assets that can provide enabling technology advancements and that also raise interesting operational challenges in both the near-term and long-term. The GER presently features a variety of robotic missions and robotic assets that can provide important technology advancements as well as operational challenges and improvements, in areas ranging from: (a) leveraging the International Space Station, (b) planetary science robotic missions to potential human destinations, (c) micro-g body proximity operations (e.g. asteroids), (d) autonomous operations, (e) high and low-latency telerobotics, (f) human assisted sample return, and (g) contamination control. This presentation will highlight operational and technology challenges in these areas that have feed forward implications for human exploration.

telerobotics↗
NASA NTRS2013

Thermal Performance of the Mars Science Laboratory Rover During Mars Surface Operations

On November 26, 2011, NASA launched a large (900 kg) rover as part of the Mars Science Laboratory (MSL) mission to Mars. Eight months later, on August 5, 2012, the MSL rover (Curiosity) successfully touched down on the surface of Mars. As of the writing of this paper, the rover had completed over 200 Sols of Mars surface operations in the Gale Crater landing site (4.5 deg S latitude). This paper describes the thermal performance of the MSL Rover during the early part of its two Earth-0.year (670 Sols) prime surface mission. Curiosity landed in Gale Crater during early Spring (Ls=151) in the Southern Hemisphere of Mars. This paper discusses the thermal performance of the rover from landing day (Sol 0) through Summer Solstice (Sol 197) and out to Sol 204. The rover surface thermal design performance was very close to pre-landing predictions. The very successful thermal design allowed a high level of operational power dissipation immediately after landing without overheating and required a minimal amount of survival heating. Early morning operations of cameras and actuators were aided by successful heating activities. MSL rover surface operations thermal experiences are discussed in this paper. Conclusions about the rover surface operations thermal performance are also presented.

thermal design↗
NASA NTRS2015

Identifying Key Issues and Potential Solutions for Integrated Arrival, Departure, Surface Operations by Surveying Stakeholder Preferences

NASA's Aeronautics Research Mission Directorate (ARMD) collaborates with the FAA and industry to provide concepts and technologies that enhance the transition to the next-generation air-traffic management system (NextGen). To facilitate this collaboration, ARMD has a series of Airspace Technology Demonstration (ATD) sub-projects that develop, demonstrate, and transitions NASA technologies and concepts for implementation in the National Airspace System (NAS). The second of these sub-projects, ATD-2, is focused on the potential benefits to NAS stakeholders of integrated arrival, departure, surface (IADS) operations. To determine the project objectives and assess the benefits of a potential solution, NASA surveyed NAS stakeholders to understand the existing issues in arrival, departure, and surface operations, and the perceived benefits of better integrating these operations. NASA surveyed a broad cross-section of stakeholders representing the airlines, airports, air-navigation service providers, and industry providers of NAS tools. The survey indicated that improving the predictability of flight times (schedules) could improve efficiency in arrival, departure, and surface operations. Stakeholders also mentioned the need for better strategic and tactical information on traffic constraints as well as better information sharing and a coupled collaborative planning process that allows stakeholders to coordinate IADS operations. To assess the impact of a potential solution, NASA sketched an initial departure scheduling concept and assessed its viability by surveying a select group of stakeholders for a second time. The objective of the departure scheduler was to enable flights to move continuously from gate to cruise with minimal interruption in a busy metroplex airspace environment using strategic and tactical scheduling enhanced by collaborative planning between airlines and service providers. The stakeholders agreed that this departure concept could improve schedule predictability and suggested several key attributes that were necessary to make the concept successful. The goals and objectives of the planned ATD-2 sub-project will incorporate the results of this stakeholder feedback.

integrated arrival/departure/surface operations↗
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

route advisories↗
NASA NTRS2017

Application of Human-Autonomy Teaming to an Advanced Ground Station for Reduced Crew Operations

Within human factors there is burgeoning interest in the "human-autonomy teaming" (HAT) concept as a way to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous systems as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as "plays." It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations, healthcare and medicine, autonomous vehicles, photography, and aviation. The current paper presents one such effort to apply HAT. It details the design of a HAT agent, developed by Human Automation Teaming Solutions, Inc., to facilitate teamwork between the automation and the human operator of an advanced ground dispatch station. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO); consequently, we have named the agent R-HATS. Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by R-HATS, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, R-HATS determines what tasks need to be completed and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. R-HATS has been designed to both support end users and researchers in RCO and HAT. Additionally, R-HATS and its underlying architecture were developed with generalizability in mind as a modular software applicable outside of RCO/aviation domains. This paper will also discuss future further development and testing of RHATS.

automation↗
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

operational acceptability↗
NASA NTRS2012

Autonomous Mission Operations

NASA's Advanced Exploration Systems Autonomous Mission Operations (AMO) project conducted an empirical investigation of the impact of time-delay on todays mission operations, and of the effect of processes and mission support tools designed to mitigate time-delay related impacts. Mission operation scenarios were designed for NASA's Deep Space Habitat (DSH), an analog spacecraft habitat, covering a range of activities including nominal objectives, DSH system failures, and crew medical emergencies. The scenarios were simulated at time-delay values representative of Lunar (1.2-5 sec), Near Earth Object (NEO) (50 sec) and Mars (300 sec) missions. Each combination of operational scenario and time-delay was tested in a Baseline configuration, designed to reflect present-day operations of the International Space Station, and a Mitigation configuration in which a variety of software tools, information displays, and crew-ground communications protocols were employed to assist both crews and Flight Control Team (FCT) members with the long-delay conditions. Preliminary findings indicate: 1) Workload of both crew members and FCT members generally increased along with increasing time delay. 2) Advanced procedure execution viewers, caution and warning tools, and communications protocols such as text messaging decreased the workload of both flight controllers and crew, and decreased the difficulty of coordinating activities. 3) Whereas crew workload ratings increased between 50 sec and 300 sec of time-delay in the Baseline configuration, workload ratings decreased (or remained flat) in the Mitigation configuration.

Mission Operations↗
NASA NTRS2018

Determining Thermal Capabilities for External Transfer Operations on the International Space Station

External transfers on the International Space Station (ISS) have a degree of difficulty caused by the severity of the radiative thermal environment and the complexity of the operational choreography to perform the installation and activation of the hardware. These transfers can be performed robotically, by astronauts during an Extra Vehicular Activity (EVA - spacewalk), or combination of robotic/crew operations. Robotic transfers may include capability to intermittently power the hardware; while the hardware remains unpowered for EVA operations. Robotic transfers can be staged to occur in a favorable thermal environment, though typically take longer than a transfer by crew during an EVA where the hardware may not be robotically compatible. The hardware is under passive thermal control, use of optics/multi-layer insulation/heaters, while being transferred from/to a visiting vehicle, airlock, stowage platform, or external ISS structure and may include additional design components, such as removable protective blankets, to meet the transfer requirements. Thermal analysis must be performed to determine the capability of the hardware being transferred to provide the Mission Control team the products necessary to plan and execute the operation while establishing an awareness for any contingency response. An overview of the thermal aspects in planning these types of transfer operations, the analytical approaches and assumptions, and examples of results are provided in this paper.

Thermal↗
NASA NTRS2015

Demonstrating Autonomous Mission Operations Onboard the International Space Station

The NASA Autonomous Mission Operations (AMO) project conducted an experiment to turn over operation and management of selected International Space Station (ISS) sys- tems to the on-board crew. ISS crews managed two different spacecraft systems: the Total Organic Carbon Analyzer (TOCA), a water quality analyzer, and Station Support Com- puters (SSC) laptops, which are non-critical crew computer systems. These systems were selected because they are representative of systems a future crew may need to operate au- tonomously during a deep space mission. The crew autonomously operated these systems, taking on mission operations functions traditionally performed by support teams on the ground, using new software tools that provide decision support algorithms for planning, monitoring and fault management, hardware schematics, system briefs, and data displays that are normally unavailable to the crew. The experiment lasted seven months, during which ISS crews managed TOCA and SSCs on 22 occasions. The AMO software processed data from TOCA and SSCs continuously during this seven month period. The combined performance of the software and crew achieved a 88 success rate on managing TOCA activity, the system for which ground-truth was available.

Autonomous Mission Operations↗
NASA NTRS2018

Predicting the Operational Acceptance of Airborne Flight Reroute Requests Using Data Mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.

Operational Acceptability↗
NASA NTRSDate not supplied

Design for Operations

Designs are usually done in a well-lit, air conditioned, comfortable room under idealized assumptions. The reality of the operation is very different. For designers to be able to design systems and procedures that work well under realistic dynamic work conditions, they must understand the reality of the operation and of the operators. The talk discusses the gap between the idealized design and the real operation and proposes a framework, THE Model, for designing systems and procedures fit for real operations.

design↗
NASA NTRSDate not supplied

Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System

While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.

ASRS narratives↗
NASA NTRSDate not supplied

Attentional Considerations in Advanced Air Mobility Operations: Control, Manage, or Assist?

The implementation of automation will enable Advanced Air Mobility (AAM), which could alter the hu-man’s responsibilities from those of an active controller to a passive monitor of vehicles. Mature AAM operations will likely rely on both experienced and novice operators to supervise multiple aircraft. As AAM constitutes a complex and increasingly autonomous system, the human operator’s set of responsibilities will transition from those of a controller, to a manager, and eventually to an assistant to highly automated systems. The development of AAM will require system designers to characterize these three sets of human responsibilities. The present work proposes different human responsibilities across various roles (i.e., pilot in command, system operator, system assistant) in the context of AAM along with pertinent attention-related constructs that could contribute to each of the three identified roles of AAM operators including situation awareness, workload, complacency, and vigilance.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Uncrewed Lunar Surface Operations and Support Activities

A sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The number of astronauts and the availability of the surface crew to perform tasks will be limited, and extravehicular activities are burdensome and time-consuming. A balance of crewed and uncrewed surface operations will maximize crew exploration time by reducing their time dedicated to routine maintenance and support tasks. Certain sustaining activities that consume valuable crew time and preparation tasks such as staging and prepositioning equipment and materials can be performed without the crew, either before their arrival on the lunar surface or after their departure, thus improving task efficiency and mission effectiveness. This paper will define uncrewed operations and support activities and examine the functions, features, and capabilities that support sustained human and robotic surface operations. It will also discuss the extreme environmental challenges and the complexities associated with the development and operation of autonomous systems, as well as some suggested methods, techniques, and tools to overcome these challenges.

Lunar↗
NASA NTRS2023

Lunar Landing and Operations Policy Analysis

At least 22 lunar missions are planned in the next four years. Half of these missions will occur in the Moon’s south polar region. Due to this upcoming proliferation of actors and activities at or near the lunar south pole, and due to the potential close proximity of operations, NASA and other operators will face challenges never faced before. NASA’s Deputy Administrator and Associate Administrator of the Science Mission Directorate tasked the Office of Technology, Policy, and Strategy with answering two questions related to the Artemis campaign: (1) what technical and policy considerations should NASA take into account in the selection of lunar landing and operations sites, and (2) what technical and policy considerations should NASA take into account when implementing tools such as safety zones in order to protect these operations and U.S. interests? We present policy tools (options and recommendations) to NASA leadership and program planners so that they can consider policy measures to respond to challenges inherent in landing and operating at the lunar South Pole—this answers question one. We also describe a set of options (transparency, coordination, and implementation) to increase the effectiveness of these tools—this answers question two.

Lunar policy↗
DOE OSTI2018

Potential of Wind Power to Provide Flexible Ramping Products and Operating Reserve: Preprint

With increasing penetration levels of variable renewable energy sources, such as wind and solar, electric power systems need flexible resources that can provide fast-response ramping capabilities to counter the variability and uncertainty of renewable generation. Although the California Independent System Operator and Midcontinent System Operator have lauched ramp products, determining how wind power can participate using their flexible ramping models is still largely unclear. This paper proposes a security-constrained unit commitment (SCUC) model considering a flexible ramping constraint representing the system's flexible ramping requirements that account for the net load probabilistic forecast uncertainty. Through this explicit modeling of the uncertainty, the true cost of the flexible ramp service can be determined after solving the SCUC problem. In addition, the potential of wind power to provide operating reserves is analyzed. Numerical case studies demonstrate that with wind power providing both flexible ramping products and operating reserve, the system operating costs and ramp shortages will be reduced significantly.

17 WIND ENERGY↗
DOE OSTI2020

How Wrong Can the Operational AEP Uncertainty Estimate Be When We Ignore the Correlations Between the Uncertainty Components?

Calculations of wind farm annual energy production (AEP) on operational data are essential for a variety of financial transactions during the life of wind plants. The AEP estimate is associated with an uncertainty value, which is calculated by combining contributions connected to on-site measurements, long-term reference measurements, losses, regression, windiness adjustment, and wind resource interannual variability. Although very limited documentation on the topic exists, the conventional approach currently used by the wind energy community to assess the uncertainty connected to the operational AEP estimate assume that the different uncertainty components are uncorrelated and therefore calculates the overall uncertainty with a sum of squares approach. In this analysis, we contrast the traditional technique to estimate the overall AEP uncertainty by ignoring the correlation between its different components with a novel Monte Carlo based approach, which can instead take into account the correlation between different uncertainty categories. We consider monthly operational data from 472 wind farms from the Energy Information Administration (EIA) 923 database to assess the difference between the two approaches. Long-term wind speed data needed for the AEP assessment are taken from three reanalysis products: the Modern-Era Retrospective analysis for Research and Applications v2 (MERRA-2), the European Reanalysis Interim (ERA-interim), and the National Centers for Environmental Prediction v2 (NCEP-2). The results of the Monte Carlo approach show that three pairs of AEP uncertainty components do show a statistically significant correlation: the uncertainty connected with wind resource inter-annual variability is positively correlated with the one related to the windiness correction and negatively correlated with the one due to the regression, and the wind measurement uncertainty is positively correlated with the regression uncertainty. All these correlations, which are found between uncertainty components which are not only part of an operational analysis, but also of a wind resource assessment, are currently ignored in the conventional techniques used as industry standard. We further investigate the causes of these correlations, in terms of common dependencies of different uncertainty components on wind resource variability, number of data points, and quality of the regression between wind speed and energy production data. Next, we quantify the error in the current industry standard technique, in terms of the percentage difference in total uncertainty calculated with the two considered approaches, for all the analyzed wind farms. We find a mean absolute percentage difference of about 6%, with the largest differences being greater than 20%. The data clearly confirm that ignoring the actual correlation between the uncertainty components can lead to large errors in the assessment of the operational AEP uncertainty, and the proposed Monte Carlo approach should be preferred.

Monte Carlo↗