SEARCH · Search NASA
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.
Continuous online monitoring of complex dynamic systems is common in applications as diverse as industrial plant operations, telecommunications network, and biomedical health monitoring. For monitoring purposes, the exact nature of the system under observation is typically not relevant provided there exist some measurements or symptoms which provide diagnostics information regarding the underlying system state.
Earth is a complex dynamical system exhibiting continuous change in its atmosphere, ocean,and surface elements. Nearly all (99.97%) of the energy driving these systems is linked to the Sun. Measurements of reflected sunlight contain a unique signature of wavelength-specific scattering and absorption interactions occurring between incoming solar energy and atmospheric (molecules, aerosols,clouds) and surface features Clouds can affect significantly both shortwave and long wave radiation, depending on altitude/vertical structure, thermodynamic phase, and optical properties. Low, warm, and optically thick clouds predominantly have a cooling effect, while high, cold, optically thin clouds can cause warming by absorbing warmer radiation emitted from the surface and lower atmosphere.When the net difference between outgoing and incoming solar radiation is matched by the net infrared radiation emitted to space, the Earth's climate is in radiative balance. While radiative forcing components (GHGs, aerosols - direct and indirect) contribute to a net radiative imbalance, climate sensitivity is ultimately determined by the contribution of various system feed backs. The role of cloud feedback in a warming climate is currently the largest inter-model uncertainty in climate sensitivity and therefore in climate prediction [Bony and Dufresne 2005]. A comprehensive understanding of current cloud propertiesand dynamic/microphysical processes requires a global perspective from satellites.
Explore the source record for details and available documents.
In order to build massive multi-agent systems, considered as complex and dynamic systems, one needs a method to analyze and control the system. We suggest an approach using morphology to represent and control the state of large organizations composed of a great number of light software agents. Morphology is understood as representing the state of the multi-agent system as shapes in an abstract geometrical space, this notion is close to the notion of phase space in physics.
Many complex systems can be modeled as a collection of interacting bodies, where the relative motion of the bodies may be large. The dynamics of such systems are simulated using multibody dynamic formulations. Many of these treat each body as a rigid component, but recently the flexibility of the components has been incorporated. This paper presents a residual flexibility formulation of the multibody dynamics problem. The formulation is very simple and offers great computational efficiency since it treats each body as a free structure in space, interacting with other bodies only through interface forces. Each body's accelerations can be solved independently, as can each set of interface forces. We have applied the technique successfully to several special applications, and the initial implementation in a general mechanisms code has given excellent results in comparison to a direct finite element representation of flexibility.
The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.
The Solar Dynamics Observatory (SDO) includes three advanced instruments, massive science data volume, stringent science data completeness requirements, and a custom ground station to meet mission demands. The strict instrument science requirements imposed a number of challenging drivers on the overall mission system design, leading the SDO team to adopt an integrated systems engineering presence across all aspects of the mission to ensure that mission science requirements would be met. Key strategies were devised to address these system level drivers and mitigate identified threats to mission success. The global systems engineering team approach ensured that key drivers and risk areas were rigorously addressed through all phases of the mission, leading to the successful SDO launch and on-orbit operation. Since launch, SDO s on-orbit performance has met all mission science requirements and enabled groundbreaking science observations, expanding our understanding of the Sun and its dynamic processes.
The Solar Dynamics Observatory (SDO) includes three advanced instruments, massive science data volume, stringent science data completeness requirements, and a custom ground station to meet mission demands. The strict instrument science requirements imposed a number of challenging drivers on the overall mission system design, leading the SDO team to adopt an integrated systems engineering presence across all aspects of the mission to ensure that mission science requirements would be met. Key strategies were devised to address these system level drivers and mitigate identified threats to mission success. The global systems engineering team approach ensured that key drivers and risk areas were rigorously addressed through all phases of the mission, leading to the successful SDO launch and on-orbit operation. Since launch, SDO's on-orbit performance has met all mission science requirements and enabled groundbreaking science observations, expanding our understanding of the Sun and its dynamic processes.
Self-reorganization methods for complex linear dynamic systems to compensate for component failures
Due to their structural flexibility, spacecraft and space manipulators are multibody systems with complex dynamics and possess a large number of degrees of freedom. Here the spatial operator algebra methodology is used to develop a new dynamics formulation and spatially recursive algorithms for such flexible multibody systems. A key feature of the formulation is that the operator description of the flexible system dynamics is identical in form to the corresponding operator description of the dynamics of rigid multibody systems. A significant advantage of this unifying approach is that it allows ideas and techniques for rigid multibody systems to be easily applied to flexible multibody systems. The algorithms use standard finite-element and assumed modes models for the individual body deformation. A Newton-Euler Operator Factorization of the mass matrix of the multibody system is first developed. It forms the basis for recursive algorithms such as for the inverse dynamics, the computation of the mass matrix, and the composite body forward dynamics for the system. Subsequently, an alternative Innovations Operator Factorization of the mass matrix, each of whose factors is invertible, is developed. It leads to an operator expression for the inverse of the mass matrix, and forms the basis for the recursive articulated body forward dynamics algorithm for the flexible multibody system. For simplicity, most of the development here focuses on serial chain multibody systems. However, extensions of the algorithms to general topology flexible multibody systems are described. While the computational cost of the algorithms depends on factors such as the topology and the amount of flexibility in the multibody system, in general, it appears that in contrast to the rigid multibody case, the articulated body forward dynamics algorithm is the more efficient algorithm for flexible multibody systems containing even a small number of flexible bodies. The variety of algorithms described here permits a user to choose the algorithm which is optimal for the multibody system at hand. The availability of a number of algorithms is even more important for real-time applications, where implementation on parallel processors or custom computing hardware is often necessary to maximize speed.
Fault-tolerant computer structure called RAMPS (for redundant asynchronous microprocessor system) has simplicity of static redundancy but offers intermittent-fault handling ability of complex, dynamically redundant systems. New structure useful wherever several microprocessors are employed for control - in aircraft, industrial processes, robotics, and automatic machining, for example.
NASA missions are extremely complex and prone to sudden, catastrophic failure if equipment falters or if an unforeseen event occurs. For these reasons, NASA trains to expect the unexpected. It tests its equipment and systems in extreme conditions, and it develops risk-analysis tests to foresee any possible problems. The Space Agency recently worked with an industry partner to develop reliability analysis software capable of modeling complex, highly dynamic systems, taking into account variations in input parameters and the evolution of the system over the course of a mission. The goal of this research was multifold. It included performance and risk analyses of complex, multiphase missions, like the insertion of the Mars Reconnaissance Orbiter; reliability analyses of systems with redundant and/or repairable components; optimization analyses of system configurations with respect to cost and reliability; and sensitivity analyses to identify optimal areas for uncertainty reduction or performance enhancement.
NASA's human exploration initiatives will invest in technologies, public/private partnerships, and infrastructure, paving the way for the expansion of human civilization into the solar system and beyond. As it is has been for the past half century, the Kennedy Space Center will be the embarkation point for humankind's journey into the cosmos. Functioning as a next generation space launch complex, Kennedy's launch pads, integration facilities, processing areas, launch and recovery ranges will bustle with the activities of the world's space transportation providers. In developing this complex, KSC teams work through the potential operational scenarios: conducting trade studies, planning and budgeting for expensive and limited resources, and simulating alternative operational schemes. Numerous tools, among them discrete event simulation (DES), were matured during the Constellation Program to conduct such analyses with the purpose of optimizing the launch complex for maximum efficiency, safety, and flexibility while minimizing life cycle costs. Discrete event simulation is a computer-based modeling technique for complex and dynamic systems where the state of the system changes at discrete points in time and whose inputs may include random variables. DES is used to assess timelines and throughput, and to support operability studies and contingency analyses. It is applicable to any space launch campaign and informs decision-makers of the effects of varying numbers of expensive resources and the impact of off nominal scenarios on measures of performance. In order to develop representative DES models, methods were adopted, exploited, or created to extend traditional uses of DES. The Delphi method was adopted and utilized for task duration estimation. DES software was exploited for probabilistic event variation. A roll-up process was used, which was developed to reuse models and model elements in other less - detailed models. The DES team continues to innovate and expand DES capabilities to address KSC's planning needs.
Optimization of dynamic systems involving complex non-hermitian matrices is often computationally expensive. Major contributors to the computational expense are the sensitivity analysis and reanalysis of a modified design. The present work seeks to alleviate this computational burden by identifying efficient sensitivity analysis and approximate reanalysis methods. For the algebraic eigenvalue problem involving non-hermitian matrices, algorithms for sensitivity analysis and approximate reanalysis are classified, compared and evaluated for efficiency and accuracy. Proper eigenvector normalization is discussed. An improved method for calculating derivatives of eigenvectors is proposed based on a more rational normalization condition and taking advantage of matrix sparsity. Important numerical aspects of this method are also discussed. To alleviate the problem of reanalysis, various approximation methods for eigenvalues are proposed and evaluated. Linear and quadratic approximations are based directly on the Taylor series. Several approximation methods are developed based on the generalized Rayleigh quotient for the eigenvalue problem. Approximation methods based on trace theorem give high accuracy without needing any derivatives. Operation counts for the computation of the approximations are given. General recommendations are made for the selection of appropriate approximation technique as a function of the matrix size, number of design variables, number of eigenvalues of interest and the number of design points at which approximation is sought.
Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improving the way in which one NASA organization, the Flight Dynamics Division (FDD), develops, maintains, and manages complex flight dynamics systems. The SEL is composed of three member organizations: NASA/GSFC, the University of Maryland, and Computer Sciences Corporation. During the past 18 years, the SEL's overall goal has remained the same: to improve the FDD's software products and processes in a measured manner. This requires that each development and maintenance effort be viewed, in part, as a SEL experiment which examines a specific technology or builds a model of interest for use on subsequent efforts. The SEL has undertaken many technology studies while developing operational support systems for numerous NASA spacecraft missions.
Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improving the way in which one NASA organization, the Flight Dynamics Division (FDD) at Goddard Space Flight Center, develops, maintains, and manages complex flight dynamics systems. It has done this by developing and refining a continual process improvement approach that allows an organization such as the FDD to fine-tune its process for its particular domain. Experimental software engineering and measurement play a significant role in this approach. The SEL is a partnership of NASA Goddard, its major software contractor, Computer Sciences Corporation (CSC), and the University of Maryland's (LTM) Department of Computer Science. The FDD primarily builds software systems that provide ground-based flight dynamics support for scientific satellites. They fall into two sets: ground systems and simulators. Ground systems are midsize systems that average around 250 thousand source lines of code (KSLOC). Ground system development projects typically last 1 - 2 years. Recent systems have been rehosted to workstations from IBM mainframes, and also contain significant new subsystems written in C and C++. The simulators are smaller systems averaging around 60 KSLOC that provide the test data for the ground systems. Simulator development lasts up to 1 year. Most of the simulators have been built in Ada on workstations. The SEL is responsible for the management and continual improvement of the software engineering processes used on these FDD projects.
Since 1976 the Software Engineering Laboratory (SEL) has been dedicated to understanding and improving the way in which one NASA organization the Flight Dynamics Division (FDD) at Goddard Space Flight Center, develops, maintains, and manages complex flight dynamics systems. This paper presents an overview of recent activities and studies in SEL, using as a framework the SEL's organizational goals and experience based software improvement approach. It focuses on two SEL experience areas : (1) the evolution of the measurement program and (2) an analysis of three generations of Cleanroom experiments.
Complexity of dynamical system performance can be significantly enriched by exploiting a non-.