A System Approach to Dynamic Response Variability Under Parameter Uncertainty and Variability

Tether system is a critical component for the TLPs, since its failure may lead to the collapse of the whole structure involving human lives, economic losses and damages to the environment. Due to this fact, reliability methods have been proposed to design TLP tethers and new codes are being developed to increase their safety level. The objective of this paper is to compare the probability of failure for TLP tethers considering the maximum tension limit state obtained with three methods, which are: a methodology based on Evolutionary Strategies and the Monte Carlo Importance Sampling, the First Order Reliability Method, and the Second Order Reliability Method. Von-Mises failure criterion is used as limit state function for the most loaded tether of a TLP submitted to different sea states. Efficiency of the ES algorithm to find design points and probabilities of failure obtained with the reliability methods are discussed.

This paper develops Mean and Range control charts in fuzzy environment using different transformation methods. It is assumed that the observations of each sample are fuzzy random variables, which have triangular membership functions. After calculating fuzzy mean and fuzzy range of each sample using fuzzy arithmetic, their representative values are calculated exploiting the transformation methods. Then, using statistical properties of the obtained representative values and basic structure of Shewhart control charts, new Mean and Range control charts are constructed to monitor the process mean and variation. After that, the power of control charts constructed based on different transformation methods are examined applying average run length ARL criterion. According to this criterion, it is concluded that, contrary to the previous claim, these different transformation methods lead to mean and rang control charts with various performances. Moreover, it is represented that the value of α --level has significant influence on the performance of obtained control charts. Finally, it is derived that incorporating fuzziness into observations results in less powerful control charts.

The transformation method has been proposed as a practical tool for the simulation and the analysis of systems with uncertain, fuzzy-valued model parameters using fuzzy arithmetic. Up to now, this method has been available in two forms: in a general form, which can be used for the simulation and the analysis of arbitrarily non-monotonic problems, and in a reduced form, which reduces the computational costs of the method to a large extent, but requires, instead, an additional condition to be fulfilled. In this paper, the extended transformation method will be introduced as an advanced version of the previously presented formulations of the transformation method. This extended version includes the former versions as marginal cases and allows a pre-adjustment of the method subject to the number of model parameters that are expected to cause non-monotonic behavior of the model output. Furthermore, to set up the method properly, a novel approach, again based on the transformation method, is presented to practically detect those parameters that are responsible for a non-monotonic behavior of the model output. Finally, to show its effectiveness, the method is applied to a static and a dynamic model with uncertain parameters.

The steam turbine and a boiler are important components of a power generation plant. Improved effciency of a power plant leads to increase of energy production and less waste. In this paper, a model of the power plant as a multivariable system using fuzzy arithmetic which is based on the Transformation Method (TM) is presented. The analytical solution is used to evaluate the state space model. The TM is then used to quantify the in uence of each parameter and their gain factors are calculated to allow estimation of relative measures of uncertainty. The method is applied to a boiler and a steam turbine systems for simulation, analysis and indication of TM's efficiency. The efficiency of TM is presented in this paper.

With the development of large-scale sensor networks, there is an increasing interest for high dimensional distributed decision fusion problem in statistical decision, machine learning, control theory, etc. In this article, we investigate the optimal sensor quantized rules of the distributed decision under a given fusion rule for conditionally dependent and high dimensional sensor observations. We provide a Monte Carlo importance sampling method to reduce the computational complexity of the distributed decision fusion. For the K-out-of-L rule, we derive a group of analytic sensor rules based on the necessary and sufficient condition of the optimal sensor rules. In term of efficiency, it is significantly better than the traditional iterative search algorithms, such as the Riemann sum approximation iteration algorithm. The analytic solution is also a general result since it does not rely on the specific K value of the K-out-of-L rule, the specific conditional probability density function and the dimension of sensor observations. Thus, it can be applied to multisensor decision fusion problems with high dimensional dependent sensor observations. For the general fusion rules rather than the K-out-of-L rule, the Monte Carlo Gauss-Seidel algorithm is also provided. The numerical examples demonstrate the effectiveness of the analytic solution and the Monte Carlo Gauss-Seidel algorithm.

The authors propose a distributed state estimation algorithm based on optimal track-to-track fusion for local posteriors in terms of Gaussian mixtures. The track-to-track fusion system is implemented with both parallel and sequential structure based on generalised covariance intersection rule. They obtain the optimal fusion coefficients in a computationally efficient manner via Monte Carlo importance sampling method. The Dirac mixture approximation method is proposed for the computation of arbitrary power of a Gaussian mixture density. The resulting Gaussian mixture fusion rule is analytical and applicable to the multi-sensor case. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with existing track-to-track fusion algorithms.

Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method

To localize damages in engineering structures, such as power lines, ultrasonic wave-based techniques are widely used for Structural Health Monitoring applications. In a cylindrical waveguide, longitudinal, flexural and torsional modes may propagate. Additionally, the wave propagation is generally of dispersive nature. Since cable structures usually consist of several smaller wires, coupling in between individual wires must also be considered. Friction contact causes energy transfer and dissipation as well as mode conversion of propagating waves. Precise transient simulation of wave propagation in multi-wire cables with common FE software results in tremendous computational costs. Therefore, recent research follows a different strategy: only power flows due to propagating waves are considered. Since these models incorporate significant simplifications, a model assessment strategy through advanced fuzzy arithmetic is applied. Uncertainty in model parameters, especially due to simplification and idealization during the modeling process, is considered as epistemic uncertainty. Therefore, the effects of uncertain model parameters represented as fuzzy numbers are investigated by simulations with the use of the Transformation Method. Different models are simulated and an inverse fuzzy arithmetical technique is used to identify parameter uncertainty based on experimentally derived data. Finally, the models' validity is verified.

An excellent airfoil with a high lift-to-drag ratio may decrease oil consumption and enhance the voyage. Based on NACA 0012, an improved airfoil is explored in this paper. The class/shape function transformation has been proved to be a good method for airfoil parameterization, and in this paper it is modified to improve imitation accuracy. The computational fluid dynamics method is applied to obtain numerically the aerodynamic parameters of the parameterized airfoil, and the result is proved credible by comparison with available experimental data in the open literature. A polynomial-based response surface model and the uniform Latin hypercube sampling method are employed to decrease computational cost. Finally, the nonlinear programming by quadratic Lagrangian method is utilized to modify the multi-island genetic algorithm, which has an improved optimization effect than the method used on its own. The obtained result shows that the modified class/shape function transformation method produces a better imitation of an airfoil in the nose and tail regions than the original method, and that it will satisfy the tolerance zone of the model in a wind tunnel. The response surface model based on the uniform Latin hypercube sampling method gives an accurate prediction of the lift-to-drag ratio with changes in the design variables. The numerical result of the flow around the airfoil shows reasonable agreement with the experimental data graphically and quantitatively. Ultimately, an airfoil with better capacity than the original one is acquired using the multi-island genetic algorithm based nonlinear programming by quadratic Lagrangian optimization method. The pressure contours and lift-to-drag ratio along with the attack angle have been compared with those of the original airfoil, and the results demonstrate the strength of the optimized airfoil. The process for exploring an improved airfoil through parameterization to optimization is worth referencing in future work.