Search results

The purpose of this study is to calculate the normal force of a two degree of freedom direct drive induction motor considering coupling effects based on an analytical model. Compared with the traditional single degree of freedom motor, normal force characteristics of two-degree-of-freedom direct drive induction motor (2DOFDDIM) is affected by coupling effect when the machine is in a helical motion. To theoretically explain the influence mechanism of coupling effect, this paper conducts a quantitative analysis of the influence of coupling effect on normal force based on the established analytical model of normal force considering coupling effect.

Firstly, the normal forces generated by 2DOFDDIM in linear motion, rotary motion and helical motion are investigated and compared to prove the effect of the coupling effect on the normal force. During this study, several coupling factors are established to modify the calculation equations of the normal force. Then, based on the multilayer theoretical method and Maxwell stress method, a novel normal force calculation model of 2DOFDDIM is established taking the coupling effect into account, which can easily calculate the normal force of 2DOFDDIM under different motions conditions. Finally, the calculation results are verified by the results of 3D finite element model, which proves the correctness of the established calculating model.

The coupling effect produced by the helical motion of 2DOFDDIM affects the normal force.

In this paper, the analytical model of the normal force of 2DOFDDIM considering the coupling effect is established, which provides a fast calculation for the design of the motor.

In order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.

By combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.

Based on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.

Due to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.

The main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.

The purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval multivariable gray model (IMGM(1,m,k) model) is constructed to simulate and forecast original interval gray number sequences in this paper.

Firstly, the interval gray number is regarded as a three-dimensional column vector, and the parameters of multivariable gray model are expressed in matrix form. Based on the dynamic gray action and optimized background value, the interval multivariable gray model is constructed. Finally, two examples and comparisons are carried out to verify the effectiveness of IMGM(1,m,k) model.

The model is applied to simulate and predict expert value, foreign direct investment, automobile sales and steel output, respectively. The results show that the proposed model has better simulation and prediction performance than another two models.

Due to the uncertainty information and continuous changing of reality, the interval gray numbers are used to characterize full information of original data. And the IMGM(1,m,k) model not only considers the characteristics of parameters changing with time but also takes into account information on lower, middle and upper bounds of interval gray numbers simultaneously to make better suitable for practical application.

The main contribution of this paper is to propose a new interval multivariable gray model, which considers the interaction between the lower, middle and upper bounds of interval numbers and need not to transform interval gray number sequences into real sequences. According to combining different characteristics of each bound of interval gray numbers, the matrix form of interval multivariable gray model is established to simulate and forecast interval gray numbers. In addition, the model introduces dynamic gray action to reflect the changes of parameters over time. Instead of white equation of classic MGM(1,m), the difference equation is directly used to solve the simulated and predicted values.