Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Xiaogang Li

3 Apparent Ageing Mechanism of Polyurethane Coating in Typical Atmospheric Environment

Authors: Jin Gao, Jin Zhang, Xiaogang Li

Abstract:

Outdoor exposure experiments were conducted in three extreme environments, namely the Chinese plateau mountain environment (Lhasa), the cold–temperate environment (Mohe), and the marine atmospheric environment (Wanning), to track a new long-life environment-friendly polyurethane coating. The relationship between apparent properties, namely gloss and microstructural changes, was analyzed, and the influence of typical climatic environment on the aging mechanism of polyurethane coatings was discussed. Results show that the UV radiation in the Lhasa area causes photoaging degradation, micropores are formed on the coating surface, and the powdering phenomenon is obvious. Photodegradation occurs in the Wanning area, and a hydrolysis reaction is observed. The hydrolysis reaction catalyzes the photoaging, the coating surface becomes yellow, and the powdering becomes serious. Photoaging is also present in the Mohe area, but it is mainly due to temperature changes that in turn change the internal stress of the coating. Microcracks and bumps form on the coating surface.

Keywords: aging, atmospheric environment, outdoor exposure, polyurethane coating

Procedia PDF Downloads 36
2 The Effect of Bottom Shape and Baffle Length on the Flow Field in Stirred Tanks in Turbulent and Transitional Flow

Authors: Jie Dong, Binjie Hu, Andrzej W Pacek, Xiaogang Yang, Nicholas J. Miles

Abstract:

The effect of the shape of the vessel bottom and the length of baffles on the velocity distributions in a turbulent and in a transitional flow has been simulated. The turbulent flow was simulated using standard k-ε model and simulation was verified using LES whereas transitional flow was simulated using only LES. It has been found that both the shape of tank bottom and the baffles’ length has significant effect on the flow pattern and velocity distribution below the impeller. In the dished bottom tank with baffles reaching the edge of the dish, the large rotating volume of liquid was formed below the impeller. Liquid in this rotating region was not fully mixing. A dead zone was formed here. The size and the intensity of circulation within this zone calculated by k-ε model and LES were practically identical what reinforces the accuracy of the numerical simulations. Both types of simulations also show that employing full-length baffles can reduce the size of dead zone formed below the impeller. The LES was also used to simulate the velocity distribution below the impeller in transitional flow and it has been found that secondary circulation loops were formed near the tank bottom in all investigated geometries. However, in this case the length of baffles has smaller effect on the volume of rotating liquid than in the turbulent flow.

Keywords: baffles length, dished bottom, dead zone, flow field

Procedia PDF Downloads 223
1 Life Prediction Method of Lithium-Ion Battery Based on Grey Support Vector Machines

Authors: Xiaogang Li, Jieqiong Miao

Abstract:

As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines.For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

Procedia PDF Downloads 361