245 research outputs found
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Characterization of nonstationary random processes
Current methods for shock test specification and shock testing treat the shock environment as a deterministic source. The present study proposes to treat shock sources as nonstationary random processes. A model for a realistic nonstationary random process shock source is specified, and the effect of variation of parameters in the shock source is shown. A method for estimating the parameters of the random process is established, and some numerical examples show that the method yields reasonable results. The use of this model in shock testing is discussed
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Random Vibrations: Assessment of the State of the Art
Random vibration is the phenomenon wherein random excitation applied to a mechanical system induces random response. We summarize the state of the art in random vibration analysis and testing, commenting on history, linear and nonlinear analysis, the analysis of large-scale systems, and probabilistic structural testing
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Simulation of nonlinear strutures with artificial neural networks
Structural system simulation is important in analysis, design, testing, control, and other areas, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks offer a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and simulation a much simpler problem. A numerical example is also presented
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System characterization in nonlinear random vibration
Linear structural models are frequently used for structural system characterization and analysis. In most situations they can provide satisfactory results, but under some circumstances they are insufficient for system definition. The present investigation proposes a model for nonlinear structure characterization, and demonstrates how the functions describing the model can be identified using a random vibration experiment. Further, it is shown that the model is sufficient to completely characterize the stationary random vibration response of a structure that has a harmonic frequency generating form of nonlinearity. An analytical example is presented to demonstrate the plausibility of the model
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Simulation of nonlinear random vibrations using artificial neural networks
The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented
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Utilizing Gauss-Hermite Quadrature to Evaluate Uncertainty in Dynamic System Response
Probabilistic uncertainty is a phenomenon that occurs to a certain degree in many engineering!~ applications. The effects that the uncertainty has upon a given system response is a matter of some concern. Techniques which provide insight to these effects will be required as modeling and prediction become a more vital tool in the engineering design process. As might be expected, this is a difficult proposition and the focus of many research efforts. The purpose of this paper is to outline a procedure to evaluate uncertainty in dynamic system response exploiting Gauss-Hermite numerical quadrature. Specifically numerical integration techniques are utilized in conjunction with the Advanced Mean Value method to efficiently and accurately estimate moments of the response process. A numerical example illustrating the use of this analytical tool in a practical framework is presented
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Numerical Techniques to Evaluate Moments of Dynamic System Response
Probabilistic uncertainty is a phenomenon that occurs to a certain degree in many engineering applications. The effects that this uncertainty has upon a given system response are a matter of some concern. Techniques which provide insight to these effects will be required as modeling and prediction becomes a more vital tool in the engineering design process. The purpose of this paper is to outline a procedure to evaluate uncertainty in dynamic system response exploiting various numerical methods. Specifically, the goal is to attain the statistics of the response with minimal computational effort. Numerical interpolation and integration techniques are utilized in conjunction with the iterative form of the Advanced Mean Value (AMV+) method to efficiently and accurately estimate statistical moments of the response random process. A numerical example illustrating the use of this analytical tool in a practical framework is presented
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Neural Network Modeling of the Lithium/Thionyl Chloride Battery System
Battery systems have traditionally relied on extensive build and test procedures for product realization. Analytical models have been developed to diminish this reliance, but have only been partially successful in consistently predicting the performance of battery systems. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models a significant challenge. Advanced simulation tools are needed to more accurately model battery systems which will reduce the time and cost required for product realization. Sandia has initiated an advanced model-based design strategy to battery systems, beginning with the performance of lithiumhhionyl chloride cells. As an alternative approach, we have begun development of cell performance modeling using non-phenomenological models for battery systems based on artificial neural networks (ANNs). ANNs are inductive models for simulating input/output mappings with certain advantages over phenomenological models, particularly for complex systems. Among these advantages is the ability to avoid making measurements of hard to determine physical parameters or having to understand cell processes sufficiently to write mathematical functions describing their behavior. For example, ANN models are also being studied for simulating complex physical processes within the Li/SOC12 cell, such as the time and temperature dependence of the anode interracial resistance. ANNs have been shown to provide a very robust and computationally efficient simulation tool for predicting voltage and capacity output for Li/SOC12 cells under a variety of operating conditions. The ANN modeling approach should be applicable to a wide variety of battery chemistries, including rechargeable systems
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Use of artificial neural networks for analysis of complex physical systems
Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs
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Statistical validation of physical system models
It is common practice in applied mechanics to develop mathematical models for mechanical system behavior. Frequently, the actual physical system being modeled is also available for testing, and sometimes the test data are used to help identify the parameters of the mathematical model. However, no general-purpose technique exists for formally, statistically judging the quality of a model. This paper suggests a formal statistical procedure for the validation of mathematical models of physical systems when data taken during operation of the physical system are available. The statistical validation procedure is based on the bootstrap, and it seeks to build a framework where a statistical test of hypothesis can be run to determine whether or not a mathematical model is an acceptable model of a physical system with regard to user-specified measures of system behavior. The approach to model validation developed in this study uses experimental data to estimate the marginal and joint confidence intervals of statistics of interest of the physical system. These same measures of behavior are estimated for the mathematical model. The statistics of interest from the mathematical model are located relative to the confidence intervals for the statistics obtained from the experimental data. These relative locations are used to judge the accuracy of the mathematical model. A numerical example is presented to demonstrate the application of the technique
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