stochastic modeling

In this article, the strength of the filament produced by the extrusion method has been investigated. For this purpose, testing samples were produced using ABS and carbon nanotubes as printable nanocomposite filaments in 3D printer. Carbon nanotubes were incorporated into ABS, and nanocomposite filaments were produced and their strengths were measured. In order to predict the strength of a nanocomposite filament, a suitable multi-scale model was developed. This model started from the microscale and after passing the in-between scale of meso, ended at the macroscale. For each scale, a suitable and separate representative volume element was defined, and its strength was predicted. Two parameters, CNT length and orientation, were captured at the microscale, and CNT agglomeration was taken into account at the mesoscale. Finally, at the macroscale, the strength of the nanocomposite filament was estimated using a genetic algorithm. In this study, the strength of the nanocomposite was calculated with two weight fractions of 0.1% and 0.5%, and it was observed that the final strength of the nanocomposite with weight fractions of 0.1% and 0.5% increased by 14% and 20%, respectively. The outputs of the modeling procedure were in very good agreement with experimental observations.

The process of health monitoring and correct prognosis of time to failure occurrence is still considered by many researchers of systems reliability to seek more effective use of available facilities. In the present study, a one-component system with four general failure mechanisms has been considered, one of which is excessive degradation under normal operating conditions and the other is accelerating the degradation process. In this paper, continuous degradation of the Gamma process is considered with two discrete and continuous noise factors or stresses, which create three different failure mechanisms in the accelerated lifetime condition. The discrete noise factor follows the Poisson distribution function and the continuous noise factor follows the normal distribution function. These four situations are investigated in the present paper and in each of them, the estimation of the reliability function and the remaining useful lifetime (RUL) or health prognosis of the equipment is obtained in order to reduce the probability of failure in this single-component system. A hybrid approach using statistical process control (SPC) with data transformation method is used to monitor the noise factors. It was also shown that with the significance of the noise factor or factors the scale parameter of the gamma distribution increases and, the reliability and remaining useful lifetime decrease. An example is solved at the end to illustrate the proposed method.

In this paper, the operation problem of a micro-grid in standalone mode considering uncertainty is modeled. For this purpose, the problem is modeled as a stochastic mathematical problem based on a set of probabilistic scenarios for the consideration of the uncertainties of load and renewable power sources. To model uncertainties and reducing difference between the operation cost in the best and worst scenario, the value at risk (CVaR) as an appropriate tool for risk management is used. Some of the production capacity of micro-grid is considered in the mathematical model as a reserve in order to cover the shortage of micro-grid production due to uncertainty, equipment failure and no distribution network connection. Finally, the results of micro-grid operation have been analyzed and a sensitivity analysis is presented to observe the relationship between costs of the micro-grid with some parameters of the mathematical model.

In this paper, Given the high importance of joint persistence characteristics on the mechanical behavior of the rock mass, geometric-stochastic joint network model, based on Veneziano method, has been developed by considering the statistical characteristics of joint size. Using surveyed data in the access tunnel of Rudbar Lorestan dam & hydropower plant and estimation of the best probability distribution function on geometric characteristics of existing joint sets in the study area, three-dimensional (3-D) geometric model of joint network has been presented. Nowadays, modeling is increasingly put forward as a method of determination of rock mass strength characteristics. An accurate description of the rock mass structure does provide a better starting point for modeling of rock mass. Besides, there will always be some random variation in the geometric properties of joints by virtue of rock mass heterogeneous nature. Therefore, it is necessary to describe the ordered properties stochastically and to use in rock mass modeling. 3-D stochastic joint network modeling technique represents the most optimal choice for simulating the probability nature of joint geometric properties. Methodology and Approaches: Using field measurements and statistical analysis, stochastic parameters of discontinuities are detemined. In this paper, collected data is obtained using a linear surveying. Data processing including matching different probability distribution function on geometric properties of surveyed joint sets has been performed using Dips and EasyFit software packages. In order to calculate volumetric joint density, joints, which have been visible to the naked eye in the field, have been counted within an area of 1 m2 using a square frame (1×1 m) with grid lines at 10 cm intervals. Using the areal joint density and existing equations, the volumetric joint density has been calculated for each joint set, separately. Thus, to prepare the geometric-stochastic model, the required inputs,mentioned above, have been used. The generated joints by the developed model, are coincided on the ordered probability distribution function of joint persistence. Results and In this paper, a 3-D geometrical- stochastic discontinuities network model for the access tunnel of Rudbar Lorestan dam has been presented. In order to model implementation, a computer code written in C++, named DFN-FRAC3D, has been developed to represent the joint network in different directions and to generate digital outputs. The results of this paper can be a useful input for numerical stability analysis and hydraulic behavior studies of rock mass.

Talog Dam located in Khuzestan province plays an important role in power generation and water supply of the region. Some studies are performed on this dam. These studies produce Artificial Time Series and accurate estimates of inflow to the dam which will have an important role in water resources planning in the future. In this study, using the statistics estimated Talog river at the dam site Talog, stochastic modeling was performed for both of them. Various stochastic models such as Autoregressive Model (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) were fitted to the data. According to the Akaike and Schwarz information criteria, the results of Modeling showed that ARMA model (2,3) was the best model in comparison with the other models. Therefore, according to the selected Model, five artificial time series and forecasting future time series for the model were performed the results of which are presented in the present study.

One of the solution for decreasing pollution in big cities is using of electric vehicles. Old combustion engine vehicles decreased Contamination of the environment. By using Hybrid Electric vehicles environmental pollution will reduce economically. One of these HEVs is plug-in HEV or PHEV. These types of vehicles are using electricity charger to charge their batteries from electricity distribution networks. So they impact on distribution transformer loading. Because many of vehicle owners start to charge their vehicles as arrive to home. In this paper assumed that vehicle owners can charge their vehicles at work and in home. This uncoordinated charging impact on distribution transformers daily aging and daily trip cost of owner vehicles are simulated based on stochastic modeling of PHEVs owner behavior at any day. Results of this topic on IEEE RBTS distribution network are presented.

An important issue in supply chain risk management is how to allocate the orders for parts and raw materials among various suppliers to meet all customer demands for products at a low cost and also to decrease the impact of supply chain risks. The selection of suppliers and allocation of orders to them with considering the risk of delay, is based on quality of purchased parts (defect rate), reliability of on time delivery and costs, that may be in conflic with each other in some cases. In addition in order to decrease fixed costs of ordering,the number of suppliers and the total number of orders should be minimized, although in some cases to reduce the impact of delay risk the selection of more suppliers may lessen the risk of unreliable supplies. In this paper a mathematical model is presented for supplier selection problem in make-to-order systems and Value-at-Risk (VaR) is applied as a measure of risk.

The proper characterization of reservoir heterogeneities plays an important role in reservoir evaluation and forcasting of its performance. However, the heterogeneity of a reservoir and its property distributions in the large scale of reservoir volume could not be properly determined and estimated with known data from a few wells. In such a case, one of the newest and roboust methods in reservoir characterization is the use of fractal methods whenever the geostatistics fails. The objective of this paper is to introduce and discuss a new method that is the combination of fractal geometry (small scale) and geostatistics (large scale) to characterize the reservoir property distributions in a gian reservoir with a few known well data. The developed method was applied to a real reservoir in the south west of Iran with three exploration well data. In this method, first of all, the reservoir zonation and formation units including carbonate, sandstone and shale must be identified from the petrophysical data by using multivariate statistics analysis. So that, the porosity, permeability, and water saturation for the known wells could be estimated from the core and log data. Then, spatial correlation using variogram analysis and quantify heterogeneities using R/S analysis, can be determined for each zone in the wells. Eventually, merging kriging method and fGn noise of fractal will lead to develope our new method to estimates and predict the reservoir property distributions between wells and throughout the reservoir more accurate and very precise than the previouse methods. The produced error is in acceptable and accurate range that was less than 4.93 percent for porosity for instance. The output of the model may be used as an input for geomodeling and simulation softwares. Also, heterogeneity distributions of reservoir characteristics are well visualized by fractal distributions.