Publications

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Mechanical systems behave randomly and it is desirable to capture this feature when making response predictions. Currently, there is an effort to develop predictive mathematical models and test their validity through the assessment of their predictive accuracy relative to experimental results. Traditionally, the approach to quantify modeling uncertainty is to examine the uncertainty associated with each of the critical model parameters and to propagate this through the model to obtain an estimate of uncertainty in model predictions. This approach is referred to as the "bottom-up" approach. However, parametric uncertainty does not account for all sources of the differences between model predictions and experimental observations, such as model form uncertainty and experimental uncertainty due to the variability of test conditions, measurements and data processing. Uncertainty quantification (UQ) based directly on the differences between model predictions and experimental data is referred to as the "top-down" approach. This paper discusses both the top-down and bottom-up approaches and uses the respective stochastic models to assess the validity of a joint model with respect to experimental data not used to calibrate the model, i.e. random vibration versus sine test data. Practical examples based on joint modeling and testing performed by Sandia are presented and conclusions are drawn as to the pros and cons of each approach.

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Sandia National Laboratories has been conducting studies on performance of laboratory and commercial lithium-ion and other types of electrochemical cells using inductive models [1]. The objectives of these investigations are: (1) To develop procedures and techniques to rapidly determine performance degradation rates while these cells undergo life tests; (2) To model cell voltage and capacity in order to simulate cell performance characteristics under variable load and temperature conditions; (3) To model rechargeable battery degradation under charge/discharge cycles and many other conditions. The inductive model and methodology are particularly useful when complicated cell performance behaviors are involved, which are often difficult to be interpreted from simple empirical approaches. We find that the inductive model can be used effectively: (1) To enable efficient predictions of battery life; (2) To characterize system behavior. Inductive models provide convenient tools to characterize system behavior using experimentally or analytically derived data in an efficient and robust framework. The approach does not require detailed phenomenological development. There are certain advantages unique to this approach. 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. We used artificial neural network for inductive modeling, along with ancillary mathematical tools to improve their accuracy. This paper summarizes efforts to use inductive tools for cell and battery modeling. Examples of numerical results will be presented. One of them is related to high power lithium-ion batteries tested under the U.S. Department of Energy Advanced Technology Development Program for hybrid vehicle applications. Sandia National Laboratories is involved in the development of accelerated life testing and thermal abuse tests to enhance the understanding of power and capacity fade issues and predict life of the battery under a nominal use condition. This paper will use power and capacity fade behaviors of a Ni-oxide-based lithium-ion battery system to illustrate how effective the inductive model can interpret the cell behavior and provide predictions of life. We will discuss the analysis of the fading behavior associated with the cell performance and explain how the model can predict cell performance.

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Practical structural dynamic phenomena involve excitations and responses that are random processes. Nonstationary random processes are most frequently encountered, and an accurate and efficient model for them is the Karhunen-Loeve (KL) expansion. The KL expansion can be obtained using experimentally measured random process realizations, but if it is, there is some level of statistical error associated with the identified parameters of the model. This paper shows how bootstrap techniques can be used to perform confidence analysis on the parameters of a KL model. Laboratory-measured data are used to demonstrate use of the model and statistical analysis of the model parameters.

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The constitutive behavior of mechanical joints is largely responsible for the energy dissipation and vibration damping in weapons systems. For reasons arising from the dramatically different length scales associated with those dissipative mechanisms and the length scales characteristic of the overall structure, this physics cannot be captured adequately through direct simulation of the contact mechanics within a structural dynamics analysis. The only practical method for accommodating the nonlinear nature of joint mechanisms within structural dynamic analysis is through constitutive models employing degrees of freedom natural to the scale of structural dynamics. This document discusses a road-map for developing such constitutive models.

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