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NASA NTRSDate not supplied

Helmet-Mounted Display Technology for Eva Training in NASA's Neutral Buoyancy Lab

Currently, during extravehicular activity (EVA) on the International Space Station (ISS), astronauts are in constant communication with mission control; however, this paradigm will significantly change during future NASA exploration missions due to limited and time-delayed communications. As exploration missions will thereby require an increase in crew autonomy, it is hypothesized that heads-up display (HUD) technology can supplement communications with mission control. Before introducing novel display technology to EVA operations, it must first be demonstrated and evaluated in an EVA training environment. The UC Davis Center for Spaceflight Research, in collaboration with NASA Johnson Space Center’s Human Physiology, Performance, Protection, and Operations (H-3PO) Lab has developed a low-cost and rapid development helmet-mounted display (HMD) for EVA training at NASA’s Neutral Buoyancy Lab (NBL). The first two phases of HMD testing (Phase I and II) at the NBL focused on technology and capability development and demonstration. Crew member feedback was obtained that evaluated aspects of HMD including its user satisfaction, visibility, readability, and usefulness. Overall, feedback indicated HMD was readable, un-obtrusive, and the available display modes had potential to be useful and enhance EVA training. HMD display modes currently available include: real-time biofeedback (metabolic rate), phase elapsed time (PET), ability to set timers, task procedural aids (e.g. Pistol Grip Tool settings) and custom messages. The goal for Phase III, which is currently underway, is to evaluate the effects that access to bioinformatics through HMD has on astronaut training and performance at the NBL. For this phase of testing, crewmembers will be asked to perform a go/no-go task while wearing HMD throughout the NBL run to characterize crew member state of situational awareness with respect to HMD. This information could be used to inform how best to display relatively important or supplemental, but not critical, data in the future.

Heads up display↗

NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

A new Exploration Extravehicular Activity Suit (xEVAS) is being designed to replace the current Extravehicular Mobility Unit (EMU) for the National Aeronautics and Space Administration’s (NASA’s) Artemis program to return astronauts to the lunar surface. This new suit will allow for increased range of motion compared the current EMU and Apollo era suits and will have additional features that will enhance the health and safety of exploration. With the design of lunar missions and the xEVAS progressing, it is important to consider possible injuries and injury mechanisms that could occur in the suit. To address these concerns, the suited Injury Modes and Effects Analysis (IMEA) was developed to outline suited injury scenarios and rank them based on risk score. The IMEA documents possible scenarios and underlying mechanisms of injury while wearing an extravehicular activity (EVA) suit. Tasks during lunar surface EVA as well as training events to prepare for lunar missions were considered as history has shown that more suit injuries occur during training than in flight. Each scenario is ranked with a consequence and likelihood scoring based on our current understanding of the suit and Artemis design reference missions to identify high-risk cases that will drive further work in suited injury. Injuries, mechanisms of injury, and mitigation strategies are evaluated within each scenario. The Suited Injury Summit was held on January 5, 2022, to vet the IMEA with external experts. This was an all-day virtual meeting with the suited injury team, ergonomists, suit engineers, safety engineers, the flight operations directorate, flight doctors, astronauts, astronaut strength, conditioning, and rehabilitation specialists (ASCRS), and external subject matter experts (SMEs). External SMEs consisted of surgeons with varying specialties. The intent of this meeting was to walk through the top injury risks identified in the analysis, identify any gaps that were not captured, and discuss mitigations. With participation from all groups, countless lessons-learned came from the Summit meeting. Using the lessons-learned and discussion from the Summit, the top 10 risks have been identified: neutral buoyancy laboratory training, hand/glove injuries, poor suit fit, field training, specific EVA tasks/design of task, boots/ankle injuries, falls from heights, background radiation, repetitive contact, and ambulation/long-distance ambulation. Mitigation steps have also been determined for each of the top risks. The IMEA and documentation of top risks is a living document. Yearly meetings are planned to update the analysis and reevaluate top risks and mitigations. The IMEA is being used to drive work in suited injury, and this work will continue to evolve with IMEA and lunar mission updates.

Teresa Reiber↗

NASA NTRSDate not supplied

Feasibility Study of a Multi Tilt-rotor Aircraft as the Artemis Lunar Training Vehicle

The Lunar Landing Research Vehicles (LLRVs) and the Lunar Landing Training Vehicles (LLTVs) provided astronaut candidates for the Apollo program with essential experience and confidence required to complete the missions, and contributed to six successful manned landings on the moon. The primary challenge in terrestrial training was being able to replicate the ratio of bank angle to linear acceleration that a pilot would experience in lunar gravity. Presently, as the Artemis program seeks to return humans to the Moon by 2025, engineers are evaluating suitable platforms to serve as an In-Flight Trainer (IFT) or Artemis Lunar Training Vehicle (ALTV) for astronauts training in the task of manual landing. The program is investigating the viability of current technology in the field of electric vertical takeoff and landing (eVTOL) vehicles and is evaluating using a multi tilt-rotor aircraft platform as a candidate platform for a preliminary ALTV. The tilt-rotor capability enables the vehicle attitude to be decoupled from its flight path, which is a crucial requirement in realistically simulating lunar gravity on Earth. Other key considerations include compensating for a lack of aerodynamic forces while flying through the atmosphere of Earth, as well as the ability to simulate the dynamics of multiple different lander designs for the Human Landing System (HLS) program. This paper details the feasibility study and presents a preliminary flight control architecture for an IFT based on a notional multi tilt-rotor platform. The modeling-following control law, based on nonlinear dynamic inversion (NDI), removes the need for gain scheduling because the vehicle operates across a wide range of flight conditions. The inner-loop dynamic control allocation strategy consists of a static portion that is optimized offline for trim while compensating for the difference in gravity and a dynamic portion that is computed in real time. The reference model consists of the full closed-loop dynamics of a generic HLS design. The modularity of the flight control architecture enables evaluation of multiple HLS concepts with minimal modifications to the control law. Simulation results of the multi tilt-rotor configuration following the final portion of the Apollo 11 descent trajectory are shown.

Jing Pei↗

NASA NTRSDate not supplied

Feasibility Study of a Multi-Tilt-Rotor Aircraft as the Artemis Lunar Training Vehicle

The Lunar Landing Research Vehicles (LLRVs) and the Lunar Landing Training Vehicles (LLTVs) provided astronauts of the Apollo program with essential experience and confidence required to complete the missions, and contributed to six successful manned landings on the moon. The primary challenge in terrestrial training was being able to replicate the ratio of tilt angle to linear acceleration that a pilot would experience in lunar gravity. Presently, as the Artemis program seeks to return humans to the Moon by 2025, engineers are evaluating suitable platforms to serve as an In-Flight Trainer (IFT) or Artemis Lunar Training Vehicle (ALTV) for astronauts training in the task of manual landing. The program is investigating the viability of current technology in the field of electric vertical takeoff and landing (eVTOL) vehicles and is evaluating using a multi-tilt-rotor aircraft platform as a candidate for a preliminary ALTV. The tilt-rotor capability enables the vehicle attitude to be decoupled from its flight path, which is a crucial requirement in realistically simulating lunar gravity on Earth. Other key considerations include compensating for a lack of aerodynamic forces while flying through the atmosphere of Earth, as well as the ability to simulate the dynamics of multiple different lander designs for the Human Landing System (HLS) program. This paper details the feasibility study and presents a preliminary flight control architecture for an IFT based on a notional multi-tilt-rotor platform. The model-following control law, based on nonlinear dynamic inversion (NDI), removes the need for gain scheduling. The inner-loop dynamic control allocation strategy consists of a static portion that is optimized offline for trim while compensating for the difference in gravity and a dynamic portion that is computed in real time. The reference model consists of the full closed-loop dynamics of a generic HLS design. The modularity of the flight control architecture enables evaluation of multiple HLS concepts with minimal modifications to the control law. Simulation results of the multi-tilt-rotor configuration following the final portion of the Apollo 11 descent trajectory are shown.

Jing Pei↗

NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

Tessa Reiber↗

NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗

DOE OSTI2024

Geometry-aware training of factorized layers in tensor Tucker format

Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during train and inference of large networks. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format and parameterizes it as the product of two rank-r matrices. However, this family of approaches often requires an initial full-model warm-up phase, prior knowledge of a feasible rank, and it is sensitive to parameter initialization.In this work, we introduce a novel approach to train the factors of a Tucker decomposition of the weight tensors. Our training proposal proves to be optimal in locally approximating the original unfactorized dynamics and stable for the initialization. Furthermore, the rank of each mode is dynamically updated during training.We provide a theoretical analysis of the algorithm, showing convergence, approximation and local descent guarantees. The method's performance is further illustrated through a variety of experiments, showing remarkable training compression rates and comparable or even better performance than the full baseline and alternative layer factorization strategies.

Zangrando, Emanuele [Gran Sasso Science Institute ↗

DOE OSTI2025

The tensor-train stochastic finite volume method for uncertainty quantification

The stochastic finite volume method offers an efficient one-pass approach for assessing uncertainty in hyperbolic conservation laws. Still, it struggles with the curse of dimensionality when dealing with multiple stochastic variables. Here, we introduce the stochastic finite volume method within the tensor-train framework to counteract this limitation. This integration, however, comes with its own set of difficulties, mainly due to the propensity for shock formation in hyperbolic systems. To overcome these issues, we have developed a tensor-train-adapted stochastic finite volume method that employs a global WENO reconstruction, making it suitable for such complex systems. This approach represents the first step in designing tensor-train techniques for hyperbolic systems and conservation laws involving shocks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

When and Why Hiring and Training Occur: A Survey of Building Efficiency Contractors

As state and local governments, along with homeowners, work to enhance building energy performance, a sufficiently skilled energy-efficiency workforce is essential for implementing solutions at scale. In recent years, employers in the energy efficiency sector, including building performance contractors, have frequently reported challenges finding qualified workers. Additionally, as building performance technologies evolve and become more sophisticated, current workers need to acquire new skills and practices to maintain high-quality work. This study surveyed 209 contractors involved in building energy performance projects across 37 U.S. states, Washington, D.C., Guam, and the Marshall Islands. The survey aimed to understand their experiences in the labor market, their business practices, and their decision-making processes when hiring new workers and training existing employees. The findings highlight contractors' motivations and considerations in hiring and training, as well as the most significant challenges they face in the labor market. These insights can help workforce development practitioners design training programs that are aligned with workforce needs and responsive to employer concerns.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

DOE Enhancement and Innovation: Toledo Healthy Homes Training Center

The initial goal of the Toledo Healthy Homes Training Center initiative was to provide weatherization instruction opportunities in Northwest Ohio. The focus of the search for a suitable location was centered on Toledo. Toledo Is the largest population center of the 19 counties that make up that region of the state. During the DOE funded grant period, a total of 103 individuals attended 121 separate training events hosted by the Toledo Healthy Homes Training Center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI1991

Pressure Safety Training (Rev. 8)

This is the workbook for the Pressure Safety Training Course. It is intended as a reference manual and guide for all work with pressurized liquids or gases. This workbook contains basic references to make work with pressure safer. It is intended to supplement classroom instruction, rather than serve as a definitive text on pressure. Earlier versions of the manual were intended specifically for training at Lawrence Livermore National Laboratory. This revision is a generic version intended for training at all DOE facilities. The information in the Standards chapter is from the LLNL Health and Safety Manual. It is included here as a convenient reference and guide for developing similar standards at your own facility.

42 ENGINEERING↗

NASA NTRS2017

Airspace Technology Demonstration II (ATD-2): American Airlines CLT Ramp Training RTC/RMTC: Baseline Integrated Arrival/ Departure/Surface (IADS) - ATD-2 Tech Transfer 1 - Phase 1

This training material was created to train CLT (Charlotte Douglas International Airport) ramp controllers on the ATD-2 (Airspace Technology Demonstration-2) system. It includes an overview of ATD-2 and the RTC/RMTC (Ramp Traffic Console/Ramp Manager Traffic Console). It discusses many interface details, data exchange and integration, metering modes, and scheduler advisories. It concludes with interactive exercises on all topics of the training course.

RTC/RMTC↗

NASA NTRSDate not supplied

Response of a Bifurcated Shock Train to Combustion Drive Force Dynamics

A bifurcated normal shock train in a Mach 2.2 constant area, rectangular duct is investigated in the combustion driven Air Force Research Lab RC-18 Sub-scale Direct Connect Supersonic Combustion Facility. High-speed shadowgraph images are captured simultaneously with high-speed wall static pressure measurements along the isolator and combustor walls. The establishment of the shock train following ignition is studied to better understand the flow pathways and fluid dynamical mechanisms that link combustor dynamics to the motions and stability of the isolator shock train. To that end, cross-spectral and wavelet analysis is leveraged to determine the spectral and temporal characteristics of upstream traveling disturbances on the isolator-combustor system in RC-18.

Shock Train↗

NASA NTRS2021

Methods for Evaluating the Effectiveness of Programs to Train Pilot Monitoring

This report provides a compendium of methods for evaluation of training programs. It addresses training programs for developing pilot monitoring skills and has a focus on monitoring flight path management. It presents the Sensemaking Model of Monitoring as a framework for organizing the targets of training, and thus also the types of change in performance useful for assessing program effectiveness. It provides guidance for designing program evaluations, which can be tailored to address the specific monitoring topics or content for an application to fleet or airline needs.

monitoring↗

NASA NTRS2020

Analysis of Pilot Monitoring Skills and a Review of Training Effectiveness

The commercial aviation industry world-wide has identified a need for improved pilot monitoring and awareness (e.g., FAA, 2013, ICAO, 2016). More specifically, aviation safety data indicate that failures in pilots’ flight path management (FPM) monitoring and awareness have contributed to a range of undesired outcomes: accidents, major upsets, and non-compliance with air traffic control (ATC) guidance. The Federal Aviation Administration (FAA) has further stated that these types of FPM failures are likely to worsen with the increasingly complex air traffic control systems and FPM concepts proposed for NextGen (https://www.faa. gov/nextgen/what_is_nextgen/) operations (e.g., see Hah et al., 2017). Adding to this complexity is the introduction of increasingly automated aircraft systems that can increase monitoring burdens. One potential mitigation for this situation is to enhance pilot training for effective monitoring. NASA Ames Research Center was asked to identify and evaluate training approaches that have the potential to enhance pilots’ ability to effectively monitor for FPM (with the result of improved awareness). The focus of this work is to identify, develop or validate training guidance to improve pilot monitoring/awareness regarding FPM and mitigate the recent trend of accidents and incidents, especially loss of control (LOC) events. The result of this work should be input for improved industry standards and FAA guidance to reduce the risk of incidents and accidents due to inadequate pilot monitoring/awareness. This is the first of three reports that were developed for this project.

aviation human factors↗

NASA NTRSDate not supplied

Artemis Geology Training Introduction to A3GT

Artemis Internal Science Training Team partners closely with the xEVA Trainers and Crew Training Office in FOD; together, all parties acknowledge the importance of integrated science and operations skill-building within training for surface operations.

Geology↗

NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗

NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗