Orthogonal experimental design of liquid-cooling structure on the cooling effect of a liquid-cooled battery thermal management system

Effects of the different air cooling strategies on cooling performance of a lithium-ion battery module with baffle

Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process

With the vigorous development of the national economy, the pace and scale of urban construction have been unfolded at an unprecedented speed. A large number of construction projects have made the urban engineering geological exploration activities reach a considerable scale in depth and breadth. The survey results of these projects are very valuable information resources, which not only played an important role in urban planning and construction at that time, but also had high reuse value. Based on BP neural network theory, this paper uses engineering geological database as the research and development platform. Based on the theory of BP neural network and the engineering geological database as the research and development platform, this paper establishes the prediction of geotechnical parameters based on the analysis of the characteristics of geotechnical materials and the distribution of geotechnical sediments and geotechnical parameters. Based on the survey data and specific engineering information, the prediction model of the project was established, and the distribution of the stratum and the relevant geotechnical parameters were predicted. Based on the study of geotechnical properties and BP neural network, a new parameter prediction model is established. Taking the engineering geological database as the platform, using the programming language such as MATLAB, the preliminary research and construction of this prediction system were carried out. The results show that the generalization ability of the prediction model meets the requirements.

Research on prediction model of geotechnical parameters based on BP neural network

A review on ice detection technology and ice elimination technology for wind turbine

A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares (FLS)-support vector machine (SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.

Multidisciplinary design optimization on production scale of underground metal mine

To identify the instability on large scale underground mined-out area in the metal mine effectively, the parameters of radial basis function were determined through clustering method and the improved fuzzy radial basis function neural network (FRBFNN) model of instability identification model about large scale underground mined-out area in the metal mine was built. The improved FRBFNN model was trained and tested. The results show that the improved FRBFNN model has high training accuracy and generalization ability. Parameters such as pillar area ratio, filling level and the value of rock quality designation have strong influence on instability of large scale underground mined-out area. Correctness of analysis about the improved FRBFNN model was proved by the practical application results about instability discrimination of surrounding rock in large-scale underground mined-out area of a metal mine in south China.

Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN

For the number and width of the implicit layer RBF centers have a direct impact on the approximation capability of RBF neural network, clustering method is adopted to determine the radial basis function parameters, adaptive variable weight method is used to improve the conventional fuzzy RBF neural network learning algorithm, adaptive variable weight fuzzy RBF neural network prediction model is built, and simulation experiments are performed on its non-linear function approximation performance. Results show that the adaptive variable weight fuzzy RBF neural network prediction model has high accuracy in both single-input single-output nonlinear prediction and multiple-input multiple-output nonlinear prediction. Instability analysis results of surrounding rock in a massive metal mine gob area in southern China verify the effectiveness and practicality of the nonlinear fitting of the model.

http://www.joics.com/publishedpapers/2013_10_11_3239_3252.pdf

Yi-wei Wang

Papers

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

Multidisciplinary design optimization on production scale of underground metal mine

Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN

Nonlinear Fitting Characteristics Analysis on Prediction Model of Adaptive Variable Weight Fuzzy RBF Neural Network

Characteristics of soil extracellular enzyme activities and their stoichiometry during rocky desertification in southwestern Guizhou, China