Готові списки джерел за темами / Simulation of dynamic systems

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Добірка наукової літератури з теми "Simulation of dynamic systems"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Simulation of dynamic systems"

1

Alzbutas, R., and V. Janilionis. "THE SIMULATION OF DYNAMIC SYSTEMS USING COMBINED MODELLING." Mathematical Modelling and Analysis 5, no. 1 (December 15, 2000): 7–17. http://dx.doi.org/10.3846/13926292.2000.9637123.

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The new approach to the problems of dynamic systems simulation is proposed. The analytical and imitation modelling of non‐linear complex dynamic systems which comprise simulation of continuous and discrete processes with constant and variable parameters, are investigated. The aggregate mathematical modelling scheme [1] and the method of control sequences for discrete systems specification and simulation are used as well as the dynamic mathematical modelling scheme for continuous process formalization and modelling. According to them the investigated systems are presented as the set of interacting piecewise linear aggregates, which can include processes described with differential equations. The above mentioned approach is used in developing software for the construction and research of the models. The modelling of the dynamic systems’ control is also analyzed and developed software for the dynamic systems’ simulation is presented. It is related to the proposed combined modelling methodology. The developed dynamical simulation system ADPRO (Automatic Differentiation PROgram) extends applicability of the system SIMAS (SIMulation of the Aggregate Systems) [2] with dynamical simulation means realized with APL2 (A Programming Language 2) and based on automatic differentiation [3]. The created model of service process and its control can be used as a base for other models of wide class complex dynamics’ systems [4], the parts of which are described with differential equations.
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2

Rükgauer, A., and W. Schiehlen. "Simulation of modular dynamic systems." Mathematics and Computers in Simulation 46, no. 5-6 (June 1998): 535–42. http://dx.doi.org/10.1016/s0378-4754(98)00082-2.

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3

Toby, Sidney, and Frina S. Toby. "The Simulation of Dynamic Systems." Journal of Chemical Education 76, no. 11 (November 1999): 1584. http://dx.doi.org/10.1021/ed076p1584.

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4

Alzbutas, Robertas, and Vytautas Janilionis. "Dynamic systems simulation using APL2." ACM SIGAPL APL Quote Quad 29, no. 2 (December 1998): 20–25. http://dx.doi.org/10.1145/379277.312699.

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5

Nishitani, Hirokazu, Eiichi Kunugita, Yuan-Chen Wan, and Masahiro Kujime. "Dynamic simulation of large systems." KAGAKU KOGAKU RONBUNSHU 17, no. 1 (1991): 149–56. http://dx.doi.org/10.1252/kakoronbunshu.17.149.

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6

Skelton, Robert E., Fa Ming Li, and Mauricio de Oliveira. "Optimal Simulation for Large Dynamic Systems." Advances in Science and Technology 56 (September 2008): 147–53. http://dx.doi.org/10.4028/www.scientific.net/ast.56.147.

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Like most engineering design problems, simulation design for large dynamic systems should seek a trade- o® between performance and cost. Here the perfor- mance is de¯ned by simulation accuracy; and the cost is related to computational resource, measured by the total wordlength. The simulation accuracy depends on model complexity, model realization and computational implementation. The optimal simula- tion problem is to determine all these factors to en- sure desired accuracy with available computational resource. When computational cost is the primary concern, one can minimize the computational re- source with simulation accuracy constraint. We de- ¯ne the economical simulation problem (ESP) as de- signing the simulation of a stable linear system and distributing computational resources (wordlength) among the digital devices such that the computa- tional cost(memory) is minimized without violat- ing the required simulation accuracy. This problem is generally not convex because of the scaling con- straint. By exploring the special structure of this joint optimization of the choice of the realizations and the computational resources to be applied, and under a scaling assumption, the ESP is converted to a convex problem. Numerical results are given which compare this method with existing approaches.
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7

Bhatti, Muhammad Akram, Li Chang Xi ., and Ye lin . "Modeling and Simulation of Dynamic Systems." Journal of Applied Sciences 6, no. 4 (February 1, 2006): 950–54. http://dx.doi.org/10.3923/jas.2006.950.954.

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8

Deckmann, S. M., V. F. da Costa, and D. A. Alves. "Dynamic Simulation for Interconnected Power Systems." IFAC Proceedings Volumes 18, no. 7 (July 1985): 261–68. http://dx.doi.org/10.1016/s1474-6670(17)60444-0.

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Lubachevsky, Boris D. "Fast simulation of multicomponent dynamic systems." Bell Labs Technical Journal 5, no. 2 (August 28, 2002): 134–56. http://dx.doi.org/10.1002/bltj.2227.

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Rosenberg, Ronald C., Joseph Whitesell, and John Reid. "Extendible simulation software for dynamic systems." SIMULATION 58, no. 3 (March 1992): 175–83. http://dx.doi.org/10.1177/003754979205800307.

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Дисертації з теми "Simulation of dynamic systems"

1

Wilhelmij, Gerrit Paul. "Symbolic simulation of dynamic systems." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305630.

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2

Gupta, Amit. "Model reduction and simulation of complex dynamic systems /." Online version of thesis, 1990. http://hdl.handle.net/1850/11265.

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3

Wiegand, Mark Eric. "Constructive qualitative simulation of continuous dynamic systems." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/868.

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4

Ramírez, Muñoz Patricio D. (Patricio Dario). "Dynamic simulation of nuclear hydrogen production systems." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62733.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2011.
"September 2010." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 261-265).
Nuclear hydrogen production processes have been proposed as a solution to rising CO 2 emissions and low fuel yields in the production of liquid transportation fuels. In these processes, the heat of a nuclear reactor is used to run the chemical reactions in a hydrogen plant. The resulting system is tightly interconnected and operates at very high temperature and pressure, which can lead to operational disruptions and accidents. For this reason, computational studies validating the safe operation of the system are required by regulatory authorities. In the past, safety studies have been conducted by using legacy codes, such as RELAP and MELCOR, and their focus has been the operation of nuclear power plants. However, traditional legacy codes are not appropriate to simulate nuclear hydrogen production. The simulation of a nuclear reactor itself is already complex because it involves simulating reactor kinetics and transport phenomena. To that complexity, nuclear hydrogen production adds the need to simulate chemical reactions in the hydrogen plant. These chemical reactions cannot be represented easily in legacy codes because these codes lack the flexibility, speed and accuracy required to simulate them. Therefore, only a limited number of studies on the safety of these systems exist. Instead of using legacy codes, this thesis proposes using equation-based simulators developed by the chemical engineering community to model and study the safety of a nuclear hydrogen production plant. Equation-based simulators were designed to be flexible, extensible and fast because they have to simulate a vast range of processes from the chemical industry. Thus, they provide a good platform for the simulation of nuclear hydrogen production systems. This thesis explains the models used for the different parts in the nuclear hydrogen production plant, and then presents the response of this plant model to different accident scenarios. The first contribution of this thesis is a novel equation-based model for the heat transfer loop connecting a nuclear reactor and a hydrogen production plant. This heat transfer loop uses helium as the heat transfer fluid, which makes simulating its behavior difficult because of the need to model gas dynamics. To resolve this, three models for gas dynamics and two set of coupling conditions for boundary variables were tested in JACOBIAN, an equation-based simulator. The three models for gas dynamics in combination with a novel approach to set coupling conditions for boundary variables were able to represent the interesting time scales accurately in transient scenarios. The accuracy and computational speed of these simulations outperformed those produced by a reference model created in RELAP, a legacy code. The second contribution is a model of a nuclear hydrogen production plant using high-temperature steam electrolysis to produce hydrogen. This model was created to study the effect of potential accidents on the nuclear reactor. It included detailed models of the nuclear reactor and heat transfer loop, and a partial model of the electrolysis plant. The nuclear reactor was modeled as a pebble bed modular reactor, which is one of the safest designs available. The reactor was connected to the hydrogen production plant using the heat transfer loop model already developed in this thesis. The hydrogen production plant was partially represented as a steam superheater in the heat transfer loop. The third contribution is the demonstration of the safety characteristics of the nuclear hydrogen production plant by subjecting the plant model to three accident scenarios. The scenarios involved disruptions in the hydrogen plant or in the heat transfer loop, and all of them-directly or indirectly-lead to a loss of heat sink capacity for the nuclear reactor. This resulted in an increase of the nuclear reactor core temperature, which was quickly moderated by the fission power reduction at the fuel pebbles and by the safe design of the nuclear reactor. As a consequence, the maximum temperature reached in the core was always less than the fuel melting point and the reactor was always in a safe condition. The heat transfer loop could suffer the rupture of a pipe in one of the scenarios, and design modifications to address this were suggested. This thesis' results partially prove that nuclear hydrogen production plants could be safe, and simultaneously, that equation-based simulators are good platforms to demonstrate the safety of these plants. Developing these models and tests further will help guarantee the safety of the plant and obtain regulatory and public approval for this new nuclear application.
by Patricio D. Ramírez Muñoz.
Ph.D.
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5

McCoy, Timothy J. (Timothy John). "Dynamic simulation of shipboard electric power systems." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12495.

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Swanson, Davin Karl. "Dynamic simulation of an improved passive haptic display." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/17292.

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Siu, Daniel. "Stochastic Hybrid Dynamic Systems: Modeling, Estimation and Simulation." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4405.

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Stochastic hybrid dynamic systems that incorporate both continuous and discrete dynamics have been an area of great interest over the recent years. In view of applications, stochastic hybrid dynamic systems have been employed to diverse fields of studies, such as communication networks, air traffic management, and insurance risk models. The aim of the present study is to investigate properties of some classes of stochastic hybrid dynamic systems. The class of stochastic hybrid dynamic systems investigated has random jumps driven by a non-homogeneous Poisson process and deterministic jumps triggered by hitting the boundary. Its real-valued continuous dynamic between jumps is described by stochastic differential equations of the It\^o-Doob type. Existing results of piecewise deterministic models are extended to obtain the infinitesimal generator of the stochastic hybrid dynamic systems through a martingale approach. Based on results of the infinitesimal generator, some stochastic stability results are derived. The infinitesimal generator and stochastic stability results can be used to compute the higher moments of the solution process and find a bound of the solution. Next, the study focuses on a class of multidimensional stochastic hybrid dynamic systems. The continuous dynamic of the systems under investigation is described by a linear non-homogeneous systems of It\^o-Doob type of stochastic differential equations with switching coefficients. The switching takes place at random jump times which are governed by a non-homogeneous Poisson process. Closed form solutions of the stochastic hybrid dynamic systems are obtained. Two important special cases for the above systems are the geometric Brownian motion process with jumps and the Ornstein-Uhlenbeck process with jumps. Based on the closed form solutions, the probability distributions of the solution processes for these two special cases are derived. The derivation employs the use of the modal matrix and transformations. In addition, the parameter estimation problem for the one-dimensional cases of the geometric Brownian motion and Ornstein-Uhlenbeck processes with jumps are investigated. Through some existing and modified methods, the estimation procedure is presented by first estimating the parameters of the discrete dynamic and subsequently examining the continuous dynamic piecewisely. Finally, some simulated stochastic hybrid dynamic processes are presented to illustrate the aforementioned parameter-estimation methods. One simulated insurance example is given to demonstrate the use of the estimation and simulation techniques to obtain some desired quantities.
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Lilly, Kathryn Weed. "Efficient dynamic simulation of multiple chain robotic systems /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487670346873809.

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PREMKUMAR, SRIDHAR. "A UNIFIED SIMULATOR FOR MULTI-DOMAIN SIMULATION OF SYSTEMS USING DYNAMIC INTERPRETATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172859432.

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Geitner, Gert-Helge, and Guven Komurgoz. "Power Flow Modelling of Dynamic Systems." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-171305.

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As tools for dynamic system modelling both conventional methods such as transfer function or state space representation and modern power flow based methods are available. The latter methods do not depend on energy domain, are able to preserve physical system structures, visualize power conversion or coupling or split, identify power losses or storage, run on conventional software and emphasize the relevance of energy as basic principle of known physical domains. Nevertheless common control structures as well as analysis and design tools may still be applied. Furthermore the generalization of power flow methods as pseudo-power flow provides with a universal tool for any dynamic modelling. The phenomenon of power flow constitutes an up to date education methodology. Thus the paper summarizes fundamentals of selected power flow oriented modelling methods, presents a Bond Graph block library for teaching power oriented modelling as compact menu-driven freeware, introduces selected examples and discusses special features.
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Книги з теми "Simulation of dynamic systems"

1

Aburdene, Maurice F. Computer simulation of dynamic systems. Dubuque, Iowa: Wm. C. Brown, 1988.

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2

Torkel, Glad, ed. Modeling of dynamic systems. Englewood Cliffs, N.J: PTR Prentice Hall, 1994.

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3

Interactive dynamic-system simulation. 2nd ed. Boca Raton, FL: CRC Press, 2011.

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4

Korn, Granino Arthur. Interactive dynamic system simulation. New York: McGraw-Hill, 1989.

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5

Woods, Robert L. Modeling and simulation of dynamic systems. Upper Saddle River, N.J: Prentice Hall, 1997.

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6

M, Hannon Bruce, ed. Modeling dynamic economic systems. New York: Springer, 1997.

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7

Matthias, Ruth, ed. Modeling dynamic biological systems. New York: Springer, 1997.

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8

Coutinho, Murilo G. Dynamic Simulations of Multibody Systems. New York, NY: Springer New York, 2001.

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9

Systems biology: Simulation of dynamic network states. Cambridge, UK: Cambridge University Press, 2011.

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10

García de Jalón, Javier, and Eduardo Bayo. Kinematic and Dynamic Simulation of Multibody Systems. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2600-0.

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Частини книг з теми "Simulation of dynamic systems"

1

Ghosh, Asish. "Modeling and Simulation." In Dynamic Systems for Everyone, 89–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43943-3_5.

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Ghosh, Asish. "Modeling and Simulation." In Dynamic Systems for Everyone, 83–102. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10735-6_5.

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3

Birta, Louis G., and Gilbert Arbez. "Modelling of Continuous Time Dynamic Systems." In Modelling and Simulation, 283–304. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18869-6_8.

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4

Birta, Louis G., and Gilbert Arbez. "Modelling of Continuous Time Dynamic Systems." In Modelling and Simulation, 269–89. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2783-3_8.

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5

Lehmann, Axel. "Knowledge-Based Systems to Support Dynamic Process Simulation." In Nuclear Simulation, 119–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84279-5_9.

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6

Mosterman, Pieter J., Akshay Rajhans, Anastasia Mavrommati, and Roberto G. Valenti. "Simulation of Hybrid Dynamic Systems." In Encyclopedia of Systems and Control, 1–20. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_100048-1.

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7

Mosterman, Pieter J., Akshay Rajhans, Anastasia Mavrommati, and Roberto G. Valenti. "Simulation of Hybrid Dynamic Systems." In Encyclopedia of Systems and Control, 1–20. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_100048-2.

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8

Rozhdestvensky, Kirill, Vladimir Ryzhov, Tatiana Fedorova, Kirill Safronov, Nikita Tryaskin, Shaharin Anwar Sulaiman, Mark Ovinis, and Suhaimi Hassan. "Computer Simulation of Dynamic Systems." In Computer Modeling and Simulation of Dynamic Systems Using Wolfram SystemModeler, 89–130. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2803-3_3.

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Mosterman, Pieter J., Akshay Rajhans, Anastasia Mavrommati, and Roberto G. Valenti. "Simulation of Hybrid Dynamic Systems." In Encyclopedia of Systems and Control, 2047–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100048.

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Richter, Knut. "Dynamic Energy Production Model." In Systems Analysis and Simulation II, 278–81. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-8936-1_57.

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Тези доповідей конференцій з теми "Simulation of dynamic systems"

1

Acosta, Alejandro, Robert Corujo, Vicente Blanco, and Francisco Almeida. "Dynamic load balancing on heterogeneous multicore/multiGPU systems." In Simulation (HPCS). IEEE, 2010. http://dx.doi.org/10.1109/hpcs.2010.5547097.

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2

Alzbutas, Robertas, and Vytautas Janilionis. "Dynamic systems simulation using APL2." In the conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/312627.312699.

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3

Yurkovich, Benjamin J., and Yann Guezennec. "Lithium Ion Dynamic Battery Pack Model and Simulation for Automotive Applications." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2613.

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In this paper, we introduce a lumped parameter, distributed battery pack dynamic model which allows simulation of the electrical dynamics of all the cells in an arbitrarily configured series/parallel pack typical of those used in automotive applications. The dynamic pack simulator is based on the development of an analytical solution for the dynamic response of a single cell and an analytical development of such elemental solutions into a distributed dynamic pack model which can resolve the dynamics of each cell within the pack. This formulation leads to a computationally efficient simulation tool appropriate for application on large battery packs. This simulation tool is then used to perform Monte Carlo simulations on typical automotive current profiles for packs made of cells with a statistical distribution of parameters. A mild distribution of cell mismatch leads to cell unbalance development and statistical metrics for the growth unbalance, presented and related to both current severity and cell parameter distribution. The tool is ideally suited for studies in Battery Management System (BMS) algorithm development, as well as model-based fault propagation and diagnostics.
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4

Frye, J. P., and B. C. Fabien. "Modeling and Simulation of Nonholonomic Lagrangian Dynamic Systems." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-057.

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Manzano, Wallace, Valdemar Vicente Graciano Neto, and Elisa Yumi Nakagawa. "Simulation of Systems-of-Systems Dynamic Architectures." In XI Congresso Brasileiro de Software: Teoria e Prática. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/cbsoft_estendido.2020.14632.

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Systems-of-Systems (SoS) combine heterogeneous, independent systems to offer complex functionalities for highly dynamic smart applications. Due to their critical nature, SoS should be reliable and work without interruption since a failure could cause serious losses. SoS architectural design can facilitate the prediction of the impact of failures due to SoS behavior. However, existing approaches do not support such evaluation. The main contribution of this paper is to present Dynamic-SoS, an approach to predict, at design time, the SoS architectural behavior at runtime to evaluate whether the SoS can sustain their operation. Results of our multiple case studies reveal Dynamic-SoS is a promising approach that could contribute to the quality of SoS by reliably enabling prior assessment of their dynamic architecture.
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Nadeem, M. Faisal, S. Arash Ostadzadeh, Stephan Wong, and Koen Bertels. "Task scheduling strategies for dynamic reconfigurable processors in distributed systems." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999811.

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Peck, William J., and Daniel A. Finke. "Systems Dynamic Modeling: Planning Beyond the Worker." In 2019 Winter Simulation Conference (WSC). IEEE, 2019. http://dx.doi.org/10.1109/wsc40007.2019.9004685.

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Lu, Qi, Jesse McAvoy, and Ou Ma. "A Simulation Study of a Reduced-Gravity Simulator for Simulating Human Jumping and Walking in a Reduced-Gravity Environment." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2629.

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This paper presents a computer-simulation based feasibility study of a passive Reduced Gravity Simulator (RGS), which uses spring-based static gravity-balancing technology to simulate a reduced-gravity environment. The concept of the simulator was developed for providing a potentially low-cost and easy-to-use simulation method for assisting astronauts training. The proposed RGS is capable of compensating full or partial gravitational effect of the trainee, providing a similar experience or feeling as if he/she is in a real reduced-gravity environment. Due to the safety requirements, the proposed technology has to be fully studied by means of simulation and nonhuman experiments before it can be safely tested with a human subject. The work presented here is the result of such a simulation study. In the study, a physical human is modeled as a multibody dynamical system with 54 degrees of freedom. The dynamic responses of a human jumping and walking with the RGS are simulated and analyzed. The simulation results are compared to those of the same human body on free jumping and walking in the same reduced-gravity environment.
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Nunna, Krishna, and Michael J. King. "Dynamic Downscaling and Upscaling in High Contrast Systems." In SPE Reservoir Simulation Conference. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/182689-ms.

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10

Riedel, Christian, Christian Stammen, and H. Murrenhoff. "Fundamentals of Mass Conservative System Simulation in Fluid Power." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2639.

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Анотація:
This article illustrates the development of a dynamic system simulation tool for fluid power on basis of mass flows. The goal is to increase the predictability and efficiency of system simulation tools in fluid power. State of the art simulation tools make use of simplified differential equations. Especially in closed systems or long-term simulations, the volume flow based approach leads to significant variations of simulation results as balancing of flow parameters and its integrations to potentials lead to a violation of the equation of continuity. However, with a mass flow and energy conservative approach we obtain a clear and physically correct model implemented in the simulation tool DSHplus. The new basis of calculation enables further implementation of thermo-hydraulic and multi-phase flow models such as cavitation or particle transport into the concentrated parametric system simulation.
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Звіти організацій з теми "Simulation of dynamic systems"

1

Klein, Steven K., Robert H. Kimpland, and Marsha M. Roybal. Dynamic System Simulation of Fissile Solution Systems. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1127468.

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2

Klein, Steven, John Determan, Larry Dowell, and Marsha Roybal. Stand-Alone Dynamic System Simulation of Fissile Solution Systems. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1154978.

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3

Klein, Steven Karl, John David Bernardin, Robert Herbert Kimpland, and Dusan Spernjak. Extensions to Dynamic System Simulation of Fissile Solution Systems. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1212640.

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4

Paul I. Barton, Mujid S. Kaximi, Georgios Bollas, and Patricio Ramirez Munoz. Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems. Office of Scientific and Technical Information (OSTI), July 2009. http://dx.doi.org/10.2172/962650.

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5

Garbrick, D. J., and B. D. Zimmerman. Description of waste pretreatment and interfacing systems dynamic simulation model. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/104761.

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6

Klein, Steven Karl, and Robert Herbert Kimpland. Dynamic System Simulation of the KRUSTY Experiment. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1253482.

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7

Ellis, Abraham, Michael Robert Behnke, and Ryan Thomas Elliott. Generic solar photovoltaic system dynamic simulation model specification. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1177082.

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8

Klein, Steven Karl, John C. Determan, and Marsha Marilyn Roybal. Stand-Alone Dynamic System Simulation of a Fissile Solution System. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1177986.

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9

HARMSEN, R. W. System Design Description Salt Well Liquid Pumping Dynamic Simulation. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/798836.

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10

Fujimoto, Richard, Michael Hunter, and Haesun Park. Dynamic Systems for Individual Tracking via Heterogeneous Information Integration and Crowd Source Distributed Simulation. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1004753.

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