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Information is given in outline form on advanced software tools for the Space Station data management system. The Space Station data management system is identified as a highly distributed system with payload users controlling experiments and processing payload data from home facilities.
Dr. Leetmaa described current work at the U.S. National Meteorological Center (NMC) on coupled systems leading to a seasonal prediction system. He described the way in which ocean thermal data is quality controlled and used in a four dimensional data assimilation system. This consists of a statistical interpolation scheme, a primitive equation ocean general circulation model, and the atmospheric fluxes that are required to force this. This whole process generated dynamically consist thermohaline and velocity fields for the ocean. Currently routine weekly analyses are performed for the Atlantic and Pacific oceans. These analyses are used for ocean climate diagnostics and as initial conditions for coupled forecast models. Specific examples of output products were shown both in the Pacific and the Atlantic Ocean.
The pump and flow control subassembly (PFCS) is an orbital replacement unit (ORU) on the Space Station Freedom photovoltaic power module (PVM). The PFCS pumps liquid ammonia at a constant rate of approximately 1170 kg/hr while providing temperature control by flow regulation between the radiator and the bypass loop. Also, housed within the ORU is an accumulator to compensate for fluid volumetric changes as well as the electronics and firmware for monitoring and control of the photovoltaic thermal control system (PVTCS). Major electronic functions include signal conditioning, data interfacing and motor control. This paper will provide a description of each major component within the PFCS along with performance test data. In addition, this paper will discuss the flow control algorithm and describe how the nickel hydrogen batteries and associated power electronics will be thermally controlled through regulation of coolant flow to the radiator.
Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.
Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.
The X-wing circulation controlled rotor system model was tested for hover performance. During these performance tests, noise data from 12 microphones was recorded on magnetic tape for subsequent data reduction. The rotor system was operated at 4 tip speeds ranging from 529 to 650 ft./sec. (404 to 497 rpm), collective angles of attack fro 0 deg to 8.5 deg (maximum), and blade pressure ratios from 1.0 (no blowing) to a maximum of 2.1. The 12 microphones included 11 in the far field, and one in the transmission area. Following completion of the rotor and subsystem noise measurements, sound field calibration measurements were made of both the rotor 'bowl' and the loudspeaker system used in the 'bowl' calibration measurements. The location of 10 far field microphones was measured by a surveyor. Additionally, detailed tape logs were prepared for the six reels of tape used for the program.
Here, the ADAQ framework is a collection of software tools that is designed to streamline the acquisition and analysis of radiation detector data produced in modern digital data acquisition (DAQ) systems and in Monte Carlo detector simulations. The purpose of the framework is to maximize user scientific pro-ductivity by minimizing the effort and expertise required to fully utilize radiation detectors in a variety of scientific and engineering disciplines. By using a single set of tools to span the real and simulation domains, the framework eliminates redundancy and provides an integrated workflow for high-fidelity comparison between experimental and simulated detector performance. Built on the ROOT data analysis framework, the core of the ADAQ framework is a set of Cþþ and Python libraries that enable high-level control of digital DAQ systems and detector simulations with data stored into standardized binary ROOT files for further analysis. Two graphical user interface programs utilize the libraries to create powerful tools: ADAQAcquisition handles control and readout of real-world DAQ systems and ADAQAnalysis provides data analysis and visualization methods for experimental and simulated data. At present, the ADAQ framework supports digital DAQ hardware from CAEN S.p.A. and detector simulations performed in Geant4; however, the modular design will facilitate future extension to other manufacturers and simulation platforms.
A high-altitude, battery-powered, propeller-driven aircraft was designed and is being built by undergraduate students at California State University, Northridge. The aircraft will fly at an altitude of 104,000 ft at Mach 0.2 (190 ft/sec) and will be instrumented to record flight performance data, including low Reynolds number propeller and airfoil information. This project will demonstrate the feasibility of electric-powered flight in a low-density, low-temperature Earth environment that models the atmosphere of Mars. Data collected will be used to design a Mars aircraft to investigate the surface of Mars prior to manned missions. The instrumented payload and the mission profile for the high-altitude Earth flight were determined. Detailed aerodynamic and structural analyses were performed. Control, tracking, and data recording subsystems were developed. Materials were obtained and fabrication begun. The aircraft has a 32-ft wing span, a wing area of 105 sq ft, is 17.5 ft long, has a 12-in payload bay, and weighs 42 lb. It is composed primarily of lightweight materials, including Mylar, and composite materials, including graphite/epoxy and aramid core honeycomb sandwich. Low-altitude flight testing to check guidance and control systems and to calibrate data-gathering instruments will take place this summer, followed shortly by the 104,000-ft flight.
Biotube was developed for plant gravitropic research investigating the potential for magnetic fields to orient plant roots as they grow in microgravity. Prior to flight, experimental seeds are placed into seed cassettes, that are capable of containing up to 10 seeds, and inserted between two magnets located within one of three Magnetic Field Chamber (MFC). Biotube is stored within an International Space Station (ISS) stowage locker and provides three levels of containment for chemical fixatives. Features include monitoring of temperature, fixative/ preservative delivery to specimens, and real-time video imaging downlink. Biotube's primary subsystems are: (1) The Water Delivery System that automatically activates and controls the delivery of water (to initiate seed germination). (2) The Fixative Storage and Delivery System that stores and delivers chemical fixative or RNA later to each seed cassette. (3) The Digital Imaging System consisting of 4 charge-coupled device (CCD) cameras, a video multiplexer, a lighting multiplexer, and 16 infrared light-emitting diodes (LEDs) that provide illumination while the photos are being captured. (4) The Command and Data Management System that provides overall control of the integrated subsystems, graphical user interface, system status and error message display, image display, and other functions.
With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.
The Kansas City Division of Allied Signal Inc., as part of the Integrated Flexible Manufacturing Program (IFMP), is developing an integrated manufacturing environment. Several systems are being developed to produce standards and automation tools for specific activities within the manufacturing environment. The Advanced Manufacturing Development System (AMDS) is concentrating on information standards (STEP) and product data transfer; the Expert Cut Planner system (XCUT) is concentrating on machining operation process planning standards and automation capabilities; the Advanced Numerical Control system (ANC) is concentrating on NC data preparation standards and NC data generation tools; the Inspection Planning and Programming Expert system (IPPEX) is concentrating on inspection process planning, coordinate measuring machine (CMM) inspection standards and CMM part program generation tools; and the Intelligent Scheduling and Planning System (ISAPS) is concentrating on planning and scheduling tools for a flexible manufacturing system environment. All of these projects are working together to address information exchange, standardization, and information sharing to support rapid prototyping in a Flexible Manufacturing System (FMS) environment.
A main design criterion for a robust Integrated Systems Health Management (ISHM) system is summed up best by the statement "No data is better than bad data". Traditional data acquisition systems are calibrated in a controlled environment and guaranteed to perform bounded by their tested conditions. To successfully design and implement a real world ISHM system, the data acquisition and signal conditioning needs to function in an uncontrolled environment. Development and testing focuses on a design with the ability to self check in order to extend calibration times, report internal faults and drifts and notify the overall system when the data acquisition is not performing as it should. All of this will be designed in a system that is flexible, requiring little redesign to be deployed on a wide variety of systems. Development progress and testing results will be reported.
A system, method and computer program product for supporting system initiated checkpoints in high performance parallel computing systems and storing of checkpoint data to a non-volatile memory storage device. The system and method generates selective control signals to perform checkpointing of system related data in presence of messaging activity associated with a user application running at the node. The checkpointing is initiated by the system such that checkpoint data of a plurality of network nodes may be obtained even in the presence of user applications running on highly parallel computers that include ongoing user messaging activity. In one embodiment, the non-volatile memory is a pluggable flash memory card.
The future of the National Aeronautics and Space Administration (NASA) depends on its innovation and efficiency in the coming years. With ambitious goals to reach Mars and explore the vast universe, correct steps must be taken to ensure our space program reaches its destination safely. The interns in the Exploration Systems and Operations Division at the Kennedy Space Center (KSC) have been tasked with building command line tools to ease the process of managing and testing the data being produced by the ground control systems while its recording system is not in use. While working alongside full-time engineers, we were able to create multiple programs that reduce the cost and time it takes to test the subsystems that launch rockets to outer space.
The Long Duration Exposure Facility (LDEF) Experiment M0003 consists of 19 subexperiments from laboratories and contractor organizations and was designed to study the effects of the space environment on a large variety of spacecraft materials and components, both current and developmental. The experiment was housed in four LDEF trays and contained over 1250 specimens, two data systems, and two environment exposure control canisters. Nearly identical pairs of trays were located on the leading and trailing edges of LDEF. The materials in these trays span nearly all generic functions in spacecraft such as optics, thermal control, composites, solar power, and electronics. Effects of the space environment, such as vacuum, ultraviolet, atomic oxygen, meteoroids and debris, thermal cycling, and synergistic effects on various samples, will be described. Summaries of the on-board data will be presented.
A system and method for notifying a process about a creation or removal event of a named data element (NDE) in a coordination namespace distributed memory system. A controller runs methods to: generate a tuple corresponding to data generated by a requesting process, the tuple having a tuple name and data value; and generate a notification indicator in a pending notification list to indicate to one or more processes a notification of the creation or removal event associated with the corresponding tuple. Upon detecting the event performed on the tuple by a second process, the method further searches for NDEs in the distributed memory system having the same tuple name, and in response to determining an existence of an associated pending notification record in a pending notification list of records, notify each corresponding process of the one or more processes indicated in the list of the creation or removal event.
Development and testing of an adaptable vehicle health-monitoring architecture is presented. The architecture is being developed for a fleet of vehicles. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and, a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained expert system. Communication between all levels is done with wireless radio frequency interfaces. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear. The flight tests were performed to validate the following: the wireless radio frequency communication capabilities of the system, the hardware design, command and control; software operation; and, data acquisition, storage and retrieval.
Distributed engine control architecture presents a significant increase in complexity over traditional implementations when viewed from the perspective of system simulation and hardware design and test. Even if the overall function of the control scheme remains the same, the hardware implementation can have a significant effect on the overall system performance due to differences in the creation and flow of data between control elements. A Hardware-in-the-Loop (HIL) simulation system is under development at NASA Glenn Research Center that enables the exploration of these hardware dependent issues. The system is based on, but not limited to, the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k). This paper describes the step-by-step conversion from the self-contained baseline model to the hardware in the loop model, and the validation of each step. As the control model hardware fidelity was improved during HIL system development, benchmarking simulations were performed to verify that engine system performance characteristics remained the same. The results demonstrate the goal of the effort; the new HIL configurations have similar functionality and performance compared to the baseline C-MAPSS40k system.