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NASA NTRSDate not supplied

A New Concurrent Engineering Tool for the New Mission Design Center at NASA Ames Research Center

The NASA Ames Mission Design Center (MDC) has undergone a significant transformation process in recent years, culminating in becoming a true Concurrent Engineering Center. The underlying goal of this transformation is to better serve the Ames PI community in a cost-effective and rapidly-responsive way, providing quality products pivotal to the decision-making proposal strategy of our center. The four pillars of the change focus on four areas: personnel, physical places, tools, and training. Personnel: the MDC switched from using a dedicated pool of engineers—mostly entry-level career—for a matrixed approach with more experienced Subject Matter Experts, usually involved in spaceflight projects. These SMEs get detailed to the MDC for the duration of the study on a part-time or full-time basis. Physical places: The physical aspect pertains mostly to the layout of the new Engineering building and the dedicated concurrent engineering layout to foster collaboration. The current plan includes a dedicated space for concurrent engineering sessions as well as dedicated project rooms for concept studies. Tool: the MDC team is developing a new in-house concurrent engineering tool to facilitate concurrent engineering sessions. The team leveraged the many lessons learned and insight gained from using the current concurrent engineering tool, in use since 2007. SMEs have been involved throughout its design and development, ensuring accuracy and providing validity to the implementation approach. This new tool is database-driven and allows a team to work concurrently on the same model of a mission concept. “Workspaces” are provided for each of the traditional spacecraft mission design disciplines, including cost accounting and systems engineering. There are multiple tiers of fidelity available within each workspace, which can be completed parametrically or independently from the other disciplines as needed or synchronously with the other disciplines. A “commit” step synchronizes a workspace with the mission database and identifies any potential conflicts, along with the user who entered the conflicting data. A Mission Summary workspace enables a Study Lead to run an effective CE session, displaying key graphics, workspace status information, and technical and monetary budget information. This new CE tool will be undergoing Beta Testing with its primary user base and a limited Parts database in the summer of 2020. The team plans to release the first fully operational version in December 2020. Training: working in early concept development and maturation for a space mission requires a unique mindset and being comfortable with uncertainties in an environment where requirements are not yet fully developed, and where changes and trade-offs happen at a high tempo. The MDC started a weekly training program to ensure a common knowledge base on how do develop and mature early concept studies, as well as how to operate in a concurrent engineering environment. In conclusion, the MDC transformation will provide the overall PI community focused on cost-effective small satellite scientific missions with a powerful tool to address and mature early concepts more effectively and efficiently.

David Mauro↗
NASA NTRSDate not supplied

Identifying Outside Influences as Latent Factors to Risk in Human Performance

During the Acquisition Life Cycle for a program, there are several opportunities for the system in design to be adjusted in accordance with its changing landscape as it is being shaped by evolving policy and organizational culture. Human Systems Integration is integral to identifying these opportunities of change as there are a set number of activities that may account for altered operational states, human performance deviations, and overall component engagement if HSI is enacted early enough in the life cycle. Some changes that occur in the operational environment may not be accounted for since the organizational culture and practices are not currently a part of the HSI focus. Likewise, policy changes themselves from a top-to-bottom analysis may not have the appearance of effecting human performance until having gone through a trail-and-error period. Workarounds to adjust for unforeseen policy affects become the system's solution that usually includes changes in the training and education of the operators, maintainers, and support personnel. A system full of workarounds and off-normal practices, that cause operators to disregard the purpose of the design, coupled with the false notion that these activities are proven for successful system operation, is the very definition of “an accident waiting to happen”. Unforeseen changes in policies and practices that cause new and unusual activities to successfully and keep the system running, should be considered latent factors that may cause a potential mishap, and not part of the resilience that humans provide to the successful operation of the system. This presentation will explore how latent factors may find their way into system operations and how they can be identified and addressed.

Human Systems Integration↗
NASA NTRS2011

Operation Evaluation of the VEGGIE Food Production System in the Habitat Demonstration Unit

The 2010 Desert Research and Technology Studies (DRATS) of the VEGGIE Food Production System in the Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) was the first operational evaluation of salad crop production technology in a NASA analog test. A systematic evaluation of rooting media and nutrient delivery systems were evaluated for three lettuce cultivars that have shown promise as candidates for a surface based food production system. The VEGGIE nutrient delivery system worked well, was able to be maintained by multiple operators with a minimum of training, and supported excellent lettuce growth for the duration of the test. A Hazard Analysis and Critical Control Point (HACCP) evaluation was performed using ProSantm as sanitation agent prior to consumption was approved, and the crew was allowed to consume the lettuce grown using the VEGGIE light cap and gravity based nutrient delivery system at the completion of the 14-day DRAT field test. The DRAT field test validated the crew operations; Growth of all lettuce cultivars was excellent. The operational DRAT field testing in the HDU identified light quality issues related to morphology and pigment development that will need to be addressed through additional testing. Feedback from the crew, ground support personnel, and human factors leads was uniformly positive on the psychological value of having the crop production system in the excursion module. A number of areas have been identified for future work, to minimize the "footprint" of the Food Production system through creative use of unused wall and floor space in the unit.

Stutte, Gary W.↗
NASA NTRS2011

Operational Evaluation of VEGGIE Food Production System in the Habitat Demonstration Unit

The 2010 Desert Research and Technology Studies (DRATS) of the VEGGIE Food Production System in the Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) was the first operational evaluation of salad crop production technology in a NASA analog test. A systematic evaluation of rooting media and nutrient delivery systems were evaluated for three lettuce cultivars that have shown promise as candidates for a surface based food production system. The VEGGIE nutrient delivery system worked well, was able to be maintained by multiple operators with a minimum of training, and supported excellent lettuce growth for the duration of the test. A Hazard Analysis and Critical Control Point (HACCP) evaluation was performed using ProSan(tm) as sanitation agent prior to consumption was approved, and the crew was allowed to consume the lettuce grown using the VEGGIE light cap and gravity based nutrient delivery system at the completion of the 14-day DRAT field test. The DRAT field test validated the crew operations; Growth of all lettuce cultivars was excellent. The operational DRAT field testing in the HDU identified light quality issues related to morphology and pigment development that will need to be addressed through additional testing. Feedback from the crew, ground support personnel, and human factors leads was uniformly positive on the psychological value of having the crop production system in the excursion module. A number of areas have been identified for future work, to minimize the "footprint" of the Food Production system through creative use of unused wall and floor space in the unit.

Stutte, Gary W.↗
NASA NTRS2005

BEAM Technology Flight Demonstration

As technologies advance, their growing complexity makes them harder to maintain. Detection methods for isolating and identifying impending problems are needed to balance this complexity. Through comparison of signal pairs from onboard sensors, the Beacon-based Exception Analysis For Multimissions (BEAM) algorithm can identify and help classify deviations in system operation from a data-trained statistical model. The goal of this task is to mature BEAM and validate its performance on a flying test bed. A series of F-18 flight demonstrations with BEAM monitoring engine parameters in real time was used to demonstrate in-the-field readiness. Captured F-18 and simulated F-18 engine data were used in model creation and training. The algorithm was then ported to the embedded system with a data buffering, file writing, and data-time-stamp monitoring shell to reduce the impact of embedded system faults on BEAM'S ability to correctly identify engine faults. Embedded system testing identified hardware related restrictions and contributed to iterative improvements in the code's runtime performance. The system was flown with forced engine flameouts and other pilot induced faults to simulate operation out of the norm. Successful detection of these faults, confirmed through post-flight data analysis, helped BEAM achieve TRL6.

Wang, David↗
DOE OSTI2023

Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets

This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.

Majidi, Majid↗
NASA NTRS1995

Task Analytic Models to Guide Analysis and Design: Use of the Operator Function Model to Represent Pilot-Autoflight System Mode Problems

Task-analytic models structure essential information about operator interaction with complex systems, in this case pilot interaction with the autoflight system. Such models serve two purposes: (1) they allow researchers and practitioners to understand pilots' actions; and (2) they provide a compact, computational representation needed to design 'intelligent' aids, e.g., displays, assistants, and training systems. This paper demonstrates the use of the operator function model to trace the process of mode engagements while a pilot is controlling an aircraft via the, autoflight system. The operator function model is a normative and nondeterministic model of how a well-trained, well-motivated operator manages multiple concurrent activities for effective real-time control. For each function, the model links the pilot's actions with the required information. Using the operator function model, this paper describes several mode engagement scenarios. These scenarios were observed and documented during a field study that focused on mode engagements and mode transitions during normal line operations. Data including time, ATC clearances, altitude, system states, and active modes and sub-modes, engagement of modes, were recorded during sixty-six flights. Using these data, seven prototypical mode engagement scenarios were extracted. One scenario details the decision of the crew to disengage a fully automatic mode in favor of a semi-automatic mode, and the consequences of this action. Another describes a mode error involving updating aircraft speed following the engagement of a speed submode. Other scenarios detail mode confusion at various phases of the flight. This analysis uses the operator function model to identify three aspects of mode engagement: (1) the progress of pilot-aircraft-autoflight system interaction; (2) control/display information required to perform mode management activities; and (3) the potential cause(s) of mode confusion. The goal of this paper is twofold: (1) to demonstrate the use of the operator functio model methodology to describe pilot-system interaction while engaging modes And monitoring the system, and (2) to initiate a discussion of how task-analytic models might inform design processes. While the operator function model is only one type of task-analytic representation, the hypothesis of this paper is that some type of task analytic structure is a prerequisite for the design of effective human-automation interaction.

Degani, Asaf↗
NASA NTRS2016

NEEMO 21: Tools, Techniques, Technologies & Training for Science Exploration EVA

The 21st mission of the NASA Extreme Environment Mission Operations (NEEMO) was a highly integrated operational test and evaluation of tools, techniques, technologies, and training for science driven exploration during Extravehicular Activity (EVA).The 16-day mission was conducted from the Aquarius habitat, an underwater laboratory, off the coast of Key Largo, FL. The unique facility, authentic science objectives, and diverse skill-sets of the crew/team facilitate the planning and design for future space exploration.

Graff, Trevor↗
NASA NTRS1981

Issues related to line-oriented flight training

The use of a training simulator along with carefully structured, detailed, line trip scenarios was envisioned by NASA as a means of providing a controllable, repeatable way to observe line crews in a highly realistic simulation of their working environment and obtain better understanding operationally significant human factors problems and issues. Relevant research done by the agency and the results of full-mission simulation scenarios revealed potential implications for flight training. Aspects to be considered in creating training programs closely related to the actual line environment with a total crew application in real world incident experiences include: (1) operational, environmental, equipment, and crew problems in scenario design; (2) real time line oriented flight training operation; (3) performance assessment and debriefing; (4) instructor qualification and training; and (5) other issues such as ub un initial, transition, and upgrade training; procedures developent and evaluation, and equipment evaluation.

Lauber, J. K.↗
NASA NTRS1993

Phenytoin as a countermeasure for motion sickness in NASA maritime operations

Seasickness is the most prevalent form of motion sickness and is an operational problem during Space Shuttle Solid-fueled Rocket Booster (SRB) retrieval. Phenytoin has been shown to protect against motion sickness induced by Coriolis stress. We exposed SRB recovery personnel to off-vertical rotation and sea motion after phenytoin or placebo. Phenytoin blood levels of at least 9 micrograms/ml were protective against motion sickness at sea. No change in susceptibility to nitrogen narcosis was seen in divers in chamber tests at 460 KPa. Phenytoin was used during the performance of critical and hazardous tasks during training and actual SRB recovery operations. Phenytoin is an effective operational countermeasure for motion sickness for selected SRB crew members.

Woodard, Daniel↗
DOE OSTI2024

Foundations of Off-Grid Solar in Haiti: 6. Installation, Operations, and Maintenance of Off-Grid Solar Systems

This training lends insight into key information and concepts for off-grid solar in Haiti, building foundational knowledge and an ability to catalyze interest in off-grid solar for rural electrification in Haiti. This self-paced course is offered in both English and French and covers a variety of topics related to energy access in Haiti including off-grid solar products, market potential in Haiti, supply and demand side considerations, system design, installation and maintenance, off-grid solar business models, financial modeling, gender and energy access, productive use of energy, and climate adaptation. The training is intended for university students, government partners, NGOs, funders, development partners, professionals, and more. No prior knowledge of off-grid solar or Haiti is required to benefit from this training. See NREL/PR-7A40-89260 for the French translation of this document.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
NASA NTRS2001

STS-104 Crew Training Clips

The crewmembers of STS-104, Commander Steven Lindsey, Pilot Charles Hobaugh, and Mission Specialists Michael Gernhardt, James Reilly, and Janet Kavandi, are seen during various stages of their training. Footage shows the following: (1) Water Survival Training at the Neutral Buoyancy Laboratory (NBL); (2) Rendezvous and Docking Training in the Shuttle Mission Simulator; (3) Training in the Space Station Airlock; (4) Training in the Virtual Reality Lab; (5) Post-insertion Operations in the Fixed Base Simulator; (6) Extravehicular Activity Training at the NBL; (7) Crew Stowage Training in the Space Station Mock-up Training Facility; and (8) Water Transfer Training in the Crew Compartment Trainer.

Source record↗
NASA NTRS2011

Balancing Training Techniques for Flight Controller Certification

Training of ground control teams has been a difficult task in space operations. There are several intangible skills that must be learned to become the steely eyed men and women of mission control who respond to spacecraft failures that can lead to loss of vehicle or crew if handled improperly. And as difficult as training is, it can also be costly. Every day, month or year an operator is in training, is a day that not only they are being trained without direct benefit to the organization, but potentially an instructor or mentor is also being paid for hours spent assisting them. Therefore, optimization of the training flow is highly desired. Recently the Expedition Division (DI) at Johnson Space Flight Center has recreated their training flows for the purpose of both moving to an operator/specialist/instructor hierarchy and to address past inefficiencies in the training flow. This paper will discuss the types of training DI is utilizing in their new flows, and the balance that has been struck between the ideal learning environments and realistic constraints. Specifically, the past training flow for the ISS Attitude Determination and Control Officer will be presented, including drawbacks that were encountered. Then the new training flow will be discussed and how a new approach utilizes more training methods and teaching techniques. We will look at how DI has integrated classes, workshops, checkouts, module reviews, scenarios, OJT, paper sims, Mini Sims, and finally Integrated Sims to balance the cost and timing of training a new flight controller.

Gosling, Christina↗
NASA NTRS2008

An EXPRESS Rack Overview and Support for Microgravity Research on the International Space Station (ISS)

The EXpedite the PRocessing of Experiments to Space Station or EXPRESS Rack System has provided accommodations and facilitated operations for microgravity-based research payloads for over 6 years on the International Space Station (ISS). The EXPRESS Rack accepts Space Shuttle middeck type lockers and International Subrack Interface Standard (ISIS) drawers, providing a modular-type interface on the ISS. The EXPRESS Rack provides 28Vdc power, Ethernet and RS-422 data interfaces, thermal conditioning, vacuum exhaust, and Nitrogen supply for payload use. The EXPRESS Rack system also includes payload checkout capability with a flight rack or flight rack emulator prior to launch, providing a high degree of confidence in successful operations once an-orbit. In addition, EXPRESS trainer racks are provided to support crew training of both rack systems and subrack operations. Standard hardware and software interfaces provided by the EXPRESS Rack simplify the integration processes for ISS payload development. The EXPRESS Rack is designed to accommodate multidiscipline research, allowing for the independent operation of each subrack payload within a single rack. On-orbit operations began for the EXPRESS Rack Project on April 24, 2001, with one rack operating continuously to support high-priority payloads. The other on-orbit EXPRESS Racks operate based on payload need and resource availability. Over 50 multi-discipline payloads have now been supported on-orbit by the EXPRESS Rack Program. Sustaining engineering, logistics, and maintenance functions are in place to maintain hardware, operations and provide software upgrades. Additional EXPRESS Racks are planned for launch prior to ISS completion in support of long-term operations and the planned transition of the U.S. Segment to a National Laboratory.

Pelfrey, Joseph J.↗
NASA NTRS2003

Development and Utility of a Piloted Flight Simulator for Icing Effects Training

A piloted flight simulator called the Ice Contamination Effects Flight Training Device (ICEFTD), which uses low cost desktop components and a generic cockpit replication is being developed. The purpose of this device is to demonstrate the effectiveness of its use for training pilots to recognize and recover from aircraft handling anomalies that result from airframe ice formations. High-fidelity flight simulation models for various baseline (non-iced) and iced configurations were developed from wind tunnel tests of a subscale DeHavilland DHC-6 Twin Otter aircraft model. These simulation models were validated with flight test data from the NASA Twin Otter Icing Research Aircraft, which included the effects of ice on wing and tail stall characteristics. These simulation models are being implemented into an ICEFTD that will provide representative aircraft characteristics due to airframe icing. Scenario-based exercises are being constructed to give an operational-flavor to the simulation. Training pilots will learn to recognize iced aircraft characteristics from the baseline, and will practice and apply appropriate recovery procedures to a handling event.

Ratvasky, Thomas P.↗
DOE OSTI2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗
NASA NTRS2005

Enhanced Lighting Techniques and Augmented Reality to Improve Human Task Performance

One of the most versatile tools designed for use on the International Space Station (ISS) is the Special Purpose Dexterous Manipulator (SPDM) robot. Operators for this system are trained at NASA Johnson Space Center (JSC) using a robotic simulator, the Dexterous Manipulator Trainer (DMT), which performs most SPDM functions under normal static Earth gravitational forces. The SPDM is controlled from a standard Robotic Workstation. A key feature of the SPDM and DMT is the Force/Moment Accommodation (FMA) system, which limits the contact forces and moments acting on the robot components, on its payload, an Orbital Replaceable Unit (ORU), and on the receptacle for the ORU. The FMA system helps to automatically alleviate any binding of the ORU as it is inserted or withdrawn from a receptacle, but it is limited in its correction capability. A successful ORU insertion generally requires that the reference axes of the ORU and receptacle be aligned to within approximately 0.25 inch and 0.5 degree of nominal values. The only guides available for the operator to achieve these alignment tolerances are views from any available video cameras. No special registration markings are provided on the ORU or receptacle, so the operator must use their intrinsic features in the video display to perform the pre-insertion alignment task. Since optimum camera views may not be available, and dynamic orbital lighting conditions may limit viewing periods, long times are anticipated for performing some ORU insertion or extraction operations. This study explored the feasibility of using augmented reality (AR) to assist with SPDM operations. Geometric graphical symbols were overlaid on the end effector (EE) camera view to afford cues to assist the operator in attaining adequate pre-insertion ORU alignment.

Maida, James C.↗