Effect of Intercritical Annealing Time on the Microstructure and Mechanical Properties of Dual-Phase Steel Processed by Large-Strain Asymmetric Cold-Rolling

Herein, the effect of intercritical annealing time on the microstructure and mechanical properties of dual-phase steel processed by large-strain asymmetric cold-rolling is studied. It is observed that the martensite islands are uniformly distributed in the ferrite phase in the microstructures of dual-phase steels due to performing the asymmetric cold-rolling before intercritical annealing treatment. As the intercritical annealing time increases up to 10 min, the fraction of martensite increases. By increasing the holding time and fraction of martensite, the carbon content of the martensite phase is decreased. The short-term intercritical annealing eliminates the yield point phenomenon. However, intercritical annealing at 860 °C for 20 min leads to the reoccurrence of a yield point phenomenon. Increasing the intercritical annealing time to 10 min improves the yield strength to 505 MPa and ultimate tensile strength to 834 MPa. However, the strength decreases sharply after the holding time of 20 min. There is a perfect linear relationship between the mechanical properties and the fraction of martensite. Ductile failure is observed at the center of the fracture surfaces of dual-phase steels while shear failure occurs at the edges of the fracture surfaces.     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

A particle swarm–BFGS algorithm for nonlinear programming problems

This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed algorithm makes an effective use of hybrid framework when dealing with nonlinear equality constraints although its convergence cannot be guaranteed.     

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R ²) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model.     

Rock strength assessment based on regression tree technique

Uniaxial compressive strength (UCS) is one of the most important parameters for investigation of rock behaviour in civil and mining engineering applications. The direct method to determine UCS is time consuming and expensive in the laboratory. Therefore, indirect estimation of UCS values using other rock index tests is of interest. In this study, extensive laboratory tests including density test, Schmidt hammer test, point load strength test and UCS test were conducted on 106 samples of sandstone which were taken from three sites in Malaysia. Based on the laboratory results, some new equations with acceptable reliability were developed to predict UCS using simple regression analysis. Additionally, results of simple regression analysis show that there is a need to propose UCS predictive models by multiple inputs. Therefore, considering the same laboratory results, multiple regression (MR) and regression tree (RT) models were also performed. To evaluate performance prediction of the developed models, several performance indices, i.e. coefficient of determination (R ²), variance account for and root mean squared error were examined. The results indicated that the RT model can predict UCS with higher performance capacity compared to MR technique. R ² values of 0.857 and 0.801 for training and testing datasets, respectively, suggests the superiority of the RT model in predicting UCS, while these values are obtained as 0.754 and 0.770 for MR model, respectively.     

Temperature of a photovoltaic module under the influence of different environmental conditions – experimental investigation

The climate changes affect photovoltaic (PV) module temperature significantly. The module temperature is one of the most important factors that influence the PV module efficiency and a deep analysis of PV module temperature will aid in better understanding of the environmental influences on the PV module performance. The module temperature depends on many parameters such as solar radiation, ambient temperature, air humidity, speed and direction of the wind, PV module orientation, dust and sand deposition on PV module, and PV module materials. An experimental research was conducted to investigate the effect of these factors on the PV module temperature in the Renewable Energy Laboratory of the Graduate University of Advanced Technology in Iran. The results of this study highlighted that the deposited dust over the PV module surface increases the module temperature and this consequently decreases the PV module power. It was also revealed that a combination of the temperature increase and the incident solar radiation decrease due to the dust deposition over the PV module enhances significantly the module power reduction.     

Flow simulation considering adsorption boundary layer based on digital rock and finite element method

Due to the low permeability of tight reservoirs,throats play a significant role in controlling fluid flow.Although many studies have been conducted to investiga...     

Effect of salts and their interaction with ingenious surfactants on the interfacial tension of crude oil/ionic solution

Understanding the roles of asphaltene and resin as natural surfactants existed in crude oil can enlighten contradicting reported results regarding interfacial tension(IFT) of crude oil/aqueous solution as a function of salinity and ion type. In this way, this study is aimed to investigate the effect of these natural surface active agents on IFT of with special focus on SO_4~(2-)anion and Mg~(2+)cation. Two different synthetic oil solutions of 8 wt% of the extracted asphaltene and resin dissolved in toluene are prepared, and then IFT values are measured. After that,the obtained results are compared with the IFT of intact crude oil in contact with the same saline solutions examined in the previous stage. The obtained results showed a synergistic effect of Na_2SO_4+ MgCl_2 solution unlike the MgSO_4+ MgCl_2 and CaSO_4+ MgCl_2 solutions on IFT reduction of resin at MgCl_2 concentration of 15000 mg·kg~(-1). In summary, it is found that the affinity of asphaltene molecules towards the interface of oleic phase/ionic solution leads to higher IFT variation.     

First-principles insight into two-dimensional palladium phosphide tellurium (PdPTe) monolayer as a promising scavenger for detecting SF6 decompositions

Journal of Materials Science - Recently, the monitoring of SF6 decomposition components has allured notable attention. SF6 decomposition products are toxic and corrosive, which will greatly reduce...     

Stability analysis of whirling composite shells partially filled with two liquid phases

In this paper, the stability of whirling composite cylindrical shells partially filled with two liquid phases is studied. Using the first-order shear shell theory, the structural dynamics of the shell is modeled and based on the Navier-Stokes equations for ideal liquid, a 2D model is developed for liquid motion at each section of the cylinder. In steady state condition, liquids are supposed to locate according to mass density. In this study, the thick shells are investigated. Using boundary conditions between liquids, the model of coupled fluid-structure system is obtained. This coupled fluid-structure model is employed to determine the critical speed of the system. The effects of the main variables on the stability of the shell are studied and the results are investigated.