Waxy crude oil exhibits complex rheological behavior below the pour point temperature, such as viscoelasticity, yield stress, and thixotropy, owing to the formation of a three-dimensional spongelike interlock network ...Waxy crude oil exhibits complex rheological behavior below the pour point temperature, such as viscoelasticity, yield stress, and thixotropy, owing to the formation of a three-dimensional spongelike interlock network structure. This viscoelasto-thixotropic behavior is an important rheologieal behavior of waxy crude oils, determining the flow recovery and safe restart of crude oil pipelines. Up to now, the thixotropic models for waxy crude have been all viscoplastic models, without considering the viscoelastic part before the yield point. In this work, based on analyzing the variation of the elastic stress and viscous stress in the Mujumbar model, a new viscoelasto-plastic model is proposed, whose shear stress is separated into an elastic component and a viscous component. The elastic stress is the product of the shear modulus and elastic strain; the shear modulus is proportional to the structural parameter. For the elastic strain, we followed the line of Zhu and his coauthors and assumed that it may be expressed by an algebraic equation. The model is validated by stepwise shear rate tests and hysteresis loop tests on Daqing and Zhongyuan waxy crude. The results show that the model＇s fitting and predictive capability is satisfactory.展开更多
With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling,...With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling, non- parametric modeling with neural network, which is a promising development in semi-active online control of vehicles with MR suspension, has been carried out in this study. A two layer neural network with 7 neurons in a hidden layer and 3 inputs and 1 output was established to simulate the behavior of MR damper at different excitation currents. In the neural network modeling, the damping force is a function of displacement, velocity and the applied current. A MR damper for vehicles is fabricated and tested by MTS; the data acquired are utilized for neural network training and vali- dation. The application and validation show that the predicted forces of the neural network match well with the forces tested with a small variance, which demonstrates the effectiveness and precision of neural network modeling.展开更多
基金the financial support from the National Natural Science Foundation of China (Grant No.51134006)Science Foundation of China University of Petroleum (Beijing) (Grant No.LLYJ-2011-55)
文摘Waxy crude oil exhibits complex rheological behavior below the pour point temperature, such as viscoelasticity, yield stress, and thixotropy, owing to the formation of a three-dimensional spongelike interlock network structure. This viscoelasto-thixotropic behavior is an important rheologieal behavior of waxy crude oils, determining the flow recovery and safe restart of crude oil pipelines. Up to now, the thixotropic models for waxy crude have been all viscoplastic models, without considering the viscoelastic part before the yield point. In this work, based on analyzing the variation of the elastic stress and viscous stress in the Mujumbar model, a new viscoelasto-plastic model is proposed, whose shear stress is separated into an elastic component and a viscous component. The elastic stress is the product of the shear modulus and elastic strain; the shear modulus is proportional to the structural parameter. For the elastic strain, we followed the line of Zhu and his coauthors and assumed that it may be expressed by an algebraic equation. The model is validated by stepwise shear rate tests and hysteresis loop tests on Daqing and Zhongyuan waxy crude. The results show that the model＇s fitting and predictive capability is satisfactory.
基金Projects 50135030 and 60404014 supported by National Natural Science Foundation of China
文摘With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling, non- parametric modeling with neural network, which is a promising development in semi-active online control of vehicles with MR suspension, has been carried out in this study. A two layer neural network with 7 neurons in a hidden layer and 3 inputs and 1 output was established to simulate the behavior of MR damper at different excitation currents. In the neural network modeling, the damping force is a function of displacement, velocity and the applied current. A MR damper for vehicles is fabricated and tested by MTS; the data acquired are utilized for neural network training and vali- dation. The application and validation show that the predicted forces of the neural network match well with the forces tested with a small variance, which demonstrates the effectiveness and precision of neural network modeling.
