Simulation

Several process modeling techniques have been used in simulation projects. However, most of these techniques provide little specific support to the programming. The main cause of this is the fact that these techniques were not developed with the same logic used in simulation models. From this issue, this paper presents an industrial application of a new conceptual modeling technique, named IDEF-SIM (Integrated Definition Methods -- Simulation) currently under development by the authors. This adapted IDEF uses logic elements present in techniques such as IDEF0 and IDEF3, but in a way that is similar to the process interpretation logic usually used in simulation projects. This way, it can be noticed an increase in the conceptual model's utility, which might facilitate the simulation model programming, verification and validation and the scenarios creation. Additionally, the paper presents the benefits of using IDEF-SIM to create the conceptual model of a Brazilian tech company manufacturing cell.

On one hand the roles of the simulation modeller and user in logistics simulation projects seem to be pretty clear and well accepted; on the other hand general and domain-specific approaches to formalise certain steps in a simulation project return to be the subject of research and development. Typically those approaches head for software engineering and tool development in order to replace the person in model building steps or most recently in trace file analysis. Unfortunately, this quite often relinquishes and intuition of the simulating person. Against this background the paper draws attention to the benefits from combining both spheres, the one of formalized algorithms and the other one of the human instinct. Based upon a discussion of simulation user needs in the logistics application area an approach is presented that allows mediating between user and software in both ways, for specifying simulation aims and questions and for deriving simulation results from seeing behind pure simulation data. By use of an example the impact the user has on a simulation project and its outcomes is illustrated. Consequently, conclusions emphasize the irreplaceable role of the user who brings in objectives, motivation and focus of the simulation project as well as domain-specific experiences and competences to understand the real message of simulation results.

This chapter introduces a number of traditional approaches for the development of simulation models. The simulation model associated with a modelling and simulation project is a computer program that captures the structural and behavioural details of the SUI as specified by the conceptual model. An important feature of the simulation model relates to the perspective taken in ‘packaging’ the model dynamics; in particular, the management of time is a major concern. At its most fundamental level, a DEDS model evolves as a consequence of discrete events which change the value of some of the model’s state variables. Thus, time management consists of determining event times and appropriately updating the model’s state variable at these times. The fundamental algorithm which captures the time management function is the execution and time advance algorithm. Four traditional world views are introduced each with its own execution and time management algorithm. These include the Activity Scanning world view, the Event Scheduling world view, the Three-Phase world view and the Process-Oriented world view. A procedure for transforming an ABCmod conceptual model into a simulation model corresponding to both the Three-Phase world view and the Process-Oriented world view is outlined.

The key to the successful "factory of the future" will be the effective coordination of managerial functions and production operations. The research presented in this dissertation addresses this "coordination" issue, especially in terms of understanding the impact and possibilities which exist in an information-rich environment. Such an environment is often referred to in the literature as being a computer-integrated manufacturing (CIM) environment. An interactive simulation modeling program, MOSES (Manufacturing Organization Simulation and Evaluation System), was developed for the purpose of describing, analyzing, and understanding such manufacturing. MOSES is a copyrighted computer program, a menu-driven, interactive, simulation modeling tool. MOSES users are able to define the manufacturing environment to be simulated by keying in data values (e.g., products, bills of material, and production rates) which describe the organization. MOSES then uses these data values to "build the manufacturing simulation model." That is, once the manufacturing parameters have been defined, MOSES can simulate the manufacturing organization (from orders through distribution) without the user having to write any computer code or having to understand the principles of simulation modeling. MOSES users are then able to interact with the manufacturing organization as "managers" in the dynamic simulation environment. This interactive capability allows MOSES users to test various manufacturing scenarios, watch the model's progress from the perspectives of different organizational functions (e.g., from marketing or from production), override model-generated decisions (e.g., by changing the demand forecast), and view the results. The MOSES approach to simulation modeling differs from traditional approaches. In short, MOSES uses both mathematic and logical modeling principles, employs traditional simulation modeling techniques but in combination with database technology, and includes the user as an integral component in the simulation process.

This paper presents the simulation and experimental analysis of an impulse voltage generator (IVG) used for atmospheric discharge voltage tests. The electrical equivalent circuit of the IVG was simulated using the electromagnetic transients program EMTP-VR. Besides the simulation of the generator operation, it was necessary to make a detailed revision of the IVG components and determine their working condition to carry the laboratory experiments. The lack of a high DC voltage source to feed the IVG led to developing a high-voltage dc supply. Although the IVG was designed and built in February 1982, this equipment was out of operation due to its DC power supply failure. Due to the interest in rehabilitating the equipment to perform high voltage experiments, and with the need to characterize insulation materials and electrical equipment subject to electrical stress, it was decided to put it back into operation. Capacitor tests were carried out for each IVG stage to determine if they fulfilled the electrical charging function. Other components were also repaired, such as damaged resistances and sphere gaps. The impulse generator was configured with a maximum of six stages which is half of the original design. Before doing an experiment with the IVG, a simulation analysis was made to study the IVG. The simulation results allow evaluating before the experimentation whether the impulse generator is working correctly; the model allows to analyze the charge and discharge of each capacitor and the electrical currents at each branch of the IVG equivalent circuit. Performance Simulation and experimental results are presented to demonstrate the IVG functionality. The IVG will be used for carrying out insulation tests made by power engineering graduate students.

To evaluate the performance of newly designed electro-pneumatic valves (EPVs) for the air-powered engine (APE) and study laws of parameters affecting them, a simulation model was established based on the thermodynamics and mechanics theories. Experiments were set up to determine the instantaneous effective orifice area of solenoid valve by the constant volume discharge method. The simulation model was also validated by comparing the measured displacement curve with the simulated displacement curve of the valve in the pressure of 0.16 and 0.49 MPa. Simulation and experimental results showed that maximum working frequency of the designed EPV could reach 30 Hz corresponding to 2000 r/min of engine rotating speed. Based on simulation results, impacts of temperature and pressure of control air on delay time, full opening/closing time and seating velocity of EPV were analyzed. The simulation model could also act as EPV simulation prototype in designing the air exchange control system of APE.

The paper establishes a simulation model to study transport capacity of urban rail transit based on RailSys software and illustrates the core principle and quantitative standard of the method “UIC 406” and the modeling process of dynamic simulation. A concrete example is analyzed by application of RailSys dynamic simulation model. Then find out an effective way to improve transport capacity and put forward the corresponding optimization. The results indicate that RailSys software which is applied to the analysis of dynamic simulation of transport capacity of urban rail transit provides feasible suggestions for the actual operation and management.KeywordsUrban rail transitTransport capacityRailSys simulationUIC 406

In Industry 4.0, computer simulation has been changing its application. Simulation models focused on specific analysis give space to models with automation degree and they are integrated with different systems. This integration favors the constant model updating based on variations in the real system, enabling faster decision making. In this way, simulation models are part of cyber-physical systems since they represent a virtual version of the real process. In this context, the present work proposed a method adapted from Montevechi et al. (2010) to carry out simulation projects according to Industry 4.0 principles. Finally, the proposed method was applied in a supply material process in an aeronautical industry, validating it and showing how the simulation works in the Industry 4.0.

This paper analyses the effects of job release policies, priority scheduling rules and setup times on the performance of a dynamic job shop in a sequence dependent setup time environment. Two job release policies namely, immediate job release and job release based on a specified work-in-process are investigated. A simulation model of a realistic manufacturing system is developed for detailed analysis. The dynamic total work content method is adopted to assign the due dates of jobs. Six priority rules are applied for prioritizing jobs for processing on machines. Several performance criteria are considered for analyzing the system performance. The simulation results are used to conduct statistical tests. Analytical models have been formulated to represent the simulation model for post-simulation studies. These models are found to yield a satisfactory estimation of the system outputs.

The speed at which cities are growing and developing today cannot be disregarded. Human activities and natural causes are both contributors to urban growth. The relationship between these factors is complex and the complexity makes it difficult for the human mind alone to understand cities. A model that helps reveal the complexity is needed for urban studies. Main objective of this study is to understand the effects of urban planning strategies on the future of the city by utilizing a Cellular Automata and Artificial Neural Networks based simulation model. Driving factors of urban growth according to development scenarios were used in the simulation process. Six different development scenarios were formulated according to the strategic plan of Izmir. Land use and driving factor data used in simulating scenarios were acquired from EarthExplorer and OpenStreetMap databases, and produced in QGIS. Future Land Use Simulation Model (FLUS) based on Cellular Automata (CA) and Artificial Neural Networks (ANN) was used. The results were assessed both by using FRAGSTATS which helped calculate fractal dimensions and visual analysis. Fractal dimension results of each scenario showed that the simulation model respected the overall urban complexity. A closer look at each scenario indicated the diverse local growth possibilities for different scenarios. The results show that urban simulation models when used as decision support tools promise a more inclusive and explicit planning process.

In order to effectively analyze the fluorescence distribution of sea surface oil film detected by laser-induced fluorescence (LIF), a novel, to the best of our knowledge, simulation model of the oil film fluorescence was established based on the Monte Carlo method. Using this simulation model, the fluorescence distribution of oil film with different thickness in emission direction and spatial distribution were analyzed. Based on the fluorescence mechanism model of oil film detected by LIF, a criterion for the LIF system calibration, i.e., the fluorescence intensity ratio between oil film and clean seawater (FIR) using the fluorescence collected from clean seawater as a reference was proposed. The validity of the fluorescence simulation model was verified by using the FIR results of theory and simulation. The fluorescence spectra of oil films with different thickness and FIR parameters of corresponding thickness were obtained by experiments. By analyzing the fluorescence spectra of different oil products and oil film thickness, the fluorescence influencing factors of oil film detected by LIF were obtained. The results show that the fluorescence coverage area increases gradually with the increase of oil film thickness. When the incident light is in the same direction as the fluorescence receiving direction, the obtained fluorescence intensity is larger. Moreover, the FIR used as the calibration criterion of the LIF monitoring system can effectively characterize the thickness of oil film on the sea surface for LIF to detect sea surface oil film in real applications.

*† ‡ We develop an approach for estimating model parameters which result in the “best distribution fit” between experimental and simulation data. Best distribution fit means matching moments of experimental data to those of a simulation (and possibly matching a full probability distribution). This approach extends typical nonlinear least squares methods which identify parameters maximizing agreement between experimental points and computational simulation results. Several analytic formulations for the distribution matching problem are provided, along with results for solving test problems and comparisons of this parameter estimation technique with a deterministic least squares approach. I. Introduction Nonlinear models are frequently used to model physical phenomena, including engineering applications. In this paper, we refer to a nonlinear model very broadly: the output of the model is a nonlinear function of the parameters [Draper98]. Thus, nonlinear models can include systems of partial differential equations (PDEs). Some examples include CFD (computational fluid dynamics), groundwater flow, heat transport, etc. Nonlinear models also include functional approximations of uncertain data via regression or response surface models. In most cases, we have some type of simulation model which is a nonlinear model, so we use the term nonlinear model and simulation model interchangeably in this paper. In addition to a simulation model, we assume that we have experimental data which may be used to calibrate the model. The calibration often requires the solution of an optimization problem to determine the optimal parameter settings for the simulation model. In this paper, we are concerned with identifying model parameters which result in a “best fit” between experimental data and simulation results in a nondeterministic context. That is, instead of matching point estimates we are concerned with matching moments (e.g., mean or variance) between experimental and simulation data where the variability in the model output is due to parametric uncertainty. We develop an approach extending the typical nonlinear least squares formulation to allow for this distribution matching. Note that “parameters” may be parameters in an approximation model such as a regression model, or physics modeling parameters which are used in physical simulation models such as PDEs. We distinguish data from parameters: data are physical data which are input either to a regression or physical simulation. For example, in groundwater flow modeling, hydraulic conductivity is a parameter and data may include measured flow rates from well tests. In this paper, we denote parameters that will be calibrated as θ, and the independent input data (e.g., state variables, configuration data, boundary conditions) as x. We also assume that there are uncertain variables, denoted by u, that represent inherent variability or lack of exact knowledge influencing the simulation, but which we cannot observe. The effect of the uncertain variables is reflected in both the output variability of the nonlinear model and the experimental data, but we can only explicitly account for these uncertain variables in the simulation model. Thus, our simulation model, f,

Sulfur recovery units (SRU) have an important role in the industrial production of elemental sulfur from hydrogen sulfide, whereas the generated acidic gas emissions must be controlled and treated based on local and international environmental regulations. Herein, Aspen HYSYS V.11 with Sulsim software is used to simulate the industrial and treatment processes in a refinery plant in the Middle East. In the simulation models, in temperature, pressure, flow, energy, and gas emissions were monitored to predict any expected change that could occur during the industrial processes. The simulation models were validated by comparing the obtained data with actual industrial data, and the results showed low deviation values. The simulation results showed that the current process temperature conditions can work efficiently for sulfur production without causing any environmental consequences. Interestingly, the simulation results revealed that sulfur can be produced under the optimized temperature conditions (20° less than design temperatures) with a total amount of steam reduction by 1040.12 kg/h and without any negative impact on the environment. The steam reduction could have a great economic return, where an average cost of 7.6 $ per ton could be saved with a total estimated cost savings by 69,247.03 $ per year. The simulation revealed an inaccurate production capacity calculated by real data in the plant during the performance test guarantee (PTG) where the real data achieved around 1 ton/h higher capacity than the simulation result, with an overall recovery efficiency of 99.96%. Based on this significant result, a solution was raised, and the level transmitters were calibrated, then the test was repeated. The simulation models could be very useful for engineers to investigate and optimize the reaction conditions during the industrial process in sulfur production facilities. Hence, the engineers can utilize these models to recognize any potential problem, thereby providing effective and fast solutions. Additionally, the simulation models could participate in assessing the performance test guarantee (PTG) calculations provided by the contractor.

Gear boxes, i. e. the realization of their functions, especially gear shift, have a big impact on vehicle operation quality through their effects on the performance of vehicles and their comfort. This paper shows a method of modeling the transition process during gear shifting in planetary gear trains. The simulation model is developed with in order to provide virtual research of planetary gear trains, which would positively decrease the number of real prototypes, thus considerably saving time and contributing to the quality improvement of the final product (planetary gear train) and vehicles in general. Introduction Modeling of gear shift processes has been carried out on the planetary gear type Ravigneaux used in planetary gear trains of motor vehicles. The model was developed modularly, so that more simulation models can be added to the whole. At the same time each subsystem is a model for itself and can be used independently from the main model. Simplified engine model Bearing in mind that, for the simulation of the planetary gear which is the subject of this paper, the necessary input parameters are in the form of engine torque Mm, angular velocity and motor ωm moment of inertia of rotating engine components reduced to the input shaft gear, a simplified model of an internal combustion engine has been formed. Model of external load External load resistance is in the form of the torque Mt occurring on the drive wheel of the vehicle. On the other hand, this load is defined by the moment of inertia of rotating elements from the planetary gear to the drive wheel, reduced to the output shaft. Model of friction transmission simulation Transmission power control in planetary gears is achieved through friction components. The output size of this subsystem is the moment of carrying the friction assembly. Simulation model of the planetary gear train The model simulation of the planetary gear type Ravigneaux was formed in accordance with the kinematic scheme of the gear train. The gears are presented as a solid body defined by mass, moment of inertia, position with respect to the system and the center of gravity. Subsystem for monitoring the simulation results Measuring and recording the simulation results are simulated with the simulation tracking subsystem. The simulation results are described through the torque and the angular velocity as a function of time. Analysis of the simulation model Forming a simulation model enables virtual testing of the planetary gear and the analysis of the impact of certain parameters on the behavior of the gear during gear changes. In other words, an opportunity has been created to examine the behavior of the model while simulating different conditions. Conclusion This paper presents the modeling of the gear change process in a planetary gear using computers in the Matlab / Simulink environment. Computer-aided modeling of the gear change process enables the generation of different versions of virtual gear models with relevant data about their characteristics thus helping designers in their decision making in the iterative process of design, i. e. in making appropriate decisions in the early stages of design.

The article describes the systems of structural and simulation modeling (SSSM); AnyLogic, ExtendSim, Simulink. In the structural models constructed in these SSSMs, the routes of movement of dynamic objects and the processes of their servicing are reflected in a graphic form. The comparison of SSSM is based on the structural models of the queuing system (QS) of the M/M/5/2 type obtained in these SSSMs and the difference between the results of simulation and analytical modeling. The reliability estimation was carried out on 13 QS parameters: the failure probability and the mathematical expectations and standard deviations of quantitative and temporal indices. The article is intended to facilitate the selection of the SSSM for the simulation of probabilistic objects in accordance with the subject area to which the object belongs, taking into account the personal preferences of the user and taking into account the reliability of the simulation results.