Papers by Keyword: Stochastic Simulation

Abstract: Unique corrosion conditions in oil refining processes lead to the necessity of using passive and active protection systems, aimed at preventing from damages and ensuring the correct operating conditions of machines. To prevent from the development of corrosion processes on the internal surfaces of horizontal settlers, sacrificial protection is used. Before installing such a protection, they usually calculate the number of protectors to be installed in parallel. Some inputs are stochastic by their nature, which should be considered in assessing the risk of non-achieving the required protection level. The probabilistic model proposed to calculate the parameters of sacrificial tank protection that allows performing an exploratory design based on considering various environmental aspects to decide on the efficiency of sacrificial protection and to assess the achievement of the required protection level.

Abstract: Underground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the 3D spatial distribution of soil organic matter in coal arable land is crucial for agricultural production and environmental management. However, little research has been done on the three-dimensional modeling of soil organic matter. In this study, 3D kriging interpolation method and 3D stochastic simulation method were used to develop the 3D model of soil organic matter , and the root-mean-square error (RMSE) and mean error (ME) were used as evaluation indexes to compare the simulation accuracy of the two methods. Results showed that the spatial distribution of soil organic matter obtained by using 3D kriging interpolation method is relatively smooth, which reduce the difference of spatial data; while the spatial distribution of soil organic matter obtained by using 3D stochastic simulation method is relatively discrete and highlights the volatility of spatial distribution of raw data, the RMSE obtained by 3D kriging interpolation method and 3D stochastic simulation method respectively is 2.7711 g/kg and 1.8369 g/kg. The prediction accuracy of organic matter interpolation obtained by 3D stochastic simulation method is higher than that by 3D kriging interpolation method; so the 3D stochastic simulation method can reflect the spatial distribution characteristics of soil organic matter more realistically, and more suitable for 3D modeling of soil organic matter. According to the 3D modeling of soil organic matter, the content of soil organic matter has obvious spatial difference in different soil depth（0-20 cm、20-40 cm、40-60 cm） and decreases with the increase of soil depth; The result also showed that the content of soil organic matter decreased rapidly from the upper slope to the middle slope, and gradually increased from the middle slope to the bottom, so the soil organic matter content was obviously lost in the middle slope. This result may provide useful data for land reclamation and ecological reconstruction in coal mining subsidence area.

Abstract: Advanced semiconductor materials including silicon carbide and gallium nitride possess excellent properties like high hardness, and high heat and chemical resistance compared to silicon. Such properties reversely prevent efficient production of wafers, therefore a new wafer finishing method, tape grinding, is developed to improve productivity. This paper proposes a simulation method of tape grinding, which method is developed by modifying the stochastic approach developed for plunge grinding. The distribution of abrasive grains on the grinding tape is represented by number density, and the material existence probability that is represented by Abbott-Firestone curve is modified by considering machining parameters. Tape grinding process were then conducted to confirm the calculation method.

Abstract: This paper presents the technique of decomposition of form deviation based on the use of wavelet filter and Fourier transform. The method allows decomposing the total deviation into systematic, random systematic (waviness) and random components. The method testing was carried out for the statistics of the deviation of the suction side of the turbine engine compressor blade. The accuracy check of the estimates of form deviations generated by the method in a series of simulated surfaces with pre-laid characteristics was carried out. The results showed that the method of decomposition allows one to estimate exactly the components of the form deviation of the simulated surfaces in their series.

Abstract: Forces and temperatures in specific orthogonal cutting conditions and calculated by finite element analysis, have been evaluated taking into account the uncertainty of some process conditions. A traditional deterministic approach, in machining simulations, is not able to explain the uncertain physical variations related to material characteristics (yield and tensile strenght, hardness, etc.) and tool/chip/workpiece interface conditions (friction and tool wear). During machining operations many different sources of non-controllable process variations usually display their effect leading to a degree of uncertainty in the final parts quality. Statistical tools and methods are increasingly being used in combination with FE numerical simulation, in order to take in account of the variability of the process. Then, if one of the purposes of process design is to study and model robustness or reliability of a given process in aleatory conditions, a CAE study might become a feasible way to do it. Today, the evaluation of the performances of a metal cutting process is possible using several commercial FEA packages. These software tools automatically allow the preventive evaluation of the robustness of technological decisions. In this work the authors, by means the integration between stochastic simulation tools and machining FE codes, have evaluated the process sensitivity to a random variation of uncontrollable parameters or conditions. Furthermore, a specific experimental and numerical activity has been performed in order to better understand the technical capabilities in terms of process simulation in stochastic conditions.

Abstract: The scatter of experimental results using specimens made of quasi-brittle materials, such as concrete, fibre-reinforced concrete, ultra high performance concrete etc., can be due to their heterogeneity rather high. An assessment of fracture-mechanical parameters is then difficult and problematic. To remain at deterministic level is therefore unrealistic and without virtual statistical approach, simulation and probabilistic result assessment the consequent practical design of quasi-brittle material-based structures can be risky. A key parameter of nonlinear fracture mechanics modeling is fracture energy of concrete. Numerical simulation of concrete failure and fracture phenomena in concrete as well as other cementitious materials became a field of an intensive research in the recent years. With respect to accuracy and efficiency of corresponding numerical models some few still open questions have to be focused. How the heterogeneity of cementitious materials can be taken into consideration in the most realistic way using commercially available finite element programs A sophisticated option to get the parameters of the computational model indirectly is based on combination of fracture test with inverse analysis. This paper describes a methodology to get such parameters using experimental data from three-point bending tests used in inverse analysis based on combination of artificial neural networks and stochastic analysis.

Abstract: Stochastic simulation using authors’ empirically-specified deterministic model estimates technical risk, i.e. failure risk. Simulating a crack growth in hydrogenating and stress cycling high-strength steels requires a great computing performance. Cloud computing based on Microsoft Azure enables authors to provide a lot of required calculations to complete design tasks in appropriate time to ensure safety operation of the structure component under consideration. Obtained results illustrate benefits of using cloud computing for stochastic simulation of crack growth in high-strength steels in comparison with the same calculations executed on a workstation. The article emphasizes the prospects of cloud computing applications to technical risk management.

Abstract: Aiming at the randomicity of rainfall and evapotranspiration in the formulation of crop irrigation schemes, the precipitation and evapotranspiration were randomly treated, and simulated using random hydrology methods based on time series. The simulated values were used into the water balance equation so as to determine the irrigation time and amount. Compared with the measured sequence, the result indicates that the simulated values and actual values fit well. It can provide a reference for the scientific crop irrigation schemes.

Abstract: The paper builds the model of direct reuse reverse logistics center location followed by the uncertainty random variables of the demand of retailers and the recovery of collection points. This model assumes that the enterprises are expanding on the traditional network. For the random variables, they will be solved by using stochastic simulation, genetic algorithm and linear programming, and numericai exampie is presented.

Abstract: Stochastic chance-constrained programming which is one of important stochastic programming widely exits in different fields. For searching an algorithm that can more effectively solve this problem,a new algorithm for its combined stochastic particle swarm optimization with stochastic simulation for approximation of the fitness function and checking feasibility of solution is presented. It overcomes the defaults such as needing a long time, complex calculation,resapsing into local optimum in the hybrid intelligence algorithm based on GA. After testing its performance and comparing with GA, the results show that the algorithm is more preferable.