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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 2: Influence of horizontal tail location for Model D

The influence of horizontal tail location on the rotational flow aerodynamics is discussed for a 1/6-scale general aviation airplane model. The model was tested using various horizontal tail positions, with both a high and a low-wing location and for each of two body lengths. Data were measured, using a rotary balance, over an angle-of-attack range of 8 to 90 deg, and for clockwise and counter-clockwise rotations covering an Omega b/2V range of 0 to 0.9.

Barnhart, B.↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
NASA NTRS2010

System and method for responding to ground and flight system malfunctions

A system for on-board anomaly resolution for a vehicle has a data repository. The data repository stores data related to different systems, subsystems, and components of the vehicle. The data stored is encoded in a tree-based structure. A query engine is coupled to the data repository. The query engine provides a user and automated interface and provides contextual query to the data repository. An inference engine is coupled to the query engine. The inference engine compares current anomaly data to contextual data stored in the data repository using inference rules. The inference engine generates a potential solution to the current anomaly by referencing the data stored in the data repository.

Fussell, Ronald M.↗
NASA NTRS1992

Determination of YAV-8B Reaction Control System bleed flow usage

Using a calibrated Rolls-Royce Pegasus engine, total Reaction Control System (RCS) bleed flow rates have been measured on a YAV-8B Harrier during typical short takeoff, transition, hover and vertical landing maneuvers. Using existing aircraft instrumentation and pressure taps located in the RCS ducts, bleed flow rates at each RCS valve were also measured directly during flight and ground tests. These data were compared with the calibrated engine data and with the RCS part of a YAV-8B mathematical model used in piloted simulation at NASA Ames Research Center. Areas of disagreement were small, being confined to the estimation of closed RCS valve leakages and the modeling of the RCS butterfly valve pressure losses.

Borchers, Paul F.↗
DOE OSTI2024

Dataset for "Large Language Models as molecular design engines"

This dataset contains data and results associated with the paper "Large Language Models as molecular design engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 1: Influence of airplane components for model D

The influence of airplane components, as well as wing location and tail length, on the rotational flow aerodynamics is discussed for a 1/6 scale general aviation airplane model. The airplane was tested in a built-up fashion (i.e., body, body-wing, body-wing-vertical, etc.) in the presence of two wing locations and two body lengths. Data were measured, using a rotary balance, over an angle-of-attack range of 8 deg to 90 deg, and for clockwise and counter-clockwise rotations covering an omega b/2V range of 0 to 0.9.

Ralston, J.↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗
NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗
NASA NTRS1988

Cruise noise of an advanced counterrotation turboprop measured from an adjacent aircraft

Acoustic test results are presented for a full-scale counterrotation demonstrator engine installed on a Boeing 727 aircraft in place of the right-side turbofan engine. Sideline acoustic data were acquired from a Learjet chase aircraft instrumented with noise and wing-tip flush mount microphones. Data are presented for a 47.2-m sideline at several engine operating conditions and flight Mach numbers of 0.50 and 0.72.

Woodward, Richard P.↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
DOE OSTI2020

Multi-mode advanced combustion engine with supervisory control

An engine system uses data associated with at least one operating condition of an engine to set the engine system to an AI mode when the engine is in an SI mode 1) within first operating condition limits, and 2) when a rate of change of a first operating condition is within rate of change limits, maintain the engine system in the SI mode when the engine is outside of first operating condition limits or when the rate of change of the first operating condition is not within rate of change limits, set the engine system to the SI mode when the engine is in the AI mode outside second operating condition limits, and maintain the engine system in the AI mode when the engine is within second operating condition limits, wherein the second operating condition limits are different from the first operating condition limits.

42 ENGINEERING↗
DOE OSTI2015

Integrated sampling and data flow for Hanford vitrification - 15337

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is a one-of-a-kind nuclear facility being designed, built, and commissioned for the U.S. Department of Energy (DOE) by Bechtel National, Inc. and its subcontractor URS Corporation. The WTP facilities are under construction to complete the design and construction, as well as planning to support the startup, commissioning, and operation for vitrifying the radioactive waste stored in aging underground tanks at the Hanford Site. The work progress includes development of waste feed acceptance and qualification program, including methodologies for processing feed batches. Qualification data from staged feed samples will support the verification that waste acceptance criteria (WAC) and processability requirements are met. This verification is required to ensure safe operation of WTP processes for vitrifying the waste, while establishing specific campaign and batch sheets to comply with glass form acceptance requirements. Sampling and data integration are critical for process control and monitoring of the feed through unit operations. Data collection begins with process knowledge of staged feed for determining the required WAC analyses and testing for unit operations. The waste feed qualification data form the baseline information for WTP operation. The qualification data will be tracked and compared with sample analyses at different steps in vitrification operation. All aspects of data life cycle will be managed by the WTP Plant Engineering organization. The data management approach is established in this manner to allow maintaining campaign traceability through glass production records, while safely processing the corresponding feed campaigns and complying with permitting requirements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2007

NASA Tech Briefs, March 2007

Topics include: Advanced Systems for Monitoring Underwater Sounds; Wireless Data-Acquisition System for Testing Rocket Engines; Processing Raw HST Data With Up-to-Date Calibration Data; Mobile Collection and Automated Interpretation of EEG Data; System for Secure Integration of Aviation Data; Servomotor and Controller Having Large Dynamic Range; Digital Multicasting of Multiple Audio Streams; Translator for Optimizing Fluid-Handling Components; AIRSAR Web-Based Data Processing; Pattern Matcher for Trees Constructed From Lists; Reducing a Knowledge-Base Search Space When Data Are Missing; Ground-Based Correction of Remote-Sensing Spectral Imagery; State-Chart Autocoder; Pointing History Engine for the Spitzer Space Telescope; Low-Friction, High-Stiffness Joint for Uniaxial Load Cell; Magnet-Based System for Docking of Miniature Spacecraft; Electromechanically Actuated Valve for Controlling Flow Rate; Plumbing Fixture for a Microfluidic Cartridge; Camera Mount for a Head-Up Display; Core-Cutoff Tool; Recirculation of Laser Power in an Atomic Fountain; Simplified Generation of High-Angular-Momentum Light Beams; Imaging Spectrometer on a Chip; Interferometric Quantum-Nondemolition Single-Photon Detectors; Ring-Down Spectroscopy for Characterizing a CW Raman Laser; Complex Type-II Interband Cascade MQW Photodetectors; Single-Point Access to Data Distributed on Many Processors; Estimating Dust and Water Ice Content of the Martian Atmosphere From THEMIS Data; Computing a Stability Spectrum by Use of the HHT; Theoretical Studies of Routes to Synthesis of Tetrahedral N4; Estimation Filter for Alignment of the Spitzer Space Telescope; Antenna for Measuring Electric Fields Within the Inner Heliosphere; Improved High-Voltage Gas Isolator for Ion Thruster; and Hybrid Mobile Communication Networks for Planetary Exploration.

Source record↗
NASA NTRS1990

Architecture independent environment for developing engineering software on MIMD computers

Engineers are constantly faced with solving problems of increasing complexity and detail. Multiple Instruction stream Multiple Data stream (MIMD) computers have been developed to overcome the performance limitations of serial computers. The hardware architectures of MIMD computers vary considerably and are much more sophisticated than serial computers. Developing large scale software for a variety of MIMD computers is difficult and expensive. There is a need to provide tools that facilitate programming these machines. First, the issues that must be considered to develop those tools are examined. The two main areas of concern were architecture independence and data management. Architecture independent software facilitates software portability and improves the longevity and utility of the software product. It provides some form of insurance for the investment of time and effort that goes into developing the software. The management of data is a crucial aspect of solving large engineering problems. It must be considered in light of the new hardware organizations that are available. Second, the functional design and implementation of a software environment that facilitates developing architecture independent software for large engineering applications are described. The topics of discussion include: a description of the model that supports the development of architecture independent software; identifying and exploiting concurrency within the application program; data coherence; engineering data base and memory management.

Valimohamed, Karim A.↗
NASA NTRS1981

Advanced oxygen-hydrocarbon rocket engine study

This study identifies and evaluates promising LO2/HC rocket engine cycles, produces a consistent and reliable data base for vehicle optimization and design studies, demonstrates the significance of propulsion system improvements, and selects the critical technology areas necessary to realize an improved surface to orbit transportation system. Parametric LO2/HC engine data were generated over a range of thrust levels from 890 to 6672 kN (200K to 1.5M 1bF) and chamber pressures from 6890 to 34500 kN (1000 to 5000 psia). Engine coolants included RP-1, refined RP-1, LCH4, LC3H8, LO2, and LH2. LO2/RP-1 G.G. cycles were found to be not acceptable for advanced engines. The highest performing LO2/RP-1 staged combustion engine cycle utilizes LO2 as the coolant and incorporates an oxidizer rich preburner. The highest performing cycle for LO2/LCH4 and LO2/LC3H8 utilizes fuel cooling and incorporates both fuel and oxidizer rich preburners. LO2/HC engine cycles permitting the use of a third fluid LH2 coolant and an LH2 rich gas generator provide higher performance at significantly lower pump discharge pressures. The LO2/HC dual throat engine, because of its high altitude performance, delivers the highest payload for the vehicle configuration that was investigated.

Obrien, C. J.↗