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The operational mode of thermal power plants has been changed from base load to duty cycle. From the changeover, fossil power plants cannot avoid frequent thermal transient states, for example, start up and stop, which results in thermal fatigue damage at the heavy section components. The rotor is the highest capital cost component in a steam turbine and requires long outage for replacing with a new one. For an optimized power plant operational life, inspection management of the rotor is necessary. It is known in general that the start-up and shutdown operations greatly affect the steam turbine life. The start-up operational condition is especially severe because of the rapid temperature and rotational speed increase, which causes damage and reduction of life of the main components life of the steam turbine. The start-up stress of a rotor which is directly related to life is composed of thermal and rotational stresses. The thermal stress is due to the variation of steam flow temperature and rotational stress is due to the rotational speed of the turbine.

In this paper, the analysis method for the start-up stress of a rotor is proposed, which considers simultaneously temperature and rotational speed transition, and includes a case study regarding a 500MW fossil power plant steam turbine rotor. Also, the method of quantitative damage estimation for fatigue-creep damage to operational conditions, is described. The method can be applied to find weak points for fatigue-creep damage. Using the method, total life consumption can be obtained, and can be also be used for determining future operational modes and life extension of old fossil power units.

The durability and reliability of thermal barrier coatings(TBCs) have become a major concern of hot-section components due to lack of a reliable life prediction model. In this paper, it is found that the failure location of TBCs is at the TBC/TGO interface by a sequence of crack propagation and coalescence process. The critical crack length of failure samples is 8.8mm. The crack propagation rate is 3-10µm/cycle at the beginning and increases largely to 40µm/cycle near coating failure. A life prediction model based a simple fracture mechanics approach is proposed.

Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method.

In critical conditions of variable stress-state, fluctuating temperature and hostile environment, where the objective is to design components and structures for longevity, durability, and reliability — structural integrity — then the balance between empirical engineering design based on continuum and mathematical modeling (sometimes called "distilled empiricism"), and physical modeling (sometimes called "mechanism modeling" or simply "micromechanics"), is shifted in favor of physical modeling. When combined with experimental evidence, physical modeling has the economic advantage of reducing the high cost of vast experimental programs of duration of many thousands of hours. Furthermore, existing empirical design methodologies at the higher (macroscopic) structural size scales can be supported and justified by fundamental understanding at lower (microscopic) size scales through the physical model. Armed with this information, together with knowledge of the mechanical behavior of the material over time, we follow the path of "physical model-informed empiricism", sometimes called "intelligent-informed design". Proof of identity of individual cracking processes based on their direct observation and an understanding of coupling between them is the first step in formulating a complete physical model of fracture.

Improving and stabilizing the life of the die has always been the key to increasing the output of cold precision forging products and reducing the production cost of forgings. The stress state in pre-stressed composed dies during cold extrusion process is investigated in this paper, it shows that the combined die can greatly reduce the tangential tensile stress of the inner wall of the die and reduce the strain energy density of the die, thereby improving the strength of the die and extending the life of the die. By increasing the number of pre-stressed rings, the amount of interference can be changed, which indirectly changes the pre-stress applied to the die. The relationship between the die fatigue life and the number of pre-stressed rings indicates that the design of the pre-stressed composed structure above the inflection point is an excess design, and the optimal design should be near the inflection point.

Damage to composite structures can accumulate over time and lead to fatigue failure in their actual use environment. Therefore, it is critical to establish a suitable fatigue life prediction model. This work developed an improved fatigue life prediction model based on the effects of equivalent damage and load interactions. Validation and comparison of the improved fatigue life prediction model were carried out using test data of composites subjected to secondary and tertiary loading. The analysis indicates that the accuracy of fatigue life prediction for composites under variable amplitude load is improved by the damage equivalence prediction model, which accounts for the influence of load application sequence and load interaction. Furthermore, a comparison with existing fatigue life prediction models reveals that the proposed model predicts fatigue life more accurately under different amplitude loads.

In order to ensure an accurate prediction of the life of the transmission system, a gray BP neural network model is created. Flexible rate adjustment factor and adaptive inertia factor are introduced to improve the optimization performance of the particle swarm optimization. Once more, the gray BP neural network model is optimized by the particle swarm optimization. Finally, the prediction model is trained for the optimal solution. Simulation results show that this method gets good convergence rate while improving the prediction accuracy.

Current correction to the Fick's second law was summarized to research chloride diffusion in concrete. Theoretical diffusion model was established especially for bonded strengthened concrete. The method to consider the impact of externally bonding to the diffusion was first put forward. Numerical simulation of chloride diffusion in a reinforced concrete beam strengthened by externally bonding was finished by ABAQUS, MATLAB PDE tool, MATLAB programming respectively. The characters of chloride diffusion were indicated in both strengthened and non-strengthened specimens. The simulation result shows externally bonding decreases the chloride concentration of steel surface and suspends it to reach the threshold value that leads to corrosion, so that the service life of RC specimens can be extended. The earlier bonding is carried out, the longer service life is achieved. This research could facilitate the durability design and life prediction of boned strengthened concrete in chloride environment as reference.

USB was designed to standardize the connection of computer peripherals (including keyboards, pointing devices, digital cameras, printers, portable media players, disk drives and network adapters) to personal computers, both to communicate and to supply electric power. It has become commonplace on other devices, such as smartphones, PDAs and video game consoles. USB has effectively replaced a variety of earlier interfaces, such as parallel ports, as well as separate power chargers for portable devices. Since USB is hot pluggable, the connectors would be used more frequently. In this context, its reliability and life span is vital. Generally, an exhaustive series of circular insertion/extraction testing can be carried out to evaluate the USB life span. However, this method is time consuming and costly. This paper proposes a novel testing method based on BPNN for USB connector life span, which can estimate the useful life by predicting the residual life of the connector. This method allows the entire test process to stop before the specimen fails, and predicts the specimen’s life in advance based on pre-test data. Modeling process is described in detail in this paper and the test results show that the model can realize accurate prediction within a certain range.