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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 NTRS2019

Evaluation Studies of Solid Oxide-Based Fuel Cells for Light-Weight Electrical Power in Aviation: Concepts and Opportunities for the Application of Additive Manufacturing

Currently NASA is investigating the feasibility of hybrid-electric, solid oxide fuel cell power (SOFC) systems for generating electrical power for airborne propulsion and secondary/auxiliary power. As part of this project, researchers are investigating the performance of SOFC hardware in aviation-like environments, to establish the barriers, and potential suitability, of this technology. Immediate findings indicate the unsuitability of current SOFC stack architectures with regards to ease of manufacture, specific energy, as well as environmental and mechanical durability. One possible solution is to create light-weight high specific energy density solid oxide fuel cells. The possibility may be achievable by the use of modern processing techniques such as additive manufacturing. Here I discuss the use additive manufacturing to fabricate ceramic anode, electrolytes, and cathode cells supported on various metal foams with predicted performance based upon variable porosities

Goldsby, Jon C.↗

NASA NTRS2021

Mars 2020 Perseverance trajectory reconstruction and performance from launch through landing

The Mars 2020 (M2020) Mission carrying Perseverance, the most advanced rover ever sent to Mars, successfully launched on an Atlas V 541 (AV-088) launch vehicle from the Eastern Test Range (ETR) at Cape Canaveral Air Force Station (CCAFS) in Florida at 11:50:00 UTC (T-Zero time) on July 30, 2020. After some station reconfiguration, carrier/telemetry were locked at both Deep Space Network (DSN) Canberra and Goldstone stations. Perseverance entered the Martian atmosphere at 20:36:50 Spacecraft Event Time (SCET) UTC, and landed inside Jezero Crater at 20:43:49 SCET UTC on February 18, 2021. Confirmation of nominal landing was received at the DSN Goldstone and Madrid tracking stations via the Mars Reconnaissance Orbiter at 20:55:11 Earth Received Time (ERT) UTC. This paper summarizes in detail the actual vs. predicted performance in terms of launch vehicle events, launch vehicle injection performance, actual DSN spacecraft lockup, trajectory correction maneuver performance, Entry, Descent, and Landing events, and overall trajectory and geometric characteristics.

Abilleira, Fernando↗

NASA NTRS2021

Mars 2020 Perseverance Trajectory Reconstruction and Performance from Launch through Landing

Wong, Mau↗

NASA NTRS2022

A High-fidelity Performance and Sensitivity Analysis of X-ray Pulsar Navigation in Near-Earth and Cislunar Orbits

Millisecond X-ray pulsars are rapidly rotating neutron stars that emit broadband electromagnetic radiation, including X-rays. These emissions can appear as pulsations to distant observers with long-term stabilityrivaling laboratory atomic clocks. The concept of using X-ray pulsars for navigation, commonly referred to as XNAV, has a long history in the research literature. Notably, in 2017, NASA’s Station Explorer for X-ray Timing and Navigation (SEXTANT) mission,a technology enhancement to the Neutron Star Interior Composition Explorer, made a successful on-orbit demonstration of XNAV on the International Space Station (ISS) in Low-Earth-Orbit. This paper investigates the performance of XNAV in Earth and lunar regimes using a new high-fidelity XNAV analysis tool that builds on tools developed for SEXTANT and uses recent updates to the Goddard Enhanced Onboard Navigation System flight software. Specifically, we study the use of XNAV in an ISS-like orbit for comparison with SEXTANT results, in a highly-inclined geosynchronous orbit, and in an Earth-Moon L1 libration point Near Rectilinear Halo Orbit similar to that proposed for the planned lunar Gateway space station. This paper provides a high-level description of the XNAV analysis tool, baseline predicted performance results, selected sensitivity analyses, and conclusions.

Luke B Winternitz↗

NASA NTRS1993

Evaluation of the national launch system as a booster for the HL-20

The capability of a proposed national launch system (NLS) to boost the personnel launch system (PLS) manned vehicle has been examined. A reference NLS configuration, the NLS-2 1.5 stage vehicle, and a reference HL-20 PLS configuration were used for the study. Performance has been analyzed for several PLS insertion orbits to support the Space Station Freedom resupply mission. The reliability of the NLS launch vehicle and its contribution to crew safety requirements have been determined. The launch-processing and launch facility requirements of these combined systems were also analyzed. Previous studies of these two systems have focused on either the PLS manned element or NLS launch vehicle. This paper combines the results of prior studies in an analysis of the integrated NLS/PLS configuration. This analysis has found the proposed NLS 1.5 stage launch vehicle to be an excellent booster for the PLS. Predicted performance margins for this launch-vehicle configuration are more than adequate, and acceptable reliability and safety levels are anticipated. Integration of this NLS/PLS configuration into NASA mixed-fleet launch architectures is feasible.

Duffy, James B.↗

NASA NTRS1990

Earth Observing Scanning Polarimeter (EOSP), phase B

Evaluations performed during a Phase B study directed towards defining an optimal design for the Earth Observing Scanning Polarimeter (EOSP) instrument is summarized. An overview of the experiment approach is included which provides a summary of the scientific objectives, the background of the measurement approach, and the measurement method. In the instrumentation section, details of the design are discussed starting with the key instrument features required to accomplish the scientific objectives and a system characterization in terms of the Stokes vector/Mueller matrix formalism. This is followed by a detailing of the instrument design concept, the design of the individual elements of the system, the predicted performance, and a summary of appropriate instrument testing and calibration. The selected design makes use of key features of predecessor polarimeters and is fully compatible with the Earth Observing System spacecraft requirements.

Source record↗

NASA NTRS2008

DSN Array Simulator

The DSN Array Simulator (wherein 'DSN' signifies NASA's Deep Space Network) is an updated version of software previously denoted the DSN Receive Array Technology Assessment Simulation. This software (see figure) is used for computational modeling of a proposed DSN facility comprising user-defined arrays of antennas and transmitting and receiving equipment for microwave communication with spacecraft on interplanetary missions. The simulation includes variations in spacecraft tracked and communication demand changes for up to several decades of future operation. Such modeling is performed to estimate facility performance, evaluate requirements that govern facility design, and evaluate proposed improvements in hardware and/or software. The updated version of this software affords enhanced capability for characterizing facility performance against user-defined mission sets. The software includes a Monte Carlo simulation component that enables rapid generation of key mission-set metrics (e.g., numbers of links, data rates, and date volumes), and statistical distributions thereof as functions of time. The updated version also offers expanded capability for mixed-asset network modeling--for example, for running scenarios that involve user-definable mixtures of antennas having different diameters (in contradistinction to a fixed number of antennas having the same fixed diameter). The improved version also affords greater simulation fidelity, sufficient for validation by comparison with actual DSN operations and analytically predictable performance metrics.

Tikidjian, Raffi↗

NASA NTRS2017

Propulsion Powertrain Real-Time Simulation Using Hardware-in-the-Loop (HIL) for Aircraft Electric Propulsion System

It is essential to design a propulsion powertrain real-time simulator using the hardware-in-the-loop (HIL) system that emulates an electrified aircraft propulsion (EAP) systems power grid. This simulator would enable us to facilitate in-depth understanding of the system principles, to validate system model analysis and performance prediction, and to demonstrate the proof-of-concept of the EAP electrical system. This paper describes how subscale electrical machines with their controllers can mimic the power components in an EAP powertrain. In particular, three powertrain emulations are presented to mimic 1) a gas turbo-=shaft engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft, 2) a motor driving a propulsive fan, and 3) a turbo-shaft engine driven fan (turbofan engine) operation. As a first step towards the demonstration, experimental dynamic characterization of the two motor drive systems, coupled by a mechanical shaft, were performed. The previously developed analytical motor models1 were then replaced with the experimental motor models to perform the real-time demonstration in the predefined flight path profiles. This technique can convert the plain motor system into a unique EAP power grid emulator that enables rapid analysis and real-time simulation performance using hardware-in-the-loop (HIL).

turbo-electric propulsion↗

NASA NTRS1989

Three-Man Solid Electrolyte Carbon Dioxide Electrolysis Breadboard

The development of the Three-Man (2.2 lb CO2/man-day) Solid Electrolyte CO2 Electrolysis Breadboard consisted of a Phase 1 and 2 effort. The Phase 1 effort constituted fabrication of three electrolysis cell types and performing parametric testing, off-design testing, and cell life testing. The Phase 2 consisted of the preliminary design, incorporation of palladium (Pd) tubes for hydrogen separation from the electrolyzer cathode feed gases, design support testing, final design, fabrication, and performance testing of the breadboard system. The results of performance tests demonstrated that CO2 electrolysis in an oxygen reclamation system for long duration space-based habitats is feasible. Closure of the oxygen system loop, therefore, can be achieved by CO2 electrolysis. In a two step process the metabolic CO2 and H2O vapor are electrolyzed into O2, H2, and CO. The CO can subsequently be disproportionated into carbon and CO2 in a carbon deposition reactor and the CO2 in turn be recycled and electrolyzed for total O2 recovery. The development effort demonstrated electrolyzer system can be designed and built to operate safely and reliably and the incorporation of Pd tubes for hydrogen diffusion can be integrated safely with predictable performance.

Isenberg, Arnold O.↗

NASA NTRS2016

Development and Use of Engineering Standards for Computational Fluid Dynamics for Complex Aerospace Systems

Computational fluid dynamics (CFD) and other advanced modeling and simulation (M&S) methods are increasingly relied on for predictive performance, reliability and safety of engineering systems. Analysts, designers, decision makers, and project managers, who must depend on simulation, need practical techniques and methods for assessing simulation credibility. The AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations (AIAA G-077-1998 (2002)), originally published in 1998, was the first engineering standards document available to the engineering community for verification and validation (V&V) of simulations. Much progress has been made in these areas since 1998. The AIAA Committee on Standards for CFD is currently updating this Guide to incorporate in it the important developments that have taken place in V&V concepts, methods, and practices, particularly with regard to the broader context of predictive capability and uncertainty quantification (UQ) methods and approaches. This paper will provide an overview of the changes and extensions currently underway to update the AIAA Guide. Specifically, a framework for predictive capability will be described for incorporating a wide range of error and uncertainty sources identified during the modeling, verification, and validation processes, with the goal of estimating the total prediction uncertainty of the simulation. The Guide's goal is to provide a foundation for understanding and addressing major issues and concepts in predictive CFD. However, this Guide will not recommend specific approaches in these areas as the field is rapidly evolving. It is hoped that the guidelines provided in this paper, and explained in more detail in the Guide, will aid in the research, development, and use of CFD in engineering decision-making.

Lee, Hyung B.↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2012

Progress on the Development of a Simulation Environment for Optical Communications

Deep space laser communications require extremely accurate beam pointing to take advantage of the narrow beams achievable at optical wavelengths. This pointing accuracy must be achieved in the presence of spacecraft basebody motion which may exceed laser pointing requirements by orders of magnitude.In this paper a model of an optical band transceiver pointing control system is developed that can be used to predict performance under various operating scenarios. The transceiver model consists of an electro-mechanical model of the telescope platform and isolator. A novel photon counting detector array is used in the simulation as the focal plane detector. In the simulation we are able to inject various cases of spacecraft basebody motion based on both flight data and future mission spacecraft jitter requirements. Various models of uplink beacon flux levels and atmospheric scintillation are also available for analysis.Using these models, detector processing and control functions are implemented in the simulation. A complete acquisition sequence is demonstrated with blind acquisition and tracking of the modulated uplink beam and positioning of the downlink beam on the focal plane array. These simulations predict that pointing requirements will be met with representative disturbance models and uplink beam scintillation.

pointing control↗

NASA NTRS2011

Orion Parachute Riser Cutter Development

This paper presents the tests and analytical approach used on the development of a steel riser cutter for the CEV Parachute Assembly System (CPAS) used on the Orion crew module. Figure 1 shows the riser cutter and the steel riser bundle which consists of six individual cables. Due to the highly compressed schedule, initial unavailability of the riser material and the Orion Forward Bay mechanical constraints, JSC primarily relied on a combination of internal ballistics analysis and LS-DYNA simulation for this project. Various one dimensional internal ballistics codes that use standard equation of state and conservation of energy have commonly used in the development of CAD devices for initial first order estimates and as an enhancement to the test program. While these codes are very accurate for propellant performance prediction, they usually lack a fully defined kinematic model for dynamic predictions. A simple piston device can easily and accurately be modeled using an equation of motion. However, the accuracy of analytical models is greatly reduced on more complicated devices with complex external loads, nonlinear trajectories or unique unlocking features. A 3D finite element model of CAD device with all critical features included can vastly improve the analytical ballistic predictions when it is used as a supplement to the ballistic code. During this project, LS-DYNA structural 3D model was used to predict the riser resisting load that was needed for the ballistic code. A Lagrangian model with eroding elements shown in Figure 2 was used for the blade, steel riser and the anvil. The riser material failure strain was fine tuned by matching the dent depth on the anvil with the actual test data. LS-DYNA model was also utilized to optimize the blade tip design for the most efficient cut. In parallel, the propellant type and the amount were determined by using CADPROG internal ballistics code. Initial test results showed a good match with LS-DYNA and CADPROG simulations. Final paper will present a detailed roadmap from initial ballistic modeling and LS-DYNA simulation to the performance testing. Blade shape optimization study will also be presented.

Oguz, Sirri↗

NASA NTRS1991

Decision paths in complex tasks

Complex real world action and its prediction and control has escaped analysis by the classical methods of psychological research. The reason is that psychologists have no procedures to parse complex tasks into their constituents. Where such a division can be made, based say on expert judgment, there is no natural scale to measure the positive or negative values of the components. Even if we could assign numbers to task parts, we lack rules i.e., a theory, to combine them into a total task representation. We compare here two plausible theories for the amalgamation of the value of task components. Both of these theories require a numerical representation of motivation, for motivation is the primary variable that guides choice and action in well-learned tasks. We address this problem of motivational quantification and performance prediction by developing psychophysical scales of the desireability or aversiveness of task components based on utility scaling methods (Galanter 1990). We modify methods used originally to scale sensory magnitudes (Stevens and Galanter 1957), and that have been applied recently to the measure of task 'workload' by Gopher and Braune (1984). Our modification uses utility comparison scaling techniques which avoid the unnecessary assumptions made by Gopher and Braune. Formula for the utility of complex tasks based on the theoretical models are used to predict decision and choice of alternate paths to the same goal.

Galanter, Eugene↗

NASA NTRS1975

Performance of a hypersonic hot fuselage structure with a carbon dioxide frost projected, nonintegral cryogenic tank

A model which consisted of a hot structure and a nonintegral tank protected by a carbon dioxide frost thermal protection system was tested under the following conditions: (1) room temperature loading and (2) heating and loading corresponding to the Mach 8 flight of an air-breathing launch vehicle. In the simulated flight tests, liquid nitrogen inside the tank was withdrawn at the rate fuel would be consumed. Prior to each simulated flight test, carbon dioxide was cryodeposited in the insulation surrounding the tank; during the tests, subliming CO2 frost absorbed heat and provided a purge gas for the space between the tank and the structure. A method of flame spraying the joints between panels with a nickel-aluminum material was developed to prevent excessive leakage of the purge gas through the outer structure. The tests indicated that the hot structure (with a joint repaired by riveting), the nonintegral tank and suspension system, and the carbon dioxide frost thermal protection system provide a workable concept with predictable performance.

Sharpe, E. L.↗

NASA NTRS1999

Flight Control Laws for NASA's Hyper-X Research Vehicle

The goal of the Hyper-X program is to demonstrate and validate technology for design and performance predictions of hypersonic aircraft with an airframe-integrated supersonic-combustion ramjet propulsion system. Accomplishing this goal requires flight demonstration of a hydrogen-fueled scramjet powered hypersonic aircraft. A key enabling technology for this flight demonstration is flight controls. Closed-loop flight control is required to enable a successful stage separation, to achieve and maintain the design condition during the engine test, and to provide a controlled descent. Before the contract award, NASA developed preliminary flight control laws for the Hyper-X to evaluate the feasibility of the proposed scramjet test sequence and descent trajectory. After the contract award, a Boeing/NASA partnership worked to develop the current control laws. This paper presents a description of the Hyper-X Research Vehicle control law architectures with performance and robustness analyses. Assessments of simulated flight trajectories and stability margin analyses demonstrate that these control laws meet the flight test requirements.

Davidson, J.↗

NASA NTRS1995

Experimental Aerodynamic Characteristics of the Pegasus Air-Launched Booster and Comparisons with Predicted and Flight Results

Experimental longitudinal and lateral-directional aerodynamic characteristics were obtained for the Pegasus and Pegasus XL configurations over a Mach number range from 1.6 to 6 and angles of attack from -4 to +24 degrees. Angle of sideslip was varied from -6 to +6 degrees, and control surfaces were deflected to obtain elevon, aileron, and rudder effectiveness. Experimental data for the Pegasus configuration are compared with engineering code predictions performed by Nielsen Engineering & Research, Inc. (NEAR) in the aerodynamic design of the Pegasus vehicle, and with results from the Aerodynamic Preliminary Analysis System (APAS) code. Comparisons of experimental results are also made with longitudinal flight data from Flight #2 of the Pegasus vehicle. Results show that the longitudinal aerodynamic characteristics of the Pegasus and Pegasus XL configurations are similar, having the same lift-curve slope and drag levels across the Mach number range. Both configurations are longitudinally stable, with stability decreasing towards neutral levels as Mach number increases. Directional stability is negative at moderate to high angles of attack due to separated flow over the vertical tail. Dihedral effect is positive for both configurations, but is reduced 30-50 percent for the Pegasus XL configuration because of the horizontal tail anhedral. Predicted longitudinal characteristics and both longitudinal and lateral-directional control effectiveness are generally in good agreement with experiment. Due to the complex leeside flowfield, lateral-directional characteristics are not as well predicted by the engineering codes. Experiment and flight data are in good agreement across the Mach number range.

Rhode, M. N.↗