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Buildings are the centerpiece of modern living, with more than half of the world’s population now living in urban environments. This demographic evolution has led to building use be-coming the main drain of our energy resources. According to the US Energy Information Administration, in 2016, 40 percent of the total energy consumption in the United States came from building use. However, we could overcome this energy drain by embedding new environmental technologies into future cities to realize energy-sustainable buildings.
The high frequency data acquisition system developed for the Space Shuttle Main Engine (SSME) single engine test facility at the National Space Technology Laboratories is discussed. The real time system will provide engineering data for a complete set of SSME instrumentation (approx. 100 measurements) within 4 hours following engine cutoff, a decrease of over 48 hours from the previous analog tape based system.
The policies and overall procedures that are used in distributing and in making available products of the Software Engineering Laboratory (SEL) are discussed. The products include project data and measures, project source code, reports, and software tools.
Exhaust gas pollutant emissions data under test stand conditions were obtained for the following: (1) full-rich baseline test (7-mode cycle), (2) lean-out tests for each power mode, and (3) different spark settings. The test data were also used to create a theoretical 5-mode cycle baseline. The emissions data in the framework of the theoretical 5-mode cycle were emphasized. There is no significant difference in the test results produced by data exhibited on the 7-mode cycle or 5-mode cycle. The 5-mode cycle was slightly more conservative for the carbon monoxide pollutant than the 7-mode cycle. The data were evaluated to determine which mode(s) had the greatest influence on improving general aviation piston engine emissions. Improvements that were achieved as a result of making lean-out adjustments to the fuel metering device were: (1) taxi mode only, (2) taxi and approach modes combined, and (3) leaning-out of the climb mode to best power.
These are raw (uncorrected) measurements from the DataHawk 1 sensor package. This is a level 0 data product and only minimal quality control measures have been applied. It is likely that substantial further corrections will be required to use these in a physically meaningful way. Please contact the PI if interested in using the dataset.
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC E test-stand complex and utilize the SSC file format. The programs are the following: (1) Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel. (2) QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post-test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot. (3) EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PVWAVE based plotting software.
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC E test-stand complex and utilize the SSC file format. The programs are the following: Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel. QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post-test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot (a free graphing program written by Rick Paris). EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PV-WAVE based plotting software.
Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC "E" test-stand complex and utilize the SSC file format. The programs are the following: 1) Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel; 2) QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot (a free graphing program written by Rick Paris); and 3) EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PVWAVE based plotting software.
Astrodynamical data compilation for use by system engineer for mission planning
Recent flight data on jet engine exhaust noise do not agree with projections based on classical jet noise theories. This paper demonstrates that these differences may be reconciled by considering the combined effects of jet mixing noise and internally-generated engine exhaust noise. The source strength of the internally-generated noise is assumed to be unaffected by flight, as has been shown in small-scale free-jet experiments. The directivity of the internally-generated noise is assumed to be the same statically as that given in the NASA interim prediction method for core engine noise. However, it is assumed that in flight the internally generated noise is subject to the convective amplification effect of a simple source. The absolute levels of the internally-generated noise are obtained from an empirical fit of some typical engine data. The static and flight jet noise are predicted using the NASA interim prediction method for jet noise. It is shown that in many cases, although jet mixing may be the dominant noise source statically, much of the flyover noise signature is dominated by internally-generated noise.
A nonlinear dynamic model and propulsion controller are developed for a small-scale turbofan engine. The small-scale turbofan engine is based on the Price Induction company's DGEN 380, one of the few turbofan engines targeted for the personal light jet category. Comparisons of the nonlinear dynamic turbofan engine model to actual DGEN 380 engine test data and a Price Induction simulation are provided. During engine transients, the nonlinear model typically agrees within 10 percent error, even though the nonlinear model was developed from limited available engine data. A gain scheduled proportional integral low speed shaft controller with limiter safety logic is created to replicate the baseline DGEN 380 controller. The new controller provides desired gain and phase margins and is verified to meet Federal Aviation Administration transient propulsion system requirements. In understanding benefits, there is a need to move beyond simulation for the demonstration of advanced control architectures and technologies by using real-time systems and hardware. The small-scale DGEN 380 provides a cost effective means to accomplish advanced controls testing on a relevant turbofan engine platform.
Engineering higher education increasingly produces data in the volume, variety, velocity, and need for veracity such that the output of the research is considered “Big Data”. While engineering faculty members do conceive of and direct the research producing this data, there may be gaps in faculty members’ knowledge in training graduate and undergraduate research assistants in the management of Big Data. The project described herein details the development of a Big Data education module for a group of graduate researchers and undergraduate research assistants in Electrical and Computer Engineering. This project has the following objectives: to document and describe current data management practices; to identify gaps in knowledge that need to be addressed in order for research assistants to successfully manage Big Data; and to create curricular interventions to address these gaps. This paper details the motivation, relevant literature, research methodology, curricular intervention, and pilot presentation of the module. Results indicate that, generally, students involved in Big Data projects need comprehensive introduction to the topic, which will be most effective when contextualized to the work that they are performing in the research or classroom environment.
The National Reactor Innovation Center (NRIC), established by the U.S. Department of Energy (DOE) in August 2019, accelerates the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is a national program led by Idaho National Laboratory (INL), enabling collaborators to harness the world-class capabilities of the U.S. National Laboratory System. Committed to demonstrating advanced reactors by the end of 2025, NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the Nation’s most promising advanced nuclear reactors into commercial applications by 2030. To meet these needs, NRIC is developing two reactor demonstration test beds at Idaho National Laboratory (INL), the Laboratory for Operation and Testing in the United States (LOTUS) and the Demonstration and Operation of Microreactor Experiments (DOME) test bed. Each test bed involves the modification of existing facilities at INL’s Materials and Fuels Complex (MFC). Retrofitting these facilities to accommodate novel reactors, as well as subsequent but similar NRIC projects, is a non-trivial engineering task and is expected to generate a significant amount of new and revised documentation. Leveraging digital engineering practices, this documentation will be managed in a purpose-built data management tool to facilitate origination, review, and approval while coordinating with the design contractor. The subsequent upload and review of documentation to INL’s existing document control system comprises a long and unnecessarily manual task. An opportunity exists to automate this upload process and save job-hours while continuing to adhere to INL/MFC document management procedures.
Test data for operation of a rotating detonation engine using Hydrogen-air mixtures at air mass fluxes of 40-115 kg/m2s and a range of global equivalence ratios using three different injection schemes are presented and discussed. Stable rotation of at least one detonation wave traveling at speeds of 1380-1650 m/s is achieved for each injector under certain conditions. A variety of stable, unstable, and transitory combustion processes are seen, and data collected from high-speed pressure transducers is used to characterize this behavior. Further insight into what drives the operating mode for each injector is provided with the aid of static pressure measurements and a simple model to estimate injector jet Mach numbers and momentum fluxes. Comparisons are made between the injectors using average pressure measurements taken along the detonation channel. When these data are normalized by the feed system stagnation pressure self similar profiles are seen and clear trends in pressure losses among the injectors emerge.
Metal additive manufacturing (AM) processes exhibit significant variability in the quality and properties of components that are produced. This variability has prevented the widespread adoption of AM in industry. The need for more advanced and descriptive process monitoring, part qualification, and process control has led to an increasing number of sensors on machines and subsequent data to analyze. Increasingly, data science principles are being leveraged in each of these domains in order to process this data and better understand the causes of variability and the corresponding quality inconsistencies that occur in additive manufacturing.
Video image data transmission using millimeter wave relay satellites