Journal of Engineering and Technological Sciences
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An Analysis of EEG Changes during Prolonged Simulated Driving for the Assessment of Driver Fatigue
Get PDFFatigue during driving is the main contributing factor to road accidents. It is influenced by time on task (TOT) and time of day (TOD). Recent electroencephalogram (EEG) research on fatigue assessment has shown a promising result in explaining the fatigue phenomenon. However, different findings exist regarding the best EEG parameters related to fatigue. This study examined EEG changes according to the effect of TOT and TOD and determined the best parameters to distinguish fatigue status. To generate driver fatigue, prolonged driving in the morning and at night in a simulator was conducted. The EEG signal was collected from 28 male participants at frontal and occipital areas. The EEG power (brainwave) was determined from the first and last 5 minutes of the driving task and after a break of 30 minutes. The results of this study showed a general tendency of EEG power changing throughout the driving sessions. However, changes related to fatigue were only found for the night sessions, as confirmed by q power and the subjective fatigue measurement result. This study showed that TOT (as a factor that induces fatigue) was explained by q from the frontal area, whereas TOD was differentiated by a, q, q/b, (q+a)/b and (q+a)/(b+a)
Research on Flow Characteristics of Electronically Controlled Injection Device Developed for High-Power Natural Gas Engines
Get PDFAccurate fuel supply is a key factor that influences the performance of high-power natural gas engines. The premixed and single-point natural gas supply system is the most commonly used method to ensure a large fuel supply but one of its shortcomings is the inaccuracy of the fuel supply. A new type of natural gas injection device with fungiform configuration and electronically controlled actuator was developed to achieve high efficiency and stable operation in high-power natural gas engines. Firstly, a computational fluid dynamics (CFD) model of the injection device was created. Based on this model, the key structure parameters that have a significant influence on the outlet flow were confirmed. A particle swarm optimization (PSO) model was developed to identify the optimal outflow structure. Then, a flow function for precise flow supply control was constructed based on a response surface model, according to the flow rates of the device under different control parameters. Finally, a flow-characteristic test bench and a high-power engine prototype were developed to verify the simulation and optimization results. The results indicate that the optimized outflow structure shows low pressure loss and a large flow rate, improving injection efficiency by 10.37% and mass flow by 11.78% under 0.4 Mpa pressure difference. More importantly, the cycle fuel supply could be controlled accurately for each cylinder owing to the developed flow function. Consequently, compared with the original engine using a single-point natural gas supply system, the cylinder performance imbalance was improved by 37.47%
Comparison of Formulation Methods to Produce Nano-Chitosan as Inhibitor Agent for Bacterial Growth
Get PDFChitosan is known as an antibacterial agent. The effective surface area ratio of chitosan can be increased by modification into nanoparticles. Nano-chitosan can be prepared with several simple methods, i.e. precipitation, ionic gelation, or the polyelectrolyte complex method. This study compared these three methods in terms of the targeted product characteristics, i.e. stability of the average nanoparticle size as well as the colloidal dispersion, and the antibacterial characteristics. All three methods resulted in nanoparticle formation, but in the precipitation method significant zeta potential reduction was observed due to the presence of negative ions from the alkali that neutralized the chitosan amine group. The ionic gelation method yielded higher zeta potential and higher inhibition of bacterial growth than those yielded by the polyelectrolyte complex method. Ionic gelation and the polyelectrolyte complex method resulted in much better colloidal dispersion stability than the precipitation method, where a significant particle size increase was observed after one week of storage. This result indicates that both ionic gelation and the polyelectrolyte complex method can be used for forming nano-chitosan for the purpose of food preservation. However, for fishery products it is advisable to use the polyelectrolyte complex method because the TPP usually used in ionic gelation is not allowed to be applied to fish
Prediction Model of Coal and Gas Outburst Based on Rough Set-Unascertained Measure Theory
Get PDFThis paper proposes a risk evaluation model based on rough sets (RS) and the unascertained measure theory (UMT) for solving the accuracy problem of coal and gas outburst prediction with the aim to reduce economic losses and casualties in coal mining. The coal and gas outburst prediction problem is constrained by the selection of the prediction indexes, the coupling of a single index, and the weight of each index. The proposed RS-UMT model applies two modified techniques. The first one is a method for index weight determination that was improved by rough set theory. The second one is a method for coupling a single index that was modified by the unascertained measure theory. The RS-UMT model not only well solves the problem of coupling a single index of coal and gas outbursts, but also solves the problem that the weight is susceptible to subjective factors and prior knowledge. The RS-UMT model was used to judge the risk degree of outburst of 10 mining faces in the Pingdingshan No. 8 Mine and No. 10 Mine. The predictive results of the model were basically identical to the actual measured results. The performance of the RS-UMT model was also compared to existing methods. Based on the case study it can be concluded that the RS-UMT model is an accurate and very promising method for solving the coal and gas outburst prediction problem
Improving the Lifting Capacity of Drilled Cuttings Using Henna Leaf Extracts and Lignite in Bentonite Water-based Drilling Mud
Get PDFOne of the basic functions of drilling fluids is to transport drilled cuttings to the surface. Bentonite with low solids content is preferred in carrying out this task. However, a low amount of bentonite in the drilling mud is incapable of effective cuttings lifting and suspension. In this study, a new, green, low-cost henna leaf extract and lignite in bentonite water-based drilling mud was used to transport cuttings to the surface. The effects of three hole angles (0°, 45° and 90°) were examined at different cuttings size diameters (0.5 mm, 1.0 mm, 2.0 mm and 2.4 mm) for the mud systems. The average cuttings transport efficiencies were found to be in the following order: 52-94% for 0.5 mm diameter, 45-93% for 1.0 mm diameter, 38-90% for 2.0 mm diameter, and 33-83% for 2.4 mm diameter. Viscosity and hole angle are directly related to cuttings transport efficiency. A plastic viscosity of 16 cP and yield point of 12.5 lb/100ft² were the most effective mud properties for a 45° hole angle, which needs attention while preparing the drilling mud. Addition of henna and lignite can be used to improve the rheological and filtration properties of bentonite water-based drilling mud
Non-linear Numerical Modeling of Partially Pre-stressed Beam-column Sub-assemblages Made of Reactive Powder Concrete
Get PDFThree partially pre-stressed interior beam-column sub-assemblages (SI) and two partially pre-stressed exterior beam-column sub-assemblages (SE) made of reactive powder concrete as test specimens were numerically modeled using a finite element program. The objective of this study was to investigate the behavior of the SI and SE numerical models. The numerical model inputs were: material data, details of test specimen dimensions, and test specimen reinforcements. The numerical models were subjected to the same loads as those applied experimentally. The numerical modeling results were hysteretic and backbone curves and stress distribution contours. The numerical model outputs showed good similarity with the experimental results. The stress distribution contours of the numerical models correlated with the crack patterns in the joint zone of the test specimens. The behavior of the SI numerical models differed from the SE numerical models due to various stresses on the beam plastic joints and the joint zones
Three-dimensional DC Resistivity Modeling using Galerkin Finite Element Method Composed by Tetrahedral Elements
Get PDFSuccessful interpretation of DC resistivity data depends on the availability of a proper forward modeling scheme. In this study, a three-dimensional DC resistivity forward modeling scheme was developed using the finite element method. The finite element equations were obtained using a weakened form of the weighted-residual method called the Galerkin method. Discretization of the modeling domain was carried out by dividing it into smaller three-dimensional blocks and subdividing each block into five tetrahedral elements. A linear interpolation function was employed and elemental linear equations were set up, followed by formation of global matrix systems of equation and incorporation of proper boundary conditions. The conjugate gradient method was applied to solve the global system of equations, which in this study was proven to be more efficient than a direct solver, contributing to a 67% time reduction. Using a Wenner array configuration, comparison with theoretical calculation of the electric potential for a homogeneous model yielded a relative error of 3.66%. To confirm the applicability of this forward modeling scheme, apparent resistivity profiles for several basic three-dimensional subsurface resistivity models were compared with the analytical profiles, yielding an acceptable level of fitting