Fluid-structure Interaction Analysis and Lifetime Estimation of a Natural Gas Pipeline Centrifugal Compressor under Near-choke and Near-surge Conditions

Up to present, there have been no studies concerning the application of fluid-structure interaction（FSI） analysis to the lifetime estimation of multi-stage centrifugal compressors under dangerous unsteady aerodynamic excitations. In this paper, computational fluid dynamics（CFD） simulations of a three-stage natural gas pipeline centrifugal compressor are performed under near-choke and near-surge conditions, and the unsteady aerodynamic pressure acting on impeller blades are obtained. Then computational structural dynamics（CSD） analysis is conducted through a one-way coupling FSI model to predict alternating stresses in impeller blades. Finally, the compressor lifetime is estimated using the nominal stress approach. The FSI results show that the impellers of latter stages suffer larger fluctuation stresses but smaller mean stresses than those at preceding stages under near-choke and near-surge conditions. The most dangerous position in the compressor is found to be located near the leading edge of the last-stage impeller blade. Compressor lifetime estimation shows that the investigated compressor can run up to 102.7 h under the near-choke condition and 200.2 h under the near-surge condition. This study is expected to provide a scientific guidance for the operation safety of natural gas pipeline centrifugal compressors.

Models for lifetime estimation： an overview with focus on applications to wind turbines

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes： physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.

Lifetime Enhancement of Cluster Based Wireless Sensor Network through Energy Efficient MAC Protocol

The efficient use of energy is an important performance metric to extend the lifetime of wireless sensor networks. Since major energy consumption of node is due to its transceiver, the design of MAC protocol plays a vital role in sensor network design. In cluster based sensor networks, due to the different functions of member node and cluster head node, the usage of common MAC protocol results increased energy consumption. To overcome this problem, a novel energy efficient hybrid MAC protocol (EEHMAC) for cluster based wireless sensor networks is proposed in this paper. The proposed EEHMAC protocol uses E-TDMA (Energy efficient TDMA) for intra-cluster communication and FDMA (Frequency Division Multiple Access) for inter-cluster communication. IDS (Iterative Deepening Search) based Scheduling algorithm is used for assigning time slot and frequency slot to nodes. Nodes in EEHMAC follow the periodic duty cycle, which reduces the idle listening, and overhearing problems. Simulation results reveal that an average of 18% energy saving is achieved compared to LEACH (Low Energy Adaptive Clustered Hierarchy) protocol and 10% energy is saved in comparison with GH-MAC (Graph theory based Hybrid MAC) protocol. It is evident that delay of EEHMAC is reduced by 17% and throughput is increased by 15% under all traffic conditions. These results demonstrate that EEHMAC performs better than existing MAC protocols in terms of energy efficiency, delay and throughput.

The storage lifetime model based on multi-performance degradation parameters

According to the multi-performance degradation of the bipolar transistor in the accelerating storage process, an extrapolation model of the storage lifetime is proposed. In this model, using the Wiener process simu- lates the mono-degradation process of each feature degradation; using the copula function describes the correlation among these feature degradations. The Wiener process and parameters in the copula function are considered to associate with the temperature, and their relationships can be represented by the converted equations. Through the maximum likelihood estimation, the parameters in the Wiener process can be found; introducing Kendall＇s tau, those in the copula function can be estimated. By conducting the regression analyses of the estimated values of the parameters in each stress, their corresponding converted equations can be shown. Based on the storage test data of bipolar transistors, with the estimation method, the storage lifetime is found. The findings show that the model is reasonable for the prediction of storage lifetime.