A New Computer Code for Prediction of Enthalpy of Fusion and Melting Point of Energetic Materials

The prediction of phase change properties of energetic materials is important for the assessment of hazardous energetic materials. A novel user‐friendly computer code, written in Visual Basic, is introduced to predict the melting point and the enthalpy of fusion of energetic materials by only using their molecular structure parameters. It can be used for different types of energetic compounds including polynitro arenes, polynitro heteroarenes, acyclic and cyclic nitramines, nitrate esters, and nitroaliphatic. The predicted results were compared with several of the best available methods, which confirmed the higher reliability of the new computer code for some new and well‐known energetic compounds with complex molecular structures. This code can be used for designing of energetic compounds with desirable phase change properties.     

Hot Deformation Behavior of Beta Titanium Ti-13V-11Cr-3Al Alloy

Hot compression tests were conducted on Ti-13V-11Cr-3Al beta-Ti alloy in the temperature range of 1203 K to 1353 K (930 °C to 1080 °C) and at strain rates between 0.001 and 1 s^?1 The stress–strain curves showed pronounced yield point phenomena at high strain rates and low temperatures. The yield point elongation and flow stresses at the upper and lower yield points were related to the Zener–Hollomon parameter. It was found that dynamic recovery at low strain rates and dynamic recrystallization at high strain rates were the controlling mechanisms of microstructural evolution. The results also showed that strain rate had a stronger influence on the hot deformation behavior than temperature. The microstructural observations and constitutive analysis of flow stress data supported the change in the hot deformation behavior of the studied alloy varies with strain rate. For various applied strain rates, the activation energy for hot deformation was calculated in range of 199.5 to 361.7 kJ/mol. At low strain rates (0.001 and 0.01 s^?1), the value of activation energy was very close to the activation energy for the diffusion of V, Cr, and Al in beta titanium. The higher value of activation energy for deformation at high strain rates (0.1 and 1 s^?1) was attributed to the accumulation of dislocations and the tendency to initiate dynamic recrystallization.     

Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches

Abstract

Thermal conductivity and dynamic viscosity play key role in heat transfer capacity of nanofluids. In the present study, thermal conductivity and dynamic viscosity of Fe₂O₃/water are modeled by applying various artificial neural network algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and CHPSO ANFIS algorithms. The data for modeling procedure are extracted from several experimental studies. Obtained results by the different algorithms are compared and it was concluded that the highest R-squared values belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for thermal conductivity ratio and dynamic viscosity, respectively.     

Inverse shape design for heat conduction problems via the ball spine algorithm

ABSTRACT

In this article, a novel iterative physical-based method is introduced for solving inverse heat conduction problems. The method extends the ball spine algorithm concept, originally developed for inverse fluid flow problems, to inverse heat conduction problems by employing a subtle physical-sense rule. The inverse problem is described as a heat source embedded within a solid medium with known temperature distribution. The object is to find a body configuration satisfying a prescribed heat flux originated from a heat source along the outer surface. Performance of the proposed method is evaluated by solving many 2-D inverse heat conduction problems in which known heat flux distribution along the unknown surface is directly related to the Biot number and surface temperature distribution arbitrarily determined by the user. Results show that the proposed method has a truly low computational cost accompanied with a high convergence rate.     

Optimal Porosity in an Air Heater Conduit Filled with a Porous Matrix

In the present study, flow and forced convective heat transfer in an air heater conduit filled with a porous matrix with a uniform constant solar heat flux has been investigated analytically, based on minimal entropy generation principle. While trying to decrease entropy generation due to heat transfer, pressure loss entropy generation increases, which indicates that an optimal porosity value exists. The influence of Reynolds number, fluid properties, constant uniform heat flux, flow, and geometry of the system on the optimum matrix porosity has been investigated. It was revealed that optimum matrix porosity values increase as Reynolds number increases. In the range of the present study, a correlation predicting optimal matrix porosity was proposed using least squares analysis.     

A framework for identification of high-value customers by including social network based variables for churn prediction using neuro-fuzzy techniques

Customer churn has become a significant problem and is one of the prime challenges that many in the services industry are facing. While all kinds of churn lead to incur loss, the loss of low-value customers will be naturally less damaging than the loss of loyal and high-value ones. So companies need to build a churn prediction model for their high-value customers. In this paper, a two-phase framework for prediction of high-value customer churn has been proposed. Phase 1 is the identification phase which takes into account social-network based variables of customers in identifying the high-value ones. The data of an identified high-value customer is used as the input for Phase 2 to prepare the churn prediction model. Data of a major telecommunication company has been used to implement the framework. The customers were clustered by using K-means algorithm. After ranking clusters, the top-cluster was selected according to clusters ratings. The data belonging to the top cluster is used in churn prediction model building phase. In this phase, two neuro-fuzzy techniques, namely the adaptive neuro-fuzzy inference system (ANFIS) and the locally linear neuro-fuzzy (LLNF) have been applied together with locally linear model tree (LoLiMoT) learning algorithm on churn data. A new algorithm has been devised for comparing these methods with the most widely used neural networks such as multi layer perceptron (MLP) and radial basis function (RBF) networks. Results of comparison indicate that the neuro-fuzzy techniques perform better than neural network models and they are a good candidate for churn prediction purposes.     

An In Vitro Evaluation of Fenugreek Mucilage as a Potential Excipient for Oral Controlled-Release Matrix Tablet

A polysaccharide mucilage derived from the seeds of fenugreek, Trigonella foenum-graceum L (family Fabaceae) was investigated for use in matrix formulations containing propranolol hydrochloride. Methocel® hypomellose K4M was used as a standard controlled release polymer for comparison purposes. In this study the effect of lactose on the release behaviour of propranolol hydrochloride from matrices formulated to contain the fenugreek mucilage also was investigated. An increase in concentration of the mucilage in matrices resulted in a reduction in the release rate of propranolol hydrochloride comparable to that observed with hypomellose matrices. The rate of release of propranolol hydrochloride from fenugreek mucilage matrices was mainly controlled by the drug:mucilage ratio. However, the mechanism of release from matrices containing drug:mucilage ratios of 1:1, 1:1.25, 1:1.5, and 1:2 remained the same. The kinetics of release, utilising the release exponent n, showed that the values of n were between 0.46–0.57 indicating that the release from fenugreek mucilage matrices was predominantly by diffusion. The presence of lactose in matrices containing mucilage increased the release rate of propranolol hydrochloride. This is due to a reduction in tortuoisity and increased pore size of channels caused by lactose through which propranolol diffuses and therefore diffusion of water into the tablet is facilitated.     

Analytical equations for oscillation amplitude of MOS Colpitts oscillator

New analytical equations are presented for amplitude analysis of metal–oxide–semiconductor (MOS) Colpitts oscillator. These equations are obtained from a large signal analysis that includes MOS operation in the saturation, triode and cutoff regions. The analysis is based on a reasonable estimation for the output voltage waveform. The estimated waveform must satisfy the nonlinear differential equations governing the circuit. The validity of the proposed method and the resulting equations has been verified through simulations using TSMC 0.18?µm complementary MOS process. The results are also compared with the other methods. Simulation results show high validity of the proposed equations.     

A Fast Adaptive Salt and Pepper Noise Reduction Method in Images

In this paper, an effective filtering method is proposed to remove impulse noise from images. In this two-stage method, detected noise-free pixels remain unchanged. Afterwards, a Gaussian filter with adaptive variances according to the image noise level is applied on the noisy pixels. Experimental results show that the proposed method outperforms recent impulse denoising methods in terms of PSNR, MAE, IEF, and SSIM. Moreover, the speed of the method is comparable with them, and it can be used effectively in real-time applications.