Results for “data engineering” — Page 38

NASA NTRS2022

Generating Landslide Density Heatmaps for Rapid Detection Using Open-access Satellite Radar Data in Google Earth Engine

Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

Alexander L Handwerger↗

DOE OSTI2026

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗

NASA NTRS1969

ALSEP/SIDE/CCGE /Apollo Lunar Surface Experiments Package/Suprathermal Ion Detector Experiment/Cold Cathode Gauge Experiment/. Volume 2 - Appendix Final engineering report

Data for experiments for detecting lunar atmosphere and surface ion emission

Pollack, S.↗

NASA NTRS1983

The performance and application of high speed long life LH2 hybrid bearings for reusable rocket engine turbomachinery

Data are presented for two different experimental programs which were conducted to investigate the characteristics of a hybrid (hydrostatic/ball) bearing operating in liquid hydrogen. The same bearing design was used in both programs. Analytical predictions were made of the bearing characteristics and are compared with the experimental results when possible. The first program used a bearing tester to determine the steady state, transient, and cyclic life characteristics of the bearing over a wide range of operating conditions. The second program demonstrated the feasibility of applying hybrid bearings to an actual high speed turbopump by retrofitting and then testing an existing liquid hydrogen turbopump with the bearings.

Hannum, N. P.↗

NASA NTRS1983

The performance and application of high speed long life LH2 hybrid bearings for reusable rocket engine turbomachinery

Data are presented for two different experimental programs which were conducted to investigate the characteristics of a hybrid (hydrostatic/ball) bearing operating in liquid hydrogen. The same bearing design was used in both programs. Analytical predictions were made of the bearing characteristics and are compared with the experimental results when possible. The first program used a bearing tester to determine the steady state, transient, and cyclic life characteristics of the bearing over a wide range of operating conditions. The second program demonstrated the feasibility of applying hybrid bearings to an actual high speed turbopump by retrofitting and then testing an existing liquid hydrogen turbopump with the bearings.

Hannum, N. P.↗

DOE OSTI2021

Novel Temperature Sensors and Wireless Telemetry for Active Condition Monitoring of Advanced Gas Turbines

The objective of the program is to develop and engine test hardware and software technologies that will enable active condition monitoring to be implemented on hot gas path turbine blades in large industrial gas turbines. The specific objectives are (1) to fabricate and install Smart Turbine Blades with thermally sprayed sensors and high temperature wireless telemetry systems in a gas turbine engine, (2) to integrate the component engine test data with remaining useful life (RUL) models and develop an approach for networking the component RUL data with Siemens' Power Diagnostics® engine monitoring system. These significant advances carried out in Phase 1 in temperature wide bandgap telemetry, along with new induced power driver and receiver geometry combined with an innovative approach to transmit digital data wirelessly will enable the opportunity to proceed with more technical innovation. The Phase 2 program focused on validation testing of sensor-wireless telemetry package in spin rig and advanced operation-based assessment (OBA) model utilizing artificial intelligence. Significant efforts were dedicated on the download of the technology onto components to be tested an actual gas turbine engine for full realization of active condition monitoring for Smart Turbine Blades.

03 NATURAL GAS↗

DOE OSTI2021

NRIC Digital Engineering Ecosystem

The NRIC Digital Engineering Ecosystem is a software suite designed to eliminate procedural failures in the engineering lifecycle of complex projects. It focuses on improving the flow of scheduling data, safety information, engineering requirements, and CAD files among stakeholders of complex engineering projects. The project comprises software adapters that move engineering data from disparate software tools in and out of a central database. The ecosystem is designed to efficiently incorporate data sources from many different engineering toolsets, making it software agnostic, and adaptable to many different engineering applications.

Harris, BrennanK↗

DOE OSTI2021

High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. Furthermore, this paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStock TM and ResStock TM ), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1993

NASA data exchange standards for computational fluid dynamics

This presentation covers the following topics in viewgraph format: purpose of data exchange standards; data exchange in engineering analysis/CFD; geometry data exchange through existing product data exchange standards, NASA Data Exchange Committee, and NASA-IGES (Initial Graphics Exchange Specification); CFD grid and solution data exchange; and data exchange for multi-disciplinary engineering.

Matthew Blake↗

NASA NTRS2014

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

propulsion system performance↗

NASA NTRS2014

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

propulsion system performance↗

NASA NTRS2015

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

systems health monitoring↗

NASA NTRS2017

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

SERVI↗

NASA NTRS1973

Experimental quiet engine program. Predicted engine performance

Three turbofan configurations, each incorporating alternative noise reduction features, were tested under the Quiet Engine Program. Performance data for these engines are shown over a range of flight conditions. The data are presented in tabular form for standard day flight inlet conditions. Procedures for estimating nonstandard day performance are shown. Tabular data and calculation procedures to allow determination of ram recovery, customer bleed, and customer shaft power extraction effects on engine performance can be found in the original Performance Brochure titled, Experimental Quiet Engine Program, Predicted Engine Performance, dated April 8, 1970. Predicted engine noise levels for representative take-off and approach conditions are provided.

Source record↗

DOE OSTI2024

Collection And Analysis Of Telemetry For The Cyote Heuristic

CATCH CLI focuses on gathering telemetry data, storing it in the Neo4j database, querying for Mitre ATT&CK patterns, and creating STIX 2.1 reports. Key Components: Analysis Modules: Analyze data to detect attack patterns. GoSTOTS Collection Engines: Collect telemetry data. These tools can be used together or individually. Analysis modules rely on data from specific engines to identify attack patterns. Source Code Organization: Engines: CATCH/catch/cmd/collection Modules: CATCH/catch/cmd/analysis CGUI Overview CATCH Graphical User Interface (CGUI) offers a graphical shell to execute CATCH CLI, allowing easy editing of: Analysis Modules Database configurations Profiles (collection and device settings) Neo4j Overview Neo4j is a graph database using the Cypher query language, storing data in JSON. It seamlessly integrates with STIX 2.1 data for: Data Submission: CATCH Collection Engines Data Querying: Analysis Modules CATCH modifies STIX 2.1 data for Neo4j submission and reverts it back during querying. STIG Overview Structured Threat Intelligence Graph (STIG) is a tool for creating, editing, querying, analyzing, and visualizing threat intelligence using STIX 2.1 and storing data in Neo4j. Usage Tools can be run: Manually (CLI): Refer to CATCH documentation User Interface: Run ./cgui/CGUI or go run ./cgui/ Additional Information Logging System: Detailed in the config documentation Further Documentation: Available for CATCH and CGUI

Madsen, MichaelJ. [Idaho National Laboratory (INL)↗

NASA NTRS1990

Annual Health Monitoring Conference for Space Propulsion Systems, 2nd, Cincinnati, OH, Nov. 14, 15, 1990, Proceedings

This conference discusses health management systems for liquid rocket engines, signal processing of spectroscopic data for engine diagnostic applications, real-time diagnostics of the reusable rocket engine using on-line system identification, and nonlinear parameter estimation for rocket engine health monitoring. Plume diagnostics are considered including discussion on preliminary plume diagnostics on the SSME using video digitization techniques and baseline plume emmissions. The development of a Fabry-Perot Interferometer for rocket engine plumes is also considered. The topic of computational modeling is also discussed, addressing such topics as probabalistic dynamics of material loss in the SSME turbomachinery, state-variable modelling of fluid-dynamics processes, and a model for the SSME high pressure oxidizer turbopump shaft seal system. A discussion of sensor development includes the topics of sensor development for the rocket engine condition monitoring system and the heat flux measurements in SSME turbine blade tester.

Source record↗

NASA NTRS1993

An information model for use in software management estimation and prediction

This paper describes the use of cluster analysis for determining the information model within collected software engineering development data at the NASA/GSFC Software Engineering Laboratory. We describe the Software Management Environment tool that allows managers to predict development attributes during early phases of a software project and the modifications we propose to allow it to develop dynamic models for better predictions of these attributes.

Li, Ningda R.↗

NASA NTRS1995

Overview of thermal barrier coatings in diesel engines

An understanding of delamination mechanisms in thermal barrier coatings has been developed for diesel applications through nondestructive evaluation, structural analysis modeling and engine evaluation of various thermal barrier coatings. This knowledge has resulted in improved thermal barrier coatings which survive abusive cyclic fatigue tests in high output diesel engines. Significant efforts are still required to improve the plasma spray processing capability and the economics for complex geometry diesel engine components. Data obtained from advanced diesel engines on the effect of thermal barrier coatings on engine fuel economy and emission has not been encouraging. Although the underlying metal component temperatures have been reduced through the use of thermal barrier coating, engine efficiency and emission trends have not been promising.

Yonushonis, T. M.↗