Global Sensitivity Analysis of a Multi-Cylinder Automotive Reciprocating Compressor

The objective of this paper is to perform sensitivity analysis of a compressor simulation model using a global sensitivity analysis method. In this paper, Sobol’s method of global sensitivity analysis, which is based on decomposition of variance, is applied to a compressor simulation model. A previously developed and tested compressor simulation model is used to perform sensitivity analysis of gas pulsations in the suction manifold of a multicylinder reciprocating compressor. The focus of the research is to determine the sensitivity of gas pulsations in the suction manifold of the compressor to three design parameters, namely, radius, width and depth of the suction manifold. Sobol’s method of global sensitivity analysis was used to calculate the first order effect and total effect of the suction manifold radius, width and depth on the manifold pressure response. It was also showed that suction manifold pressure response was most sensitive to changes in manifold radius, followed by manifold width and depth. This method of sensitivity analysis can be readily extended to any compressor simulation model

Implementation of Sobol’s Method of Global Sensitivity Analysis to a Compressor Simulation Model

Most simulation models are complex and nonlinear and so global sensitivity analysis is becoming a popular choice to predict the performance characteristics and behavior of the model. Global sensitivity analysis methods are generally variance-based methods that greatly rely on sampling methods and input parameter distribution. These methods don’t rely on linearity or monotonicity of the model and can be applied to a diverse range of problem. Sobol’s method—a variance based global sensitivity analysis method is applied to a nonlinear function to highlight and outline the implementation details of the method. It is used to calculate the sensitivities of the input parameter on the model output. The method is then applied to a reciprocating compressor model to determine the sensitivity of gas pulsation to suction manifold design parameters, namely radius, width and depth. The same method can be readily applied to any compressor simulation model

Performance investigation of a document retrieval system on a voice-data integrated token ring local area network

Lately, the interest in integration of voice and data on local computer networks has been on the rise. Subsequently, much research has been devoted to exploring various techniques that are implementable using the existing standards. This research has focused on the design issues in implementing a document retrieval system on a token ring network. The presence of voice and data traffic on the network complicates the protocol design further. The performance requirements of these traffic types are different. Voice creates stream traffic on a network, where as data traffic is bursty. Voice packets need to be delivered within a limited time interval, whereas the data emphasizes on error-free delivery. The necessity and the technological feasibility with off-the-shelf components has prompted this study. A possible solution is discussed in this dissertation;During the course of this research, due to the time consuming nature of simulation experiments, a need for efficient simulation techniques was felt. Thus, as a byproduct of the initial goal of protocol design, an approximate version of the regenerative simulation was developed and is discussed here in detail;Lastly, modeling difficulties encountered in forming an analytical model are listed and a performance analysis of the subsystems of interest is given

Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System

Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected from a period of time. False alarm rate resulted from using threshold determined from each distribution is used as a measure to determine which distribution is the most appropriate. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated