Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “CONSTRUCTION”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Evolution of A Distributed Live, Virtual, Constructive Environment for Human in the Loop Unmanned Aircraft Testing

NASA's Unmanned Aircraft Systems Integration in the National Airspace System Project is conducting human in the loop simulations and flight testing intended to reduce barriers associated with enabling routine airspace access for unmanned aircraft. The primary focus of these tests is interaction of the unmanned aircraft pilot with the display of detect and avoid alerting and guidance information. The project's integrated test and evaluation team was charged with developing the test infrastructure. As with any development effort, compromises in the underlying system architecture and design were made to allow for the rapid prototyping and open-ended nature of the research. In order to accommodate these design choices, a distributed test environment was developed incorporating Live, Virtual, Constructive, (LVC) concepts. The LVC components form the core infrastructure support simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing assets distributed across multiple NASA Centers. Using standard LVC concepts enable future integration with existing simulation infrastructure.

LVC↗
NASA NTRS2015

Message Latency Characterization of a Distributed Live, Virtual, Constructive Simulation Environment

A distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts was developed to execute standalone and integrated simulations and flight-tests that support unmanned aircraft research for NASA’s Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project. The LVC components form the core infrastructure that supports simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing distributed assets. The LVC concepts used for the UAS in the NAS project include live aircraft, flight simulators, and virtual air traffic control assets operating at facilities distributed across multiple NASA Centers. With a distributed network, however, there is a concern that message latency could impact the realism of a simulation and its data. The latencies associated with sending data among these distributed facilities were, therefore, measured to ensure that they fall within acceptable parameters. Several live and virtual test assets were integrated into the LVC infrastructure including NASA Armstrong’s Ikhana MQ-9 unmanned aircraft, NASA Glenn’s S3-B manned aircraft, and the B747 flight simulator at NASA Ames. Average latencies from 100 to 150 milliseconds were observed between the LVC System running at NASA Ames and each of the participating NASA Centers under a light-to-moderate (fifty aircraft) traffic sample.

Murphy, James R.↗
NASA NTRS2015

RUMS - Realtime Visualization and Evaluation of Live, Virtual, Constructive Simulation Data

This paper describes the tools and display capabilities being developed to visualize aircraft flight and trajectory information that is produced during a distributed Live, Virtual, Constructive (LVC) simulation events involving a mix of manned and unmanned aircraft. This paper discusses the design approach and general software architecture of the Remote User Monitor Service (RUMS) application. The simulations and flight tests are conducted over a nationwide network with components communicating via software gateways that translate the messages and provide a location to access and collect data. The data collected is not only archived for post-processing and analysis, but also processed in real-time by RUMS, which allows dynamic viewing of the information via a web browser. This real-time dynamic viewing of the data allows test conductors and researchers to monitor the health of the distributed system and of the data being collected not only in situ where tests are conducted but also at remote locations.

distributed simulation↗
NASA NTRS2021

Pedestal Hazard Abatement and Soil Removal Construction Completion Report for Launch Complex 34, Solid Waste Management Unit CC054, Cape Canaveral Space Force Station, Florida

This Construction Completion Report summarizes the activities associated with implementation of an interim measure (IM) for hazard abatement of the Launch Pedestal and soil removal at Launch Complex 34 (LC34), Cape Canaveral Space Force Station, Florida. The purpose of the hazard abatement IM was to mitigate any continuing source of contamination associated with residual paint on the Launch Pedestal that contained polychlorinated biphenyls (PCBs). Sampling determined that residual PCB concerns were limited to the steel elements on the top-side of the Pedestal, which included the torus ring, feed pipe, down pipes, pipe straps and shields, remaining steel cover plates and bolts, and railing support steel. The IM also mitigated the potential spread of contamination to surrounding areas by removing fallen metal fragments from the concrete launch pad, as well as soil from trenches on the launch pad where PCB-containing paint chips may have accumulated prior to or during Pedestal hazard abatement activities.

Complex 34↗
NASA NTRS2022

Hypergol Maintenance Facility North, SWMU 090, Air Sparge System Construction Completion and Performance Monitoring Report, Kennedy Space Center, Florida

This Air Sparge (AS) System Construction Completion and Performance Monitoring Report (CC-PMR) presents field implementation activities, as well as Year 1 operation, maintenance, and monitoring (OM&M) activities and performance monitoring results for the AS Interim Measure (IM) at the Hypergol Maintenance Facility North (HMFN) at Kennedy Space Center (KSC), Florida. The timeframe for activities included in this report extends from December 2019 to August 2021.

Christopher A Hook↗
NASA NTRSDate not supplied

Constructing a Knowledge Graph & Applying Graph Algorithms to Draw Insights about GES-DISC Jira Tickets

In order to assess the complexities of Jira tickets created by NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC), it was beneficial to create a knowledge graph. The knowledge graph receives ticket data through the Jira API. The creation of a knowledge graph will help to answer high-level questions about internal structure, knowledge gaps, and team organization within GES-DISC. To work towards this goal, the knowledge graph was constructed in adockerizedNeo4j graph database. Once the graph had been created, graph algorithms were applied to answer high-level questions, such as exploring the role of staff in relation to projects, which qualities of a ticket contribute to the formation of communities within the graph, etc. To answer these questions, centrality and community detection algorithms were applied using Cypher querying language. The analysis of the results of the algorithms indicated that, as expected, certain individuals were more connected to some projects, while others were serving as hub nodes between two or more projects. Similarly, specific keywords are more likely to increase a Jira ticket’s centrality in the graph. In terms of community detection, when tickets in a community have certain qualities, it is more probable for them to be grouped together. To best visualize which nodes had higher centrality scores or were grouped into certain communities, interactive graphs were created in Python using Plotly and Matplotlib. Ultimately, the project was successful in creating and deploying a knowledge graph to better understand the relationships between data in GES-DISC Jira tickets

Rebecca Lipton↗
NASA NTRS2022

Launch Complex 39A, SWMU 008 and Launch Complex 39B, SWMU 009 Performance Monitoring and Air Sparge Expansion Construction Completion Report Kennedy Space Center, Florida

This Performance Monitoring and Construction Completion Report (PM-CCR) presents the findings, observations, and results for air sparging (AS) operations and sitewide groundwater monitoring for Launch Complex 39A (LC39A), Solid Waste Management Unit (SWMU) 008, and Launch Complex 39B (LC39B), SWMU 009, at Kennedy Space Center (KSC), Florida. Results from direct push technology (DPT) sampling and AS system expansion activities at LC39A are also included in this report. The reporting period covered under this PM-CCR is from January 1, 2021, to December 31, 2021.

Andrew M Walters↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRS2023

Paint and Oil Locker, SWMU 067 Southern Treatment Area Air Sparge System Construction Completion and Performance Monitoring and Paint and Oil Locker, Northern Area (SWMU 067) and Supply Warehouse #3, SWMU 088 Long-Term Monitoring Report Kennedy Space Center, Florida

This Air Sparge (AS) System Construction Completion, Performance Monitoring, and Long-Term Monitoring (LTM) Report presents activities conducted at the Paint and Oil Locker (POL) (Solid Waste Management Unit [SWMU] 067) and Supply Warehouse #3 (SW3) (SWMU 088) sites located at the Kennedy Space Center (KSC), Florida. Activities include implementation and Year 1 (Quarters [Q] 1 through 4) and Q5 operation, maintenance, and monitoring (OM&M) activities and performance monitoring results for the AS Interim Measure (IM) in the POL Southern Treatment Area, LTM results for the POL Northern Area and SW3, and supplemental direct push technology (DPT) sampling at both sites to support the LTM and performance monitoring programs. The timeframe for activities included in this report extends from August 2019 to August 2022. The scope for these sites currently includes two main components, which are documented within this report: (1) LTM at SW3 and POL Northern Area where an AS system began operation at these sites in May 2009 and June 2013, respectively, and subsequently turned off for both sites in March 2018 (during the previous reporting period); and (2) Active AS operations and performance monitoring in the POL Southern Treatment Area where an AS system was installed and began operation in 2021. The AS IM in the POL Southern Treatment Area was implemented between 2019 and 2021 to treat a chlorinated solvent groundwater plume that resulted from historic operations supporting the National Aeronautics and Space Administration’s (NASA) Space Program. The objective of the AS IM is to remediate groundwater within the treatment zone to support transition to monitored natural attenuation (MNA). The overall Corrective Action objective is to reduce concentrations of trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), trans-1,2- dichloroethene (tDCE), and vinyl chloride (VC) present in groundwater to levels below their respective State of Florida Groundwater Cleanup Target Levels (GCTLs). The AS IM was installed in the POL Southern Treatment Area to target the high concentration plume (HCP), which includes a source zone (SZ) area where TCE was found to exceed 11,000 micrograms per liter (µg/L), and extend to a depth of 30 feet below land surface (bls).

TCE↗
NASA NTRSDate not supplied

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

We are the Coronado Ecological Conservation team at the GA, Athens Node. We have been Utilizing NASA Earth observations to assess invasive species spread and change in vegetation health in southeastern Arizona due to the construction of the US-Mexico border wall. The NPS at Coronado National Memorial will use our end products to bolster their plans and funding requests for restoration work in the disturbed areas.

Carson Schuetze↗
NASA NTRSDate not supplied

Thermal Vacuum Testing Strategy and Thermal Desktop Model Correlation for the Moon to Mars Planetary Autonomous Construction Technologies (MMPACT) Robotic Terrestrial Arm

This paper discusses the initial thermal vacuum testing of the MMPACT robotic terrestrial arm. The robotic arm is part of a construction system designed for the lunar south pole surface. The first thermal vacuum test was a risk mitigation test to ensure the arm could operate in vacuum, with all other data collection as secondary priorities. 44 thermocouples (TCs) were attached to the arm. Installation was done with additional care to account for both the extra wiring harness weight on the arm with the TC wires and increased focus on stabilizing the TC attachments to the moving components. Thermal steady state of <= 0.01°C/hour was reached for the hot set of testing conditions. This data was used to correlate the Thermal Desktop (TD) model to the test results within +/- 5°C.

Thermal analysis↗
NASA NTRSDate not supplied

Thermal Vacuum Testing Strategy and Thermal Desktop Model Correlation for the Moon to Mars Planetary Autonomous Construction Technologies (MMPACT) Robotic Terrestrial Arm

This paper discusses the initial thermal vacuum testing of the MMPACT robotic terrestrial arm. The robotic arm is part of a construction system designed for the lunar south pole surface. The first thermal vacuum test was a risk mitigation test to ensure the arm could operate in vacuum, with all other data collection as secondary priorities. 44 thermocouples (TCs) were attached to the arm. Installation was done with additional care to account for both the extra wiring harness weight on the arm with the TC wires and increased focus on stabilizing the TC attachments to the moving components. Thermal steady state of <= 0.01°C/hour was reached for the hot set of testing conditions. This data was used to correlate the Thermal Desktop (TD) model to the test results within +/- 5°C.

Thermal analysis↗
DOE OSTI2014

Constructing a logical, regular axis topology from an irregular topology

Constructing a logical regular topology from an irregular topology including, for each axial dimension and recursively, for each compute node in a subcommunicator until returning to a first node: adding to a logical line of the axial dimension a neighbor specified in a nearest neighbor list; calling the added compute node; determining, by the called node, whether any neighbor in the node's nearest neighbor list is available to add to the logical line; if a neighbor in the called compute node's nearest neighbor list is available to add to the logical line, adding, by the called compute node to the logical line, any neighbor in the called compute node's nearest neighbor list for the axial dimension not already added to the logical line; and, if no neighbor in the called compute node's nearest neighbor list is available to add to the logical line, returning to the calling compute node.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Constructing a logical, regular axis topology from an irregular topology

Constructing a logical regular topology from an irregular topology including, for each axial dimension and recursively, for each compute node in a subcommunicator until returning to a first node: adding to a logical line of the axial dimension a neighbor specified in a nearest neighbor list; calling the added compute node; determining, by the called node, whether any neighbor in the node's nearest neighbor list is available to add to the logical line; if a neighbor in the called compute node's nearest neighbor list is available to add to the logical line, adding, by the called compute node to the logical line, any neighbor in the called compute node's nearest neighbor list for the axial dimension not already added to the logical line; and, if no neighbor in the called compute node's nearest neighbor list is available to add to the logical line, returning to the calling compute node.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Introduction to Cost Control Strategies for Zero Energy Buildings: High-Performance Design and Construction on a Budget (Fact Sheet)

Momentum behind zero energy building design and construction is increasing, presenting a tremendous opportunity for advancing energy performance in the commercial building industry. At the same time, there is a lingering perception that zero energy buildings must be cost prohibitive or limited to showcase projects. Fortunately, an increasing number of projects are demonstrating that high performance can be achieved within typical budgets. This factsheet highlights replicable, recommended strategies for achieving high performance on a budget, based on experiences from past projects.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗