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The new competitive environment changes the paradigm of power system operation. In the transmission area, open access process provides fair accessibility for all market participants. Congestion management is one of the most important side effects of this new process. This paper proposes a new approach for congestion management which is based on both active and reactive re-dispatch of the network critical buses. The critical buses including both generation and interruptible loads, are determined through a sensitivity analysis. Due to the important effects of the market pricing rules on the congestion cost and its allocation, two types of these rules, namely uniform and pay as bid pricing are investigated in the paper. The numerical results of case studies show the effectiveness of the proposed approach.

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Prediction of stress-strain behavior of geotechnical material is one of the major efforts of engineers and researchers in the field of geomechanics. Experimental tests like tri-axial shear strength tests are the most effective apparatus to prepare the mechanical characteristics of gravelly material; but due to difficulties in preparing test samples and costs of the tests, only several tests will be done in a new project. Artificial neural network is a kind of method, in which engineer could judge the results based on numerous data from other similar projects, which enable the engineer to have a good judgment on the material properties. In this research, the behavior of gravelly material was simulated by use of multi-layer perceptron neural network, which is the most useful kind of artificial neural networks in the field of geotechnical engineering. For instance, first exact information was provided from laboratory tests of various barrow areas of embankment dams in the country and effective parameters on shear strength of coarse-grained material were studied. After omitting incorrect or weak data, 95, 20 and 23 sets of data were used for learning, testing and evaluating data, respectively. Input parameters for the model were as follows: particle-size distribution curve, dry density, relative density, Los-angles abrasion percent, confining pressure, axial strain; and outputs were selected as deviator stress. In order to reach a steady state in the model and force the model to behave homogenous to the all inputs, data was normalized to the value between .05 and 0.95. In the simulation, back-propagation algorithm was used for learning or error reduction. The aim of the simulations was defined to reduce error between real data and predicted values; for instance root mean square error (RMS) was used to be minimized through simulation and predicted versus real graphs were used to observe the global error of the model. After modeling the data based on some criteria, it was shown that curves of stress-strain from simulation tests were in good agreement with those from laboratory. These close coherencies were observed in all training, testing and evaluation data, in which the RMS errors were 0.038, 0.037 and 0.026, respectively. To reach this ultimate step, a 10*19*1 multilayer perceptron was used via trial and error. In order to determine quality and quantity of the effect of inputs on outputs, and prove that the results were in good agreement with soil mechanic principles, sensitivity analyses were done on the average data of the inputs. Results show that confine pressure, uniformity coefficient and relative density of the material were the most effective parameters on the stress-strain curves; thus the model has enough capability to predict the stress-strain behavior of gravelly soils.

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In the present paper, the parameters of Johnson - Cook (JC) constitutive model for two steels have been identified, based on the Hopkinson pressure bar test results. The experimental data has been taken from the split Hopkinson pressure bar data found in the literature. Using the measured strain pulses, the experimental stress - strain and deformation - time curves can be extracted. The experimental data have been processed using two different methods. In the first method strain rate assume to be constant during deformation and in the other one the deformation has been applied to a modeled specimen. In each method, an optimal set of material constants for JC constitutive model have been computed by minimizing the standard deviation of the numerically obtained stress - strain curve from the experimental data. Also a sensitivity analysis has been performed on JC constitutive model parameters and temperature changes during test have been investigated. The obtained results show that using constant strain rate method, leads to considerable error in results; for example in this study the minimum error is about 14%.

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Ramsey model is one of the most important basic models to study intertemporal resource allocation. This model is derived from microeconomic optimal principle so it has a key role in macroeconomics with micro foundations. Hence, in many economic researches it is considered as a reference theory. Application of this model in economy of Iran will provide an appropriate theorem framework for explaining empirical facts of the Iranian economy and will introduce a new approach to researchers. The main idea of this study is generalizing Ramsey model through including terms of trade and its calibration in the economy of Iran. For this purpose first, the model is explained. Then, the first order condition is derived and mathematical optimal path of variables is solved.  Finally, the model is calibrated by GAMS package for economy of Iran in time period (2006-2036). The results indicate that there is a feasible solution for model and the optimal path of variables can be observed. The optimal path of Gross National Production and Consumption are increasing but the optimal path of capital stock and investment is primarily increasing then decreasing. In the final section of this paper a sensitivity analysis is presented. Some scenarios are designed for the important parameters of model like time preferences rate, intertemporal substitution elasticity of consumption, labour growth rate and output elasticity of capital. Sensitivity analysis shows that output elasticity of capital and labour growth rate increased the social welfare and shifted optimal path of variables upward. But time preferences rate and intertemporal substitution elasticity of consumption had inverse effect on social welfare and optimal path of variables.

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Creating and supporting small and medium-sized industries in economic development programs and the emphasis on improving efficiency and productivity in the policies adopted, shows the vital position of such industries in developed as well as developing economies. In general, the most important role of these industries in the economic development process can be summarized as; effective employment, production and supply chain management, creating value added and reducing dependence on unnecessary commodity imports. Since any improvement in the efficiency of small scale industries will bring more equitable distribution of income, the main purpose of this study is to measure the performance of various technical, management and scale efficiencies in subsectors of small scale industries and provide policy recommendations to the inefficient industries. For this purpose, the principal component analysis (PCA) and factor analysis is used to determine the variables of the model and the DEA is used for evaluation and sensitivity analysis of the factors affecting the efficiency and productivity of small scale industries during the period 2002 - 2007. The results show that out of 22 industries, only 8 ones are found to be perfectly efficient. Productivity measurement of these industries according to the "Malmquist" index reveals that, the trend of productivity enjoys positive growth.

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Determining the bearing capacity of piles is an important issue that always Geotechnical engineers focus on. Effect of factors such as environmental dissonance of soil which contains a pile, pile implementation, pile gender and its shape make correct estimation of bearing capacity difficult. Pile load testing as a reliable method could be used in various stages of analysis, design and implementation of piles to determine the axial bearing capacity of piles. On the other hand, pile load testing, despite high accuracy, imposes high cost and long duration for development projects and it causes limitations in this experiment. Thus acceptance of numerical analysis at geotechnical studies is increasing. In this study serious models of multi-layer perception neural network, one of the most commonly used neural networks, was used. In all models four parameters are used as input data which are length and diameter of the pile, the coefficient of elasticity and internal friction angle of soil and the bearing capacity of piles is used as output data. Models have reasonable success in predicting the bearing capacity of piles. To increase the accuracy of predicting bearing capacity, for the network training stage the real tests that has been done at the geotechnical studies of dry dock area Hormozgan by POR Consulting Engineers were used. According to (Because we) need of more data for training and testing network, several tests on pile bearing capacity, in smaller dimensions were performed in the laboratory. To perform these tests the device of pile bearing capacity, made in university of Tarbiat Modarres, was used. Models based on neural networks, unlike traditional models of behavior don’t explain effect of input parameters on output parameters. In this study, by the sensitivity analysis on the optimal structure of introduced models in each stage it has been somewhat trying to answer this question.

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This paper presents the sensitivity analysis of a sample hydraulic servo valve. The sensitivity of the flow-current curve with respect to variations of geometrical and functional parameters is evaluated. This analysis is done by use of a model which simulates the valve behavior in terms of geometrical parameters and properties of components. With attention to multiplicity of the valve components and the complexity of the system, the more effective parameters have been studied more precisely. Deviation of “flow gain” And “saturation flow” -which are the main characteristics of the valve behavior- from their nominal values in the flow-current curve, is the criterion for the valve sensitivity assessment. The curve of characteristic variations versus parameter variations has been plotted. The evaluations indicated that all of the sensitivity curves are linear in the limited intervals. Also between the evaluated parameters, “nozzle-flapper equilibrium distance”, “orifice diameter” and “stiffness of spool spring” have more sensitivity effect on the valve performance, respectively.

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Seasonal variations of climatic parameters are significant in arid and semi-arid regions and sensitivity of each parameter may differ in different seasons. No work has been done in this regard in Iran. Therefore, in this study, sensitivity analysis of the ASCE-Penman-Monteith grass reference evapotranspiration (ETo) equation was investigated on the basis of variation of mean air temperature (Tmean), vapor pressure deficit (VPD), wind speed at 2 meter height (U2), and short wave solar radiation (Rs) in the semi-arid climate of Kerman, southeast of Iran. The sensitivity coefficients were derived for each variable on a daily basis. The results showed that the computed ETo was sensitive to VPD in all months, to U2 during March to November, and to Rs during the summer months. The change in ETo was linearly related to the change in the climatic variables, with in most cases. The sensitivity coefficient for Rs was higher during the summer months and lower during the winter months. Increase in ETo with respect to the increase in the aforementioned climate variable changed by month. On an annual average, 1 C increase in Tmean, 1 ms-1 increase in U2, and one MJ m-2d-1 increase in Rs resulted in, respectively, 0.11, 0.37, and 0.09 mm d-1increases in ETo. A 0.4 kPa increase in VPD resulted in 0.85 mm d-1 increase in ETo. Generally, various meteorological parameters should be measured with high accuracy in order to use the combination model.

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Concrete buttress dams are constructed in large numbers at medium sites in many countries such as Iran because of their considerable technical and economical benefits in previous century. This type of dams is exposed to damages due to earthquakes as other structures. Some buttress dams such as Sefidroud dam in Iran, Hsinfengkiang dam in China and Honenike dam in Japan have undergone some damages due to recent earthquakes. After these incidents, some investigations have been carried out. However, these investigations have just mentioned the manner of incidents and the resulting damages. Therefore, the seismic behavior and sensitivity recognition of these dams with respect to different factors have been ignored; however the study of behavior and seismic sensitivity of this type of dams is important. In this paper, the tallest monolith of the Sefidroud concrete buttress dam is analyzed using a 3D model with massless foundation to study the seismic behavior and sensitivity of this type of dam. The interaction of the dam with the reservoir, the reservoir bottom absorption and upstream radiation of hydrodynamic waves are considered, but the cross-canyon component of earthquake is neglected. The applied accelerograms to the system are scaled according to the Sefidroud dam site DBE response spectrum. To determine the initial conditions before occurring earthquake, a series of detailed static analyses are done under the effect of dam body weight, hydrostatic pressure, uplift pressure and ambient temperature. Seismic loading due to longitudinal and vertical components of earthquake is applied and the nonlinear behavior of dam under various factors such as different seismic loading scenarios and different properties of dam body and also foundation materials is investigated. The results of analyses show that the dam body downstream kink, heel, toe and buttress web are sensitive and vulnerable zones. The results also demonstrate that the compressive stresses in the dam body are usually much less than the compressive strength of concrete. Therefore, the possibility of compressive failure is almost zero. But the conditions of tensile and shear stresses are different and large stresses may occur at the mentioned zones and considerable tensile and shear damages to the dam body are possible. According to the results of analyses, it is apparent that when the ratio of dam body modulus to that of the foundation (called softness modulus) is small, i.e. when the foundation modulus is high and near to that of dam body, the construction of concrete buttress dams at highly seismic zones may cause local failure and unfavorable situations for the tensile stresses at the kink, the heel and the toe of the dam body. Therefore, adaptation of this dam type in such sites should be carefully studied and in these circumstances, the modulus of the concrete of dam body should be kept more than usual practice. Furthermore, the shear damage at the dam-foundation contact surface is highly dependent to the applied earthquake type, but increasing the softness modulus could reduce this type of damage. The compressive strength of concrete has no effect on the shear damage at the dam-foundation contact surface.

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optimization of arch dams.

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In-situ tests play important role in any geotechnical investigation. Pressuremeter test can be considered one of the most important in situ tests.This test can be considered one of the most important in situ tests in Geotechnical Engineering. This test is capable to properly estimate deflection parameters of soil. Three types of pressuremeters exist based on their placement in the boreholes: Predrilled pressuremeters (P.D.P), Self-boring pressuremeters (S.B.P) and Push-in pressuremeters (P.I.P). The Predrilled pressuremeters (P.D.P) have been used in this project. Based on expansion of a cylinder that is placed inside the borehole the pressure-volume variation during testing is recorded. In this research, the results of approximately 500 conducted Pressuremeter tests on the soils by Pajohesh Omran Rahvar Ltd (2006-2007) are employed. The number of tests decreased to 400 due to lack of accuracy and also high changes in the range of Pressuremeter modules. The tests have been carried out on the soils of Northwest Iran (Tabriz), South Iran (Kharg Island) and Northeast Iran (Mashhad). The Pressuremeter instrument used is menard pre-boring. Conducted tests in accordance with ASTM-D4719 represented acceptable accuracy.  In the current paper, three types of Artificial Neural Network (ANN) are employed in interpretation of pressuremeter test results. First, multi layer perceptron neural network, one of the most applicable neural networks, is used. Then, neuro-fuzzy network, combination of neural-phase network is employed and finally radial basis function, a successful network in solving nonlinear problems, is applied. Neural network models showed prosperity to interpret Pressuremeter test. Soil physical and compaction properties are used in all these models. The applied models own 5 input parameters and 1 output parameters. Hidden layers with different number of neurons are tested in both one and two layers networks so as to select the most proper network architecture. It has been shown that a three-layer perceptron with differential transfer functions and sufficient number of neurons in hidden layer can approximate any nonlinear relationship. Consequently, one hidden layer is used in the present study. The neural network toolbox of MATLAB7.4, a popular numerical computation and visualization software, is used for training and testing of the MLPs. Transfer functions of networks are selected by trial and error.  A large complex of carried out tests on the extensive range of fine and course grained soils is used as database. In order to determine the most exact network in the perceptron neural network, some networks with different architecture are employed. Of all neural network models, multi-layer perceptron neural network proved to be the most effective. However, other applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance is determined. In order to evaluate the capability of model generalization, the performance of mentioned network against inexperienced data has been compared with empirical results. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models. Also, derivation of governing equation for neural network model give more assurance to user to employ such models and consequently this facilitates the application of models in the engineering practices.

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Determining the bearing capacity of piles is an important issue that always Geotechnical engineers focus on. Effect of factors such as environmental dissonance of soil which contains a pile, pile implementation, pile gender and its shape make correct estimation of bearing capacity difficult. Pile load testing as a reliable method could be used in various stages of analysis, design and implementation of piles to determine theaxial bearing capacity of piles. On the other hand, pile load testing, despite high accuracy, imposes high cost and long duration for development projects and it causes limitations in this experiment. Thus acceptance of numerical analysis at geotechnical studies is increasing. The modeling using artificial neural networks is the method that is based on previous data and don’t need to simplify and improve the high reliability coefficient. In this study serious models of multi-layer perceptron neural network, one of the most commonly used neural networks, was used. Network design and factors influencing its behavior in this issue has been studied as a summary. In this study, artificial neural networks are used for prediction of bearing capacity of driven steel piles in sandy soil, in all models four parameters are used as input data which are length and diameter of the pile, the coefficient of elasticity and internal friction angle of soil and the bearing capacity of piles is used as output data. Models have reasonable success in predicting the bearing capacity of piles. In order to evaluation of networks, the different indices such as RMSE, MAE, MAXAE and SDAE were used. To increase the accuracy of predicting bearing capacity, for the network training stage the real tests that has been done at the geotechnical studies of dry dock area hormozgan by POR Consulting Engineers were used.Acording to (Because we) need of more data for training and testing network, several tests on pile bearing capacity, in smaller dimensions were performed in the laboratory. The sixty tests have been performed on piles with various length (35, 40, 45 and 50 cm), various diameters (20, 25 and 32 mm) and different relative compacted sandy beds (50, 60, 70, 75 and 80%). To perform these tests the device of pile bearing capacity, made in university of TarbiatModarres, was used. Models based on neural networks, unlike traditional models of behavior don’t explain effect of input parameters on output parameters. In this study, by the sensitivity analysis on the optimal structure of introduced models in each stage it has been somewhat trying to response this question. .

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This study was conducted to determine a relationship between energy input and yield in greenhouse basil production in Esfahan Province, Iran. Data were collected from 26 greenhouse basil producers through a face-to-face questionnaire. The data collected belonged to the production period of 2009–2010 with the following results obtained. A total energy input of 236,057 MJ ha^-1 was estimated to be required for basil production. The share of electricity (75.68% of the total energy input) was the highest form of energy required. The expense was followed by plastic cover (9.69%) and chemical fertilizer spending (7.28%), respectively. The energy ratio, productivity, specific, and net energies were found out as 0.25, 0.11 kg MJ^-1, 9 MJ kg^-1 and -177377 MJ ha^-1, respectively. A determination of the efficient allocation of energy resources was modeled through Cobb–Douglas production function. The results of econometric model estimation revealed that the impact of energies spent in the form of human labour and plastic coverings on yield was significantly positive at 1% level. Sensitivity analysis of the energy inputs revealed that the marginal physical productivity (MPP) value related to human labour was estimated as the highest.

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The Hargreaves-Samani (HS) equation, which estimates reference evapotranspiration (ET0) using only temperature as input, should be most suitable for ET0 prediction based on weather forecasting data. In the current study, the HS equation is calibrated with daily ET0 by the Penman-Monteith equation, and is evaluated to check the possibility of predicting daily ET0 based on weather forecast data. The HS equation is likely to overestimate daily ET0 in the humid regions of China. Coefficients a and c are calculated as 0.00138 and 0.5736 according to local calibration. The calibrated HS equation performs considerably better than the original one. The proposed equation could be an alternative and effective solution for predicting daily ET0 using public weather forecast data as inputs. The error of daily ET0 prediction increases with the increase in the error of daily temperature range (TR) or daily mean temperature (Tmean). This error is likely to be more sensitive to the error in TR than in the Tmean. Ensuring that TR errors are less than 2°C is necessary for perfect estimations of ET0 based on public weather forecast data using the calibrated HS equation.

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In this article, after the design of a CCHP system for office buildings in Tehran, a mathematical analysis of the CCHP system following thermal demand management in comparison to separate system is presented. In order to have a comprehensive evaluation of the performance of the CCHP system, four criteria including primary energy saving, CO2 emission reduction, operational cost reduction and rate of return are employed for a typical office building in Iran. Also a sensitivity analysis of rate of return based on increasing natural gas and electric price is performed. Results show that the CCHP system with selling electricity to grid has much better performance than separate system when all of the criteria are involved. Also without selling electricity to grid the CCHP system achieves more benefits than separate system but these benefits are less than the benefits of the situation with selling electricity to grid. The sensitivity analysis shows that in the situation with selling electricity to grid, with increasing natural gas and electric price the ROR will be increased but in the situation without selling electricity to gird, with 40% increase in natural gas price the ROR will become less than Interest Rate.

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Abstract The lack of an accurate equation of state for predicting the thermodynamic properties of materials in a wide range of temperature and pressure caused the researchers study on new equations. . In this investigation, a reverse Brayton cryocooler was simulated as a system that prepares the sub-cooled liquid nitrogen supplying the operation condition for high temperature superconductor cables. A computational code was developed for predicting thermodynamic properties of Helium and Neon using fundamental equation of state. Comparing the results with experimental data validate the accuracy of these equations in predicting the thermodynamic properties. Then, using the developed computational code, a reverse Brayton cycle with 10 kW cooling capacity, was designed and simulated and the effect of various parameters on its performance was evaluated. Performance characteristic curves were plotted to illustrate the sensitivity analysis under different operation conditions, and the influence of various parameters such as compression ratio in compressor, maximum pressure, working fluid, efficiency of the heat recovery exchanger and efficiency of expander on the performance of the cycle was addressed. The results showed that the use of neon as a refrigerant gives a better performance than helium. Efficiency of heat recovery exchanger has a significant effect on the performance of cycle, so that 3 percent increase of this parameter increases 11 percent figure of merit (FOM) of the cycle.

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In the last few years, public awareness has been on the increase about short- and long-term effects of forest roads construction on the environment. Therefore, forest road managers have to be concerned about the negative impacts and mitigate them as much as possible. This research conducted multi-criteria analysis techniques in a useful way to define the effective criteria and propose a model for forest road network planning and assessment so that both economic and environmental costs are minimized. The model was used for evaluating the alternatives and a sensitivity analysis was then performed to verify the model. Results of sensitivity analysis showed that, there were two alternatives out of nine, with the lowest negative impacts. As a result, analytic hierarchy process and sensitivity analysis (AHP-SA) revealed that the criteria slope, soil texture and landslide susceptibility had the highest weight values, respectively, and the criteria soil texture and distance from stream networks and distance from faults were especially sensitive to the changes. In addition, the sensitivity analysis proved that the model proposed in our analysis was almost reliable and stable, and only the first and second priorities were replaced in priority levels when the weight values of criteria were changed. Results showed that the methodology was useful for identifying road networks that met environmental and cost considerations. Based on this work, the authors suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified by the experts in this study can be useful.

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Existence of huge reserves of natural gas in the country and also the extent of its distribution lines has caused the use of natural gas as the main energy carrier. Seasonal fluctuations in gas consumption in domestic sector and giving priority to this sector has led that the gas supply to other sectors such as thermal power plants is faced with many problems in the cold season. One way to deal with this issue (shortage of natural gas) is the liquefaction and storage of surplus natural gas in the summer, using peak-shaving gas liquefaction plants. In this study, SMR and N2-expander processes have been evaluated. Changing in operational and environmental parameters (such as changes in flow rate, pressure, temperature and composition of the feed gas and working fluid of the cycle) are the main problems that peak-shaving plants will be permanently encountered with them, thus low sensitivity to changing conditions is the one of the important criteria in the selection of suitable process for peak-shaving. In this study, the sensitivity of liquefaction processes has been investigated using normalized sensitivity analysis. The results indicate that SMR process, despite lower power consumption is more sensitive to changes of the environmental and operational parameters and even, in some cases, the applied perturbation in the probable error range of measurement devices (such as 20 kPa uncertainty or fluctuation in compressor suction pressure), causes malfunction of the liquefaction process (wet entering the compressor).