Showing papers on "Modeling and simulation published in 2022"

Composing Modeling And Simulation With Machine Learning In Julia

Abstract: In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build surrogates from component-based models, including Functional Mockup Units, using continuous-time echo state networks (CTESN). The foundation of this environment, Modeling-Toolkit.jl, is an acausal-modeling language which can compose the trained surrogates as components. We present the JuliaSim model library, consisting of differential-algebraic equations and pre-trained surrogates, which can be composed using the modeling system. We demonstrate a surrogate-accelerated approach on HVAC dynamics by showing that the CTESN surrogates capture dynamics at less than 4% error with an acceleration of 340x, and speed up design optimization by two orders of magnitude. We showcase the surrogate deployed in a co-simulation loop allowing engineers to explore the design space of a coupled system. Together this demonstrates a workflow for automating the integration of machine learning into traditional modeling and simulation.

VGEs as a New Platform for Urban Modeling and Simulation

Abstract: The complexity of interrelationships between urban natural environments and human environments is increasing with rapid urbanization. This brings new challenges to urban modeling and simulation in simultaneously meeting the comprehensive needs of the dual integration of data and models, multi-type visualizations, human-centered simulation, geographic collaboration, and interactions between physical and virtual spaces. We here propose virtual geographic environments (VGEs) as a new platform of urban modeling and simulation. After discussing the evolution, definition, and features of VGEs, we design a VGE framework for urban system modeling and simulation. Two typical cases are provided to illustrate how VGEs support urban modeling and simulation on different scales: VGE-based collaborative modeling and the simulation of air pollution dispersion in the Pearl River Delta (PRD) urban agglomeration, and fire emergency crowd evacuation simulation. In the future, VGEs may also play an important role in digital twin cities and urban metaverses.

Modeling and Simulation of an Automotive RADAR

Abstract: An autonomous car, well known as self-driving or driverless car, is a vehicle that is skilled in sensing its environment with various sensors and moves with little or no human input. It makes use of Radar, Lidar and camera for its function. The use of Automotive RADAR is harmless to human health as its power is less than 10mW. Validating ADAS in real-life traffic conditions is prolonged process which leads to safety critical conditions and high costs. To speed up its development, virtual driving simulation tools are being used. The RADAR System working at 77GHz is simulated in the MATLAB simulation platform and presented in this paper. It also includes waveform generation, antenna characterization and processing algorithms for range and speed determination. For the development of algorithm, realistic sensor models and simulation tools are required to generate input data. Using this algorithm, given the driving scenario as input data for the ego vehicle, RADAR scans in its field of view. The radar signal is computed by incorporating radar systems specifics, like antenna pattern, transmitted power, etc. Basic radar signal processing methods then extract generic object information to be used for the following processing unit. The developed RADAR simulator is used in real-time driving simulators for the growth of driving assistance systems and in turn used in the radar signal processing techniques. This paper presents the development of RADAR sensor model using MATLAB-based driving simulator.

A Model-Driven Approach for Conducting Simulation Experiments

Abstract: With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed.

Simulation Modeling

Abstract: The book presents some recent specialized works of a theoretical and practical nature in the field of simulation modeling, which is being addressed to a large number of specialists, mathematicians, doctors, engineers, economists, professors, and students. The book comprises 11 chapters that promote modern mathematical algorithms and simulation modeling techniques, in practical applications, in the following thematic areas: mathematics, biomedicine, systems of systems, materials science and engineering, energy systems, and economics. This project presents scientific papers and applications that emphasize the capabilities of simulation modeling methods, helping readers to understand the phenomena that take place in the real world, the conditions of their development, and their effects, at a high scientific and technical level. The authors have published work examples and case studies that resulted from their researches in the field. The readers get new solutions and answers to questions related to the emerging applications of simulation modeling and their advantages.

Recent progress in modeling and simulation of biomass conversion to biohydrogen

Abstract: A systematic review of modeling and simulation for biohydrogen production from a wide variety of biomass using different biochemical and thermochemical conversion processes is presented in this study. Research through mathematical modeling and computational simulation approaches such as reaction kinetic modeling, equilibrium modeling, and computational fluid mechanics techniques are reviewed and presented in this study. These models can play an essential role in identifying the optimum operational conditions to produce maximum biohydrogen through fermentation, pyrolysis, and gasification process, and make these processes economically feasible at the industrial level. Moreover, this current study can help scholars to identify the appropriate modeling and simulation approach for their research, based on the type of biomass, desired product, and operating conditions. The detailed comparison and analysis of results can provide a future direction on sustainable and renewable biofuel resources and techniques for the biohydrogen production.

Exploring geometric, kinematic and behavioral scalability of microscopic traffic simulation systems

Abstract: Even with today's remarkable advancement in computing power, microscopic simulation modeling remains a computationally intensive process that imposes limitations on its potential use for modeling large-scale transportation networks. Research and practice have repeatedly demonstrated that microscopic simulation runs can be excessively time-consuming, depending on the network size, the number of simulated entities (vehicles), and the computational resources available. While microscopic features of a simulated system collectively define the overall system characteristics, it is argued that the microscopic simulation process itself is not necessarily free of redundancy, which if reduced, could substantially improve the computational efficiency of simulation systems without compromising the overall integrity of the simulation process. This research study explores the concept of scalability for microscopic traffic simulation systems in order to improve their computational efficiency and cost-effectiveness. More specifically, we present an optimized downsampling procedure for transforming the full-scale simulation system (prototype) into a geometrically, kinematically, and behaviorally equivalent reduced-scale system (microcosm). The ultimate goal is to execute the microscopic simulation process in the microcosm environment, observe all necessary macroscopic characteristics and performance measures, and upsample the results back to the prototype environment. Experimental analysis was conducted on a homogeneous freeway corridor to examine the effect of different operating conditions on the optimal solutions for the downsampling procedure. The study also investigates the tradeoff between performance and scalability of microscopic simulation systems.

XDEVS: A hybrid system modeling framework

Abstract: When Discrete Event System Specification (DEVS) is used as a modeling tool, there is a semantic gap between a DEVS model and the mathematical representation, which may result in understanding difficulties. To provide a more intuitive form of modeling, XDEVS expands the concept of states in DEVS. The continuous state is introduced in XDEVS, which enhances the ability to model hybrid systems. Based on the DEVS simulation framework, a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model. A hybrid model is constructed and simulated using XDEVS. A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.

Solving Electrical Engineering Problems Using Mathematical Simulation

Abstract: The paper considers solving electrical engineering problems using mathematical simulation and the MatLab software package. Simulation is used as a tool to solve differential equations in the Simulink environment. The MatLab software package provides sufficient simulation accuracy, which may reach 95% in the calculation of electrical engineering systems as compared to simulation and calculation using specialized software. The solution to a heterogeneous linear differential equation with second-order constants has been considered as a simulation technique and a basis for further building complex models, it describes a series-oscillatory circuit comprising an LRC circuit and a series-connected sinusoidal AC source. The basic part of the study results considers more complex models of the electric circuit graph and the separately excited DC motor control systems. All simulation results have been compared with real calculations performed using specialized software or simulation on laboratory installations. The efficiency of solving electrical engineering differential equations using the MatLab software package with an acceptable simulation accuracy exceeding 95% has been confirmed.

Detection of anomalous modeling behavior: a goal-driven data mining approach

Abstract: Precise and timely detection of anomalous modeling behaviors is critical for both teaching and application of modeling methods. Existing methods usually focus on evaluating the modeling results rather than mining the knowledge hidden in the modeling process. In this paper, we propose to monitor and analyze the modeling process in order to timely detect anomalous modeling behaviors, potentially contributing to a comprehensive assessment of the modeling practice. Specifically, we propose to systematically build a goal model for characterizing the normal modeling behaviors, which establishes the connections between modelers' high-level modeling behaviors and low-level modeling operations. On top of such a goal model, we propose a data mining-based approach to semi-automatically validate the design of the goal model and explore other normal modeling behaviors. Then, we propose to automatically detect anomalous modeling behaviors by capturing normal modeling behaviors obtained from the goal model and actual modeling sequences. We have developed and deployed a data-flow diagram modeling platform, which implemented our proposed approach. We have conducted an experiment with 57 participants, the preliminary results of which show that our approach can effectively detect modelers' anomalous behaviors. The experiment results are beneficial for not only assessing the modelers' performance but also identifying the usability issues of the modeling tool.

Computer Modeling of DC and AC Motor Systems by Different Methods and Determination of Errors them

Abstract: In this paper the MatLab software package is considered from the point of view of a universal environment for modeling DC and AC motor systems. The analysis of methods of mathematical, structural and physical modeling in MatLab was carried out using the Simulink block library and the SimPowerSystems application. For the study, two electromechanical systems were selected - a direct-current motor with independent excitation and an asynchronous motor with a squirrel-cage rotor, as one of the most common for driving technological installations of mining. Using a laboratory facility, a simulation model has been developed. It makes possible to evaluate, analyze and compare the results obtained during modeling with real data. The qualitative and quantitative characteristics of the transient process, such as the regulation time, the amount of overshoot, the number of oscillations, were subject to evaluation. During the evaluation and analysis of the modeling results, the advantages and disadvantages of various methods for modeling DC and AC motor systems in the MatLab software package were established.

Agile Modeling and Simulation Technology of Heterogeneous Systems Based on Meta-model Modeling Method

Abstract: Modeling and simulation of heterogeneous systems are faced with the problems of long modeling and simulation cycle, complex and changeable modeling fields and complex domain knowledge. In order to support the requirements analysis and initial design of the system, build the system model as the only authoritative truth source throughout the whole life cycle, and quantitatively characterize the requirements, functions, structure and performance of the system, this paper proposes an agile modeling and simulation technology for heterogeneous systems based on the meta-model modeling method, and on this basis, constructs an agile modeling and simulation system framework for heterogeneous systems.

Uncertainty on Discrete-Event System Simulation

Abstract: Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic S...

Modeling Plant Tissue Development Using VirtualLeaf.

Abstract: Cell-based computational modeling and simulation are becoming invaluable tools in analyzing plant development. In a cell-based simulation model, the inputs are behaviors and dynamics of individual cells and the rules describing responses to signals from adjacent cells. The outputs are the growing tissues, shapes, and cell-differentiation patterns that emerge from the local, chemical, and biomechanical cell-cell interactions. In this updated and extended version of our previous chapter on VirtualLeaf (Merks and Guravage, Methods in Molecular Biology 959, 333-352), we present a step-by-step, practical tutorial for building cell-based simulations of plant development and for analyzing the influence of parameters on simulation outcomes by systematically changing the values of the parameters and analyzing each outcome. We show how to build a model of a growing tissue, a reaction-diffusion system on a growing domain, and an auxin transport model. Moreover, in addition to the previous publication, we demonstrate how to run a Turing system on a regular, rectangular lattice, and how to run parameter sweeps. The aim of VirtualLeaf is to make computational modeling more accessible to experimental plant biologists with relatively little computational background.

Modeling of electrical interface of electromechanical management system based on Saber

Abstract: The design and function verification of aircraft electromechanical management system mainly based on test have the disadvantages of long developing cycle and high cost. Therefore, combined with the idea of Model-based Systems Engineering (MBSE), it is of great engineering significance to establish a simulation platform of integrated control management system to replace the test and form an integrated simulation support environment for the research of integrated control management. A modeling and simulation method of electrical interface function and performance is developed based on Saber, which aims at aircraft electrical interface characteristics of electromechanical management system. With analog modeling, digital modeling, analog-digital modeling and embedded modeling, mechanical and electrical management electrical interface components and circuits are modelled. The electromechanical management electrical interface integrated simulation system, which is in high precision, fine granularity, and high degree of confidence, is built to promote the process of aviation simulation instead of test.

Modeling and Simulation of Hybrid Magnetic Bearing as Physical Model in Simscape

Abstract: In this paper, the Hybrid Magnetic Bearing (HMB) model is established in Simscape with the physical object-based modeling method. The HMB model is used to analyze the change of the air gap magnetic flux density under the different control currents. The physical model simulation result is highly consistent with the results of the Maxwell Finite element method analysis, and the simulation speed is much faster. The physical object-based modeling method provided a new solution for the rapid modeling, simulation, and dynamic characteristics analysis of the electromagnetic coupling devices.

System Analysis of the Process of Mathematical Modeling of Electromechanical Converters

Abstract: The paper raises the problem of strengthening a practice-oriented approach to the modeling process and using the results of modeling technical objects using the example of electromechanical converters based on an asynchronous electric motor. For the synthesis of a generalized algorithm for modeling a technical object, a multi-criteria system analysis of registered software systems designed to simulate an electromechanical converter based on an asynchronous motor was carried out. The functionality of software systems and the applied software is analyzed. Based on the criterion of functionality and practice orientation, a generalized algorithm for modeling a technical object is constructed and this algorithm is concretized for modeling an electromechanical converter based on an asynchronous electric motor. The fundamental difference of the proposed generalized algorithm is the combination of mathematical and visual modeling. Thus, the optimal use of the capabilities of modern information technologies is achieved both for the implementation of the most reliable mathematical models and for obtaining a visual representation of the object due to augmented reality technology

Application of multibody simulation tool for dynamical analysis of tethered aerostat

Abstract: This paper presents a dynamical analysis of a tethered aerostat using a multibody simulation tool called SimscapeMultibody™. Tethered aerostats are lighter than air vehicles that float in the air and are connected to the ground through the tether. Atmospheric winds are a source of disturbance to the aerostat. The wind produces drag force that causes the aerostat to displace from its mean position. This phenomenon is called blow-by. Solving the mathematical model of the motion dynamics with time step is the conventional method used for simulating the flight dynamics of the aerostat. This paper discusses the application of Simscape Multibody™ for the three-degrees-of-freedom flight dynamic simulation of spherical tethered aerostat. Simscape Multibody™, an extension of MATLAB®/Simulink, is a multibody modeling tool in which physical bodies and their interactions have to be defined to simulate the dynamics. The mathematical model of the defined system is automatically generated by the SimscapeMultibody™ solver, and the system dynamics is simulated. The simulation of dynamics using multibody modeling tools provides high fidelity simulation for the tethered aerostats.