MCC-H computer support requirements for mission control and training
Explore the source record for details and available documents.
Pauly, R. J.↗
SEARCH · Engineering Papers
Results for “MCC”
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.
OPS MCC level B/C formulation requirements: Area targets and space volumes processor
The level B/C mathematical specifications for the area targets and space volume processor (ATSVP) as well as the characteristics of the system are provided. The mathematical equation necessary to determine whether the spacecraft lies within the area target or space volume is presented. A semianalytical technique for predicting the acquisition of signal (AOS) and loss of signal (LOS) time periods is discussed. A functional overview of the ATSVP which includes an outline of the process required to determine precise AOS and LOS times are given.
Bishop, M. J., Jr.↗
MCC level C formulation requirements. Shuttle TAEM targeting
The level C requirements for the shuttle orbiter terminal area energy management (TAEM) guidance and flight control functions to be incorporated into the Mission Control Center entry profile planning processor are described. This processor is used for preentry evaluation of the entry through landing maneuvers, and includes a simplified three degree-of-freedom model of the body rotational dynamics that is necessary to account for the effects of attitude response on the trajectory dynamics. This simulation terminates at TAEM-autoland interface.
Carman, G. L.↗
Shuttle program. MCC level C formulation requirements: Shuttle TAEM guidance and flight control
The Level C requirements for the shuttle orbiter terminal area energy management (TAEM) guidance and flight control functions to be incorporated into the Mission Control Center entry profile planning processor are defined. This processor will be used for preentry evaluation of the entry through landing maneuvers, and will include a simplified three degree-of-freedom model of the body rotational dynamics that is necessary to account for the effects of attitude response on the trajectory dynamics. This simulation terminates at TAEM-autoland interface.
Carman, G. L.↗
MCC flight operations automation of STS orbiter systems monitoring
The integration of new capabilities into the Mission Control Center at the Johnson Space Center in Houston, Texas in order to improve the data monitoring and analysis of the STS is discussed. User interface application software and systems, such as the flight data file Shuttle mission simulator and failure analysis program, to be utilized for flight operations automation are examined. Consideration is given to task automation, the trend monitoring system, the data retrieval system, and the launch processing system. The development and functions of the Shuttle data analysis system, which is to operate in real time, are described.
Bates, William V., Jr.↗
MPAD MCC workstation system: ADDAM and EEVE
The flight control consoles in mission control at NASA's Johnson Space Center have evolved in sophistication to the level where expert systems are now being incorporated into them as flight controller assistants. The evolution of the gateway mode, designed to obtain and redistribute numerous kinds of data provided by Mission Control computers over a laser-optical network to enable rapid prototyping development of the above application expert systems is described. This automated distribution and management system serves as an effective buffering system for assuring the necessarily separate requirements of the operational and developmental environments.
Kozick, Bill, Jr.↗
NASA Earth Science Mission Control Center Enterprise Emerging Technology Study Study (MCC Technology Study)
This paper reports on the results of the study to identify technologies that could have a significant impact on Earth Science mission operations when looking out at the 5-15 year horizon (through 2025). The potential benefits of the new technologies will be discussed, as well as recommendations for early research and development, prototyping, or analysis for these technologies.
Smith, Dan↗
Characterizing Mesoscale Cellular Convection in Marine Cold Air Outbreaks With a Machine Learning Approach
Abstract During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.
Meteorology & Atmospheric Sciences↗
Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site
Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.
54 ENVIRONMENTAL SCIENCES↗
James Webb Space Telescope Initial Mid-Course Correction Monte Carlo Implementation using Task Parallelism
The James Webb Space Telescope will be launched into a highly elliptical orbit that does not possess sufficient energy to achieve a proper Sun-Earth/Moon L2 libration point orbit. Three mid-course correction (MCC) maneuvers are planned to rectify the energy deficit: MCC-1a, MCC-1b, and MCC-2. To validate the propellant budget and trajectory design methods, a set of Monte Carlo analyses that incorporate MCC maneuver modeling and execution are employed. The first analysis focuses on the effects of launch vehicle injection errors on the magnitude of MCC-1a. The second on the spread of potential V based on the performance of the propulsion system as applied to all three MCC maneuvers. The final highlights the slight, but notable, contribution of the attitude thrusters during each MCC maneuver. Given the possible variations in these three scenarios, the trajectory design methods are determined to be robust to errors in the modeling of the flight system.
James Webb Space Telescope↗
James Webb Space Telescope Initial Mid-Course Correction Monte Carlo Implementation using Task Parallelism
The James Webb Space Telescope will be launched into a highly elliptical orbit that does not possess sufficient energy to achieve a proper Sun-Earth L2 libration point orbit. Three mid-course correction (MCC) maneuvers are planned to rectify the energy deficit: MCC-1a, MCC-1b, and MCC-2. To validate the propellant budget and trajectory design methods, a set of Monte Carlo analyses that incorporate MCC maneuver modeling and execution are employed. The first analysis focuses on the effects of launch vehicle injection errors on the magnitude of MCC-1a. The second on the spread of potential V based on the performance of the propulsion system as applied to all three MCC maneuvers. The final highlights the slight, but notable, contribution of the attitude thrusters during each MCC maneuver. Given the possible variations in these three scenarios, the trajectory design methods are determined to be robust to errors in the modeling of the flight system.
Libration Point Orbit↗
Digital Multicasting of Multiple Audio Streams
The Mission Control Center Voice Over Internet Protocol (MCC VOIP) system (see figure) comprises hardware and software that effect simultaneous, nearly real-time transmission of as many as 14 different audio streams to authorized listeners via the MCC intranet and/or the Internet. The original version of the MCC VOIP system was conceived to enable flight-support personnel located in offices outside a spacecraft mission control center to monitor audio loops within the mission control center. Different versions of the MCC VOIP system could be used for a variety of public and commercial purposes - for example, to enable members of the general public to monitor one or more NASA audio streams through their home computers, to enable air-traffic supervisors to monitor communication between airline pilots and air-traffic controllers in training, and to monitor conferences among brokers in a stock exchange. At the transmitting end, the audio-distribution process begins with feeding the audio signals to analog-to-digital converters. The resulting digital streams are sent through the MCC intranet, using a user datagram protocol (UDP), to a server that converts them to encrypted data packets. The encrypted data packets are then routed to the personal computers of authorized users by use of multicasting techniques. The total data-processing load on the portion of the system upstream of and including the encryption server is the total load imposed by all of the audio streams being encoded, regardless of the number of the listeners or the number of streams being monitored concurrently by the listeners. The personal computer of a user authorized to listen is equipped with special- purpose MCC audio-player software. When the user launches the program, the user is prompted to provide identification and a password. In one of two access- control provisions, the program is hard-coded to validate the user s identity and password against a list maintained on a domain-controller computer at the MCC. In the other access-control provision, the program verifies that the user is authorized to have access to the audio streams. Once both access-control checks are completed, the audio software presents a graphical display that includes audiostream-selection buttons and volume-control sliders. The user can select all or any subset of the available audio streams and can adjust the volume of each stream independently of that of the other streams. The audio-player program spawns a "read" process for the selected stream(s). The spawned process sends, to the router(s), a "multicast-join" request for the selected streams. The router(s) responds to the request by sending the encrypted multicast packets to the spawned process. The spawned process receives the encrypted multicast packets and sends a decryption packet to audio-driver software. As the volume or muting features are changed by the user, interrupts are sent to the spawned process to change the corresponding attributes sent to the audio-driver software. The total latency of this system - that is, the total time from the origination of the audio signals to generation of sound at a listener s computer - lies between four and six seconds.
Macha, Mitchell↗
Operating and Managing a Backup Control Center
Due to the criticality of continuous mission operations, some control centers must plan for alternate locations in the event an emergency shuts down the primary control center. Johnson Space Center (JSC) in Houston, Texas is the Mission Control Center (MCC) for the International Space Station (ISS). Due to Houston s proximity to the Gulf of Mexico, JSC is prone to threats from hurricanes which could cause flooding, wind damage, and electrical outages to the buildings supporting the MCC. Marshall Space Flight Center (MSFC) has the capability to be the Backup Control Center for the ISS if the situation is needed. While the MSFC Huntsville Operations Support Center (HOSC) does house the BCC, the prime customer and operator of the ISS is still the JSC flight operations team. To satisfy the customer and maintain continuous mission operations, the BCC has critical infrastructure that hosts ISS ground systems and flight operations equipment that mirrors the prime mission control facility. However, a complete duplicate of Mission Control Center in another remote location is very expensive to recreate. The HOSC has infrastructure and services that MCC utilized for its backup control center to reduce the costs of a somewhat redundant service. While labor talents are equivalent, experiences are not. Certain operations are maintained in a redundant mode, while others are simply maintained as single string with adequate sparing levels of equipment. Personnel at the BCC facility must be trained and certified to an adequate level on primary MCC systems. Negotiations with the customer were done to match requirements with existing capabilities, and to prioritize resources for appropriate level of service. Because some of these systems are shared, an activation of the backup control center will cause a suspension of scheduled HOSC activities that may share resources needed by the BCC. For example, the MCC is monitoring a hurricane in the Gulf of Mexico. As the threat to MCC increases, HOSC must begin a phased activation of the BCC, while working resource conflicts with normal HOSC activities. In a long duration outage to the MCC, this could cause serious impacts to the BCC host facility s primary mission support activities. This management of a BCC is worked based on customer expectations and negotiations done before emergencies occur. I.
Marsh, Angela L.↗
James Webb Space Telescope Orbit Determination Analysis
The James Webb Space Telescope (JWST) is designed to study and answer fundamental astrophysical questions from an orbit about the Sun-Earth/Moon L2 libration point, 1.5 million km away from Earth. This paper describes the results of an orbit determination (OD) analysis of the JWST mission emphasizing the challenges specific to this mission in various mission phases. Three mid-course correction (MCC) maneuvers during launch and early orbit phase and transfer orbit phase are required for the spacecraft to reach L2. These three MCC maneuvers are MCC-1a at Launch+12 hours, MCC-1b at L+2.5 days and MCC-2 at L+30 days. Accurate OD solutions are needed to support MCC maneuver planning. A preliminary analysis shows that OD performance with the given assumptions is adequate to support MCC maneuver planning. During the nominal science operations phase, the mission requires better than 2 cm/sec velocity estimation performance to support stationkeeping maneuver planning. The major challenge to accurate JWST OD during the nominal science phase results from the unusually large solar radiation pressure force acting on the huge sunshield. Other challenges are stationkeeping maneuvers at 21-day intervals to keep JWST in orbit around L2, frequent attitude reorientations to align the JWST telescope with its targets and frequent maneuvers to unload momentum accumulated in the reaction wheels. Monte Carlo analysis shows that the proposed OD approach can produce solutions that meet the mission requirements.
Libration Point Orbit↗
James Webb Space Telescope Orbit Determination Analysis
The James Webb Space Telescope (JWST) is designed to study and answer fundamental astrophysical questions from an orbit about the Sun-EarthMoon L2 libration point, 1.5 million km away from Earth. Three mid-course correction (MCC) maneuvers during launch and early orbit phase and transfer orbit phase are required for the spacecraft to reach L2. These three MCC maneuvers are MCC-1a at Launch+12 hours, MCC-1b at L+2.5 days and MCC-2 at L+30 days. Accurate orbit determination (OD) solutions are needed to support MCC maneuver planning. A preliminary analysis shows that OD performance with the given assumptions is adequate to support MCC maneuver planning. During the nominal science operations phase, the mission requires better than 2 cmsec velocity estimation performance to support stationkeeping maneuver planning. The major challenge to accurate JWST OD during the nominal science phase results from the unusually large solar radiation pressure force acting on the huge sunshield. Other challenges are stationkeeping maneuvers at 21-day intervals to keep JWST in orbit around L2, frequent attitude reorientations to align the JWST telescope with its targets and frequent maneuvers to unload momentum accumulated in the reaction wheels. Monte Carlo analysis shows that the proposed OD approach can produce solutions that meet the mission requirements.
James Webb Space Telescope↗
An approach to a real-time distribution system
The requirements of a real-time data distribution system are to provide fast, reliable delivery of data from source to destination with little or no impact to the data source. In this particular case, the data sources are inside an operational environment, the Mission Control Center (MCC), and any workstation receiving data directly from the operational computer must conform to the software standards of the MCC. In order to supply data to development workstations outside of the MCC, it is necessary to use gateway computers that prevent unauthorized data transfer back to the operational computers. Many software programs produced on the development workstations are targeted for real-time operation. Therefore, these programs must migrate from the development workstation to the operational workstation. It is yet another requirement for the Data Distribution System to ensure smooth transition of the data interfaces for the application developers. A standard data interface model has already been set up for the operational environment, so the interface between the distribution system and the application software was developed to match that model as closely as possible. The system as a whole therefore allows the rapid development of real-time applications without impacting the data sources. In summary, this approach to a real-time data distribution system provides development users outside of the MCC with an interface to MCC real-time data sources. In addition, the data interface was developed with a flexible and portable software design. This design allows for the smooth transition of new real-time applications to the MCC operational environment.
Kittle, Frank P., Jr.↗
Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor imagery
High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.
Pope, P. A.↗