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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

DEVELOP’s Approach to Experiential Learning

The NASA DEVELOP Program addresses environmental decision making needs and geoscience workforce development through 10-week feasibility studies that apply Earth observations to environmental issues at hand. The program builds capacity to use geospatial information in both its participants (students, recent graduates, early career professionals, and transitioning career professionals) and partner organizations (federal agencies, state & local governments, non-profits, and private industry). This is accomplished through a structured project execution model that provides opportunities for participants to have autonomy, learn “on the job,” and gain new skillsets for working with remote sensing data. A pipeline of leadership positions enhances opportunities for individuals engaged in the program to get hands-on experience conducting data analyses, communicating their work, leading technical projects, and building their knowledge bank of Earth-observing satellite capabilities. These skillsets and knowledge are then transferred to partners through the projects. This panel contribution will introduce the DEVELOP model, highlight experiences of participants, and share the program’s insights into good practices for effective experiential learning.

Capacity Building↗
NASA NTRS2017

Training Early Career Scientists in Flight Instrument Design Through Experiential Learning: NASA Goddard's Planetary Science Winter School.

The NASA Goddard Planetary Science Winter School (PSWS) is a Goddard Space Flight Center-sponsored training program, managed by Goddard's Solar System Exploration Division (SSED), for Goddard-based postdoctoral fellows and early career planetary scientists. Currently in its third year, the PSWS is an experiential training program for scientists interested in participating on future planetary science instrument teams. Inspired by the NASA Planetary Science Summer School, Goddard's PSWS is unique in that participants learn the flight instrument lifecycle by designing a planetary flight instrument under actual consideration by Goddard for proposal and development. They work alongside the instrument Principal Investigator (PI) and engineers in Goddard's Instrument Design Laboratory (IDL; idc.nasa.gov), to develop a science traceability matrix and design the instrument, culminating in a conceptual design and presentation to the PI, the IDL team and Goddard management. By shadowing and working alongside IDL discipline engineers, participants experience firsthand the science and cost constraints, trade-offs, and teamwork that are required for optimal instrument design. Each PSWS is collaboratively designed with representatives from SSED, IDL, and the instrument PI, to ensure value added for all stakeholders. The pilot PSWS was held in early 2015, with a second implementation in early 2016. Feedback from past participants was used to design the 2017 PSWS, which is underway as of the writing of this abstract.

Flight↗
DOE OSTI2022

Workforce Development Bioenergy Experiential Learning Tool (Final Report)

The "Pathways to Bio-Power" program was designed as a workforce development initiative aimed at addressing the workforce gap in the bioenergy sector while promoting diversity and inclusion. The program incorporated advanced technologies, such as artificial intelligence (AI) and learning algorithms, to provide individualized training plans and pathways for Minority Business Enterprises (MBEs) and the current workforce to enter the bioenergy industry. However, after careful consideration, it was determined that proceeding to Phase II was not feasible due to several reasons related to resource allocation, market demand, and technical challenges.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Experiential Learning

The DEVELOP Program, part of NASA’s Applied Sciences’ Capacity Building Program, connects decision makers and early career individuals through rapid 10-week feasibility studies. These projects utilize Earth observation assets to address environmental issues and are conducted by small, interdisciplinary teams who work autonomously under the guidance of a cohort of science advisors. The teams tailor their research to create end products that support partner decision making processes. The connection to real-world decision making and direct communication with partner organizations drives meaningful experiences for DEVELOP participants that are directly applicable to future work in the geosciences. This poster will share DEVELOP’s model for offering meaningful experiences to its participants, address lessons learned over the past 20+ years, and highlight a variety of testimonials.

Capacity Building↗
NASA NTRS2023

Designing Learning Experiences With A Low-Cost Robotic Arm

Robots have gained immense popularity in Hollywood and growth globally in both industrial manufacturing and non-manufacturing environments and applications such as healthcare, service sector, and space exploration. To date, there are several examples of simple and low-cost educational robotic platforms and commercially available platforms (e.g., Lego Mindstorms, VEX Robotics) for incorporation into existing curriculum. However, most low-cost examples require access to rapid-prototyping tools, such as 3D printers to manufacture the structure of the robot, while commercially available platforms are relatively expensive (> $1,000). Although the low-cost, open-source examples provide increased access, these examples require design and manufacturing tasks that would be considered outside the learning objectives of an upper-level robotics course and are better suited for other introductory courses. Thus, we asked, how can a robotic platform be incorporated into existing robotics curriculum to enhance students' learning experiences? To explore this research question, we introduce a low-cost (<$200), untethered, and transportable robotic platform that is easy to assemble using off-the-shelf components. This kit can be powered from a laptop computer and does not rely on access to rapid-prototyping tools such as 3D printers or laser cutters, making this a more accessible option in undergraduate engineering courses. Specifically, we aimed to investigate the design of experiential learning experiences for the mathematical modeling of the forward and inverse kinematics of a serial robotic arm that complements existing robotics curriculum. The experiential learning experience focuses on traditional written answer, simulation in MATLAB, and finally implementation on a robotic platform. Few studies examined this accessible option when evaluating experiential learning experiences that complement existing robotics curricula. To assess the impact of this robotic arm kit in an undergraduate course, we implemented an educational intervention that allowed us insight into student perceptions, takeaways on the course and activities involving the robotic arm, and the impact of the course on their career outlook when comparing activities that involved use of the robotic arm and those that did not. Details on the design, development and implementation of the learning activities is provided. Both quantitative and qualitative data were collected and analyzed. The results presented in this paper discuss students finding the learning activities on the robotic arms more helpful than those without, and that students found high value in the hands-on experiences and real-world scenarios offered by the activities using the robotic arm. Challenges to implementation of the robotic arms are discussed, including students’ prior knowledge of using robotic arms.

Eric J. Markvicka↗
NASA NTRSDate not supplied

Benchmark Problem Development for Testing Maturity of Intelligent Contingency Management Tools

This paper presents a process used to develop appropriate scenarios and metrics for evaluating the maturity of intelligent contingency management algorithms. A benchmark scenario is a reference point against which something can be measured, compared, or assessed. Creating an accurate benchmark requires considerable research and expertise. The scenario itself is an artificial representation of a real-world event, designed to achieve a set of learning objectives through experiential learning. Designing an effective benchmark simulation scenario requires careful planning, including identification of clear objectives; capability assessment of the algorithm/tool being evaluated; assessment of necessary levels of fidelity; development of a process flow map of events and event interactions; and identification of metrics that map back to objectives. Thus, a benchmark scenarios for contingency management might consist of one or several commonly used functions taken from real world applications, used for evaluation, characterization and performance measurement of a contingency management algorithm. Behavior of the contingency management algorithm under different environmental conditions should then be able to be predicted using a set of benchmark functions. The paper describes the resulting benchmark problem as an illustration of the application of this process.

Jon Holbrook↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS2015

NASA Space Science Day Events-Engaging Students in Science

The NASA Space Science Day Event follows the same format of planning and execution at all host universities and colleges. These institutions realized the importance of such an event and sought funding to continue hosting NSSD events. In 2014, NASA Johnson Space Center ARES team has supported the following universities and colleges that have hosted a NSSD event; the University of Texas at Brownsville, San Jacinto College, Georgia Tech University and Huston-Tillotson University. Other universities and colleges are continuing to conduct their own NSSD events. NASA Space Science Day Events are supported through continued funding through NASA Discovery Program. Community Night begins with a NASA speaker and Astromaterials display. The entire community surrounding the host university or college is invited to the Community Night. This year at the Huston-Tillotson (HTU) NSSD, we had Dr. Laurie Carrillo, a NASA Engineer, speak to the public and students. She answered questions, shared her experiences and career path. The speaker sets a tone of adventure and discovery for the NSSD event. After the speaker, the public is able to view Lunar and Meteorite samples and ask questions from the ARES team. The students and teachers from nearby schools attended the NSSD Event the following day. Students are able to see the university or college campus and the university or college mentors are available for questions. Students rotate through hour long Science Technology Engineering and Mathematics (STEM) sessions and a display area. These activities are from the Discovery Program activities that tie in directly with k- 12 instruction. The sessions highlight the STEM in exploration and discovery. The Lunar and Meteorite display is again available for students to view and ask questions. In the display area, there are also other interactive displays. Angela Green, from San Jacinto College, brought the Starlab for students to watch a planetarium exhibit for the NSSD at Huston-Tillotson University. Many HTU mentors were leading activities in the display room such as build a comet, volcano layering and robotics manipulation. Students were exposed to a variety STEM career possibilities and information. The students could relate the displays and sessions to what they were learning in school. The HTU mentors made the connection clear for the students. The students ended the event with a mission design presentation. They were able to take what they had learned during the day and were able to create a mission. Students presented their Mission Design and gained confidence in STEM. Conclusion: NASA Space Science Day Events provides an out of school experiential learning environment for students to enhance their STEM curriculum and let students see a college campus. The experiences students gain from attending NSSD gives them the confidence to see themselves on a college campus, possibly majoring in a STEM degree, and understand the importance of completing school.

Foxworth, S.↗
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2022

Artificial Intelligence for Power Electronics in Electric Vehicles: Challenges and Opportunities

We report progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Operation and Maintenance of PV Systems: Data Science, Analysis, and Standards

This effort improves the effectiveness and reduce uncertainty in O&M cost through four primary objectives/tasks: 1) institutionalize standards for reliability and availability reporting for large PV power plants; 2) bridge systemic O&M knowledge gaps around important topics affecting O&M; 3) characterize systemic failure modes and patterns and accelerate O&M experiential learning cycles using field data; and 4) establish a baseline understanding of UPVS O&M cost drivers. Key results of this effort include publication of IEC standards, published topical papers on O&M topics, training, and characterize field data for climate- and service-related patterns (additional details below). Integrating these results serves to reduce performance risk and facilitate improvement in the way solar projects are operated and maintained. Results are well received and two publications are among the most successful SETO publications at NREL ("Model of Operation and Maintenance Costs for Photovoltaic Systems with over 40,000 downloads and "Best Practices in Operation and Maintenance of PV Systems, 3rd Ed." with over 90,000 downloads).

14 SOLAR ENERGY↗
NASA NTRS2010

Modeling the Effects of Stress: An Approach to Training

Stress is an integral element of the operational conditions experienced by combat medics. The effects of stress can compromise the performance of combat medics who must reach and treat their comrades under often threatening circumstances. Examples of these effects include tunnel vision, loss of motor control, and diminished hearing, which can result in an inability to perceive further danger, satisfactorily treat the casualty, and communicate with others. While many training programs strive to recreate this stress to aid in the experiential learning process, stress inducement may not always be feasible or desired. In addition, live simulations are not always a practical, convenient, and repeatable method of training. Instead, presenting situational training on a personal computer is proposed as an effective training platform in which the effects of stress can be addressed in a different way. We explore the cognitive and motor effects of stress, as well as the benefits of training for mitigating these effects in real life. While many training applications focus on inducing stress in order to "condition" the stress response, the author explores the possibilities of modeling stress to produce a similar effect. Can presenting modeled effects of stress help prepare or inoculate soldiers for stressful situations in which they must perform at a high level? This paper investigates feasibility of modeling stress and describes the preliminary design considerations of a combat medic training system that utilizes this method of battlefield preparation.

Cuper, Taryn↗
NASA NTRSDate not supplied

Advancing STEM in Bhutan through Increased Earth Observation Capacity: Applying NASA DEVELOP’s Model to Bhutanese Scholars

The NASA DEVELOP Program, a dual capacity building effort within NASA’s Applied Sciences, fosters the development of skills and knowledge to use Earth observations for environmental decision making. The program engages students, recent graduates, early career individuals, and transitioning career professionals through 10-week feasibility studies that assess the potential for integrating NASA Earth observing satellite data into partner organization decision making processes. DEVELOP projects provide an experiential learning opportunity for participants while building the institutional capacity of partner organizations. Since 2020, DEVELOP has worked with the Government of Bhutan to engage 30 Bhutanese scholars on a series of projects focused on environmental issues in Bhutan. These projects have offered an opportunity for Bhutanese scholars studying in the US to gain geospatial knowledge and skills, project management and leadership skills, and science communication experience. This presentation will highlight the DEVELOP model of empowering its participants, collaborative projects, and highlight example outcomes of the Bhutan engagement.

Capacity Building↗
NASA NTRSDate not supplied

Expressing Functionality Using Design Patterns, A Real-World Example

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting tacit knowledge from existing systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. This study enables such a technique by demonstrating the use of architectural theory in expressing tacit knowledge from existing systems and presenting that knowledge according to the model-based view offered by the Functional Architecture of Systems methodology. By validating the functional information expressed by the design pattern, this work solidifies design patterns as a critical component of a technical architecture and proposes a functional architecture viewpoint for describing mined knowledge in a usable, model-based, way suitable for archival in a modern knowledge management system.

Systems Engineering↗
NASA NTRSDate not supplied

The Emphasis of Design Patterns in Expressing Expert Knowledge From A Technical Solution – A Framework for Continued Research

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting expert knowledge from proven systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. Motivated by performance gains observed during a multi-year, sequential development activity, this survey reviews knowledge, architecture, and pattern literature to establish a framework for research of architectural methods for expert knowledge identification and description using Model Based System Engineering. The multi-year sequential development activity is offered as the experimental system of interest for this research. This work aims to enable a digital engineering strategy that improves concept phase decision making, accelerates knowledge acquisition from lessons learned repositories, and eases the burden of generational knowledge loss.

Lithium Ion↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
DOE OSTI2025

Development of a New Criticality Safety Training Program for College Students

Nuclear criticality safety (NCS) expertise remains a crucial workforce need within the US Department of Energy (DOE) laboratory complex. To address this challenge, a novel university/laboratory-based nuclear criticality training certificate program is being developed through a collaborative effort between the Georgia Institute of Technology, Texas A&M University, and Oak Ridge National Laboratory. This comprehensive program implements a two-tiered certification approach that combines online theoretical coursework with hands-on experimental training to create a sustainable pipeline of nuclear criticality specialists. The program specifically targets undergraduate and graduate students in engineering, physics, and mathematics disciplines across the United States. Through integration of fundamental nuclear physics principles, practical safety applications, and experiential learning opportunities, this initiative aims to establish a standardized pathway for developing the next generation of NCS professionals.

K-Effective↗