Generation Of Simulation Models Research Articles

A Tool for xMAS-Based Modeling and Analysis of Communication Fabrics in Simulink

The eXecutable Micro-Architectural Specification (xMAS) language developed in recent years finds an effective way to model on-chip communication fabrics and enables performance-bound analysis with network calculus at the micro-architectural level. For network-on-Chip (NoC) performance analysis, model validation is essential to ensure correctness and accuracy. In order to facilitate the xMAS modeling and corresponding analysis validation, this work presents a unified platform based on xMAS in Simulink. The platform provides a friendly graphical user interface for xMAS modeling and parameter setup by taking advantages of the Simulink modeling environment. The regulator and latency-rate sever are added to the xMAS primitive set to support typical flow and service behaviors. Hierarchical model build-up and Verilog-HDL code generation are essentially supported to manage complex models and to conduct cycle-accurate bit-accurate simulations. Based on the generated simulation models of xMAS, this tool is applied to evaluate the tightness of analytical delay bound results. We demonstrate the application as well as the work flow of the xMAS tool through a two-agent communication example and an all-to-one communication example with a tree topology.

Life Loss Estimation Based on Dam-Break Flood Uncertainties and Lack of Information in Mountainous Regions of Western China

Compared with urban floods, dam-break floods are associated with greater uncertainties, including variable dam-break modes and hydrological characteristics, so conventional flood estimation methods cannot be directly applied in the estimation of dam-break flood loss. In particular, there is scant information regarding the conditions of affected area and hydrological characteristics in southwest China. In this paper, we introduce an integrated model for estimating flood loss that is adapted to the mountainous regions of southwestern China in light of the relative lack of available information. This model has three major components: a basic information model, a dam-routed flood propagation simulation model, and a loss estimation model. We established the basic information model despite the relative lack of available information using 3S technology [remote sensing (RS); geographical information system (GIS); global positioning system (GPS)], data mining technology, and statistical analysis techniques. Our dam-routed flood propagation simulation model consists of major hydrologic processes and their governing equations for flow propagation, which we solve using finite-difference schemes. In this model, the flood propagation area is divided into grids and each grid is determined by the characteristic parameters obtained from the propagation simulation. We present a case study of the Lianghekou hydropower station in Sichuan Province, China to illustrate the practical application of this integrated model for life loss estimation.

Simulation-Based Computational Design Synthesis Using Automated Generation of Simulation Models From Concept Model Graphs

Current approaches in computational design synthesis (CDS) enable the human designer to explore large solution spaces for engineering design problems. To extend this to support designers in embodiment and detail design, not only the generation of solution spaces is needed but also the automated evaluation of engineering performance. Here, simulation methods can be used effectively to predict the behavior of a product. This paper builds on a general approach to automatically generate solution spaces for energy and signal-based engineering design tasks using first-order logic and Boolean satisfiability. The generated concept model graphs (CMGs) are now in this paper automatically transformed into corresponding bond-graph-based simulation models. To do this, guidelines for creating partial simulation models for the available synthesis building blocks are presented. The guidelines ensure valid causality in the final simulation model. Considering the connections in the concept model graphs, the simulation models are automatically generated and simulated. The simulation results are then used to calculate different objectives, constraints, and performance metrics. The method is validated using automotive powertrains as a case study. One hundred and sixty-two different powertrain concepts are generated and evaluated, showing the advantages of electric powertrains with respect to CO2 emissions and the importance of considering intelligent control strategies in the future for hybrid ones.

Automating agent-based modeling: Data-driven generation and application of innovation diffusion models

Simulation modeling is useful to understand the mechanisms of the diffusion of innovations, which can be used for forecasting the future of innovations. This study aims to make the identification of such mechanisms less costly in time and labor. We present an approach that automates the generation of diffusion models by: (1) preprocessing of empirical data on the diffusion of a specific innovation, taken out by the user; (2) testing variations of agent-based models for their capability of explaining the data; (3) assessing interventions for their potential to influence the spreading of the innovation. We present a working software implementation of this procedure and apply it to the diffusion of water-saving showerheads. The presented procedure successfully generated simulation models that explained diffusion data. This progresses agent-based modeling methodologically by enabling detailed modeling at relative simplicity for users. This widens the circle of persons that can use simulation to shape innovation.

Emulation of control strategies through machine learning in manufacturing simulations

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of dynamic behavior of the modeled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.

Automatic Generation of Pipelines Into a 3D Industrial Process Model

Simulation has become an established technique to support the design of complex, mechatronic or cyber-physical systems. Ideally, simulations should already be performed at an early design phase before high-cost design commitments are made, and the recent advances in the digitalization of design information open possibilities for automatic generation of simulation models. However, high-fidelity simulation model building depends on accurate data. In particular, first-principles models are desirable source information for simulations, but such models generally are not available at an early design stage. This paper investigates the automatic generation of first-principles 3-D models for piping intensive systems based on design information that is available at an early design stage, namely piping & instrumentation diagrams (P&ID). An algorithm is presented for the generation of such 3-D models based on machine-readable P&ID information. The main focus of the algorithm is the automatic generation of feasible pipelines into the 3-D models, so that the model has sufficient information, which can be exploited in further work to automatically generate high fidelity first-principles thermo-hydraulic simulations.

Compact solid-state Marx-bank sub-nanosecond pulse generator based on gradient transmission line theory

For non-invasive treatments in the biomedical field, sub-nanosecond pulse electric field matching ultra-wideband antennas have aroused great interest for their excellent directivity and targeted treatment. Sub-nanosecond pulse generators have proved difficult to control in pulsed power technologies despite many studies of pulse generators based on avalanche transistor theory and the Marx circuit. However, no solution exists to the problems of non-ideal waveform of the output sub-nanosecond pulse and the relatively low effectiveness of the Marx circuit. To resolve these problems, the paper describes a sub-nanosecond pulse generator simulation model based on gradient transmission line theory in PSPICE simulation software and the development of a compact solid-state sub-nanosecond pulse generator (18 cm×18 cm×4 cm). From experimental tests, trigger signals of 5-V amplitude, 5-ns rise and fall times (pulse-width 10 ns-50 ns) were generated by Field-Programmable Gate Array (FPGA). A pulse with 1600-V voltage amplitude, 300-ps full width at half maximum, 150-ps rise time, and 10-kHz high-stability repetition rate was generated in an attenuator load with high-bandwidth (8 GHz, 50 Ω). The effectiveness (Output pulse voltage amplitude / Charge voltage × Stages) of this Marx circuit reached to 41.7%, improving considerably on previous efforts.

Smart home simulation model for synthetic sensor datasets generation

World population is ageing due to longer life expectancy worldwide. There is a trend in elderly people to live alone in their habitual residences in spite of health and safety risks. Smart Homes, intelligent environment systems deployed at elderly homes can act as early warning systems trying to forecast the worsening or exacerbation of the resident chronic conditions. Access to sensor datasets is essential for the development of an efficient real smart home. Procurement of such datasets is subject to several restrictions and difficulties. This paper describes the generation of synthetic datasets by means of a simulation model as a suitable alternative previous to the deployment of a real monitoring system. The collection of synthetic datasets will be used during the next project step to train and evaluate activity recognition methods and algorithms.

Graph transformation based simulation model generation

The graph transformation based method presented in this paper can automatically generate simulation models assuming that the models are intended for a certain domain. The method differs from other methods in that: the data used for model generation does not contain specifications of the model structures to be generated; the generated simulation models have structures that are dynamically constructed during the model generation process. Existing data typically has quality issues and does not contain all types of information, particularly in terms of model structure, that are required for modelling. To solve the problem, transformation rules are designed to infer the required model selection and structure information from the data. The rules are specified on meta-models of the original data structure, of intermediate structures and of the simulation model. Graph patterns, pattern composites and graph pattern matching algorithms are used to define and identify potential model components. Model composite structures are represented by hypergraphs according to which simulation models are generated using model components as building blocks. The method has been applied practically in the domain of light-rail transport.

Conifer Presence May Negatively Affect Sugar Maple’s Ability to Migrate into the Boreal Forest Through Reduced Foliar Nutritional Status

The discipline of ecology suffers from a lack of knowledge of non-climatic factors (for example, plant–soil, plant–plant and plant–insect interactions) to predict tree species range shifts under climate change. The next generation of simulation models of forest response to climate change must build upon local observations of species interactions and growth along climatic gradients. We examined whether sugar maple (Acer saccharum) seedlings were disadvantaged with respect to soil nutrient uptake under coniferous canopies, as this species would need to migrate northward into conifer-dominated forests in response to climate change. An experimental design was applied to 3 sites, forming the largest possible latitudinal/climatic gradient for sugar maple in Quebec (Canada) and isolating the effect of conifer presence on its seedling’s nutritional status. We tested whether: (1) both soil and climate and (2) presence of conifers affected foliar nutrient levels of sugar maple seedlings. Climate and soil (through pH) strongly affected nutrient availability for sugar maple seedlings and predicted 63.7% of their foliar nutrient variability. When controlling for site effects, we found a significant negative effect of conifers on foliar Ca and Mg levels of maple seedlings, which can adversely affect their overall health and vigour. When considering projected modifications of the forest environment due to climate change, we suggest that northward migration of sugar maple will be negatively affected by the presence of conifers through reduced foliar nutrition.

Simulation Model of Propagation of Vibrations in a System of Connected Bodies for the Solution of Problems of Vibration Diagnostics

An approach to the construction of a mathematical model of the process of propagation of acoustic vibrations in equipment that is in the form of a system of connected bodies is considered. The proposed structure of the mathematical model is implemented as a graph whose edges are links between the elements of a system of connected bodies formalized by nonhomogeneous wave equations that take into account dissipative losses. Through the use of the mathematical model together with the introduction of sources of an external disturbance in any node of the graph, it becomes possible to describe a variation in the vibration signal at a contact point, estimate its properties, and select on a well-defined basis the point of attachment of the vibration signal sensor. The mathematical model is tested to assure it corresponds qualitatively to the actual processes in the propagation of vibration signals by means of test disturbances, and it is shown that it may be used to evaluate the selection of the points of attachment of the vibration signal sensors in problems of vibration diagnostics of equipment.

System entity structure extension to integrate abstraction hierarchies and time granularity into DEVS modeling and simulation

The modeling and simulation (M&S) of complex systems often requires models described at different levels of detail characterized by differences in abstraction hierarchies and/or time granularity. The discrete-event system specification (DEVS) is a framework based on mathematical systems theory that offers a computational basis for application of M&S to systems engineering and that has become widely adopted for its support of discrete-event, continuous, and hybrid applications. A fundamental representation of DEVS hierarchical modular model structures is the system entity structure (SES), which represents a design space via the elements of a system and their relationships in a hierarchical and axiomatic manner. As has been described in a number of publications, the SES supports development, pruning, and generation of DEVS simulation models. The goal of this paper is to propose an extension of SES in order to integrate both the concepts of abstraction hierarchies and time granularity into DEVS. This paper explains in detail: (i) the concepts of abstraction hierarchies and time granularity; (ii) the extension of SES in order to take into account these concepts; (iii) DEVS M&S of complex systems according to different levels of detail (abstraction hierarchies and time granularity); (iv) the use of a Python DEVS simulator (DEVSimPy) to implement the management of abstraction hierarchies and time granularity. A real case study is given to illustrate the proposed approach, and follow-on research needed to implement the concepts is discussed.

Semi-automatic simulation model generation of virtual dynamic networks for production flow planning

Computer modelling, simulation and visualization of production flow allowing to increase the efficiency of production planning process in dynamic manufacturing networks. The use of the semi-automatic model generation concept based on parametric approach supporting processes of production planning is presented. The presented approach allows the use of simulation and visualization for verification of production plans and alternative topologies of manufacturing network configurations as well as with automatic generation of a series of production flow scenarios. Computational examples with the application of Enterprise Dynamics simulation software comprising the steps of production planning and control for manufacturing network have been also presented.

A study for production simulation model generation system based on data model at a shipyard

Simulation technology is a type of shipbuilding product lifecycle management solution used to support production planning or decision-making. Normally, most shipbuilding processes are consisted of job shop production, and the modeling and simulation require professional skills and experience on shipbuilding. For these reasons, many shipbuilding companies have difficulties adapting simulation systems, regardless of the necessity for the technology. In this paper, the data model for shipyard production simulation model generation was defined by analyzing the iterative simulation modeling procedure. The shipyard production simulation data model defined in this study contains the information necessary for the conventional simulation modeling procedure and can serve as a basis for simulation model generation. The efficacy of the developed system was validated by applying it to the simulation model generation of the panel block production line. By implementing the initial simulation model generation process, which was performed in the past with a simulation modeler, the proposed system substantially reduced the modeling time. In addition, by reducing the difficulties posed by different modeler-dependent generation methods, the proposed system makes the standardization of the simulation model quality possible.

OpenGridMap: towards automatic power grid simulation model generation from crowdsourced data

OpenGridMap is an open source project that crowdsources realistic power grid data to be used for research purposes. In this paper, we propose an approach for the automatic generation of power gird simulation models from crowdsourced data. The proposed approach orders the crowdsourced data into a power circuit relation which is then used to produce a CIM description file and subsequently a power grid simulation model. We provide experiments which demonstrate the effectiveness of the approach on OpenGridMap data. Given the large amount of crowdsourced data available, our approach has the potential to generate power grid simulations of larger size, more variety and more accuracy than the currently available state-of-the-art test power grids.

Generic data structure and validation methodology for simulation of manufacturing systems

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis (M w 7.5–8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).

Prediction of the oil injection time of FDBs in an HDD spindle motor with a tied shaft by utilizing Kirchhoff’s pressure law

We propose a method to predict the oil injection time of fluid dynamic bearings (FDBs) with a tied shaft by applying Kirchhoff's pressure law. Since the oil is injected by capillary phenomenon, the volume flow rate can be calculated by utilizing Kirchhoff's pressure law. Then, we calculated the oil injection time of the FDBs with a tied shaft by dividing the volume flow rate by the clearance volumes of the journal bearing, the thrust bearing, and the recirculation channel (RC), respectively. We generated simulation models of the FDBs used in a 2.5? HDD spindle motor with a tied shaft. The total oil injection times of the FDBs with and without a RC were 0.302 and 0.335 s, respectively. Also, we verified the proposed method by measuring the oil injection time of FDBs with a RC. We applied the proposed method to predict and improve the oil injection time of the FDBs with a tied shaft due to the variation of major parameters affecting the oil injection time.

Online Simulation of Fluids in Early Design

Shorter product life cycles as well as growing range of product varieties are shifting the product development in the focus. Furthermore, risks of new developments can be reduced by evaluating different solutions. An effective instrument for the validation of different concepts and designs is simulation. Nowadays, there is a huge drawback in the area of fluid simulations. Tools used in the area of Computations Fluid Dynamics (CFD) have two disadvantages: on the one hand it takes a lot of effort to prepare the simulation; on the other hand, the computational execution of simulation takes a long time. Thus, fluids are mainly simulated in special cases like aerodynamics, not in special cases like filling processes because of not being economical. We present a new approach for simulating fluids in the early phases of the product development process based on existing Lagrangian methods. In addition a new method for a fast generation and modification of simulation models is described. We evaluate the new method for online simulation in an example showing a design process with a real-time simulation of the design, which is updated online. Thus, the design of products is supported by the What-You-See-Is-What-You-Get approach, helping to reduce the effort put into different concepts.

Getting the Human Factor into Traffic Flow Models: New Open-Source Design to Simulate Next Generation of Traffic Operations

Automated driving may lead to much higher road capacities, combined with increased road safety, increased driver comfort, and lower costs. Although this vision may hold ground in the long run, first a transitional period will take place in which increasing percentages of vehicles with many levels of automation will drive on the world’s road networks. This transition poses a fundamental scientific challenge. The models used today to simulate and predict vehicular traffic are not valid to predict emergent properties of traffic flows under increasing amounts of vehicle automation. For example, there is no idea of how drivers of nonautomated vehicles will respond to other drivers reading their morning papers behind the steering wheel or the consequences of such interactions on traffic safety and capacity. In this paper, the authors do not propose a new behavioral theory with which the effects of increasing vehicle automation can be predicted. What the authors propose is an advanced open-source simulation framework, OpenTrafficSim, which makes it possible to extend microscopic models incrementally with explanatory mental models, such that new behavioral theories can be tested and shared within the community. Given the societal importance of predicting the effects on safety and efficiency of vehicle automation, the authors sincerely hope this paper will fuel the discussion on how both open-source and closed-source simulation software can be adapted and made ready for the next generation of traffic simulation models that are needed in the coming decades.