Simulation Of Response Characteristics Research Articles

Numerical Simulation of Response Characteristics of Audio-magnetotelluric for Gas Hydrate in the Qilian Mountain Permafrost, China

Audio-magnetotelluric (AMT) method is a kind of frequency-domain sounding technique, which can be applied to gas hydrate prospecting and assessments in the permafrost region due to its high frequency band. Based on the geological conditions of gas hydrate reservoir in the Qilian Mountain permafrost, by establishing high-resistance abnormal model for gas hydrate and carrying out numerical simulation using finite element method (FEM) and nonlinear conjugate gradient (NLCG) method, this paper analyzed the application range of AMT method and the best acquisition parameters setting scheme. When porosity of gas hydrate reservoir is less than 5%, gas hydrate saturation is greater than 70%, occurrence scale is less than 50 m, or bury depth is greater than 500 m, AMT technique cannot identify and delineate the favorable gas hydrate reservoir. Survey line should be more than twice the length of probable occurrence scale, while tripling the length will make the best result. The number of stations should be no less than 6, and 11 stations are optimal. At the high frequency section (10∼1000 Hz), there should be no less than 3 frequency points, 4 being the best number.

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiCp metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (Ra, micrometers), maximum peak–valley surface roughness height (Rt, micrometers), improvement in Ra (i.e., ΔRa), and improvement in Rt (i.e., ΔRt), the networks are trained for finishing of Al/SiCp-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.