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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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NASA NTRS2020

Lessons Learned from Sonic Boom Flight Research Projects conducted by NASA Armstrong Flight Research Center

Over the course of four years, a team of aerospace engineers at the National Aeronautics and Space Administration Armstrong Flight Research Center completed four projects, each with the objective to research sonic boom signatures from a ground and building level perspective. The relatively compressed timeline of these projects resulted in the team amassing a large number of lessons learned. With each successive project, these lessons have been more relied upon and referenced. This report serves as a written record of the teams tribal knowledge capturing the relevant lessons learned and their importance for future projects.

Erin R Waggoner↗
NASA NTRS2018

Nguyen Xuan Vinh - A Life In Hypersonic Flight

Nguyễn Xuân Vinh (born January 1930 in Yên Bái, Vietnam) is a noted Vietnamese-American aerospace scientist and educator whose seminal work on the guidance, dynamics and optimal control of space vehicles and their interaction with the atmosphere has played a fundamental role in space exploration. Vinh is Professor Emeritus of Aerospace Engineering at the University of Michigan, where he taught for nearly thirty years. Among his many publications was "Hypersonic and Planetary Entry Flight Mechanics” (1980. Vinh, N. X.; Busemann, A.; Culp, R. D. University of Michigan Press) which contains equations for hypersonic flight that came to be known as “the Vinh equations”.

Lu, Ping↗
NASA NTRSDate not supplied

AAM NC ATI TechTalk - Aerograph Architecture v1

Aerograph is NASA’s data management system for Advanced Air Mobility. Its mission is to support AAM research by providing a reliable and secure system that collects, stores, protects, and shares AAM data. Its vision is to provide a system that AAM research scientists, aerospace engineers, data scientists, and analysts trust for obtaining NC data and performing key analyses. The types of data Aerograph manages involves data related to flight test events, including: Aircraft Performance and Characterization (e.g., position reports) Airspace (e.g., operation intent, waypoints, and constraints) Environment (e.g., surface and wind weather) Infrastructure (e.g., surveillance coverage) Derivative Analytical Artifacts (e.g., glide path performance chart, 3D position chart, Integrated Data Product)

Aerograph↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRS2021

Role of Cyber-Physical Testing in Developing Resilient Extraterrestrial Habitats

Extraterrestrial long-term habitat systems (henceforth referred to as habitat systems) require groundbreaking technological advances to overcome the extreme demands introduced by isolation and challenging environments. A habitat system must operate as intended under continuous disruptive conditions. Designing for the demands that challenging environments will place on habitat systems (e.g., wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts, vibrations, and solar particle events) represents one of the greatest challenges in this endeavor. This engineering problem necessitates that we design and manage habitat systems to be resilient. System resilience requires a comprehensive approach that accounts for disruptions through the design process and adapts to them in operation. As the habitat system evolves—growing in physical size, complexity, population, and connectivity—and diversifies in operations, it must continue to be safe and resilient. In this endeavor, we should take advantage of lessons learned in developing civil infrastructure responsive to catastrophic natural hazards, autonomous robotics platforms, smart buildings, cyber-physical testing, complex systems, and diagnostics and prognostics for intelligent health management. This study highlights the importance of system resilience and cyber-physical testing to address the grand challenge of developing habitat systems.

Public health and safety↗
NASA NTRSDate not supplied

Overview of the Research Aircraft for eVTOL Enabling techNologies (RAVEN) Activity

The Research Aircraft for eVTOL Enabling techNologies (RAVEN) activity is a collaboration between Georgia Tech and NASA to design and develop a 1,000 lb gross weight class eVTOL research aircraft. The vision for RAVEN is that the aircraft will serve as a “flying laboratory” for enduring research and technology development applications across the realm of eVTOL technologies. A major goal of RAVEN is to disseminate the aircraft design geometry and data from flight tests for the benefit of the broader aeronautics community. Initial research applications will include flight dynamics, controls, acoustics, and automation/autonomy. The aircraft is based on the airframe of a fixed-wing experimental homebuilt airplane that will be modified to incorporate a distributed propulsion system, battery system, fly-by-wire flight control system, and avionics to enable remotely piloted operation. The aircraft is being designed to use commercial off-the-shelf components to the maximum extent practicable to save costs and to accelerate the development schedule without compromising the goal of publishing design geometry and test data. The RAVEN activity is also focused on workforce development by training the next generation of aerospace engineers in eVTOL technologies.

eVTOL↗
NASA NTRS2023

Vertical Habitability Layout Studies and Neutral Buoyancy/Parabolic Flight Habitat Studies

In the Summer of 2013, the University of Maryland was selected for two projects under the NASA X-Hab 2014 program, administered by the National Space Grant Foundation: Vertical Habitability Layout Studies (XHab201406) and Neutral Buoyancy/Parabolic Flight Habitat Studies (XHab201407). This document, at the direction of the NASA Technical Monitor, comprises the final report for both of these contracts. Recognizing from the outset the mismatch between the desired scope of research activities under the contracts and the severely limited funding and duration, the University of Maryland leveraged the X-Hab support by integrating the programs into a number of academic classes throughout the 2013-2014 academic year. By far the most significant interaction was with the Department of Aerospace Engineering Senior capstone experience in spacecraft design sequence, ENAE 483/484. Throughout the academic year, 42 students in this sequence worked on both projects in conjunction with their senior project to design an artificial gravity research station in a distant lunar retrograde orbit. As part of this research project, the students worked with previously created 1-G habitats and underwater simulations to better understand habitat design from microgravity to full Earth gravity, as well as at lunar and Mars gravity levels between those two endpoints.

David L. Akin↗
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↗
NASA NTRSDate not supplied

An Overview of Plume-Surface Interaction Testing and Research

NASA has studied rocket plume-surface interactions caused by spacecraft since the Apollo era to reduce risk to landing vehicles. The study of plume-surface interactions is a multi-discipline effort, spanning aerospace engineering to planetary science. Historical testing for Apollo and Viking produced methods, terms, and datasets that influence the field today. Research conducted over the past two decades has expanded our understanding of plume-surface interaction phenomena, but the problem remains resistant to a robust predictive capability. Contemporary experimental and modeling efforts to understand and retire strategic knowledge gaps will be discussed, with a focus on work conducted under NASA’s Plume-Surface Interaction Project and the upcoming Human Landing System Plume-Surface Interaction Risk Reduction Ground Test.

Wesley A Chambers↗
NASA NTRSDate not supplied

Experimental Calibration of Flow Rule Coefficients for LS-Dyna Mat_213

Composite materials have become a popular choice in aerospace engineering applications due to their low weight, high specific stiffness, high specific energy absorption, and economic synthesis. As use of composite materials has proliferated, the need for accurate simulation under extreme loading conditions is of increasing importance. *MAT_213 is a tabulated plasticity and damage material model in LS-DYNA designed for such use cases. To account for the nonlinear material response, a plasticity algorithm is used, which employs a non-associative flow rule with coefficients requiring experimental calibration. This work outlines an experimental methodology for calibrating these coefficients using the macroscopic stress-strain response and microscopic in-plane strain measurements from coupon scale experiments. The model material used for the development and demonstration of this methodology is a plain weave woven fabric with T300 carbon fiber tows in the warp direction and Kevlar© 49 fiber tows in the weft direction, and an Epon© 828/Epikure© 3223 thermoset epoxy matrix. Coupon-scale tensile testing performed at multiple fiber angles (carbon fibers oriented 0º, 22.5º, 45º, 67.5º, and 90º relative to the loading axis) provides data for multiple calibration processes. The measured plastic strain increments in the principal material coordinate are used to set limits for ratios between flow rule coefficients. The identified ranges are used to bound the search for a set of coefficients, leading to convergence of different coupon responses in the effective stress – effective plastic strain space. Flow rule coefficients found by this methodology are then used as (i) input for LS-DYNA simulations of uniaxial tension coupon tests at varying fiber angles, and (ii) comparison of predicted values for the plastic Poisson’s ratio in global coordinates with the experimentally measured one for all off-axis orientations. These results are quantitatively and qualitatively compared to macroscopic stress-strain curves and experimental strain components. The conformity of the experimentally measured quantities with those derived from the simulations provide a means for assessing the quality of calibrated coefficients.

Polymer Matrix Composites↗
NASA NTRS2001

Advanced Engineering Environments: Implications for Aerospace Manufacturing

There are significant challenges facing today's aerospace industry. Global competition, more complex products, geographically-distributed design teams, demands for lower cost, higher reliability and safer vehicles, and the need to incorporate the latest technologies quicker all face the developer of aerospace systems. New information technologies offer promising opportunities to develop advanced engineering environments (AEEs) to meet these challenges. Significant advances in the state-of-the-art of aerospace engineering practice are envisioned in the areas of engineering design and analytical tools, cost and risk tools, collaborative engineering, and high-fidelity simulations early in the development cycle. These advances will enable modeling and simulation of manufacturing methods, which will in turn allow manufacturing considerations to be included much earlier in the system development cycle. Significant cost savings, increased quality, and decreased manufacturing cycle time are expected to result. This paper will give an overview of the NASA's Intelligent Synthesis Environment, the agency initiative to develop an AEE, with a focus on the anticipated benefits in aerospace manufacturing.

Thomas, D.↗
NASA NTRS2012

A Model-Based Approach to Engineering Behavior of Complex Aerospace Systems

One of the most challenging yet poorly defined aspects of engineering a complex aerospace system is behavior engineering, including definition, specification, design, implementation, and verification and validation of the system's behaviors. This is especially true for behaviors of highly autonomous and intelligent systems. Behavior engineering is more of an art than a science. As a process it is generally ad-hoc, poorly specified, and inconsistently applied from one project to the next. It uses largely informal representations, and results in system behavior being documented in a wide variety of disparate documents. To address this problem, JPL has undertaken a pilot project to apply its institutional capabilities in Model-Based Systems Engineering to the challenge of specifying complex spacecraft system behavior. This paper describes the results of the work in progress on this project. In particular, we discuss our approach to modeling spacecraft behavior including 1) requirements and design flowdown from system-level to subsystem-level, 2) patterns for behavior decomposition, 3) allocation of behaviors to physical elements in the system, and 4) patterns for capturing V&V activities associated with behavioral requirements. We provide examples of interesting behavior specification patterns, and discuss findings from the pilot project.

SysML↗
NASA NTRS1978

Applications of hybrid and digital computation methods in aerospace-related sciences and engineering

The computing equipment in the engineering systems simulation laboratory of the Houston University Cullen College of Engineering is described and its advantages are summarized. The application of computer techniques in aerospace-related research psychology and in chemical, civil, electrical, industrial, and mechanical engineering is described in abstracts of 84 individual projects and in reprints of published reports. Research supports programs in acoustics, energy technology, systems engineering, and environment management as well as aerospace engineering.

Huang, C. J.↗
NASA NTRS2023

Forward for the new edition of: “What Every Engineer Should Know About Risk Engineering and Management"

In aerospace and engineering in general, the major metrics are capability, cost, and safety/risk. With the increasingly rapid emergence and utilization of new technologies and systems of increasing complexity, ensuring safety becomes more difficult. The technology and practice/applications of safety/risk technologies require updating in concert with capability and systems technology changes. Hence the new, updated edition of this risk engineering book. Major changes in technology and applications, now and going forward, increasingly involve artificial intelligence (AI)/autonomy and complex systems, which are rapidly developing and moving targets when it comes to risk/safety analysis. These introduce both new risks and safety issues, and they alter more usual ones. The current reality is that often the best AI is when it is “Black Boxed”, developed “independently”, without detailed human understanding of how and by what processes decisions and results are produced. There are ongoing efforts to make the AI processes more understandable by humans, with results to be determined. Also, trusted and true autonomy is free of human intervention, which would require machines to ideate to solve in real time issues that arise due to unknown unknowns and even known unknowns. Machines using generative adversarial network (GANs) and other approaches are beginning to ideate. Overall, the capabilities and practice of AI/autonomy utilization is a work in progress with the rate of progress substantial and the impacts upon system risk/safety major.

Dennis M. Bushnell↗
NASA NTRS1977

Mother, Earth, Father Sky Symposium

A conference was held in which minority aerospace scientists and engineers interacted with the minority community, particularly at the junior high, high school, and college levels. There were two presentations in the biological sciences, two in the physical and environmental sciences, seven in engineering and computer sciences, and nine in aerospace science and engineering. Aerospace technology careers and aerospace activities were discussed as to how they are relevant to minorities and women.

Bowman, B.↗