Simulation and experimental study on the surface morphology and energy lost of the target material under non-overlapping impact of angular particles

In order to further understand the effect of solid impurities on pipeline wall during erosion,the particle impact process without fluid was extracted for specific study.The effect of multi-impact particles on the wall of pipeline was studied experimentally and simulated.In this experiment,an improved ejection apparatus was implemented to carry out multi-impacts non-overlapping impingement by rhombic particles made of high speed steel(W18Cr4V)on the AA6061 aluminum alloy plate through changing particle angle,incident angle,orientation angle and impact velocity.As a result,each particle's penetration depth was investigated and particles' rebound trajectory can be described through this experiment as well as surface morphology of the target material after impingement.The ductile damage criterion,shear damage criterion and MSFLD damage criterion were jointly implemented in ABAQUS/CAE software to simulate the whole process of collision which proved to be effective by getting consistent result compared with experimental data.It is found that under the condition of continuous non-overlapping impact,the target material produces a small work hardening effect in the impact area by converting kinetic energy of moving particles into internal energy of plate so as to reduce the penetration depth of each impact particle.

Can the Hypothetical Protons Emitted by the Shroud’s Man Furnish an I(z) Correlation?

Recently, we have investigated the hypothesis radiative demonstrating that the two penetrated thicknesses (in air and linen) are not compatible with a single energy of the protons. Furthermore, we deduced that the distribution of energy, released by the above particles, on the burial linen has not a linear trend when the body-burial linen distance changes. Now, in this article we want to deduce the I(z) relationship, between the Image Intensity of the colour produced by protons on a linen and the z distance from the source (of Protons) and the same linen. To achieve the result in an analytical form and make a comparison with the same function extracted from the Shroud, we used the empirical expression Range-Energy for protons in air of Wilson-Brobeck. Thus, we obtain a result I(z) = Im [1 − (z/R)5/9] that is different from the one extracted from the Turin Linen I(z) = IM (1 − z/R0). We have also the same information using the Range-Energy curves for protons of Rogozinski. The result is negative for the radiative hypothesis that is unable to produce the Shroud Body Image. Therefore, to investigate the above unknown process of formation, it is necessary to think about another one.

Solving Nonlinear Differential Equation Governing on the Rigid Beams on Viscoelastic Foundation by AGM

In the present paper a vibrational differential equation governing on a rigid beam on viscoelastic foundation has been investigated. The nonlinear differential equation governing on this vibrating system is solved by a simple and innovative approach, which has been called Akbari-Ganji＇s method （AGM）. AGM is a very suitable computational process and is usable for solving various nonlinear differential equations. Moreover, using AGM which solving a set of algebraic equations, complicated nonlinear equations can easily be solved without any mathematical operations. Also, the damping ratio and energy lost per cycle for three cycles have been investigated. Furthermore, comparisons have been made between the obtained results by numerical method （Runk45） and AGM. Results showed the high accuracy of AGM. The results also showed that by increasing the amount of initial amplitude of vibration （A）, the value of damping ratio will be increased, and the energy lost per cycle decreases by increasing the number of cycle. It is concluded that AGM is a reliable and precise approach for solving differential equations. On the other hand, it is better to say that AGM is able to solve linear and nonlinear differential equations directly in most of the situations. This means that the final solution can be obtained without any dimensionless procedure Therefore, AGM can be considered as a significant progress in nonlinear sciences.

Applied machine learning in greenhouse simulation;new application and analysis

Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions.The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it.So the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,Iran.The environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data samples.In this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was selected.The results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,respectively.Comparing RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices.The range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12
C and 0.28-0.50%,respectively.Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.The performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy.