Hybrid simulation which combines experimental and numerical modeling is a powerful and relatively new test method for evaluating the seismic performance of structural systems. In this method only critical components of structure are tested experimentally while the rest of the structure is numerically modeled in the computer. In this method the response of the structure is achieved by numerically integrating the equation of motion of the whole system. Among numerical integration methods, operator splitting (OS) method is of great interest for hybrid simulation, since not only its results are more accurate and stable in comparison with explicit methods but also its application for hybrid simulation is much more easier than implicit methods; the reason is that in OS method it is not required to conduct iteration on experimental element or estimate its tangent stiffness matrix during the simulation, the tasks which limit the application of implicit methods for hybrid simulation. But OS method suffers from the shortcoming that the use of initial stiffness matrix in its corrector step decreases the accuracy of results in nonlinear range. This paper presents a modified form of OS method which is termed MOS integration method in which by proposing a new procedure in the predictor step, the accuracy of this step is increased. When the accuracy of the predictor step increases, the difference between predictor and corrector displacements decreases and as a result the effect of initial stiffness approximation becomes less important. This would finally result in the improved accuracy of the whole simulation, as is shown in the paper.The performance, accuracy and stability characteristics of the proposed integration method were studied through numerical simulations, where it was assumed that the restoring force of the system is achieved experimentally and no information about the experimental stiffness is available. The results showed that for the wide range of considered systems including various natural periods, various ductility ratios and various degrees of freedom, MOS results are more accurate than OS method. This shows that the employed method of the predictor step of MOS method has successfully decreased the length of the corrector step with initial stiffness assumption. All the employed error indices also verified that not only the results of MOS are in great harmony with the reference solution but also its accuracy is improved over regular OS method, especially in simulations involving severe nonlinearity. Furthermore results of multi degree of freedom systems with high frequency modes show that MOS results are quite stable as long as the accuracy of important modes of the system is maintained, which is usually the case.As in a real hybrid simulation, experimental errors also affect the accuracy and stability of integration methods, in this paper a hybrid simulation algorithm is numerically modeled and the effect of actuator time delay on the performance of MOS method is investigated. It was observed that in the presence of actuator delay, which is known to be one of the most important sources of experimental errors in hybrid simulation, MOS integration method has solved the equation of motion in an accurate and stable manner with very small level of errors in comparison with the reference solution.

The particles within the rock samples are present in extensive ranges of shapes and sizes, and their characterization and analysis exist with a considerable diversity. The prior research works have appraised the significance of the particle shape types and their effects on the geotechnical structures and deficiencies by evaluating the uncertainty-related rock particle shape descriptors (PSDs). In this work, the Monte Carlo simulation (MCS) is used in order to present a framework to integrate the inherent uncertainty associated with PSDs. A tabletop microscope is used to measure the primary particle shape distribution for the sandstone samples. An open-source processing tool, ImageJ, is used in order to analyze PSDs. The probabilistic distribution of PSDs is acquired using MCS according to the relative frequency histogram of the input parameters. Additionally, a probabilistic sensitivity analysis is performed in order to evaluate the importance of the input parameters in PSDs. The sensitivity analysis results demonstrate that the major axis and area are the most influential parameters involved. The simulation results obtained have revealed that the proposed framework is capable of integrating the inherent uncertainties related to the particle shape.

Introduction: Musculoskeletal problems are the common complaints of patients refer to internal medicine clinics and the pain is the most important of them. There are different physiotherapy methods for reduction of pain and action potential simulation therapy (APS therapy) is one of newest methods. The aim of this study was to determine reduction of pain with APS therapy. Materials & Methods: In 47 patients with different musculoskeletal pain APS therapy performed in 6 days each for 16 minutes and with current of 0.7-1.2 mA. Pain (VAS), Global functional status (ACR) and relief of pain (VAS) before and after study compared.Results: APS therapy reduced pain (p<0.001) and increased Global functional status (p<0.001) and increased relief of pain (p<0.005) but there was not any correlation between this reduction of pain with APS therapy and educational status and past history of physiotherapy and duration of illness in these patients.Conclusion: APS therapy is a useful physiotherapy modality for reduction of pain in musculoskeletal problems.

This paper presents an optimisation methodology for simulating the integration of distributed generation and electric vehicles (EVs) in a residential district. A model of a smart residential district is proposed. Different charging scenarios (CS) for private cars are considered for simulating different power demand distributions during the day. Four different case studies are investigated, namely the Base Case, in which no EVs are present in the district and three study cases with different CSs. A global optimisation method based on a genetic algorithm approach was applied on the model to find the total power from PV panels installed and co-generative micro gas turbines while minimising the annual energy cost in the district for the four different scenarios. In conclusion, the results showed that the use of EVs in the district introduces considerable savings with respect to the Base Case. Moreover, the impact of the chosen CS is nearly insignificant under a purely economic perspective even if it is relevant for grid management. Additionally, the optimum amounts of installed power vary in a limited range if the distance travelled by EVs, users’ departure and arrival time change broadly.

The study aims to use simulation-based optimization methodology for modeling Automative-wheel rig supply chain. Simulation-based optimization approach consists of both simulation and optimization models that transform information repetitively until stop criterion is fulfilled. Simulation technique is based on system dynamics and optimization comprised of multi objective optimization with the aim of minimizing cost, minimizing cash conversion cycle as well as maximizing inventory turnover for two members of supply chain which is solved by genetics algorithm. Powersim Studio 10 is utilized to combine simulation and optimization models. After using the methodology and acquiring optimal solutions, decision maker chooses the optimal solution based on priority discussed for members. The study claims optimal solutions generated by simulation-based optimization are superior in comparison with scenario making in system dynamics model.

Flood simulation using the hydrological model requires an appropriate rainfall dataset and unfortunately, in most parts of the Iran country the spatiotemporal resolution and density of ground gauges aren’ t suitable. Hence, using remotely sensed high spatiotemporal resolution datasets can be useful for filling this gap. The main objective of this research is the assessment of PERSIANN-CCs hourly rainfall dataset for simulation of flood hydrograph using HEC-HMS event-based model at the Asalem river basin (ARB), Iran. Furthermore, to better evaluation of this model, three different spatial scenarios (including lumped, Thiessen and link-lumped) at 1, 3 and 6 hour time steps are used. Findings showed that using of link-lumped scenario at 3 and 6-hour time steps for simulation of flood hydrograph leads to the best results. For example, in this case, the average values of Nash-Sutcliffe efficiency (NSE) and Correlation Coefficient (CC) values for all events are about 0. 58 and 0. 78 (for Δ t= 3 hr) and 0. 56 and 0. 74 (for Δ t= 6 hr), respectively. Moreover, if the main purpose of modeling is the accurate estimation of peak flow, using the second spatial scenario leads to minimum error (with the relative error ranging between 0. 2 and 7. 6 %) at 1 and 3 hr time steps, while the third spatial scenario(link-lumped) hasn’ t the required potential for simulation of peak flow. Also, combing the first spatial scenario and PERSIANN-CCs dataset is the best case for estimation of time to peak (Tp) which is very important in flood warning systems. Findings of this study indicate that in the lack of ground observations, the high spatiotemporal resolution rainfall datasets such as PERSIANN-CCs can be used for flood simulation.