Papers by Keyword: Correlations

Abstract: Geotechnical structures, design of embankment, earth and rock fill dam, tunnels, and slope stability require further attention in determining the shear strength of soil and other parameters that govern the result. The shear strength of soil commonly obtained by conducting laboratory testing such as Unconfined Compression Strength (UCS) Test and Unconsolidated Undrained (UU) Test. However, random errors and systematic errors can occur during experimental works and caused the findings imprecise. Besides, the laboratory test also consuming a lot of time and some of them are quite costly. Therefore, soft computational tools are developed to improve the accuracy of the results and time effectively when compared to conventional method. In this study, Artificial Neural Network (ANN) was employed to develop a predictive model to correlate the moisture content (MC), liquid limit (LL), plastic limit (PL), and liquidity index (LI) of cohesive soil with the undrained shear strength of soil. A total of 10 databases was developed by using MATLAB 7.0 - matrix laboratory with 318 of UCS tests and 451 of UU tests which are collected from the verified site investigation (SI) report, respectively. All the SI reports collected were conducted in Sarawak, Malaysia. The datasets were split into ratio of 3:1:1 which is 60:20:20 (training: validation: testing) with one hidden layer and eight hidden neurons. The input parameter of Liquidity index (LI) has shown the highest R-value (regression coefficient) which are 0.926 and 0.904 for UCS and UU model, respectively. In addition, the predictive models were tested and compare with the predicted and observed cohesion obtained from the collected experimental results. In summary, the ANN has the feasibility to be used as a predictive tool in estimating the shear strength of the soil.

Abstract: Modern assembly lines are usually optimized towards output and tact time as well as process capability and quality. Yet, approaches for energy saving are hardly used in assembly presses. Therefore, assembly lines are using more electric power and compressed air than necessary. Especially at high load, during handling phases and in different idle modes there is a huge potential for energy savings. Current research is focusing on high-power consuming turning and milling machines as well as laser welding. Energy saving projects usually focus on whole factory halls instead of manufacturing lines and single assembly machines. Therefore, this paper presents a new methodology using a top-down-approach and data mining analysis regarding a conventional assembly press as well as a whole assembly line. Here, relevant information types like process data, quality factors, expenditure of energy per produced part and power consumption are used to generate more insight into chained assembly processes. Various tools like energy analysis, process flow and correlation analysis are used to identify focus stations of a whole assembly line for energy saving projects and quality improvements. This novel holistic approach regards the electrical power and compressed air consumption of each relevant station and its machine components during different operating states as well as its correlations between process data, quality factors and energy consumption. Besides tact-time-analysis of the process, the scheduled and unplanned downtimes of the machine are also regarded. Furthermore, it enables predictions of tool wear and breakdown, quality impacts of supplied parts, as well as energy savings on process and machine level. Due to an increased quality, the material efficiency may rise as well.

Abstract: The one-dimensional mixture model efficiently predicts gas-liquid flows dominated by gravity force. The advantages of the mixture model are the absence of interfacial terms and the reduced number of transport equations, but its weakness lies on the constitutive laws to predict the wall shear force of a gas-liquid mixture. The objective of this work is to realize a sensitivity analysis of the wall shear model (based on the intermittent behavior of the gas and liquid structures) to the correlations for frequency and slug holdup in the one-dimensional, steady state mixture model applied to an isothermal gas-liquid mixture flowing in the slug regime. The numerical results for the pressure gradient obtained here are compared against experimental data from previous work.

Abstract: This paper presents an overview of recent computer simulations of grain boundary (GB)diffusion focusing on atomistic understanding of diffusion mechanisms. At low temperatures when GBstructure is ordered, diffusion is mediated by point defects inducing collective jumps of several atomsforming a chain. At high temperatures when GB structure becomes highly disordered, the diffusionprocess can be analyzed by statistical methods developed earlier for supercooled liquids and glasses.Previous atomistic simulations reported in the literature as well as the new simulations presented in thispaper reveal a close similarity between diffusion mechanisms in GBs and in supercooled liquids. GBdiffusion at high temperatures is dominated by collective displacements of atomic groups (clusters),many of which have one-dimensional geometries similar to strings. The recent progress in this fieldmotivates future extensions of atomistic simulations to diffusion in alloy GBs, particularly in glassformingsystems.

Abstract: Four kinds of granular activated carbon GAC were treated by water taken from surface drinking-water sources in Ji’nan. Five adsorption performance indicators of granular activated carbon (GAC) were investigated. The correlation between the indicators and the removal rate of organic matter in raw water was analysed and the results showed that the removal rates of COD_Mn and DOC were well correlated with iodine value, the coefficient correlations, R² were 0.8745 and 0.8474 respectively, the removal rates of UV₂₅₄ was well correlated with methylene blue value (R² = 0.9454), indicating the capability as indicator for GAC selection. Additionally, one original GAC was used to adsorb organic matter in raw water, as well as the GAC samples treated with four different saturation time in raw water, to investigate the residue rate of adsorption ability. Of all five adsorption performance indicators, it was found that the iodine value, methylene blue value, and tannic acid were negatively related to the residual rate of the GAC absorption ability (R² was over 0.9). Based on the research, the activated carbon filters in Quehua Water plant in Ji’nan was evaluated and the replacement of them was suggested.

Abstract: This paper presents a new method in damage detection by taking the sound signals of the rolling bearings in different levels. The tested bearing was put on the end of the shaft rotated by permanent magnet synchronous motor. The sound signal produced by this rig was recorded separately for each bearing condition with the same experimental environment. The sound data signals are compared each other. Based on the cross-correlation analysis, the recorded sound signal proved that the signals were recorded with the same environment. The power spectra calculation has shown different harmonic frequencies according to various bearing conditions. The total power of the sound is decreased along with the increasing damage. This is also confirmed by the auto-correlation of each sound signal that shows the appearance of the sounds impulse repetition with a wider period.