AQUA A-Train Exit Options September 28, 2021
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Earth Observing System (EOS), Aqua Spacecraft, Mis↗
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Artemis Internal Science Team Update: Hardware and Integrated Testing
The Artemis program will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis program is a collaboration of space agencies and companies from around the world. In support of the Artemis program a cross disciplinary effort integrating science, engineering, operations, and human factors is currently being developed to identify methods, facilities, and field locations to test hardware, train astronauts, and evaluate concepts of operations. NASA’s Science Mission Directorate (SMD) and Exploration Systems Development Mission Directorate (ESDMD) established a comprehensive Science Team structure to support the Artemis program. This structure includes 1) an Artemis Internal Science Team (AIST), 2) a Geology Team (GT) with additional Participating Scientists (PS) roles, and 3) Payload Teams (PT) for the inclusion of scientific instruments. The GT, PS and PT will be competitively selected for each mission as part of NASA’s Research Opportunities in Space and Earth Sciences (ROSES) solicitations. The AIST provides multi-mission continuity and has been working closely with Artemis program elements to best integrate science into all aspects of planning and development. This abstract serves to provide an AIST update on science relevant hardware developments and integrated testing efforts that occurred in 2022.
Trevor George Graff↗
Artemis Internal Science Team Update: Deployed Payloads
Artemis will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis effort includes a collaboration of space agencies and companies from around the world [1]. In support of Artemis, a cross-disciplinary effort of science, engineering, operations, and human factors personnel is currently developing the best methods, facilities, and field locations to test hardware, train astronauts, and evaluate concepts of operations. This abstract, as part of the Artemis Internal Science Team (AIST) [2], provides an update to NASA’s plans for the solicitation, development, and operations of deployed surface payloads. In calendar year 2023, NASA’s Science Mission Directorate will solicit proposals for instruments to be deployed by crew on the surface of the Moon (Fig. 1), beginning with Artemis III [3]. The Artemis III mission will land in the south polar region of the Moon, within 6º of latitude from the south pole, in the vicinity of both persistently illuminated and permanently shadowed areas of the Moon, with potential access to surface-accessible volatile deposits [4]. Several of the proposed landing regions are located among some of the oldest parts of the Moon, and together with the permanently shadowed regions, provide the opportunity to learn about the history of the Moon through previously unexplored lunar regions [5]. Deployed payloads will be a critical part of a notional program that captures the highest-priority science for Artemis III and provides the greatest feedforward to follow-on missions and the build-up to the Artemis Base Camp
R. C. Weber↗
Artemis Internal Science Team Update: Deployed Payloads
Artemis will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis effort includes a collaboration of space agencies and companies from around the world. In support of Artemis, a cross-disciplinary effort of science, engineering, operations, and human factors personnel is currently developing the best methods, facilities, and field locations to test hardware, train astronauts, and evaluate concepts of operations. This poster, as part of the Artemis Internal Science Team (AIST), provides an update to NASA’s plans for the solicitation, development, and operations of deployed surface payloads. In calendar year 2023, NASA’s Science Mission Directorate will solicit proposals for instruments to be deployed by crew on the surface of the Moon (Fig. 1), beginning with Artemis III. The Artemis III mission will land in the south polar region of the Moon, within 6º of latitude from the south pole, in the vicinity of both persistently illuminated and permanently shadowed areas of the Moon, with potential access to surface-accessible volatile deposits. Several of the proposed landing regions are located among some of the oldest parts of the Moon, and together with the permanently shadowed regions, provide the opportunity to learn about the history of the Moon through previously unexplored lunar regions. Deployed payloads will be a critical part of a notional program that captures the highest-priority science for Artemis III and provides the greatest feed-forward to follow-on missions and the build-up to the Artemis Base Camp.
R. C. Weber↗
EVA Skills Training
Dr. Parazynski and a colleague from Extravehicular Activity (EVA), Robotics, & Crew Systems Operations (DX) worked closely to build the EVA Skills Training Program, and for the first time, defined the gold standards of EVA performance, allowing crewmembers to increase their performance significantly. As part of the program, individuals had the opportunity to learn at their own rate, taking additional water time as required, to achieve that level of performance. This focus on training to one's strengths and weaknesses to bolster them enabled the Crew Office and DX to field a much larger group of spacewalkers for the daunting "wall of EVA" required for the building and maintenance of the ISS. Parazynski also stressed the need for designers to understand the capabilities and the limitations of a human in a spacesuit, as well as opportunities to improve future generations of space. He shared lessons learned (how the Crew Office engaged in these endeavors) and illustrated the need to work as a team to develop these complex systems.
Parazynski, Scott↗
Columbus Programme (current status)
A description is presented for the Columbus Program. The Columbus program comprises a space segment, a ground segment, and operations preparation program, and a utilization preparation program. The space segment consist of three elements: an Attached Pressurized Module (APM); a Man Tended Free Flyer (MTFF); and a Polar platform (PPF). The ground segment is a program shared with other European programs such as Hermes, for communications, services, training and tracking facilities. The Operations preparation program focuses on preparing the ground segment for readiness for the launch of the space segment elements. And the Utilization preparation program includes definition of candidate payload facilities, initial payload selection and precursor flights (Eureca, Spacelab).
Source record↗
Computer Based Training: Field Deployable Trainer and Shared Virtual Reality
Astronaut training has traditionally been conducted at specific sites with specialized facilities. Because of its size and nature the training equipment is generally not portable. Efforts are now under way to develop training tools that can be taken to remote locations, including into orbit. Two of these efforts are the Field Deployable Trainer and Shared Virtual Reality projects. Field Deployable Trainer NASA has used the recent shuttle mission by astronaut Shannon Lucid to the Russian space station, Mir, as an opportunity to develop and test a prototype of an on-orbit computer training system. A laptop computer with a customized user interface, a set of specially prepared CD's, and video tapes were taken to the Mir by Ms. Lucid. Based upon the feedback following the launch of the Lucid flight, our team prepared materials for the next Mir visitor. Astronaut John Blaha will fly on NASA/MIR Long Duration Mission 3, set to launch in mid September. He will take with him a customized hard disk drive and a package of compact disks containing training videos, references and maps. The FDT team continues to explore and develop new and innovative ways to conduct offsite astronaut training using personal computers. Shared Virtual Reality Training NASA's Space Flight Training Division has been investigating the use of virtual reality environments for astronaut training. Recent efforts have focused on activities requiring interaction by two or more people, called shared VR. Dr. Bowen Loftin, from the University of Houston, directs a virtual reality laboratory that conducts much of the NASA sponsored research. I worked on a project involving the development of a virtual environment that can be used to train astronauts and others to operate a science unit called a Biological Technology Facility (BTF). Facilities like this will be used to house and control microgravity experiments on the space station. It is hoped that astronauts and instructors will ultimately be able to share common virtual environments and, using telephone links, conduct interactive training from separate locations.
Mullen, Terence J.↗
Space shuttle onboard navigation console expert/trainer system
A software system for use in enhancing operational performance as well as training ground controllers in monitoring onboard Space Shuttle navigation sensors is described. The Onboard Navigation (ONAV) development reflects a trend toward following a structured and methodical approach to development. The ONAV system must deal with integrated conventional and expert system software, complex interfaces, and implementation limitations due to the target operational environment. An overview of the onboard navigation sensor monitoring function is presented, along with a description of guidelines driving the development effort, requirements that the system must meet, current progress, and future efforts.
Wang, Lui↗
The Man-machine Integration Design and Analysis System (MIDAS) Software Training Documentation
This document is a training manual for MIDAS v5 that takes an user through the hardware and software requirements for the MIDAS V5 software, the steps required to download the software, and the steps that a user needs to take to create a MIDAS simulation. The training guide also illustrates how the models interact to generate MIDAS predictions of operator performance along task, workload, and situation awareness timelines and provides the test routines that were conducted to verify the operation of the integrated MIDAS models. The training guide shows the user how the MIDAS task model interacts with and controls an anthropometric model through its use of behavioral primitives. The training documentation also illustrates one approach that has been used to filter and analyze MIDAS output.
Brian F. Gore↗
Results from an Investigation into Extra-Vehicular Activity (EVA) Training Related Shoulder Injuries
The number and complexity of extravehicular activities (EVAs) required for the completion and maintenance of the International Space Station (ISS) is unprecedented. The training required to successfully complete this magnitude of space walks presents a real risk of overuse musculoskeletal injuries to the EVA crew population. There was mounting evidence raised by crewmembers, trainers, and physicians at the Johnson Space Center (JSC) between 1999 and 2002 that suggested a link between training in the Neutral - Buoyancy Lab (NBL) and the several reported cases of shoulder injuries. The short- and long-term health consequences of shoulder injury to astronauts in training as well as the potential mission impact associated with surgical intervention to assigned EVA crew point to this as a critical problem that must be mitigated. Thus, a multi-directorate tiger team was formed in December of 2002 led by the EVA Office and Astronaut Office at the JSC. The primary objectives of this Tiger Team were to evaluate the prevalence of these injuries and substantiate the relationship to training in the NBL with the crew person operating in the EVA Mobility Unit (EMU). Between December 2002 and June of 2003 the team collected data, surveyed crewmembers, consulted with a variety of physicians, and performed tests. The results of this effort were combined with the vast knowledge and experience of the Tiger Team members to formulate several findings and over fifty recommendations. This paper summarizes those findings and recommendations as well as the process by which these were determined. The Tiger Team concluded that training in the NBL was directly linked to several major and minor shoulder injuries that had occurred. With the assistance of JSC flight surgeons, outside consultants, and the lead crewmember/physician on the team, the mechanisms of injury were determined. These mechanisms were then linked to specific aspects of the hardware design, operational techniques, and the training environment. During the 1999 to 2003 time frame many variables converged to make it impossible to determine with any accuracy which one or two root causes were primarily involved. Therefore a broad range of recommendations was established to prevent future injury to crewmembers training in the NBL in the near term. Many of these recommendations are lessons learned that are essentially timeless and therefore should be passed on to future EVA endeavors to ensure that hardware designs and operational techniques utilized in the future consider the demands of training on the human body here on earth.
Johnson, Brian J.↗
Low-latency Jet Tagging for HL-LHC Using Transformer Architectures
Transformers are the state-of-the-art model architectures and widely used in application areas of machine learning. However the performance of such architectures is less well explored in the ultra-low latency domains where deployment on FPGAs or ASICs is required. Such domains include the trigger and data acquisition systems of the LHC experiments. We present a transformer-based algorithm for jet tagging built with the HGQ2 framework, which is able to produce a model with heterogeneous bitwidths for fast inference on FPGAs, as required in the trigger systems at the LHC experiments. The bitwidths are acquired during training by minimizing the total bit operations as an additional parameter. By allowing a bitwidth of zero, the model is pruned in-situ during training. Using this quantization-aware approach, our algorithm achieves state-of-the-art performance while also retaining permutation invariance which is a key property for particle physics applications. Due to the strength of transformers in representation learning, our work also serves as a stepping stone for the development of a larger foundation model for trigger applications.
Laatu, Lauri [Imperial Coll., London]↗
Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom
Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope
In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.
20 FOSSIL-FUELED POWER PLANTS↗
A smart alarm for particle accelerator beamline operations
We present the initial results of a proof-of-concept 'smart alarm' for the Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify and diagnose unusual machine states is needed. Our approach leverages a trained neural network capable of alerting operators (a) when an anomalous condition exists in the beamline and (b) identifying the element setting that is the root cause. The tool is based on an inverse model that maps beamline readings (diagnostic readbacks) to settings (beamline attributes operators can modify). The model takes as input readings from the machine and computes machine settings which are compared to control setpoints. Instances where predictions differ from setpoints by a user-defined threshold are flagged as anomalous. Given data corresponding to 354 anomalous injector configurations, the model can narrow the root cause of an anomalous condition to three potential candidates with 94.6% accuracy. Furthermore, compared to the current method of identifying anomalous conditions which raises an alarm when machine parameters drift outside their normal tolerances, the data-driven model can identify 83% more anomalous conditions.
43 PARTICLE ACCELERATORS↗
Mission Operations, Cubed: NASA Marshall Operations Support for SmallSats
SmallSats have come a long way since the Huntsville Operations Support Center (HOSC) at NASA’s Marshall Space Flight Center supported its first “minisatellite” mission in 2010. And just as SmallSats themselves have evolved in those 12 years, so too has the HOSC’s mission support for SmallSats. Marshall Space Flight Center has a long history with payload and mission operations, including support for the Apollo missions to the moon, the Space Shuttle program, and 21 years of continuous around-the-clock science operations support for research aboard the International Space Station. Today, the HOSC is a multi-tenant facility, supporting not only ISS, but also NASA’s Commercial Crew program, the Space Launch System, the Hubble and Chandra observatories and others – including multiple SmallSat missions. Two SmallSat solar sail missions will be among those taking advantage of the HOSC’s resources for planning, training for and executing mission operations – the Near Earth Asteroid (NEA) Scout and Solar Cruiser missions. One of 10 6U CubeSats manifest on the Artemis I launch of NASA’s Space Launch System rocket this year, NEA Scout’s three-year mission will be supported through a more traditional operations concept, with a dedicated Flight Controller staff operating within the HOSC. Scheduled to launch as part of the Interstellar Mapping and Acceleration Probe (IMAP) in February 2025, Solar Cruiser’s 11-month mission will take a next-generation approach to operations by utilizing a multi-mission flight controller concept, as well as Marshall’s Telescience Resource Kit (TreK). TreK provides a suite of software applications and libraries that allow the Mission Operations Center to serve as an in-house ground system which incorporates remote and automation capability options for engineers and scientists. This presentation will compare the approaches the HOSC will use to support these two missions as a way of demonstrating the array of options NASA MSFC offers for operations support for CubeSat and SmallSat missions.
Darren S Wallace↗
Inductive monitoring system constructed from nominal system data and its use in real-time system monitoring
The present invention relates to an Inductive Monitoring System (IMS), its software implementations, hardware embodiments and applications. Training data is received, typically nominal system data acquired from sensors in normally operating systems or from detailed system simulations. The training data is formed into vectors that are used to generate a knowledge database having clusters of nominal operating regions therein. IMS monitors a system's performance or health by comparing cluster parameters in the knowledge database with incoming sensor data from a monitored-system formed into vectors. Nominal performance is concluded when a monitored-system vector is determined to lie within a nominal operating region cluster or lies sufficiently close to a such a cluster as determined by a threshold value and a distance metric. Some embodiments of IMS include cluster indexing and retrieval methods that increase the execution speed of IMS.
Iverson, David L.↗
Lunar and Mars Exploration: The Autonomy Factor
Long duration space flight crews have relied heavily on almost constant communication with ground control mission support. Ground control teams provide vehicle status and system monitoring, while offering near real time support for specific tasks, emergencies, and ensuring crew health and well being. With extended exploration goals to lunar and Mars outposts, real time communication with ground control teams and the ground s ability to conduct mission monitoring will be very limited compared to the resources provided to current International Space Station (ISS) crews. An operational shift toward more autonomy and a heavier reliance on the crew to monitor their vehicle and operations will be required for these future missions. NASA s future exploration endeavors and the subsequent increased autonomy will require a shift in crew skill composition, i.e. engineer, doctor, mission specialist etc. and lead to new training challenges and mission scenarios. Specifically, operational and design changes will be necessary in many areas including: Habitat Infrastructure and Support Systems, Crew Composition, Training, Procedures and Mission Planning. This paper will specifically address how to apply ISS lessons learned to further use ISS as a test bed to address decreased amounts of ground support to achieve full autonomous operations for lunar and Mars missions. Understanding these lessons learned and applying them to current operations will help to address the future impacts of increased crew autonomy for the lunar and Mars outposts and pave the way for success in increasingly longer mission durations.
Rando, Cynthia M.↗
Upset Recovery Human Factors
Accident and incident analyses as well as industry group concerns and recommendations have justified taking a second look at proficiency standards related to upset recovery training and performance. Quite a number of factors and theories have been suggested-- leading the NASA Aviation Safety Program to reconsider manual handling skills in highly automated aircraft particularly in conditions that can potentially lead to Loss of Control events. Our team of Subject Matter Experts (SMEs) first identified 76 Basic Recovery Skills that were important for effective crew response under five different anomaly conditions. In addition to manual handling skills, the skill set included knowledge and cognitive skills, as well as decision making and management skills. Advanced Recovery Skills were identified by combining skills, integrating with crew resource management skills, and developing heuristics for decision making.Using the Advanced Recovery Skill set, the SMEs then developed a generic process flow starting from the problem discovery phase (e.g., identifying an anomaly) through the decision making and management phase (e.g., assessing response options), through the recovery phase (e.g., controlling the aircraft). The generic process flow was refined by testing it against six additional scenarios. The next part of the project was to develop an approach for assessing and revising a generic training curriculum (we used an operators Advanced Qualification Program (AQP) as a framework). Although many of the Basic and Advanced Recovery Skills could be found in the Job Task Listing, they were not always structured or combined in the most effective way. Recommendations were developed for assessing relevant aspects of the Job Task Listing and Continuing Qualification curriculum so that the more comprehensive set of Upset Recovery skillsincluding Human Factors--could be trained and assessed in the most appropriate and effective context. The existing AQP methodology provides a natural way to insert targeted Upset Recovery skills into its system of proficiency objectives, training devices, training activities, and ultimately, into the event sets of a simulator training scenario.
AQP training↗