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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Methods for Developing Successful Systems Engineers

Systems Engineering (SE) is a complex and challenging field that incorporates the knowledge of systems engineering processes, the ability to synthesize a wide-range of engineering disciplines, and the ability to lead a team of people to successfully accomplish the goals of a project. It requires hard technical skills and soft-skill leadership savvy. As a result, three main development needs are identified: 1) Knowledge of SE processes, the benefits of these processes to a project and their tailored application 2) Knowledge of a wide-range of engineering disciplines, how they interrelate in a system, and the development of sound technical judgement 3) Team leadership to direct and motivate a team of subsystem and discipline experts This paper describes the establishment of a comprehensive training and development program for Systems Engineers at NASA Ames Research Center that addresses in part each of these three areas from the perspective of the implementing manager. A variety of methods have been utilized including the establishment of a SE Community of Practice, a unique and innovative web tool, on-line videos, classroom training in NASA’s 17 Common Technical Processes, guidance on the tailored application of these processes, monthly technical talks, mentoring in both technical judgment and team leadership, and NASA’s Leadership Development Programs. While much of professional SE development must come through project experience, the approaches listed above can accelerate development. The diversity of skills required of Systems Engineering demands a multi-faceted approach to successfully train and develop this critical skill.

development↗
NASA NTRS2016

Flight Operations

Outline the process that takes new hires with zero knowledge all the way to the point of completing missions in Flight Operations. Audience members should be able to outline the attributes of a flight controller and instructor, outline the training flow for flight controllers and instructors, and identify how the flight controller and instructor attributes are necessary to ensure operational excellence in mission prep and execution. Identify how the simulation environment is used to develop crisis management, communication, teamwork, and leadership skills for SGT employees beyond what can be provided by classroom training.

Forest, Greg↗
NASA NTRSDate not supplied

Methods for Developing Successful Systems Engineers

Systems Engineering (SE) is a complex and challenging field that incorporates the knowledge of systems engineering processes, the ability to synthesize a wide-range of engineering disciplines, and the ability to lead a team of people to successfully accomplish the goals of a project. It requires hard technical skills and soft-skill leadership savvy. As a result, three main development needs are identified: 1. Knowledge of SE processes, the benefits of these processes to a project and their tailored application 2. Knowledge of a wide-range of engineering disciplines, how they interrelate in a system, and the development of sound technical judgement 3. Team leadership to direct and motivate a team of subsystem and discipline experts This paper describes the establishment of a comprehensive training and development program for Systems Engineers at NASA Ames Research Center that addresses in part each of these three areas from the perspective of the implementing manager. A variety of methods have been utilized including the establishment of a SE Community of Practice, a unique and innovative web tool, on-line videos, classroom training in NASA’s 17 Common Technical Processes, guidance on the tailored application of these processes, monthly technical talks, mentoring in both technical judgment and team leadership, and NASA’s Leadership Development Programs. While much of professional SE development must come through project experience, the approaches listed above can accelerate development. The diversity of skills required of Systems Engineering demands a multi-faceted approach to successfully train and develop this critical skill.

development↗
NASA NTRSDate not supplied

Exploring the Moon: Training Astronauts for Artemis Missions

For the first time in over 50 years, NASA scientists are training astronauts assigned to lunar missions. The successful Artemis 1 test flight in 2022 set the stage for future human missions to the Moon. The Artemis II mission will send astronauts to fly by the Moon, providing the first opportunity since Apollo for humans to directly observe and document the lunar surface from a few thousand kilometers, including the lunar far side. Artemis III will be the first human mission to land and explore the Moon’s surface near the lunar South Pole. To prepare for Artemis exploration, geology training for the Artemis II crew and detailed training plans for Artemis III and beyond are in full swing. NASA scientists have been working for several years with the Flight Operations Directorate (FOD) at the Johnson Space Center on a progressive geology training program that starts with incoming astronaut classes, offers opportunities to maintain the geology proficiency between flight assignments, and trains the broader engineering-operations-management community in geology and field operations. In the spring of 2023, our team developed an introductory week-long class called Lunar Fundamentals and delivered this content to the Artemis II crew, as well as key NASA Flight Control Team members. The Lunar Fundamentals curriculum combines basic geographic and geologic information about the Moon; includes detailed content on major geologic processes, the lunar environment, lunar volatiles, and sample science; provides historical context of our growing knowledge about the Moon; and introduces new lunar data sets and tools for exploring the lunar surface. With the Lunar Fundamentals foundation, we continue development of the advanced planetary science classroom content for the Artemis III crew, together with field training science objectives and plans. Partnering with our FOD colleagues, we are building an integrated training plan for lunar science, exploration field methods, and operational skills to ensure that the Artemis astronauts exploring the Moon will have the necessary foundation in geology and lunar science to achieve mission success.

Astronauts training↗
NASA NTRS2019

Workforce for the Future Development of Space Access Vehicles

The development of advanced vehicles that will travel between planetary surfaces with atmospheres and space requires the availability of experimental and computational capabilities that accomplish the research leading to new technologies and the development, test, and evaluation (RDT&E) of new flight systems. The United States is at risk of not having the required RDT&E workforce – qualified and in sufficient numbers – in place and ready to meet the future market’s commercial and defense needs. Systemic challenges include uncertain Federal budgets that limit and interrupt government research and development, the projectized nature of new space access systems that drive boom/bust cycles, an aging aerospace workforce (compounded by a mid-age demographic gap), limited to declining investment in sustaining and advancing experimental and computational tools infrastructure, lack of standards for sharing (and leveraging) data, dramatically changing technologies, changing social norms, and potentially large increases in commercial market needs. Flight systems have mission-based trajectories to and from space and designers must ensure risk is properly assessed across the entire trajectory. Thus, physics questions must be answered at each stage of flight, which requires a suite of experimental and computational tools. The RDT&E workforce utilizing these tools include subject matter experts from the producers, interested in acquiring data and information on the product, and from the capabilities being utilized, interested in addressing the product customer’s needs (providing robust data collection techniques, data quality, timeliness – available when needed, efficient with cost management, and teaming on data analysis). This requires a range of skills – in addition to aerospace, other engineers, and software developers, a highly trained and certified craft and technician workforce is critical to future success. This paper will present a human resources construct that addresses the system of needs for people – including, but more than just technical skills and an application of that construct in the hypersonic Test and Evaluation (T&E) community. This is part of a larger AIAA effort to document challenges and associated best practices for the aerospace RDT&E workforce.

Steven C Dunn↗
NASA NTRSDate not supplied

Autonomous Medical Officer Support (AMOS) ISS Technology Demonstration: Enabling Earth-Independnt Procedure Guidance

Successful performance of medical procedures during missions beyond low Earth orbit requires novel solutions to replace real-time support from the ground since the communication delay will be longer as the crew travels farther from Earth. The Autonomous Medical Officer Support software Technology Demonstration project (AMOS Tech Demo) on the International Space Station (ISS) evaluates a novel software tool that shifts the emphasis from preflight training and real-time guidance (current ISS paradigm) to in-flight just-in-time (JIT) instruction (a new paradigm for crew medical autonomy). The AMOS platform introduces a novel, streamlined, skill-management archetype for exploration missions; the current AMOS version features comprehensive training and guidance modules for urinary bladder and kidney ultrasound examinations. The primary goal of the AMOS Tech Demo was to confirm telemedical proof-of-concept through initial use of the platform for autonomous imaging activities in an operational setting. Success metrics encompassed 1) successful software deployment, 2) collection of click tracking data, and 3) recording of ultrasound images. Additional aims included collecting crew feedback on a) AMOS implementation, and b) integrated training and procedure support concepts, as well as c) evaluation of AMOS use in an operational setting.

D. Ebert↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
DOE OSTI2023

AMES: MS and MENG in Advanced Manufacturing for Energy at the University of Connecticut (Final Technical Report)

The objective of the project is to develop and implement an advanced degree program (MS and MENG in Advanced Manufacturing for Energy Systems, AMES) responding to the long-term workforce and technology requirements of the nation’s advanced energy products manufacturing industry. The program provided an industry relevant research experience by leveraging existing energy (e.g. fuels, power electronics, electrochemical power sources) and advanced manufacturing (e.g. additive manufacturing, composites, sensing) research at UConn funded by federal and state agencies, and industry, as well as through our industry partnerships. The trainees have joined research teams, advised by faculty with relevant research interests and expertise and were co-advised by industrial mentors. The AMES program have developed a truly interdisciplinary curriculum, first graduate degree program at UConn School (now College) of Engineering not housed in an academic department, with concentrations focusing on various challenges in advanced manufacturing for energy systems, e.g. advanced materials and processing. The project also developed new courses focusing on common technical and professional skills, and integrated various components for an industry relevant training. The program has admitted 29 Master of Science (MS) students since inception in January 2019. Twenty six of these students were AMES fellows, who have received partial funding from DoE through this project. The program far exceeded the goal of admitting at least five new MS (with thesis) students. All students were required to complete a thesis (M.S.) or a capstone (M.Eng.) project that are defined in collaboration with industry partners to ensure industrial relevancy, addressing a current industrial challenges. Industrial mentors also participated in advising the students in their research. AMES fellows, in addition were also required to complete an industrial internship for further industrial experience.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Lagged low order control systems with powered controls

With powered controls, a high order control system can be changed to a lower order control system with a lag, which is better suited to our position control skills and strategies. Separate groups of untrained men were trained on an acceleration control system, or on a rate control system with an exponential lag of either .5, 2, or 4 sec. Acquisition time was reliably shorter with all the lagged rate control systems than with the unlagged acceleration control system.

Poulton, E. C.↗
NASA NTRS1981

Using rewards and penalties to obtain desired subject performance

Operant conditioning procedures, specifically the use of negative reinforcement, in achieving stable learning behavior is described. The critical tracking test (CTT) a method of detecting human operator impairment was tested. A pass level is set for each subject, based on that subject's asymptotic skill level while sober. It is critical that complete training take place before the individualized pass level is set in order that the impairment can be detected. The results provide a more general basis for the application of reward/penalty structures in manual control research.

Cook, M.↗
NASA NTRS1987

Attention theory and training research

This study used elements of attention theory as a methodological basis to decompose a complex training task in order to improve training efficiency. The complex task was a microcomputer flight simulation where subjects were required to control the stability of their own helicopter while acquiring and engaging enemy helicopers in a threat enviroment. Subjects were divided into whole-task, part-task, and part/open loop adaptive task groups in a transfer of training paradigm. The effect of reducing mental workload at the early stages of learning was examined with respect to the degree that subordinate elements of the complex task could be automated through practice of consistent, learnable stimulus-response relationships. Results revealed trends suggesting the benefit of isolating consistently mapped sub-tasks for part-task training and the presence of a time-sharing skill over and above the skill required for the separate subtasks.

Connelly, James G., Jr.↗
NASA NTRS1988

Issues in development, evaluation, and use of the NASA Preflight Adaptation Trainer (PAT)

The Preflight Adaptation Trainer (PAT) is intended to reduce or alleviate space adaptation syndrome by providing opportunities for portions of that adaptation to occur under normal gravity conditions prior to space flight. Since the adaptation aspects of the PAT objectives involve modification not only of the behavior of the trainee, but also of sensiomotor skills which underly the behavioral generation, the defining of training objectives of the PAT utilizes four mechanisms: familiarization, demonstration, training and adaptation. These mechanisms serve as structural reference points for evaluation, drive the content and organization of the training procedures, and help to define the roles of the PAT instructors and operators. It was determined that three psychomotor properties are most critical for PAT evaluation: reliability; sensitivity; and relevance. It is cause for concern that the number of measures available to examine PAT effects exceed those that can be properly studied with the available sample sizes; special attention will be required in selection of the candidate measure set. The issues in PAT use and application within a training system context are addressed through linking the three training related mechanisms of familiarization, demonstration and training to the fourth mechanism, adaptation.

Lane, Norman E.↗
NASA NTRS2015

Science Training History of the Apollo Astronauts

Following President Kennedy’s initiation of Project Apollo, NASA underwent substantial changes in personnel, organization, and programs and faced a major question: what to do on the Moon after landing. Once a decision that science activities, particularly geoscience, should be pursued, considerable debate ensued over how to accomplish this. Questions arose over instruments and tools required, samples and photos to be returned, landing site selection, and crew composition. Answers to these questions required major efforts for planning traverses on the Moon and training the astronauts in the extensive procedures necessary in low gravity to use tools, set up instruments, take adequate photos, collect and document samples, and provide proper descriptions. In addition to astronauts on the surface, an astronaut in lunar orbit managed additional instruments, photography and verbal descriptions. Training for these activities averaged nearly one hundred hours per month for over a year for each crew. There were many problems as the training progressed: adjusting groups and backgrounds of the training personnel for the best combination of personalities and skills, overcoming logistical troubles, revising awkward procedures, determining optimum means of communications between all involved groups, and devising contingency procedures for real-time problems. By the last mission these problems were overcome.

Phinney, William C.↗
NASA NTRSDate not supplied

Development of an Inertial Sensor-based Methodology for Spacesuited Geology Task Assessments during Simulated Lunar Extravehicular Activities

Lunar surface exploration during Artemis missions will require the specific skill set of geology sampling. Apollo astronauts had extensive training and used specialized tools to collect lunar rocks, core samples, pebbles, sand, and dust. The inflexibility of the pressurized Apollo spacesuits forced sampling to be taken at a standstill posture. However, new exploration spacesuits are expected to incorporate advanced materials and joint bearings, allowing for greater mobility and a wider range of functional postures. Thus, science and exploration during Artemis missions will likely involve a variety of standing, squatting, and kneeling postures. In preparation for future lunar exploration missions, NASA provides geologic training to astronauts and other mission personnel. This professional training with a spacesuit in simulated lunar environments will enhance performance and reduce risk of injury to astronauts on the lunar surface. However, anecdotally, untrained or newly trained people wearing prototype planetary spacesuits have been observed to performing motions differently than a trained geologist would when conducting the same geology sampling tasks. Therefore, a tool for evaluating geology postures at extravehicular activity (EVA) training facilities becomes required. In this paper, we introduce a novel inertial measurement unit (IMU)-based method of geology task assessments in spacesuited conditions during simulated lunar EVAs. As a case study, two subjects (one geologist and one non-geologist) participated and donned the Mark III prototype planetary spacesuit during offloading with the spreader bar gimbal in NASA’s Active Response Gravity Offload System (ARGOS). For automated geology task assessments, the spacesuit was instrumented with three wireless IMUs (APDM Opal, OR, USA): one on the chest and one each on the left and right ankle bearings. Then subjects performed geology tasks using various tools (rake, trench, hammer chisel, scoop, and drive tube) for 45 minutes each. The chest IMU measured the torso tilt angle in the sagittal plane. We used an ensemble learning method with the ankle IMUs to discriminate between standing and kneeling activities. IMU data were processed using custom MATLAB (Mathworks, MA, USA) software. In our case study, the developed method was able to discriminate differences in standing and kneeling activity levels between subjects who were all highly experienced with spacesuited testing. Our preliminary data showed one subject maintained the constant and lower range of the upper body tilt angle while both standing and kneeling, while the other subject showed more variation of the upper body tilt angle and preferred bending the upper body rather than changing from standing to kneeling posture and vice versa. While geology experience may be a factor, these results need further investigation as suit sizing and ARGOS offloading configurations have been proven to have a significant influence on suited ARGOS tasks. Also, more subjects will be needed to complete these tasks for validation. IMU-based geology task assessments can provide useful information for geology training programs. Additionally, our IMU-based posture analysis can provide new insights into how to evaluate spacesuited geology task characteristics of astronauts during simulated lunar EVAs.

Kyoung Jae Kim↗
DOE OSTI2023

Installer Badges Toolkit and Visualization Resources

NREL staff (Jal Desai and Cory Chovanec) will introduce the U.S. Department of Energy Weatherization Assistance Program (DOE WAP) Installer Badges Toolkit. The presentation will cover topics such as how this toolkit can be customized and implemented in a program, the potential benefits of using the Badges, and lessons learned from other implementers. This session will also engage participants to share their experiences regarding their existing training and how they are bringing their workforce up to speed in their building envelope-related programs. The Installer Badges Toolkit consists of 25 Badges, each representing different energy efficiency tasks that an installer could perform on a home. Each Badge defines the desired outcome, criteria to verify the work, applicable material requirements, and references to DOE's Standard Work Specifications (SWS) or other relevant standards. Workers earn Badges by completing each task and receiving approval from a qualified supervisor. The Badges provide a consistent approach to training by ensuring that installers are learning the same skills nationwide. Organizations can also customize the Toolkit by choosing only those Badges that are relevant to their program. NREL staff will also showcase new visualization resources like the Single-family and manufactured home job aids and the Single-family 3D house tool.

ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1995

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 46: Technical communications in aerospace: A comparison across four countries

In this paper we describe the preliminary analysis of four groups of aerospace engineering and science students -- student members of the American Institute of Aeronautics and Astronautics (AIAA) and students from universities in Japan, Russia, and Great Britain. We compare: (1) the demographic characteristics of the students; (2) factors that affected their career decision; (3) their career goals and aspirations; (4) their training in technical communication; and (5) their training in techniques for finding and using aerospace scientific and technical information (STI). Many employers in the US aerospace industry think there is a need for increased training of engineering students in technical communication. Engineers in the US and other countries believe that technical communication skills are critical for engineers' professional success. All students in our study agree about the importance of technical communication training for professional success, yet relatively few are happy with the instruction they receive. Overall, we conclude that additional instruction in technical communication and accessing STI would make it easier for students to achieve their career goals.

Kennedy, John M.↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Group Capability Model

The Group Capability Model (GCM) is a software tool that allows an organization, from first line management to senior executive, to monitor and track the health (capability) of various groups in performing their contractual obligations. GCM calculates a Group Capability Index (GCI) by comparing actual head counts, certifications, and/or skills within a group. The model can also be used to simulate the effects of employee usage, training, and attrition on the GCI. A universal tool and common method was required due to the high risk of losing skills necessary to complete the Space Shuttle Program and meet the needs of the Constellation Program. During this transition from one space vehicle to another, the uncertainty among the critical skilled workforce is high and attrition has the potential to be unmanageable. GCM allows managers to establish requirements for their group in the form of head counts, certification requirements, or skills requirements. GCM then calculates a Group Capability Index (GCI), where a score of 1 indicates that the group is at the appropriate level; anything less than 1 indicates a potential for improvement. This shows the health of a group, both currently and over time. GCM accepts as input head count, certification needs, critical needs, competency needs, and competency critical needs. In addition, team members are categorized by years of experience, percentage of contribution, ex-members and their skills, availability, function, and in-work requirements. Outputs are several reports, including actual vs. required head count, actual vs. required certificates, CGI change over time (by month), and more. The program stores historical data for summary and historical reporting, which is done via an Excel spreadsheet that is color-coded to show health statistics at a glance. GCM has provided the Shuttle Ground Processing team with a quantifiable, repeatable approach to assessing and managing the skills in their organization. They now have a common frame of reference across NASA/contractor lines to communicate and mitigate any critical skills concerns.

Olejarski, Michael↗