A Multi-Objective Optimization Approach on Spiral Grooves for Gas Mechanical Seals

Spiral groove is one of the most common types of structures on gas mechanical seals. Numerical research demonstrated that the grooves designed for improving gas film lift or film stiffness often lead to the leakage increase. Hence, a multi-objective optimization approach specially for conflicting objectives is utilized to optimize the spiral grooves for a specific sample in this study. First, the objectives and independent variables in multi-objective optimization are determined by single objective analysis. Then, a set of optimal parameters, i.e., Pareto-optimal set, is obtained. Each solution in this set can get the highest dimensionless gas film lift under a specific requirement of the dimensionless leakage rate. Finally, the collinearity diagnostics is performed to evaluate the importance of different independent variables in the optimization.

A Model for Recognizing Key Factors and Applications Thereof to Engineering

This paper presents an approach to recognize key factors in data classification. Using collinearity diagnostics to delete the factors of repeated information and Logistic regression significant discriminant to select the factors which can effectively distinguish the two kinds of samples, this paper creates a model for recognizing key factors. The proposed model is demonstrated by using the 2044 observations in finical engineering. The experimental results demonstrate that the 13 indicators such as “marital status,” “net income of borrower,” and “Engel's coefficient” are the key factors to distinguish the good customers from the bad customers. By analyzing the experimental results, the performance of the proposed model is verified. Moreover, the proposed method is simple and easy to be implemented.

The Regularization Method Based on Complex Collinearity Diagnostics and Metrics

In a large number of measurement data processing, the ill-posed problem is widespread. For such problems, this paper introduces the solution of ill-posed problem of the unity of expression and Tikhonov regularization method, and then to re-collinearity diagnostics and metrics based on proposed based on complex collinearity diagnostics and the metric regularization method is given regularization matrix selection methods and regularization parameter determination formulas. Finally, it uses a simulation example to verify the effectiveness of the method.

Forecasting the winter shower over India through a neurocomputing approach

The development of a neurocomputing technique to forecast the average winter shower in India has been modeled from 48 years of records (1950–1998). The complexities in the rainfall–sea surface temperature relationships have been statistically analyzed along with the collinearity diagnostics. The presence of multicollinearity has been revealed and a variable selection has been executed accordingly. The absence of persistence has also been revealed. For this reason, an Artificial Neural Net Model as a predictive tool for the said meteorological event in the form of a Multiple Layer Perceptron has been generated with a sea surface temperature anomaly and monthly average winter shower data over India during the above period. After proper training and testing, a Neural Net model with small prediction error is developed and the supremacy of the Artificial Neural Net over conventional statistical predictive procedures has been established statistically.