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Artículos de revistas sobre el tema "Modelica"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de mayo de 2024

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1

A. Razak, Amir. "Overview of Wind Turbine Modeling in Modelica Language". International Journal of Engineering and Technology 4, n.º 5 (2012): 551–53. http://dx.doi.org/10.7763/ijet.2012.v4.430.

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2

Tian, Guang Shu y Li Chen Zhang. "Multi-Domain Modeling and Co-Simulation Based on Modelica and Simulink". Applied Mechanics and Materials 596 (julio de 2014): 927–30. http://dx.doi.org/10.4028/www.scientific.net/amm.596.927.

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A co-simulation solution based on multi-domain modeling with Modelica is proposed to achieve the co-simulation of multi-domain modeling and simulation environment with other simulation environment . Based on the connection mechanism of multi-domain Modelica models the co-simulation under S-function co-simulation framework is implemented using the converting principle between Modelica models and Simulink modules. A co-simulation example between MWorks which is a multi-domain physical system modeling and simulation tool based on Modelica and AMESim indicates that the method can extend the application of Modelica models and achieve the collaborative work with multi-domain modeling and simulation tools and other simulation software.
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3

PEREIRA REMELHE, MANUEL A. y SEBASTIAN ENGELL. "COMBINING MODELICA MODELS WITH DISCRETE EVENT FORMALISMS FOR SIMULATION USING THE DES/M ENVIRONMENT". International Journal of Software Engineering and Knowledge Engineering 15, n.º 02 (abril de 2005): 349–55. http://dx.doi.org/10.1142/s0218194005001999.

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Technical systems that include complex physical dynamics as well as extensive discrete event control, require powerful modeling and simulation techniques. As the most adequate means for modeling hybrid physical systems, we advocate the use of object-oriented modeling languages such as Modelica. However, the discrete event models often require the use of dedicated graphical editors that cannot be defined appropriately using Modelica. The purpose of the DES/M modeling environment [10] is to provide such editors for different discrete event formalisms and to translate discrete event models automatically into Modelica components such that a discrete event controller can be integrated easily into Modelica models and simulated using standard Modelica software tools. This contribution presents the main concepts used for the representation of several discrete event formalisms in the Modelica language and discusses the class of discrete event formalisms that can be supported by the DES/M environment.
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4

Li, Zhi Hua, Hong Guang Yang, Jun Yu y You Ping Gong. "Modeling and Simulation of the PMSM-Precision Reducer System with Modelica". Applied Mechanics and Materials 201-202 (octubre de 2012): 202–7. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.202.

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There is still lack of effective modeling and simulation method for complex electromechanical coupling system. Modelica is a multi-domain unified modeling language to solve the modeling and simulation problems of the complex and heterogeneous physical systems. Dymola is a Modelica-based modeling and simulation platform for the complex physical systems. In this paper, the dynamics model of the permanent magnet synchronous motor (PMSM)-precision reducer system is established using Lagrange-Maxwell equation. The simulation model of this system is set up with Modelica language. The simulation of the system is realized in Dymola. Results show that the system can respond to good static and dynamic characteristics under a given speed for different loads. The dynamics model of the PMSM-precision reducer system can be further used in system control and optimization. The proposed modeling and simulation method based on Modelica may be commonly applied to other complex electromechanical systems.
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5

Mattsson, Sven Erik, Hilding Elmqvist y Martin Otter. "Physical system modeling with Modelica". Control Engineering Practice 6, n.º 4 (abril de 1998): 501–10. http://dx.doi.org/10.1016/s0967-0661(98)00047-1.

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6

Otter, Martin, Matthias Reiner, Jakub Tobolář, Leo Gall y Matthias Schäfer. "Towards Modelica Models with Credibility Information". Electronics 11, n.º 17 (30 de agosto de 2022): 2728. http://dx.doi.org/10.3390/electronics11172728.

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Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increase the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. The latter is also called credible simulation process. In an article at the International Modelica Conference 2021, we summarized the state of the art and best practice from the viewpoint of a Modelica language user, based on the experience gained in projects in which Modelica models were utilized in the design process. Furthermore, missing features and gaps in the used processes were identified. In this article, new proposals are presented to improve the quality of Modelica models, in particular by adding traceability, uncertainty, and calibration information of the parameters in a standardized way to Modelica models. Furthermore, the new open-source Modelica library Credibility is discussed together with examples to support the implementation of credible Modelica models.
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7

Proß, Sabrina y Bernhard Bachmann. "An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica". Journal of Integrative Bioinformatics 8, n.º 1 (1 de marzo de 2011): 1–34. http://dx.doi.org/10.1515/jib-2011-152.

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Summary Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab.The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.
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8

Tundis, Andrea, Lena Buffoni, Peter Fritzson y Alfredo Garro. "Model-Based Dependability Analysis of Physical Systems with Modelica". Modelling and Simulation in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1578043.

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Modelica is an innovative, equation-based, and acausal language that allows modeling complex physical systems, which are made of mechanical, electrical, and electrotechnical components, and evaluates their design through simulation techniques. Unfortunately, the increasing complexity and accuracy of such physical systems require new, more powerful, and flexible tools and techniques for evaluating important system properties and, in particular, the dependability ones such as reliability, safety, and maintainability. In this context, the paper describes some extensions of the Modelica language to support the modeling of system requirements and their relationships. Such extensions enable the requirement verification analysis through native constructs in the Modelica language. Furthermore, they allow exporting a Modelica-based system design as a Bayesian Network in order to analyze its dependability by employing a probabilistic approach. The proposal is exemplified through a case study concerning the dependability analysis of a Tank System.
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9

Liu, Jun, Guochen Wang y Yanyan Luo. "Multi-Domain Modeling Based on Modelica". MATEC Web of Conferences 77 (2016): 07011. http://dx.doi.org/10.1051/matecconf/20167707011.

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10

Eborn, Jonas, Hubertus Tummescheit y Karl Johan Åström. "Physical System Modeling with Modelica TM". IFAC Proceedings Volumes 32, n.º 2 (julio de 1999): 6651–56. http://dx.doi.org/10.1016/s1474-6670(17)57136-0.

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11

Jeong, WoonSeong, Jong Bum Kim, Mark J. Clayton, Jeff S. Haberl y Wei Yan. "Translating Building Information Modeling to Building Energy Modeling Using Model View Definition". Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/638276.

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This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation intoModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated fromRevit2Modelicaand (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows thatBIM2BEM(1) enables BIM models to be translated intoModelicaBEMmodels, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.
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12

Maccarini, Alessandro, Göran Hultmark, Anders Vorre, Alireza Afshari y Niels C. Bergsøe. "Modeling of active beam units with Modelica". Building Simulation 8, n.º 5 (5 de junio de 2015): 543–50. http://dx.doi.org/10.1007/s12273-015-0236-5.

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13

Ge, Jia Huan y Shan'an Zhu. "A Study of Virtual Remote Laboratory and its Subsystem by Using Modelica". Applied Mechanics and Materials 157-158 (febrero de 2012): 1506–14. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1506.

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iEELab((Internet-based Electrical Engineering Lab) at Zhejiang University is an integrated laboratory including physical and virtual remote experiments. This paper first makes a brief introduction to the overall architecture of iEELab. Then, focusing on the virtual remote laboratory which uses a unified object-oriented language Modelica for modeling and simulation, we elaborate its architecture and the internal mechanism of virtual remote lab operation. Finally, taking a subsystem of iEELab-the plate angle control experiment system as an instance of the virtual remote lab, we display the process of modeling and simulation of virtual experiments using modelica.
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14

Wetter, Michael, Wangda Zuo, Thierry S. Nouidui y Xiufeng Pang. "Modelica Buildings library". Journal of Building Performance Simulation 7, n.º 4 (13 de marzo de 2013): 253–70. http://dx.doi.org/10.1080/19401493.2013.765506.

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15

Fachini, Fernando, Marcelo de Castro, Tetiana Bogodorova y Luigi Vanfretti. "Modeling of Induction Motors and Variable Speed Drives for Multi-Domain System Simulations Using Modelica and the OpenIPSL Library". Electronics 13, n.º 9 (23 de abril de 2024): 1614. http://dx.doi.org/10.3390/electronics13091614.

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This paper introduces an innovative method for characterizing, implementing, and validating both three-phase and single-phase induction motor models, accompanied by a variable speed drive model. The primary goal is to investigate interactions between the electrical power grid and other dynamic domains (e.g., thermofluidic) that impact motor/load drive behavior. Our approach involves establishing a mechanical interface based on a physically meaningful equation linking motor torque/speed to the electrical model in the phasor domain. This allows seamless integration of diverse domain subsystems into a unified multi-domain model using Modelica v4.0.0 and the OpenIPSL library v3.0.1, overcoming co-simulation limitations. The proposed model, which requires only one Modelica-compliant tool for simulation, introduces additional dynamics through the mechanical interface, enabling explicit simulation of load disturbances based on constitutive physics. This deepens our understanding of dynamic interactions between the electrical power domain and other subsystems connected through the motor. We detail the modeled components using mathematical equations and textual descriptions, emphasizing the Modelica modeling approach. Simulation examples validate the implementation, demonstrating the multi-domain modeling capabilities of the newly developed components.
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16

Liu, Ji Hong, Ying Zhong Pang y Yu Ming Zhu. "Multi-Domain Unified Modeling and Simulation for Aircraft Landing Gear Using Modelica". Advanced Materials Research 311-313 (agosto de 2011): 2457–60. http://dx.doi.org/10.4028/www.scientific.net/amr.311-313.2457.

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Modern products become more and more complex, the modeling and simulation of them are carried out with different software on heterogeneous platforms, which always caused the heterogeneous data, separated disciplines and cannot obtain the result of unified model correctly. Therefore, a Modelica-based modeling and simulation method for aircraft landing gear is proposed. The landing gear library based on Modelica was established. The unified physical model of landing gear which is composed of structural, thermodynamics and hydromechanics disciplines is constructed. The aircraft landing process, the track of retraction mechanism and the impact work amount of the shock absorber are obtained through multi-domain unified simulation, which provides references for deisgners.
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17

Miettinen, Tuomas, Juho Salmi, Kunal Gupta, Jussi Koskela, Janne Kauttio, Tommi Karhela y Sampsa Ruutu. "Applying Modelica Tools to System Dynamics Based Learning Games: Project Management Game". Modelling and Simulation in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8324914.

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Learning simulation games are interactive simulations with game characteristics. This paper presents a learning simulation game for EPCM (engineering, procurement, and construction management) project management training. The simulation model utilises system dynamics, which is a methodology for understanding the behaviour of dynamic complex systems of different domains using modelling and simulation. The system dynamics model in turn uses the equation-based Modelica modelling language: a system dynamics model created with the graphical user interface is converted to a pure Modelica model. Two Modelica environments, namely, OpenModelica and the custom Modelica solver, have been used to simulate the generated Modelica model. The focus of this article is on how generic systems modelling and simulation platforms such as Modelica based environments can be utilised in developing a learning simulation game: what benefits do they bring and what disadvantages do they have? On the one hand, it is evaluated how the Modelica language as such is suitable for being used in a learning game development. On the other hand, the suitability of the selected implementation environments, that is, OpenModelica, the custom Modelica solver, Simantics, and Simupedia, is evaluated. The paper also shortly presents how the project management game was received by its players.
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18

Tinnerholm, John, Adrian Pop y Martin Sjölund. "A Modular, Extensible, and Modelica-Standard-Compliant OpenModelica Compiler Framework in Julia Supporting Structural Variability". Electronics 11, n.º 11 (2 de junio de 2022): 1772. http://dx.doi.org/10.3390/electronics11111772.

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Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research.
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19

Sanz, Victorino, Federico Bergero y Alfonso Urquia. "An approach to agent-based modeling with Modelica". Simulation Modelling Practice and Theory 83 (abril de 2018): 65–74. http://dx.doi.org/10.1016/j.simpat.2017.12.012.

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20

Sanz, Victorino y Alfonso Urquia. "Agent-Based Modeling of Traffic Systems Using Modelica". Computing in Science & Engineering 24, n.º 6 (noviembre de 2022): 38–43. http://dx.doi.org/10.1109/mcse.2023.3267327.

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21

Rabie, Mohamed A., Ayah E. Elshahat y Mohamed H. Hassan. "Control oriented modeling of VVER-1200 using Modelica". Nuclear Engineering and Design 420 (abril de 2024): 112980. http://dx.doi.org/10.1016/j.nucengdes.2024.112980.

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22

Gong, You Ping, Xiang Juan Bian y Guo Jin Chen. "Excavator Operator Mechanism and Hydraulic Integration Model Construction and Simulation Based on Modelica". Applied Mechanics and Materials 130-134 (octubre de 2011): 666–71. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.666.

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This paper explains and demonstrates how to construct integration simulation model of exavtor operator based on modelica language. The paper firstly introduce the history of modelic language and application in production design; secondly the paper construct exavtor operator dynamics model by D-H method and construct operator’s modelica model by dymola soft; then the paper analyse the hydraulic system of operator, and construct hydraulic simulation model which integrate to operator mechanism system; at last the paper gives simulation results of vauious operating mode. Because of consideration in coordination of mechanism and hydraulic of operator,the simulation results approach actual working process,and improve the exavtor’s design efficiency and reliability.
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23

Wang, Haosheng y Hongen Zhong. "Modeling and Simulation of Spacecraft Power System Based on Modelica". E3S Web of Conferences 233 (2021): 04033. http://dx.doi.org/10.1051/e3sconf/202123304033.

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Spacecraft power system simulation involves the coupling of electrical, thermal and control domains. At present, the modeling and simulation of multi-domain physical system mainly uses the single-domain software to establish a single-domain model, and solves the unified multi-domain modeling and simulation through the interface between the software or using HLA. But it cannot fully support the modeling and simulation of multi-domain physical system, and the model has poor reusability and extensibility. As a multi-domain modeling language, Modelica language supports acausal modelling, unified multi-domain modeling, object-oriented physical modeling and hybrid modeling. So it is widely used in the aerospace area. In this paper, Modelica language is used to establish module library of spacecraft power system on simulation platform MWorks, and the multi-domain simulation model of spacecraft power system is obtained by assembling each sub-model, and the performance of the model is simulated and analyzed so as to achieve the purpose of improving and verifying the model.
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24

Sun, Kunfeng. "Modelica Modeling of Thermodynamic Properties of LiBr-H2O Solutions". Academic Journal of Science and Technology 7, n.º 3 (29 de octubre de 2023): 305–8. http://dx.doi.org/10.54097/ajst.v7i3.13422.

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Absorption refrigeration system is one of the basic refrigeration systems, with LiBr-H2O solution being the most common working fluid. In order to perform thermodynamic design and analysis on the absorption refrigeration cycle, it is essential to develop a program to calculate the thermodynamic properties of the solution. Modelica is the most promising digital twin modeling language and has been widely used in heat flow system modeling. In this study, a new interface library for calculating the thermophysical properties of LiBr-H2O solution is introduced. This library can calculate the vapor pressure, solution temperature, enthalpy, entropy, mass concentration, etc., as well as dynamic viscosity, thermal conductivity, surface tension coefficient, and other heat transfer or thermodynamics characteristic parameters. It enables researchers to easily modeling and analyze complex LiBr-H2O absorption refrigeration cycles in Modelica within specific temperature and concentration ranges.
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25

TALAH, DJAMILA, HAMID BENTARZI y GIOVANNI MANGOLA. "MODELING AND SIMULATION OF AN OPERATING GAS TURBINE USING MODELICA LANGUAGE". REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 68, n.º 1 (1 de abril de 2023): 102–7. http://dx.doi.org/10.59277/rrst-ee.2023.68.1.17.

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This article uses Modelica to model and simulate the operating Gas Turbine (GT) in a combined cycle power plant in Ras-Djinet, Algeria. The modeling and simulation have been validated based on the data collected from this operating power plant. Details of GT modeling using Modelica language and ThermoPower library have been presented. Furthermore, the simulation results have been discussed in this article. The model has been examined in two different cases: the temperature effect and the reduction in fuel flow at a steady state. Besides, a comparison between the reel and simulation results for a different amount of fuel has been investigated. The accuracy of these simulations is noted and proven by the coherence of the simulation results with the experimental data collected from the power plant company.
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26

TALAH, DJAMILA, HAMID BENTARZI y GIOVANNI MANGOLA. "MODELING AND SIMULATION OF AN OPERATING GAS TURBINE USING MODELICA LANGUAGE". REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 68, n.º 1 (1 de abril de 2023): 102–7. http://dx.doi.org/10.59277/rrst-ee.2023.68.1.19.

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This article uses Modelica to model and simulate the operating Gas Turbine (GT) in a combined cycle power plant in Ras-Djinet, Algeria. The modeling and simulation have been validated based on the data collected from this operating power plant. Details of GT modeling using Modelica language and ThermoPower library have been presented. Furthermore, the simulation results have been discussed in this article. The model has been examined in two different cases: the temperature effect and the reduction in fuel flow at a steady state. Besides, a comparison between the reel and simulation results for a different amount of fuel has been investigated. The accuracy of these simulations is noted and proven by the coherence of the simulation results with the experimental data collected from the power plant company.
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27

Velieva, T. R., E. G. Eferina, A. V. Korolkova, D. S. Kulyabov y L. A. Sevastianov. "Modelica-based TCP simulation". Journal of Physics: Conference Series 788 (enero de 2017): 012036. http://dx.doi.org/10.1088/1742-6596/788/1/012036.

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28

Benveniste, Albert, Benoît Caillaud, Mathias Malandain y Joan Thibault. "Algorithms for the Structural Analysis of Multimode Modelica Models". Electronics 11, n.º 17 (1 de septiembre de 2022): 2755. http://dx.doi.org/10.3390/electronics11172755.

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Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In this article, we propose several contributions to this multifaceted issue, namely: an efficient and scalable multimode extension of the structural analysis of Modelica models; a systematic way of rewriting a multimode Modelica model, based on this analysis, so that the rewritten model is guaranteed to be correctly compiled by state-of-the-art Modelica tools; a proposal for the handling of the consistent initialization of multimode models; multimode structural analysis algorithms that handle both multiple modes and mode change events in a unified framework, coupled with a compile-time algorithm for identifying and quantifying impulsive behaviors at mode changes. Our approach is illustrated on relevant example models, and the performance of our implementations is assessed on a variable dimension large-scale model.
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29

Wang, Hong Jun, Guo Gang Huang, Xiang Jun Zou y Yan Chen. "Modeling and Performance Simulation for a Picking Manipulator Based on Modelica". Key Engineering Materials 579-580 (septiembre de 2013): 467–75. http://dx.doi.org/10.4028/www.scientific.net/kem.579-580.467.

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This paper presents a procedure and the results of structure design about a picking manipulator to be used for autonomous banana harvesting. Based on structure analysis and module partition for the picking manipulator, DriveLib model library was developed employing the virtual simulation and modeling language Modelica. A body structure model for the picking manipulator was established based on component models in DriveLib model library. Motion controls mathematic models for the picking manipulator were analyzed according to drive motor performance for the body structure, and then the partial differential equations were transformed into the ordinary differential equation easily solved by Modelica with the Laplace transform, mechanical motion simulation was realized. The validity models could provide a theoretical basis for picking banana manipulator primary design.
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30

Li, Yuan, Can Qu y Xingye Ruan. "ModelicaML modeling analysis of MCPS-based high-speed railroad safety monitoring system". Highlights in Science, Engineering and Technology 9 (30 de septiembre de 2022): 123–29. http://dx.doi.org/10.54097/hset.v9i.1729.

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Mobile Cyber-Physical System(MCPS) focuses on modeling of the physical world, dynamic continuity, real-time, spatial, security, and real-time predictable communication problems. Based on the example of high-speed railway system, this article mainly analyzes and studies the railway safety monitoring subsystem. Firstly, a brief analysis of the railway safety monitoring system and architecture is made, and the database connection pool and space-time analysis model are used as the software modeling system according to the requirements; then, SysML and Modelica are used to model the study, based on which a reconfigured modeling language called ModelicaML is proposed to model and simulate the modern engineering; lastly, the ModelicaML is used to physically model the safety detection system of high-speed railroad system, and finally, it is simulated and verified.
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31

Lie, Bernt. "Electric Submersible Pump Lifted Oil Field: Basic Model for Control, and Comparison of Simulation Tools". Energies 17, n.º 2 (20 de enero de 2024): 507. http://dx.doi.org/10.3390/en17020507.

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Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. Good control design for such operation requires a dynamic model of the petroleum flow from the well-bore to the separator. Here, such a dynamic model is considered for liquid production (oil/water) using an electric submersible pump (ESP) to aid in counteracting gravity and friction forces. Based on an existing model used for industrial control design, one goal is to report a complete dynamic model in a single paper. Emphasis is put on dimensionless equipment models for the simple change of units, and the model is developed from physical laws for easy replacement of sub-models, if needed. All the necessary information (equations, parameters) for model implementation is provided, and two candidate equation-based modeling languages are selected and compared: Modelica and ModelingToolkit [MTK] for Julia. The simulation results are virtually identical for the two languages and make sense from physics; however, there is a minor discrepancy in one plot—likely caused by slight differences in accuracy in handling initialization in the implicit algebraic equations. The implementation structures of the model in Modelica and MTK are similar. Modelica is a mature and excellent modeling tool, handles large-scale models, and has tools for producing C code and integration with other tools. MTK is still in rapid development, supports more model types than Modelica, and is integrated in an eco-system with excellent support for control design, optimization, model fitting, and more. To illustrate the suitability of using the developed model for control design, a simple PI controller is designed within the eco-system of MTK/Julia.
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32

Chen, Jia Xin, Hong Jun Wang, Chang Yu Liu y Xiang Jun Zou. "Modeling and Performance Analyzing of Helix Transmission Base on Modelica". Key Engineering Materials 455 (diciembre de 2010): 511–15. http://dx.doi.org/10.4028/www.scientific.net/kem.455.511.

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Picking manipulator ontology structure used rotation and translational motion pairs to join the components together, and then a multi-joint motion mechanism was built which meet the picking space requirement. According to the characters of growth space of litchi fruit, here used helix transmission as the first axes of the picking manipulator, to achieve motion in vertical direction. This study introduces the application of multi-domain modeling and simulation when constructing and simulating a helix transmission. Use object-oriented technology, multi-domain modeling and simulation language Modelica to construct the motion system which integrates motor model, signal detection module, signal processing module and parametric screw transmission model in Dymola simulation platform. Test the helix transmission by simulation, using the mechanical structure, sensor technology, signal processing technology. Simulation results show that, the modelica can provide a valid simulation for complex electromechanical system and support for parametric design.
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33

Zimmer, Dirk. "Equation-Based Modeling with Modelica – Principles and Future Challenges". SNE Simulation Notes Europe 26, n.º 2 (junio de 2016): 67–74. http://dx.doi.org/10.11128/sne.26.on.10332.

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34

Thompson, Stephen C. "An acoustics library for the Modelica multidomain modeling language". Journal of the Acoustical Society of America 139, n.º 4 (abril de 2016): 2010. http://dx.doi.org/10.1121/1.4949913.

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35

Razak, Amir A. "Library Structure of Dynamic Simulation for Combined Heat and Power Plant in Modelica Language". Applied Mechanics and Materials 110-116 (octubre de 2011): 4925–31. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.4925.

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The most common form of energy recycling system is Combined Heat and Power (CHP) plants. The CHP plant is a complex system and still under intensive development by many researchers. The system needs to be developed in quick and efficient manners with low resources based on modeling and simulation method. With the development of CHP library in open source Modelica language, it could be used as a base for further advancement of CHP technology. The aim of this work is to design a structure of initial version of a model library for the dynamic simulation of Combined Heat and Power plants (CHP). Modular approach and top-down design have been implemented in the model library development. A solid base for this work is defined which includes rules in modeling the components (e.g. robustness and reusability), default library structure arrangement and model documentation. By strictly follow the rules and concepts introduced in this work, the mistakes in modeling is minimized. The designed library in Modelica language will provide an organized environment in modeling a CHP plant.
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36

Ceraolo, Massimo y Mirko Marracci. "An Example of Modelica–LabVIEW Communication Usage to Implement Hardware-in-the-Loop Experiments". Scientific Programming 2024 (5 de febrero de 2024): 1–13. http://dx.doi.org/10.1155/2024/9648349.

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Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication.
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37

Pop, Adrian, Martin Sjölund, Adeel Ashgar, Peter Fritzson y Francesco Casella. "Integrated Debugging of Modelica Models". Modeling, Identification and Control: A Norwegian Research Bulletin 35, n.º 2 (2014): 93–107. http://dx.doi.org/10.4173/mic.2014.2.3.

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38

Bonvini, Marco y Mirza Popovac. "Fluid Flow Modelling With Modelica". IFAC Proceedings Volumes 45, n.º 2 (2012): 1047–51. http://dx.doi.org/10.3182/20120215-3-at-3016.00185.

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39

Papadopoulos, Alessandro V., Martina Maggio, Francesco Casella y Johan åkesson. "Function Inlining in Modelica Models". IFAC Proceedings Volumes 45, n.º 2 (2012): 1091–94. http://dx.doi.org/10.3182/20120215-3-at-3016.00193.

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40

Krysander, Mattias, Erik Frisk, Ingela Lind y YIva Nilsson. "Diagnosis Analysis of Modelica Models". IFAC-PapersOnLine 51, n.º 24 (2018): 153–59. http://dx.doi.org/10.1016/j.ifacol.2018.09.549.

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41

Sanz, Victorino y Alfonso Urquia. "Cyber–physical system modeling with Modelica using message passing communication". Simulation Modelling Practice and Theory 117 (mayo de 2022): 102501. http://dx.doi.org/10.1016/j.simpat.2022.102501.

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42

Arianto, S., R. Y. Yuwono y B. Prihandoko. "Modeling of Lithium Ion Battery Using Modelica and Scilab/Xcos". ECS Transactions 73, n.º 1 (15 de septiembre de 2016): 241–48. http://dx.doi.org/10.1149/07301.0241ecst.

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43

朱, 国情. "Boiler Water-Steam System Modeling and Calibration Based on Modelica". Modeling and Simulation 03, n.º 02 (2014): 25–34. http://dx.doi.org/10.12677/mos.2014.32005.

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44

黄, 林. "Modeling and Simulation of Marine Diesel Engine Based on Modelica". Modeling and Simulation 05, n.º 04 (2016): 131–42. http://dx.doi.org/10.12677/mos.2016.54018.

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45

Li, Pengfei, Yaoyu Li y John Seem. "Modelica-Based Dynamic Modeling of a Chilled-Water Cooling Coil". HVAC&R Research 16, n.º 1 (1 de enero de 2010): 35–58. http://dx.doi.org/10.1080/10789669.2010.10390891.

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46

Hilding Elmqvist. "Modelica — A unified object-oriented language for physical systems modeling". Simulation Practice and Theory 5, n.º 6 (agosto de 1997): p32. http://dx.doi.org/10.1016/s0928-4869(97)84257-7.

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47

Sanz, Victorino, Alfonso Urquia, François E. Cellier y Sebastian Dormido. "Hybrid system modeling using the SIMANLib and ARENALib Modelica libraries". Simulation Modelling Practice and Theory 37 (septiembre de 2013): 1–17. http://dx.doi.org/10.1016/j.simpat.2013.05.005.

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48

Sanz, Victorino, Alfonso Urquia, François E. Cellier y Sebastian Dormido. "Modeling of hybrid control systems using the DEVSLib Modelica library". Control Engineering Practice 20, n.º 1 (enero de 2012): 24–34. http://dx.doi.org/10.1016/j.conengprac.2010.11.014.

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49

Mo, Qiu y Fang Liu. "Modeling and optimization for distributed microgrid based on Modelica language". Applied Energy 279 (diciembre de 2020): 115766. http://dx.doi.org/10.1016/j.apenergy.2020.115766.

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50

Kopei, Volodymyr B., Oleh R. Onysko y Vitalii G. Panchuk. "Component-oriented acausal modeling of the dynamical systems in Python language on the example of the model of the sucker rod string". PeerJ Computer Science 5 (28 de octubre de 2019): e227. http://dx.doi.org/10.7717/peerj-cs.227.

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Typically, component-oriented acausal hybrid modeling of complex dynamic systems is implemented by specialized modeling languages. A well-known example is the Modelica language. The specialized nature, complexity of implementation and learning of such languages somewhat limits their development and wide use by developers who know only general-purpose languages. The paper suggests the principle of developing simple to understand and modify Modelica-like system based on the general-purpose programming language Python. The principle consists in: (1) Python classes are used to describe components and their systems, (2) declarative symbolic tools SymPy are used to describe components behavior by difference or differential equations, (3) the solution procedure uses a function initially created using the SymPy lambdify function and computes unknown values in the current step using known values from the previous step, (4) Python imperative constructs are used for simple events handling, (5) external solvers of differential-algebraic equations can optionally be applied via the Assimulo interface, (6) SymPy package allows to arbitrarily manipulate model equations, generate code and solve some equations symbolically. The basic set of mechanical components (1D translational “mass”, “spring-damper” and “force”) is developed. The models of a sucker rods string are developed and simulated using these components. The comparison of results of the sucker rod string simulations with practical dynamometer cards and Modelica results verify the adequacy of the models. The proposed approach simplifies the understanding of the system, its modification and improvement, adaptation for other purposes, makes it available to a much larger community, simplifies integration into third-party software.
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