Terraform oVirt Provider
This code creates a new "provider" plugin to the terraform utility that allows it control virtual machines running in oVirt.
Schmidt, Kenneth↗
SEARCH · Engineering Papers
Results for “virtual controls”
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.
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RAVEN: Dynamic Event Tree Approach Level III Milestone
Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.
97 MATHEMATICS AND COMPUTING↗
RAVEN. Dynamic Event Tree Approach Level III Milestone
Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.
42 ENGINEERING↗
The development of the Canadian Mobile Servicing System Kinematic Simulation Facility
Canada will develop a Mobile Servicing System (MSS) as its contribution to the U.S./International Space Station Freedom. Components of the MSS will include a remote manipulator (SSRMS), a Special Purpose Dexterous Manipulator (SPDM), and a mobile base (MRS). In order to support requirements analysis and the evaluation of operational concepts related to the use of the MSS, a graphics based kinematic simulation/human-computer interface facility has been created. The facility consists of the following elements: (1) A two-dimensional graphics editor allowing the rapid development of virtual control stations; (2) Kinematic simulations of the space station remote manipulators (SSRMS and SPDM), and mobile base; and (3) A three-dimensional graphics model of the space station, MSS, orbiter, and payloads. These software elements combined with state of the art computer graphics hardware provide the capability to prototype MSS workstations, evaluate MSS operational capabilities, and investigate the human-computer interface in an interactive simulation environment. The graphics technology involved in the development and use of this facility is described.
Beyer, G.↗
The Mars Gravity Simulation Project
Human beings who make abrupt transitions between one gravitational environment and another undergo severe disruptions of their visual perception and visual- motor coordination, frequently accompanied by "space sickness." Clearly, such immediate effects of exposure to a novel gravitational condition have significant implications for human performance. For example, when astronauts first arrive in Earth orbit their attempts to move about in the spacecraft and to perform their duties are uncoordinated, inaccurate, and inefficient. Other inter-gravitational transitions for which these difficulties can be expected include going from the 0 g of the spacecraft to the. 16 g of the Moon, from 0 g to the .38 g of Mars, and from 0 g back to the 1.0 g of Earth. However, after astronauts have actively interacted with their new gravitational environment for several days, these problems tend to disappear, evidence that some sort of adaptive process has taken place. It would be advantageous, therefore, if there were some way to minimize or perhaps even to eliminate this potentially hazardous adaptive transition period by allowing astronauts to adapt to the altered gravitational conditions before actually entering them. Simultaneous adaptations to both the altered and the normal gravitational environment as a result of repeatedly adapting to one and readapting to the other, a phenomenon known as dual adaptation. The objective of the Mars Gravity Simulator (MGS) Project is to construct a simulation of the visual and bodily effects of altered gravity. This perceptual-motor simulation is created through the use of: 1) differential body pressure to produce simulated hypo-gravity and 2) treadmill-controlled virtual reality to create a corresponding visual effect. It is expected that this combination will produce sensory motor perturbations in the subjects. Both the immediate and adaptive behavioral (postural and ambulatory) responses to these sensory perturbations will be assessed.
Korienek, Gene↗
Human Factors and Information Operation for a Nuclear Power Space Vehicle
This paper describes human-interactive systems needed for a crewed nuclear-enabled space mission. A synthesis of aircraft engine and nuclear power plant displays, biofeedback of sensory input, virtual control, brain mapping for control process and manipulation, and so forth are becoming viable solutions. These aspects must maintain the crew's situation awareness and performance, which entails a delicate function allocation between crew and automation.
Trujillo, Anna C.↗
Relationship of peroxyacetyl nitrate to active and total odd nitrogen at northern high latitudes: influence of reservoir species on NOx and O3
Measurements of peroxyacetyl nitrate (PAN), NO, NO2, HNO3, NOy (total odd nitrogen), and O3 were made in the high-latitude troposphere over North America and Greenland (35 degrees to 82 degrees N) during the Arctic Boundary Layer Expedition (ABLE 3A) (July-August 1988) throughout 0-to 6-km altitudes. These data are analyzed to quantitatively describe the relationships between various odd nitrogen species and assess their significance to global tropospheric chemistry. In the free troposphere, PAN was as much as 25 times more abundant than NOx. PAN to NOx ratio increased with increasing altitude and latitude. PAN was found to be the single most abundant reactive nitrogen species in the free troposphere and constituted a major fraction of NOy, PAN to NOy ratios were about 0.1 in the boundary layer and increased to 0.4 in the free troposphere. A 2-D global photochemical model with C1-C3 hydrocarbon chemistry is used to compare model predictions with measured results. A sizable portion (approximately 50%) of the gaseous reactive nitrogen budget is unaccounted for, and unknown organic nitrates and pernitrates are expected to be present. Model calculations (August 1, 70 degrees N) show that a major fraction of the observed NOx (50 to 70% of median) may find its source in the available PAN reservoir. PAN and the unknown reservoir species may have the potential to control virtually the entire NOx availability of the high latitude troposphere. It is predicted that the summer NOx and O3 mixing ratios in the Arctic/sub-Arctic troposphere would be considerably lower in the absence of the ubiquitous PAN reservoir. Conversely, this PAN reservoir may be responsible for the observed temporal increase in tropospheric O3 at high latitudes.
NASA Center ARC↗
International Earth Science Constellation Mission Operations Working Group: Constellation Coordination System (CCS) Status
This is a presentation at the MOWG fall meeting that will discuss CCS purpose, future status, security enhancements, arbitrary ephemeris mission features, overview of CCS 7.3, approach for the use of NORAD TLEs, account and data security, CCS System virtualization, control box visualization modification and other enhancements.
CCS↗
Performing Probabilistic Risk Assessment Through RAVEN
The Reactor Analysis and Virtual control ENviroment (RAVEN) code is a software tool that acts as the control logic driver and post-processing engine for the newly developed Thermal-Hydraulic code RELAP-7. RAVEN is now a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities: Derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures), allowing on-line monitoring/controlling in the Phase Space Perform both Monte-Carlo sampling of random distributed events and Dynamic Event Tree based analysis Facilitate the input/output handling through a Graphical User Interface (GUI) and a post-processing data mining module
99 GENERAL AND MISCELLANEOUS↗
MATHEMATICAL FRAMEWORK FOR THE ANALYSIS OF DYNAMC STOCHASTIC SYSTEMS WITH THE RAVEN CODE
RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation.
97 MATHEMATICS AND COMPUTING↗
Dynamic Event Tree Analysis Through RAVEN
Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.
42 ENGINEERING↗
Analysis of the Space Propulsion System Problem Using RAVEN
This paper presents the solution of the space propulsion problem using a PRA code currently under development at Idaho National Laboratory (INL). RAVEN (Reactor Analysis and Virtual control ENviroment) is a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities. It is designed to derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures) and to perform both Monte- Carlo sampling of random distributed events and Event Tree based analysis. In order to facilitate the input/output handling, a Graphical User Interface (GUI) and a post-processing data-mining module are available. RAVEN allows also to interface with several numerical codes such as RELAP5 and RELAP-7 and ad-hoc system simulators. For the space propulsion system problem, an ad-hoc simulator has been developed and written in python language and then interfaced to RAVEN. Such simulator fully models both deterministic (e.g., system dynamics and interactions between system components) and stochastic behaviors (i.e., failures of components/systems such as distribution lines and thrusters). Stochastic analysis is performed using random sampling based methodologies (i.e., Monte-Carlo). Such analysis is accomplished to determine both the reliability of the space propulsion system and to propagate the uncertainties associated to a specific set of parameters. As also indicated in the scope of the benchmark problem, the results generated by the stochastic analysis are used to generate risk-informed insights such as conditions under witch different strategy can be followed.
97 MATHEMATICS AND COMPUTING↗
Dynamic Probabilistic Safety Assessment Studies for Advanced Reactor Using RAVEN
Probabilistic Safety Assessment (PSA) is used extensively to evaluate the risks associated with complex engineering systems like Nuclear Power Plants (NPPs). Current PSA models are based on the Event-Tree/Fault-Tree (ET/FT) methodology. ET and FT models are static and are based on Boolean logic approaches. In the past, concerns have been raised in the literature regarding the capability of the traditional static modelling approaches to adequately account for the impact of process, hardware, software, firmware and human interactions on the stochastic system behaviour. To overcome the limitations of the traditional approach to PSA, several dynamic PSA methodologies have been proposed. One of the dynamic PSA methodologies used for dynamic evaluations is Dynamic Event Tree (DET) framework which can be used to assess the impact of the parameter variability and scenario dynamics on the PSA model for the initiating event. The DET framework couples the stochastic model (number of component/trains that start on demand, operator action timing, etc.) with a Thermal-Hydraulic (TH) model of the plant. This paper explores the use of DET along with a case study on advanced reactor. The initiating event selected for the study was Class IV power supply failure event. The TH analysis considering uncertainty in various parameters was performed using RELAP5 and Reactor Analysis and Virtual control ENvironment (RAVEN) tool. Based on the uncertainty analysis, it is concluded that the peak clad temperatures (PCT) are within the limits in all the code runs implying a high-degree of safety margin. However, variation in time to reach the PCT was observed among the code runs and the mean time to reach the PCT was found to be around 8590sec (approximately 2.4 hours). Hence, sufficient time margin is available for human intervention and the operator might have a relatively stress-free state during such an accident scenario. Due to the static nature of the traditional PSA models, the safety margin available was lesser, whereas, with the help of dynamic PSA models, one can demonstrate that the actual available safety margin is more in the present case study and is valuable input from the design point of view.
99 GENERAL AND MISCELLANEOUS↗
Mathematical framework for the analysis of dynamic stochastic systems with the RAVEN code
RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation. (authors)
97 MATHEMATICS AND COMPUTING↗
An Efficient Sampling-Based Method for Sensitivity and Uncertainty Analysis Through RAVEN
Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)
97 MATHEMATICS AND COMPUTING↗
Field Experiments using Telepresence and Virtual Reality to Control Remote Vehicles: Application to Mars Rover Missions
This paper will describe a series of field experiments to develop and demonstrate file use of Telepresence and Virtual Reality systems for controlling rover vehicles on planetary surfaces. In 1993, NASA Ames deployed a Telepresence-Controlled Remotely Operated underwater Vehicle (TROV) into an ice-covered sea environment in Antarctica. The goal of the mission was to perform scientific exploration of an unknown environment using a remote vehicle with telepresence and virtual reality as a user interface. The vehicle was operated both locally, from above a dive hole in the ice through which it was launched, and remotely over a satellite communications link from a control room at NASA's Ames Research center, for over two months. Remote control used a bidirectional Internet link to the vehicle control computer. The operator viewed live stereo video from the TROV along with a computer-gene rated graphic representation of the underwater terrain showing file vehicle state and other related information. Tile actual vehicle could be driven either from within the virtual environment or through a telepresence interface. In March 1994, a second field experiment was performed in which [lie remote control system developed for the Antarctic TROV mission was used to control the Russian Marsokhod Rover, an advanced planetary surface rover intended for launch in 1998. Marsokhod consists of a 6-wheel chassis and is capable of traversing several kilometers of terrain each day, The rover can be controlled remotely, but is also capable of performing autonomous traverses. The rover was outfitted with a manipulator arm capable of deploying a small instrument, collecting soil samples, etc. The Marsokhod rover was deployed at Amboy Crater in the Mojave desert, a Mars analog site, and controlled remotely from Los Angeles. in two operating modes: (1) a Mars rover mission simulation with long time delay and (2) a Lunar rover mission simulation with live action video. A team of planetary geologists participated in the mission simulation. The scientific goal of the science mission was to determine what could be learned about the geologic context of the site using the capabilities of imaging and mobility provided by the Marsokhod system in these two modes of operation. I will discuss the lessons learned from these experiments in terms of the strategy for performing Mars surface exploration using rovers. This research is supported by the Solar System Exploration Exobiology, Geology, and Advanced Technology programs.
Stoker, Carol↗
Virtual Machine Language Controls Remote Devices
Kennedy Space Center worked with Blue Sun Enterprises, based in Boulder, Colorado, to enhance the company's virtual machine language (VML) to control the instruments on the Regolith and Environment Science and Oxygen and Lunar Volatiles Extraction mission. Now the NASA-improved VML is available for crewed and uncrewed spacecraft, and has potential applications on remote systems such as weather balloons, unmanned aerial vehicles, and submarines.
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