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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Simscape Modeling Verification in the Simulink Development Environment

The purpose of the Simulation Product Group of the Control and Data Systems division of the NASA Engineering branch at Kennedy Space Center is to provide a realtime model and simulation of the Ground Subsystems participating in vehicle launching activities. The simulation software is part of the Spaceport Command and Control System (SCCS) and is designed to support integrated launch operation software verification, and console operator training. Using Mathworks Simulink tools, modeling engineers currently build models from the custom-built blocks to accurately represent ground hardware. This is time consuming and costly due to required rigorous testing and peer reviews to be conducted for each custom-built block. Using Mathworks Simscape tools, modeling time can be reduced since there would be no custom-code developed. After careful research, the group came to the conclusion it is feasible to use Simscape's blocks in MatLab's Simulink. My project this fall was to verify the accuracy of the Crew Access Arm model developed using Simscape tools running in the Simulink development environment.

SIMSCAPE↗
NASA NTRS2015

On-Demand Mobility (ODM) Technical Pathway: Enabling Ease of Use and Safety

On-demand mobility (ODM) through aviation refers to the ability to quickly and easily move people or equivalent cargo without delays introduced by lack of, or infrequently, scheduled service. A necessary attribute of ODM is that it be easy to use, requiring a minimum of special training, skills, or workload. Fully-autonomous vehicles would provide the ultimate in ease-of-use (EU) but are currently unproven for safety-critical applications outside of a few, situationally constrained applications (e.g. automated trains operating in segregated systems). Applied to aviation, the current and near-future state of the art of full-autonomy, may entail undesirable trade-offs such as very conservative operational margins resulting in reduced trip reliability and transportation utility. Furthermore, acceptance by potential users and regulatory authorities will be challenging without confidence in autonomous systems in developed in less critical, but still challenging applications. A question for the aviation community is how we can best develop practical ease-of-use for aircraft that are sized to carry a small number of passengers (e.g. 1-9) or equivalent cargo. Such development is unlikely to be a single event, but rather a managed, evolutionary process where responsibility and authority transitions from human to automation agents as operational experience is gained with increasingly intelligent systems. This talk presents a technology road map being developed at NASA Langley, as part of an overall strategy to foster ODM, for the development of ease-of-use for ODM aviation.

Goodrich, Ken↗
NASA NTRSDate not supplied

Human-system Interaction Issues and Proposed Solutions To Promote Successful Maturation of the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗
NASA NTRSDate not supplied

Human-system interaction issues and proposed solutions to promote successful maturation of the UTM system

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗
NASA NTRSDate not supplied

Studying Human-System Interaction within the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity. .

UTM↗
NASA NTRSDate not supplied

Studying Human-System Interaction with the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗
NASA NTRSDate not supplied

Space Launch System Day of Launch Loads for Artemis I

NASA’s Space Launch System (SLS) was successfully launched on November 16, 2022. During the years leading up to the first flight, Artemis I, a DOLILU (Day of Launch I-Load Update) process was developed to design, verify, and upload the first stage flight trajectory on day-of-launch to ensure a safe flight. The evaluation of integrated vehicle loads is a key component of the DOLILU process. The SLS Artemis I DOL loads project has involved methodology development, software development, software testing and certification, operator training, and simulation and launch support. The resulting DOL process successfully calculated loads for all launch opportunities within the window, with the robust nature of the process contributing to all opportunities being go for loads.

SLS↗
DOE OSTI2024

Unseen Winds: Harnessing High-Altitude Winds in the Southeast USA

This project aims to identify potential sites in the United States for further testing and development of airborne wind energy systems (AWES). Through industry questionnaires and interviews with AWES companies, the study assesses existing test sites' capabilities and gaps, leading to a three-tiered test site strategy: short-duration prototype demonstrations, long-duration technical assessments and power validations, and airspace interaction and deconfliction studies. Notably, the southeastern United States presents significant potential for AWES due to its lack of traditional wind turbines, which is attributed to low wind speeds at lower altitudes. However, wind conditions improve significantly at higher altitudes, making this region a promising candidate for AWES deployment. The project considers factors such as wind resources, infrastructure proximity, social acceptance, and environmental impacts to recommend sites that can support the unique needs of AWES manufacturers and facilitate their commercialization. Early findings suggest rural communities with low grid resiliency could benefit economically and technologically from hosting AWES test sites, especially with operator training centers and potential for long-term skilled employment in future disaster relief and other applications.

17 WIND ENERGY↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
NASA NTRS1990

Intelligent tutoring systems for space applications

Artificial Intelligence has been used in many space applications. Intelligent tutoring systems (ITSs) have only recently been developed for assisting training of space operations and skills. An ITS at Southwest Research Institute is described as an example of an ITS application for space operations, specifically, training console operations at mission control. A distinction is made between critical skills and knowledge versus routine skills. Other ITSs for space are also discussed and future training requirements and potential ITS solutions are described.

Luckhardt-Redfield, Carol A.↗
DOE OSTI2024

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2013

Decision Making Training in the Mission Operations Directorate

At JSC, we train our new flight controllers on a set of team skills that we call Space Flight Resource Management (SFRM). SFRM is akin to Crew Resource Management for the airlines and trains flight controllers to work as an effective team to reduce errors and improve safety. We have developed this training over the years with the assistance of Ames Research Center, Wyle Labs and University of Central Florida. One of the skills we teach is decision making/ problem solving (DM/PS). We teach DM/PS first in several classroom sessions, reinforce it in several part task training environments, and finally practice it in full-mission, full-team simulations. What I am proposing to talk about is this training flow: its content and how we teach it.

O'Keefe, William S.↗
NASA NTRS2018

Addressing Human Error in International Space Station Flight Control Teams: Advances in Ground Training for Science Operators

In flight control, as with any human in the loop system, operator error is an inevitable reality. On the International Space Station (ISS) where crew time and physical resources are precious and often irreplaceable, operator errors can result in significant, irreversible consequences. Flight controllers at the Payload Operations Integration Center (POIC) located at NASA's Marshall Space Flight Center (MSFC) in Huntsville, Alabama know this reality well. At the POIC, operator errors can be caused by a variety of factors, from poor hardware or software design to environmental factors such as time pressure or fatigue. The most difficult errors to address, however, are those which result from ineffective teamwork. To address these teamwork errors, trainers at the POIC have drawn best practices from high reliability industries as well as from our sister ISS control center at the Johnson Space Center (JSC) in Houston, Texas, to develop and implement a new training program focused specifically on teamwork skills. This training program, called Team Skills Training, is inspired by modern and historical training programs developed by NASA and is specifically tailored to the needs of the payload operations flight control team at the POIC. The program consists of training for both certified and trainee flight controllers, and covers team skills topics such as situational awareness, leadership, and communication skills. To maximize effectiveness, the program uses novel instructional techniques which extend beyond the classroom to encourage students to apply what they have learned to their day to day work. Minimizing operator errors in flight control is an endeavor which requires constant vigilance and continuous improvement. At the POIC, Team Skills Training is an important step in this journey.

Harris, Samantha S.↗
NASA NTRS2012

Evolution of Training in NASA's Mission Operations Directorate

NASA s Mission Operations Directorate provides all the mission planning, training, and operations support for NASA's human spaceflight missions including the International Space Station (ISS) and its fleet of supporting vehicles. MOD also develops and maintains the facilities necessary to conduct training and operations for those missions including the Mission Control Center, Space Station Training Facility, Space Vehicle Mockup Facility, and Neutral Buoyancy Laboratory. MOD's overarching approach to human spaceflight training is to "train like you fly." This approach means not only trying to replicate the operational environment in training but also to approach training with the same mindset as real operations. When in training, this means using the same approach for executing operations, responding to off-nominal situations, and conducting yourself in the operations environment in the same manner as you would for the real vehicle.

Hutt, Jason↗
NASA NTRS2018

Addressing Human Error in International Space Station Flight Control Teams: Advances in Ground Training for Science Operators

In flight control, as with any human in the loop system, operator error is an inevitable reality. On the International Space Station (ISS) where crew time and physical resources are precious and often irreplaceable, operator errors can result in significant, irreversible consequences. Flight controllers at the Payload Operations Integration Center (POIC) located at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama know this reality well. At the POIC, operator errors can be caused by a variety of factors, from poor hardware or software design to environmental factors such as time pressure or fatigue. The most difficult errors to address, however, are those which result from ineffective teamwork.Academic research in teamwork has resulted in the identification of many factors which make cross-functional teaming difficult, including leadership, trust building, and communication challenges. These factors, especially when combined with the challenging environmental factors flight control teams must contend with daily, make the goal of minimizing operator errors in payload operations challenging to achieve. To address such teamwork errors, trainers at the POIC have drawn best practices from high reliability industries such as commercial aviation, healthcare, and nuclear power plants, as well as from our sister ISS control center in Houston, Texas, to develop and institute a new training program focused specifically on teamwork skills.This training program, called the Team Skills Curriculum, is based on the concept of Crew Resource Management (CRM) which was developed by NASA in the 1970s for the commercial aviation industry in response to a series of aviation disasters resulting from ineffective teamwork. CRM was later tailored by the Johnson Space Center (JSC) for use in astronaut and flight control training. The result, called Space Flight Resource Management (SFRM) was formally introduced into manned spaceflight training in the late 90s. SFRM has evolved over the years, but the focus has remained on helping operators develop the skills needed to work as part of an effective team. Using these concepts as well as the latest research in cross-functional teaming and data on specific errors occurring at the POIC, trainers in the integrated flight control training branch created a custom training program for both new and certified payload operations specialists.

Harris, Samantha S.↗