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J.M. Nichols

Publications and source records attributed to J.M. Nichols.

8 recordsLinked to original sources
USGS2014

Reducing fatigue damage for ships in transit through structured decision making

Research in structural monitoring has focused primarily on drawing inference about the health of a structure from the structure’s response to ambient or applied excitation. Knowledge of the current state can then be used to predict structural integrity at a future time and, in principle, allows one to take action to improve safety, minimize ownership costs, and/or increase the operating envelope. While much time and effort has been devoted toward data collection and system identification, research to-date has largely avoided the question of how to choose an optimal maintenance plan. This work describes a structured decision making (SDM) process for taking available information (loading data, model output, etc.) and producing a plan of action for maintaining the structure. SDM allows the practitioner to specify his/her objectives and then solves for the decision that is optimal in the sense that it maximizes those objectives. To demonstrate, we consider the problem of a Naval vessel transiting a fixed distance in varying sea-state conditions. The physics of this problem are such that minimizing transit time increases the probability of fatigue failure in the structural supports. It is shown how SDM produces the optimal trip plan in the sense that it minimizes both transit time and probability of failure in the manner of our choosing (i.e., through a user-defined cost function). The example illustrates the benefit of SDM over heuristic approaches to maintaining the vessel.

Marine Structures↗
USGS2010

Bayesian estimation of weak material dispersion: Theory and experiment

This work considers the estimation of dispersion in materials via an interferometric technique. At its core, the problem involves extracting the quadratic variation in phase over a range of wavelengths based on measured optical intensity. The estimation problem becomes extremely difficult for weakly dispersive materials where the quadratic nonlinearity is very small relative to the uncertainty inherent in experiment. This work provides a means of estimating dispersion in the face of such uncertainty. Specifically, we use a Markov Chain Monte Carlo implementation of Bayesian analysis to provide both the dispersion estimate and the associated confidence interval. The interplay between various system parameters and the size of the resulting confidence interval is discussed. The approach is then applied to several different experimental samples.

Optics Express↗
USGS2010

A Bayesian approach to identifying structural nonlinearity using free-decay response: Application to damage detection in composites

This work discusses a Bayesian approach to approximating the distribution of parameters governing nonlinear structural systems. Specifically, we use a Markov Chain Monte Carlo method for sampling the posterior parameter distributions thus producing both point and interval estimates for parameters. The method is first used to identify both linear and nonlinear parameters in a multiple degree-of-freedom structural systems using free-decay vibrations. The approach is then applied to the problem of identifying the location, size, and depth of delamination in a model composite beam. The influence of additive Gaussian noise on the response data is explored with respect to the quality of the resulting parameter estimates.

Journal of Sound and Vibration↗
USGS2008

Mapping the information landscape: Discerning peaks and valleys for ecological monitoring

We investigate previously unreported phenomena that have a potentially significant impact on the design of surveillance monitoring programs for ecological systems. Ecological monitoring practitioners have long recognized that different species are differentially informative of a system's dynamics, as codified in the well-known concepts of indicator or keystone species. Using a novel combination of analysis techniques from nonlinear dynamics, we describe marked variation among spatial sites in information content with respect to system dynamics in the entire region. We first observed these phenomena in a spatially extended predator-prey model, but we observed strikingly similar features in verified water-level data from a NOAA/NOS Great Lakes monitoring program. We suggest that these features may be widespread and the design of surveillance monitoring programs should reflect knowledge of their existence.

Journal of Biological Physics↗
USGS2007

Application of information theory methods to food web reconstruction

In this paper we use information theory techniques on time series of abundances to determine the topology of a food web. At the outset, the food web participants (two consumers, two resources) are known; in addition we know that each consumer prefers one of the resources over the other. However, we do not know which consumer prefers which resource, and if this preference is absolute (i.e., whether or not the consumer will consume the non-preferred resource). Although the consumers and resources are identified at the beginning of the experiment, we also provide evidence that the consumers are not resources for each other, and the resources do not consume each other. We do show that there is significant mutual information between resources; the model is seasonally forced and some shared information between resources is expected. Similarly, because the model is seasonally forced, we expect shared information between consumers as they respond to the forcing of the resources. The model that we consider does include noise, and in an effort to demonstrate that these methods may be of some use in other than model data, we show the efficacy of our methods with decreasing time series size; in this particular case we obtain reasonably clear results with a time series length of 400 points. This approaches ecological time series lengths from real systems.

Ecological Modelling↗
USGS2005

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Theoretical Population Biology↗
USGS2003

On the use of attractor dimension as a feature in structural health monitoring

Recent works in the vibration-based structural health monitoring community have emphasised the use of correlation dimension as a discriminating statistic in seperating a damaged from undamaged response. This paper explores the utility of attractor dimension as a 'feature' and offers some comparisons between different metrics reflecting dimension. This focus is on evaluating the performance of two different measures of dimension as damage indicators in a structural health monitoring context. Results indicate that the correlation dimension is probably a poor choice of statistic for the purpose of signal discrimination. Other measures of dimension may be used for the same purposes with a higher degree of statistical reliability. The question of competing methodologies is placed in a hypothesis testing framework and answered with experimental data taken from a cantilivered beam.

Mechanical Systems and Signal Processing↗