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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Experiments on Evolving Software Models of Analog Circuits

Analog circuits are of great importance in electronic system design since the world is fundamentally analog in nature. While the amount of digital design activity far outpaces that of analog design, most digital systems require analog modules for interfacing with the external world. It was recently estimated that approximately 60% of digital application- specific integrated circuit designs incorporated analog circuits. With challenging analog circuit design problems and few analog design engineers, there are economic reasons for automating the analog design process, especially time-to-market considerations. Techniques for analog circuit design automation began appearing about two decades ago. These methods incorporated heuristics [6], knowledge bases [1], simulated annealing [5], and other algorithms. Efforts using techniques from evolutionary computation began appearing over the last few years. These include the use of genetic algorithms to select electronic component values (for example, the resistance value of a resistor), to select circuit topologies, and to design amplifiers using a limited set of canned topologies [4]. A genetic programming-based analog circuit design system has been demonstrated in which the circuit sizes, component values, and the circuit topologies are determined automatically [3]. The genetic-algorithm systems typically represent circuit structures as vectors of parameters encoded in binary strings, while the genetic programming system manipulates tree data structures.

Lohn, Jason D.↗
NASA NTRS2013

Developing IVHM Requirements for Aerospace Systems

The term Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. However, very little guidance exists for the systems engineering aspects of design with IVHM in mind. It is probably because of this that designers have to use knowledge picked up exclusively by experience rather than by established process. This motivated a group of leading IVHM practitioners within the aerospace industry under the aegis of SAE's HM-1 technical committee to author a document that hopes to give working engineers and program managers clear guidance on all the elements of IVHM that they need to consider before designing a system. This proposed recommended practice (ARP6883 [1]) will describe all the steps of requirements generation and management as it applies to IVHM systems, and demonstrate these with a "real-world" example related to designing a landing gear system. The team hopes that this paper and presentation will help start a dialog with the larger aerospace community and that the feedback can be used to improve the ARP and subsequently the practice of IVHM from a systems engineering point-of-view.

IVHM↗
NASA NTRS2007

An IEEE 1451.1 Architecture for ISHM Applications

The IEEE 1451.1 Standard for a Smart Transducer Interface defines a common network information model for connecting and managing smart elements in control and data acquisition networks using network-capable application processors (NCAPs). The Standard is a network-neutral design model that is easily ported across operating systems and physical networks for implementing complex acquisition and control applications by simply plugging in the appropriate network level drivers. To simplify configuration and tracking of transducer and actuator details, the family of 1451 standards defines a Transducer Electronic Data Sheet (TEDS) that is associated with each physical element. The TEDS contains all of the pertinent information about the physical operations of a transducer (such as operating regions, calibration tables, and manufacturer information), which the NCAP uses to configure the system to support a specific transducer. The Integrated Systems Health Management (ISHM) group at NASA's John C. Stennis Space Center (SSC) has been developing an ISHM architecture that utilizes IEEE 1451.1 as the primary configuration and data acquisition mechanism for managing and collecting information from a network of distributed intelligent sensing elements. This work has involved collaboration with other NASA centers, universities and aerospace industries to develop IEEE 1451.1 compliant sensors and interfaces tailored to support health assessment of complex systems. This paper and presentation describe the development and implementation of an interface for the configuration, management and communication of data, information and knowledge generated by a distributed system of IEEE 1451.1 intelligent elements monitoring a rocket engine test system. In this context, an intelligent element is defined as one incorporating support for the IEEE 1451.x standards and additional ISHM functions. Our implementation supports real-time collection of both measurement data (raw ADC counts and converted engineering units) and health statistics produced by each intelligent element. The handling of configuration, calibration and health information is automated by using the TEDS in combination with other electronic data sheets extensions to convey health parameters. By integrating the IEEE 1451.1 Standard for a Smart Transducer Interface with ISHM technologies, each element within a complex system becomes a highly flexible computation engine capable of self-validation and performing other measures of the quality of information it is producing.

Morris, Jon A.↗
NASA NTRS1973

Nondestructive evaluation of regeneratively cooled thrust chambers for rocket engines

Review of a development program for nondestructive tests applicable to hardware with electroformed nickel bonds. The four steps of the program include the selection of some most promising nondestructive evaluation methods out of existing methods, the fabrication of test panels for simulation of cooled thrust chamber walls with bonds of various integrities, the evaluation of the test panels by personnel without previous knowledge of the planned bond integrity, and the verification of actual bond strength in the test panels by destructive tests. Ultrasonics, acoustic emission and holography are evaluated in nondestructive testing applications.

Stauffis, C. R.↗
NASA NTRS1997

Toy Gliders

Toy designers at Hasbro, Inc. wanted to create a foam glider that a child could fly with little knowledge of aeronautics. But early in its development, the Areo Nerf gliders had one critical problem: they didn't fly so well. Through NASA's Northeast Regional Technology Transfer Center, Hasbro was linked with aeronautical experts at Langley Research Center. The engineers provided information about how wing design and shape are integral to a glider's performance. The Hasbro designers received from NASA not only technical guidance but a hands-on tutorial on the physics of designing and flying gliders. Several versions of the Nerf glider were realized from the collaboration. For instance, the Super Soaring Glider can make long-range, high performance flights while the Ultra-Stunt Glider is ideal for performing aerial acrobatics.

Source record↗
NASA NTRS2002

A Data-Driven Solution for Performance Improvement

Marketed as the "Software of the Future," Optimal Engineering Systems P.I. EXPERT(TM) technology offers statistical process control and optimization techniques that are critical to businesses looking to restructure or accelerate operations in order to gain a competitive edge. Kennedy Space Center granted Optimal Engineering Systems the funding and aid necessary to develop a prototype of the process monitoring and improvement software. Completion of this prototype demonstrated that it was possible to integrate traditional statistical quality assurance tools with robust optimization techniques in a user- friendly format that is visually compelling. Using an expert system knowledge base, the software allows the user to determine objectives, capture constraints and out-of-control processes, predict results, and compute optimal process settings.

Source record↗
DOE OSTI2021

Real Time - Optimal Power Flow Based Distributed Energy Resources Management System (DERMS) (CRADA Number CRD-20-16909 Final Report)

The integration of behind-the-meter distributed energy resources (DERs) into distribution systems brings transformative changes to power systems. This requires operators and planners to find solutions to modernize electric grids and to effectively manage DERs for grid services. NREL developed novel DER management algorithms (referred to as Real-Time Optimal Power Flow, RT-OPF) through U.S. Department of Energy (DOE)-funded efforts, including Advanced Research Projects Agency–Energy (ARPA-E) Network Optimized Distributed Energy Systems (NODES) funding. This cutting-edge control technology aims to modernize distribution systems with large amounts of DER integration, which will help utilities solve issues brought by renewable integration and build resilient and renewable-based electric grids nationwide. Utilidata worked with NREL to investigate the commercialization opportunity of this RT-OPF-based distributed energy resource management system (DERMS). In this project, NREL performs the technology transfer of the RT-OPF to Utilidata to help them fully understand the RT-OPF solution, to identify potential engineering hurdles, and to assess the expected commercial value of various RT-OPF use cases and deployment. The technology transfer work includes two major tasks. First, NREL performs an in-depth knowledge transfer of the entire RT-OPF solution to Utilidata to help them gain an extensive and detailed understanding of the complete RT-OPF solution. In this task, NREL provides exhaustive information (e.g., documentation, code packages, laboratory and field trial data, performance results) while conducting in-depth training sessions to provide a thorough explanation of the entire solution. NREL hosts meetings to present different topics related to the RT-OPF solution, and question-and-answer sessions are included in each meeting to better explain the RT-OPF-related work. Second, the RT-OPF simulations are performed in a laboratory environment. The main objectives of this task are to walk through with Utilidata engineers how to set up a simulation of RT-OPF, identifying each RT-OPF code block/component in operation, learning how these components interact with each other, and eventually running RT-OPF simulations under various system conditions. This task helps Utilidata engineers understand performance limitations and constraints while also quantifying the commercial value of the RT-OPF for different use cases. Based on these two tasks, Utilidata engineers should be able to define and prioritize the next steps of RT-OPF implementation with an eye toward commercial success and scalability of the solution. The next steps are expected to be part of a new project following the conclusion of this project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRS2026

Turbofan Engine Power Extraction Demonstration Final Report

As GE Aerospace advances toward a revolutionary step change in propulsion efficiency, the integration and demonstration of new engine architectures and technology systems are essential. The NASA Turbofan Engine Power Extraction Demonstration (PEx), conducted through the Hybrid Thermally Efficient Core (HyTEC) project, aims to develop and demonstrate megawatt-class hybrid electric capability on a modern commercial turbofan engine. The hybrid electric system is critical to meeting the needs of the U.S. aviation industry for next-generational propulsion systems with greater efficiency, durability, and range. This supports energy independence and helps ensure the security and resilience of one of America's largest export industries. The PEx project specifically targets three key objectives: mechanically integrating hybrid electric capability into a commercial turbofan engine, integrating electric machine control with turbofan control for advanced power management, and de-risking performance modeling of future hybrid electric architectures. To mature these technologies to Technology Readiness Level (TRL) 6, a series of electric power system component tests and a baseline engine performance test campaign were conducted. These efforts culminated in an integrated hybrid electric turbofan test campaign demonstrating power extraction, power insertion, and power transfer between spools. Tests of the electric power system were completed at GE Aerospace’s Electrical Power Integrated Systems Center in Dayton, Ohio and engine tests were completed at Peebles Test Operation in Peebles, Ohio. Hybrid electric trade studies extended the demonstrated capability to altitude using the validated cycle model from the PEx test campaigns, allowing for comments on expanded mission benefits not demonstrated in the ground campaign. The knowledge gained from PEx also supports GE Aerospace’s Compact Core Demonstrator as part of HyTEC Phase 2 and ultimately informs the implementation of hybrid electric systems in the next generation of GE Aerospace commercial engine products. This report provides a summary of the program background, test campaigns, trade studies, and insights into the technical maturation required to support future commercial products.

Hybrid Electric↗
NASA NTRS2016

ROS Hexapod

As an intern project for NASA Johnson Space Center (JSC), my job was to familiarize myself and operate a Robotics Operating System (ROS). The project outcome converted existing software assets into ROS using nodes, enabling a robotic Hexapod to communicate to be functional and controlled by an existing PlayStation 3 (PS3) controller. Existing control algorithms and current libraries have no ROS capabilities within the Hexapod C++ source code when the internship started, but that has changed throughout my internship. Conversion of C++ codes to ROS enabled existing code to be compatible with ROS, and is now controlled using an existing PS3 controller. Furthermore, my job description was to design ROS messages and script programs that enabled assets to participate in the ROS ecosystem by subscribing and publishing messages. Software programming source code is written in directories using C++. Testing of software assets included compiling code within the Linux environment using a terminal. The terminal ran the code from a directory. Several problems occurred while compiling code and the code would not compile. So modifying code to where C++ can read the source code were made. Once the code was compiled and ran, the code was uploaded to Hexapod and then controlled by a PS3 controller. The project outcome has the Hexapod fully functional and compatible with ROS and operates using the PlayStation 3 controller. In addition, an open source software (IDE) Arduino board will be integrated into the ecosystem with designing circuitry on a breadboard to add additional behavior with push buttons, potentiometers and other simple elements in the electrical circuitry. Other projects with the Arduino will be a GPS module, digital clock that will run off 22 satellites to show accurate real time using a GPS signal and an internal patch antenna to communicate with satellites. In addition, this internship experience has led me to pursue myself to learn coding more efficiently and effectively to write, subscribe and publish my own source code in different programming languages. With some familiarity with software programming, it will enhance my skills in the electrical engineering field. In contrast, my experience here at JSC with the Simulation and Graphics Branch (ER7) has led me to take my coding skill to be more proficient to increase my knowledge in software programming, and also enhancing my skills in ROS. This knowledge will be taken back to my university to implement coding in a school project that will use source coding and ROS to work on the PR2 robot which is controlled by ROS software. My skills learned here will be used to integrate messages to subscribe and publish ROS messages to a PR2 robot. The PR2 robot will be controlled by an existing PS3 controller by changing C++ coding to subscribe and publish messages to ROS. Overall the skills that were obtained here will not be lost, but increased.

Davis, Kirsch↗
DOE OSTI2021

Unraveling the advances of trace doping engineering for potassium ion battery anodes via tomography

Doping have been considered as a prominent strategy to stabilize crystal structure of battery materials during the insertion and removal of alkali ions. The instructive knowledge and experience acquired from doping strategies predominate in cathode materials, but doping principle in anodes remains unclear. Here, we demonstrate that trace element doping enables stable conversion-reaction and ensures structural integrity for potassium ion battery (PIB) anodes. With a synergistic combination of X-ray tomography, structural probes, and charge reconfiguration, we encode the physical origins and structural evolution of electro-chemo-mechanical degradation in PIB anodes. By the multiple ion transport pathways created by the orderly hierarchical pores from “surface to bulk” and the homogeneous charge distribution governed in doped nanodomains, the anisotropic expansion can be significantly relieved with trace isoelectronic element doping into the host lattice, maintaining particle mechanical integrity. Our work presents a close relationship between doping chemistry and mechanical reliability, projecting a new pathway to reengineering electrode materials for next-generation energy storage.

25 ENERGY STORAGE↗
DOE OSTI2021

Aero-Thermal Characterization of Accelerating and Diffusing Passages Downstream of Rotating Detonation Combustors

Cycle benefits of rotating detonation engines show up to five percentage points of efficiency gain for low-pressure ratio engines. An optimal integration between the combustor and the turbine needs to be guaranteed to realize this potential gain. The rotating detonation combustor (RDC) exhausts transonic flow with shocks rotating at frequencies ranging from a few to tens of kilohertz depending on the number of present waves. Hence, the turbine design requires precise knowledge of the fluctuations and losses downstream of the combustor. This paper focuses on the quantification of fluctuations and losses for accelerating and diffusing passages. The analysis of the combustor is performed via reactive unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The unsteady RANS equations are solved via CFD++ from Metacomp with a one-step reaction mechanism for an H2-air mixture. The resolving of the boundary layer is achieved with a structured mesh of around 36 million cells. Inlet pressure of 10 bar and two different back pressures are applied to the combustor to model the interconnection with downstream turbines. Finally, we present and assess a methodology to reduce the computational time to model these passages ten times.

Braun, James↗
NASA NTRS2008

DARPA/USAF/USN J-UCAS X-45A System Demonstration Program: A Review of Flight Test Site Processes and Personnel

The Joint Unmanned Combat Air Systems (J-UCAS) program is a collaborative effort between the Defense Advanced Research Project Agency (DARPA), the US Air Force (USAF) and the US Navy (USN). Together they have reviewed X-45A flight test site processes and personnel as part of a system demonstration program for the UCAV-ATD Flight Test Program. The goal was to provide a disciplined controlled process for system integration and testing and demonstration flight tests. NASA's Dryden Flight Research Center (DFRC) acted as the project manager during this effort and was tasked with the responsibilities of range and ground safety, the provision of flight test support and infrastructure and the monitoring of technical and engineering tasks. DFRC also contributed their engineering knowledge through their contributions in the areas of autonomous ground taxi control development, structural dynamics testing and analysis and the provision of other flight test support including telemetry data, tracking radars, and communications and control support equipment. The Air Force Flight Test Center acted at the Deputy Project Manager in this effort and was responsible for the provision of system safety support and airfield management and air traffic control services, among other supporting roles. The T-33 served as a J-UCAS surrogate aircraft and demonstrated flight characteristics similar to that of the the X-45A. The surrogate served as a significant risk reduction resource providing mission planning verification, range safety mission assessment and team training, among other contributions.

Cosentino, Gary B.↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
DOE OSTI2024

Decarbonizing Building Thermal Systems: A How-to Guide for Heat Pump Systems and Beyond

Buildings account for a substantial portion of carbon emissions, primarily due to the widespread use of fossil fuels in heating systems. Decarbonization of heating is essential to meet climate targets and reduce the environmental impact of buildings. Heat pumps are capable of leveraging renewable energy sources and can provide heating and cooling in an energy-efficient manner. By leveraging heat pump technology, buildings can significantly reduce their carbon footprint, minimize energy consumption, and decrease their reliance on fossil fuels. The design and construction community plays a pivotal role in facilitating the transition to heat pump systems for heating and cooling. However, this transition requires specialized knowledge and expertise. This resource was developed for architects, engineers, and contractors in response to an industry need for a comprehensive technical resource that guides them through the intricacies of heat pump system design, installation, and maintenance. This resource provides detailed information on system sizing, selection of appropriate heat systems, heat sources, and integration with existing building systems. Moreover, it emphasizes best practices for ensuring operational efficiency, system longevity, and reliability. The development of this recourse was a collaborative effort between NREL/DOE Better Buildings Design and Construction Allies and ASHRAE Task Force for Building Decarbonization. This resource is composed of two complimentary portions that will be completed and released on separate time frames. The first portion will be completed and released in 2023, while the second portion will be released in 2024.

building thermal systems↗
NASA NTRS1993

Integrated Intelligent training and job aiding for combustion turbine engines

This paper describes an ongoing program to augment such an expert system gas turbine startup advisor, known as the EPRI SA VANT System, by including an intelligent training package. It will give a brief background on the SA VANT development and an overview of its evolution into a full-blown Gas Turbine Information System (GTIS) for rapid access of on-line documentation, diagnostics, and training. In particular, the paper will address: (1) the conversion of the knowledge base used by the SA VANT startup advisor so that it can be used for both training and job aiding; and (2) the hypertext-oriented user manuals being incorporated into the system for rapidly accessing on-line documentation at the job site.

Mckeithan, Clifford M., Jr.↗
NASA NTRS1992

CSM solutions of rotating blade dynamics using integrating matrices

The dynamic behavior of flexible rotating beams continues to receive considerable research attention as it constitutes a fundamental problem in applied mechanics. Further, beams comprise parts of many rotating structures of engineering significance. A topic of particular interest at the present time involves the development of techniques for obtaining the behavior in both space and time of a rotor acted upon by a simple airload loading. Most current work on problems of this type use solution techniques based on normal modes. It is certainly true that normal modes cannot be disregarded, as knowledge of natural blade frequencies is always important. However, the present work has considered a computational structural mechanics (CSM) approach to rotor blade dynamics problems in which the physical properties of the rotor blade provide input for a direct numerical solution of the relevant boundary-and-initial-value problem. Analysis of the dynamics of a given rotor system may require solution of the governing equations over a long time interval corresponding to many revolutions of the loaded flexible blade. For this reason, most of the common techniques in computational mechanics, which treat the space-time behavior concurrently, cannot be applied to the rotor dynamics problem without a large expenditure of computational resources. By contrast, the integrating matrix technique of computational mechanics has the ability to consistently incorporate boundary conditions and 'remove' dependence on a space variable. For problems involving both space and time, this feature of the integrating matrix approach thus can generate a 'splitting' which forms the basis of an efficient CSM method for numerical solution of rotor dynamics problems.

Lakin, William D.↗