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JPL is constructing an Optical Communications Telescope Laboratory (OCTL) at its Table Mountain Facility complex in the San Bernadino Mountains of Southern California.
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.
The Overarching Properties (OPs) distill the essence of the implicit properties that decades of practice have determined an aviation system or subsystem must possess to be eligible for approval by relevant authorities. This document presents the fundamentals of construction and assessment for explicit arguments that conclude a system or subsystem possesses the OPs.
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.
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
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
In multifamily buildings, particularly in the Northeast, exhaust ventilation strategies are the norm as a means of meeting both local exhaust and whole-unit mechanical ventilation rates. The issue of where the 'fresh' air is coming from is gaining significance as air-tightness standards for enclosures become more stringent, and the 'normal leakage paths through the building envelope' disappear. CARB researchers have found that the majority of high performance, new construction, multifamily housing in the Northeast use one of four general strategies for ventilation: continuous exhaust only with no designated supply or make-up air source, continuous exhaust with ducted make-up air to apartments, continuous exhaust with supply through a make-up air device integral to the unit HVAC, and continuous exhaust with supply through a passive inlet device, such as a trickle vent. Insufficient information is available to designers on how these various systems are best applied. Product performance data are based on laboratory tests, and the assumption is that products will perform similarly in the field. Proper application involves matching expected performance at expected building pressures, but there is no guarantee that those conditions will exist consistently in the finished building. This research effort, which included several weeks of building pressure monitoring, sought to provide field validation of system performance. The performance of four substantially different strategies for providing make-up air to apartments was evaluated.
The 2012 IECC has an airtightness requirement of 3 air changes per hour at 50 Pascals test pressure for both single family and multifamily construction in Climate Zones 3-8. Other programs (LEED, ASHRAE 189, ASHRAE 62.2) have similar or tighter compartmentalization requirements, thus driving the need for easier and more effective methods of compartmentalization in multifamily buildings.
More than 7 cameras recorded the two year long process of constructing the world's largest self-supporting plastic structure, the NOvA experiment's far detector.