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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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Studies conducted as part of the NASA-Lewis CF6 jet engine diagnostics program are summarized. An 82-engine sample of DC-10-10 aircraft engine checkout data that were gathered to define the extent and magnitude of CF6-6D short term performance deterioration were analyzed. These data are substantiated by the performance testing and analytical teardown of CF6-6D short term deterioration engine serial number (ESN) 451507.
The development of the Mod I 4-cylinder automotive Stirling engine is discussed and illustrated with drawings, block diagrams, photographs, and graphs and tables of preliminary test data. The engine and its drive, cold-engine, hot-engine, external-heat, air/fuel, power-control, electronic-control, and auxiliary systems are characterized. Performance results from a total of 1900 h of tests on 4 prototype engines include average maximum efficiency (at 2000 rpm) 34.5 percent and maximum output power 54.4 kW. The modifications introduced in an upgraded version of the Mod I are explained; this engine has maximum efficiency 40.4 percent and maximum power output 69.2 kW.
This report documents the work done on the bearing assessment program over the past two and a half years. The objective of the program is to develop a nondestructive evaluation system for the SSME HPOTP's which would be used to detect anomalies in installed bearings without engine disassembly. Data bases of various signatures are obtained by slowly turning the pump shafts before and after an engine firing. These signatures are then analyzed and compared to the original signatures to more accurately predict bearing wear.
COMBUSTOR and CNOISE are computer codes that predict far-field noise that originates in the combustors of modern aircraft turbine engines -- especially modern, low-gaseous-emission engines, the combustors of which sometimes generate several decibels more noise than do the combustors of older turbine engines. COMBUSTOR implements an empirical model of combustor noise derived from correlations between engine-noise data and operational and geometric parameters, and was developed from databases of measurements of acoustic emissions of engines. CNOISE implements an analytical and computational model of the propagation of combustor temperature fluctuations (hot spots) through downstream turbine stages. Such hot spots are known to give rise to far-field noise. CNOISE is expected to be helpful in determining why low-emission combustors are sometimes noisier than older ones, to provide guidance for refining the empirical correlation model embodied in the COMBUSTOR code, and to provide insight on how to vary downstream turbinestage geometry to reduce the contribution of hot spots to far-field noise.
The major changes in the NASP program caused mostly by budget pressures are described and current technology developments are highlighted. Particular attention is given to the program funding, vehicle and engine design, and the technology program. It is concluded that the success of the X-30 vehicle design depends on the success of the technology development effort. The major accomplishments of the NASP technology program include the T-5 Shock Tunnel at California Institute of Technology and Mach 16 injection and mixing experiments conducted in this facility; the low speed component integration rig which provide data on engine operation from static conditions to Mach 3.5; the module to module interaction test which provided data on the effects of inlet unstart in one engine module on an adjacent module at Mach 4.3; and the direct-connect module used for fuel injection and mixing studies at Mach 6-8.
The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.
Pressures and temperatures throughout the X24C-4B turbojet engine are presented in both tabular and graphical forms to show the effect of altitude, flight Mach number, and engine speed on the internal operation of the engine. These data were obtained in the NACA Cleveland altitude wind tunnel at simulated altitudes from 5000 to 45,000 feet, simulated flight Mach numbers from 0.25 to 1.08, and engine speeds from 4000 to 12,500 rpm. Location and detail drawings of the instrumentation installed at seven survey stations in the engine are shown. Application of generalization factors to pressures and temperatures at each measuring station for the range of altitudes investigated showed that the data did not generalize above an altitude of 25,000 feet. Total-pressure distribution at the compressor outlet varied only with change in engine speed. At altitudes above 35,000 feet and engine speeds above 11,000 rpm, the peak temperature at the turbine-outlet annulus moved inward toward the root of the blade, which is undesirable from blade-stress considerations. The temperature levels at the turbine outlet and the exhaust-nozzle outlet were lowered as the Mach number was increased. The static-pressure measurements obtained at each stator stage of the compressor showed a pressure drop through the inlet guide vanes and the first-stage rotor at high engine speeds. The average values measured by the manufacturer's instrumentation werein close agreement with the average values obtained with NACA instrumentation.
Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.
Digital data from LANDSAT for each 1.1-acre cell in Georgia were processed and the land cover conditions were categorized. Several test cases were completed and an operational hardware and software processing capability was established at the Georgia Institute of Technology. The operational capability was developed to process the entire state (60,000 sq. miles and 14 LANDSAT scenes) in a cooperative project between eleven divisions and agencies at the regional, state, and federal levels. Products were developed for State agencies such as in both mapped and statistical formats. A computerized geographical data base was developed for management programs. To a large extent the applications of the data base evolved as users of LANDSAT information requested that other data (i.e., soils, slope, land use, etc.) be made compatible with LANDSAT for management programs. To date, geographic data bases incorporating LANDSAT and other spatial data deal with elements of the municipal solid waste management program, and reservoir management for the Corps of Engineers. LANDSAT data are also being used for applications in wetland, wildlife, and forestry management.
Wide-ranging sets of data for aerospace industry described. Document describes Materials and Processes Technical Information System (MAPTIS), computerized set of integrated data bases for use by NASA and aerospace industry. Stores information in standard format for fast retrieval in searches and surveys of data. Helps engineers select materials and verify their properties. Promotes standardized nomenclature as well as standarized tests and presentation of data. Format of document of photographic projection slides used in lectures. Presents examples of reports from various data bases.
Modern high speed propeller (advanced turboprop) aircraft are expected to operate on 50 to 60 percent less fuel than the 1980 vintage turbofan fleet while at the same time matching the flight speed and performance of those aircraft. Counterrotation turboprop engines offer additional fuel savings by means of upstream propeller swirl recovery. This paper presents acoustic sideline results for a full-scale counterrotation turboprop engine at cruise conditions. The engine was installed on a Boeing 727 aircraft in place of the right-side turbofan engine. Acoustic data were taken from an instrumented Learjet chase plane. Sideline acoustic results are presented for 0.50 and 0.72 Mach cruise conditions. A scale model of the engine propeller was tested in a wind tunnel at 0.72 Mach cruise conditions. The model data were adjusted to flight acquisition conditions and were in general agreement with the flight results.
The exhaust characteristics of the F-18 aircraft with an F404 engine are examined with reference to the results of an acoustic flight testing program. The discussion covers an overview of the flight test planning, instrumentation, test procedures, data analysis, engine modeling codes, and results. In addition, the paper presents the exhaust velocity and Mach number data for the climb-to-cruise, Aircraft Noise Prediction Program validation, and ground tests.
During some flight programs, researchers have encountered problems in the throttle response characteristics of high-performance aircraft. To study and to help solve these problems, the National Aeronautics and Space Administration Ames Research Center's Dryden Flight Research Facility (Ames-Dryden) conducted a study using a TF-104G airplane modified with a variable-response electronic throttle control system. Ames-Dryden investigated the effects of different variables on engine response and handling qualities. The system provided transport delay, lead and lag filters, second-order lags, command rate and position limits, and variable gain between the pilot's throttle command and the engine fuel controller. These variables could be tested individually or in combination. Ten research flights were flown to gather data on engine response and to obtain pilot ratings of the various system configurations. The results should provide design criteria for engine-response characteristics. The variable-response throttle components and how they were installed in the TF-104G aircraft are described. How the variable-response throttle was used in flight and some of the results of using this system are discussed.
Generation and interpretation of input and output data for Structural Analysis and Matrix Interpretive System /SAMIS/
Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.
National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.
Improvements in fuel economy for a broad spectrum of truck engines and waste heat utilization concepts are evaluated and compared. The engines considered are the diesel, spark ignition, gas turbine, and Stirling. The waste heat utilization concepts include preheating, regeneration, turbocharging, turbocompounding, and Rankine engine compounding. Predictions were based on fuel-air cycle analyses, computer simulation, and engine test data. The results reveal that diesel driving cycle performance can be increased by 20% through increased turbocharging, turbocompounding, and Rankine engine compounding. The Rankine engine compounding provides about three times as much improvement as turbocompounding but also costs about three times as much. Performance for either is approximately doubled if applied to an adiabatic diesel.
A computerized method was developed to estimate weight and envelope dimensions of large and small gas turbine engines within + or - 5% to 10%. The method is based on correlations of component weight and design features of 29 data base engines. Rotating components were estimated by a preliminary design procedure which is sensitive to blade geometry, operating conditions, material properties, shaft speed, hub tip ratio, etc. The development and justification of the method selected, and the various methods of analysis are discussed.