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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 NTRS2023

Developing a Hybrid Space Suit Simulator as a Research Tool for Assessing Extravehicular Activity Relevant Workload

Suited testing time is limited and hard to come by, expensive, and requires a whole team to operate and monitor the suit; however, pressurized spacesuits are not always necessary for initial developmental efforts or assessment of informatics. The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a Hybrid Space Suit Simulator (HS3) to support characterization of human performance during planetary surface exploration analogs. The primary goal of HS3 is to create a low cost, modular, and unpressurized suit simulator for use as a research tool which provides relevant physical and cognitive workload approximations with EVA-like immersion during planetary extravehicular activity (EVA) simulations. HS3 consists of a soft suit, communication, thermal control, gloves, boots, helmet, and integrated bioinformatics/sensors. The modular design of HS3 allows it to be modified to support test objectives as needed. HS3 enables H-3PO and NASA to complete critical human health and performance testing to address open risks and knowledge gaps in a timely, repeatable, and controlled manner. Four engineering runs were completed with HS3 as a baseline for capability. General workload was assessed while traversing on a passive treadmill and conducting geology tasks. Liquid cooling temperature, suit temperature, and metabolic rates were collected during one extended three-hour engineering run. For the duration of the engineering run the suit temperature was 26.6 ± 1.58 °C while suit humidity was 53.7 ± 5.11 %. Helmet temperature was higher than suit temperature at 27.8 ± 1.58 °C. Helmet humidity was lower than average suit humidity at 50.12 ± 11.3 °C. The liquid cooling temperature remained stable at 22.4 ± 1.62 °C. Resting metabolic rate was 597 ± 88 BTU/hr while metabolic rate increased during traverse tasks at 1500 ± 319 BTU/hr and geology tasks at 921 ± 129 BTU/hr. Following the engineering runs, a characterization study will be conducted. The subjects will perform EVA-like scenarios in both a shirtsleeve only and donned HS3, consisting of EVA task circuit focused on traversing, geological rock sampling, and task board manipulation. The characterization study will provide data capability for implementing HS3 in analog EVA testing environments.

Monica Hew↗

NASA NTRSDate not supplied

A Review Towards the Design Optimization of High-Performance Additively Manufactured Rotating Detonation Rocket Engine Injectors

Rotating Detonation Rocket Engines (RDRE) have been marketed primarily for their higher specific impulse potential over constant pressure (CP) liquid rocket engines. However, several other performance advantages exist such as heat transfer advantages for gas expander cycle, increased completeness of combustion at low chamber L*, compact engine design, reduced coolant channel pressure drop potential, and improved injector C* performance. NASA has paved the way for liquid engine system performance enhancement since the Apollo program and continues to do so with metal additive manufacturing (AM), new advanced materials, and advanced propulsion concepts. A team of propulsion development engineers at NASA are in the process of developing high-performance 7K lbf class RDRE hardware for their potential use in lander, upper stage, and even launch vehicle applications. Clear advantages have been demonstrated with AM including program cost and schedule reductions of up to 50%. It is well known that injector performance is integrally linked to the global performance of a combustion device. This is especially the case for RDREs since detonation stability is heavily dependent on the mixedness of propellants. A major goal of this work is to identify what has been done in the open experimental literature and what injectors design features are conducive to high performance in the detonation cycle. This paper reviews the available literature and reports the primary gaps in the knowledge base needed by the pressure gain combustion (PGC) community. Major conclusions are documented, and suggestions given towards the design of high-performance liquid RDRE injectors. In addition, the integration of metal AM into the design of liquid RDRE injector schemes is included.

rotating detonation rocket engine↗

NASA NTRS1997

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Broderick, Ron↗

NASA NTRS2013

Shock Position Control for Mode Transition in a Turbine Based Combined Cycle Engine Inlet Model

A dual flow-path inlet for a turbine based combined cycle (TBCC) propulsion system is to be tested in order to evaluate methodologies for performing a controlled inlet mode transition. Prior to experimental testing, simulation models are used to test, debug, and validate potential control algorithms which are designed to maintain shock position during inlet disturbances. One simulation package being used for testing is the High Mach Transient Engine Cycle Code simulation, known as HiTECC. This paper discusses the development of a mode transition schedule for the HiTECC simulation that is analogous to the development of inlet performance maps. Inlet performance maps, derived through experimental means, describe the performance and operability of the inlet as the splitter closes, switching power production from the turbine engine to the Dual Mode Scram Jet. With knowledge of the operability and performance tradeoffs, a closed loop system can be designed to optimize the performance of the inlet. This paper demonstrates the design of the closed loop control system and benefit with the implementation of a Proportional-Integral controller, an H-Infinity based controller, and a disturbance observer based controller; all of which avoid inlet unstart during a mode transition with a simulated disturbance that would lead to inlet unstart without closed loop control.

Csank, Jeffrey T.↗

NASA NTRS2013

Exploring Mission Concepts with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts called the A-Team. The A-Team combines innovative collaborative methods with subject matter expertise and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an A-Team study. Methods are grouped by Concept Maturity Level (CML), from 1 through 3, including idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for each CML are presented, and the key team roles are described from two points of view: innovative methods and technical expertise. A-Team roles for providing innovative methods include the facilitator, study lead, and assistant study lead. A-Team roles for providing technical expertise include the architect, lead systems engineer, and integration engineer. In addition to these key roles, each A-Team study is uniquely staffed to match the study topic and scope including subject matter experts, scientists, technologists, flight and instrument systems engineers, and program managers as needed. Advanced analysis and collaborative engineering tools (e.g. cost, science traceability, mission design, knowledge capture, study and analysis support infrastructure) are also under development for use in A-Team studies and will be discussed briefly. The A-Team facilities provide a constructive environment for innovative ideas from all aspects of mission formulation to eliminate isolated studies and come together early in the development cycle when they can provide the biggest impact. This paper provides an overview of the A-Team, its study processes, roles, methods, tools and facilities.

Team Eureka↗

NASA NTRS1992

An expert system for integrated structural analysis and design optimization for aerospace structures

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

Source record↗

NASA NTRS1992

Ramp Technology and Intelligent Processing in Small Manufacturing

To address the issues of excessive inventories and increasing procurement lead times, the Navy is actively pursuing flexible computer integrated manufacturing (FCIM) technologies, integrated by communication networks to respond rapidly to its requirements for parts. The Rapid Acquisition of Manufactured Parts (RAMP) program, initiated in 1986, is an integral part of this effort. The RAMP program's goal is to reduce the current average production lead times experienced by the Navy's inventory control points by a factor of 90 percent. The manufacturing engineering component of the RAMP architecture utilizes an intelligent processing technology built around a knowledge-based shell provided by ICAD, Inc. Rules and data bases in the software simulate an expert manufacturing planner's knowledge of shop processes and equipment. This expert system can use Product Data Exchange using STEP (PDES) data to determine what features the required part has, what material is required to manufacture it, what machines and tools are needed, and how the part should be held (fixtured) for machining, among other factors. The program's rule base then indicates, for example, how to make each feature, in what order to make it, and to which machines on the shop floor the part should be routed for processing. This information becomes part of the shop work order. The process planning function under RAMP greatly reduces the time and effort required to complete a process plan. Since the PDES file that drives the intelligent processing is 100 percent complete and accurate to start with, the potential for costly errors is greatly diminished.

Rentz, Richard E.↗

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↗

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

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↗

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.↗