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Journal articles on the topic 'Simulation of dynamic systems'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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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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9

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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10

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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11

Drab, C. B., H. W. Engl, J. R. Haslinger, G. Offner, R. U. Pfau, and W. Zulehner. "Dynamic simulation of crankshaft multibody systems." Multibody System Dynamics 22, no. 2 (April 8, 2009): 133–44. http://dx.doi.org/10.1007/s11044-009-9152-8.

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12

Wu, S. Z., D. N. Wormley, D. Rowell, and H. M. Paynter. "Dynamic Modeling and Simulation of Gaseous Systems." Journal of Dynamic Systems, Measurement, and Control 107, no. 4 (December 1, 1985): 262–66. http://dx.doi.org/10.1115/1.3140733.

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A general computer-based mathematical modeling system for analyzing air/gas system dynamics has been developed. A set of generic lumped and distributed elements are interconnected by generalized junction structures to represent system configurations. The dynamic response of pressure, flow, temperature, and heat transfer rate at any point in a system, due to control actions, or fluid, thermal, or mechanical disturbances can be determined. The model has been used to analyze furnace implosion and disturbance propagation problems in fossil fuel power plants. To illustrate the modeling techniques, a model of a coal-fired plant has been constructed and pressure transients computed following a fuel trip. The model simulation predictions of the furnace pressure excursions are in close agreement with the data from field tests.

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13

Jin, Yong Fu. "Simulation Research on Dynamic Tribological Systems of Plain Bearing." Advanced Materials Research 426 (January 2012): 297–302. http://dx.doi.org/10.4028/www.scientific.net/amr.426.297.

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The research method of the inner friction and wear characteristics of plain bearing which is mainly conducted currently, is to analyze the all factors that affect the friction and wear of plain bearing from the perspective of wear, and to build friction and wear model, and then to conduct the research on the model. The article establishes the friction and wear dynamics model on base of the tribological systems approach, and on the base of analyzing the internal friction system and inner process of Tribology system of plain bearing. The article conducts the research on the friction and wear characteristics of plain bearings, and studies the relationship between the inner temperature rise and the heat on the base of simulation of the dynamics model. The consistency of the simulation results and academic calculation of the dynamic model proves that the dynamic model, which is established in the article, is correct.

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14

Dinkelbach, Jan, Ghassen Nakti, Markus Mirz, and Antonello Monti. "Simulation of Low Inertia Power Systems Based on Shifted Frequency Analysis." Energies 14, no. 7 (March 27, 2021): 1860. http://dx.doi.org/10.3390/en14071860.

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New types of power system transients with lower time constants are emerging due to the replacement of synchronous generation with converter interfaced generation and are challenging the modeling approaches conventionally applied in power system simulation. Quasi-stationary simulations are based on classical phasor models, whereas EMT simulations calculate the instantaneous values of models in the time domain. In addition to these conventional modeling approaches, this paper investigates simulation based on dynamic phasor models, as has been proposed by the Shifted Frequency Analysis. The simulation accuracy of the three modeling approaches was analyzed for characteristic transients from the electromagnetic to the electromechanical phenomena range, including converter control as well as low inertia transients. The analysis was carried out for systems with converter interfaced and synchronous generation whilst considering the simulation step size as a crucial influence parameter. The results show that simulations based on dynamic phasors allow for larger step sizes than simulations that calculate the instantaneous values in the time domain. This can facilitate the simulation of more complex component models and larger grid sizes. In addition, with dynamic phasors, more accurate simulation results were obtained than with classical phasors, in particular—but not exclusively—in a low inertia case. Overall, the presented work demonstrates that dynamic phasors can enable fast and accurate simulations during the transition to low inertia power systems.

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15

Vanhooren, H., Z. Yuan, and P. A. Vanrolleghem. "Benchmarking nitrogen removal suspended-carrier biofilm systems using dynamic simulation." Water Science and Technology 46, no. 1-2 (July 1, 2002): 327–32. http://dx.doi.org/10.2166/wst.2002.0497.

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We are witnessing an enormous growth in biological nitrogen removal from wastewater. It presents specific challenges beyond traditional COD (carbon) removal. A possibility for optimised process design is the use of biomass-supporting media. In this paper, attached growth processes (AGP) are evaluated using dynamic simulations. The advantages of these systems that were qualitatively described elsewhere, are validated quantitatively based on a simulation benchmark for activated sludge treatment systems. This simulation benchmark is extended with a biofilm model that allows for fast and accurate simulation of the conversion of different substrates in a biofilm. The economic feasibility of this system is evaluated using the data generated with the benchmark simulations. Capital savings due to volume reduction and reduced sludge production are weighed out against increased aeration costs. In this evaluation, effluent quality is integrated as well.

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16

Peasgood, Mike, Eric Kubica, and John McPhee. "Stabilization of a Dynamic Walking Gait Simulation." Journal of Computational and Nonlinear Dynamics 2, no. 1 (July 6, 2006): 65–72. http://dx.doi.org/10.1115/1.2389230.

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Forward dynamic simulations of human walking gait have typically simulated and analyzed a single step of the walking cycle, assuming symmetric and periodic gait. To enable simulations over many steps, a stabilizer is required to maintain the balance of the walking model, ideally mimicking the human balance control mechanism. This paper presents a feedback control system that stabilizes the torso orientation during a human walking gait dynamic simulation, enabling arbitrarily long simulations. The model is a two-dimensional mechanical simulation, in which the desired joint trajectories are defined as functions of time; the only external forces on the model are gravitational and ground reaction forces. Orientation or postural control is achieved by modulation of the rate at which lower limb joints move through angular trajectories. The controller design is based on a sequence of simple linear feedback controllers, each based on an intuitive control law. Controller parameters were determined iteratively using an optimization algorithm and repeated executions of the forward dynamics simulation to minimize control term errors. Results show the use of feedback control and joint speed modulation to be effective in maintaining balance for walking simulations of arbitrary length, allowing for analysis of steady-state walking.

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17

Manzano, Wallace, Valdemar Vicente Graciano Neto, and Elisa Yumi Nakagawa. "Dynamic-SoS: An Approach for the Simulation of Systems-of-Systems Dynamic Architectures." Computer Journal 63, no. 5 (April 12, 2019): 709–31. http://dx.doi.org/10.1093/comjnl/bxz028.

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Abstract Systems-of-Systems (SoS) combine heterogeneous, independent systems to offer complex functionalities for highly dynamic smart applications. Besides their dynamic architecture with continuous changes at runtime, SoS should be reliable and work without interrupting their operation and with no failures that could cause accidents or losses. SoS architectural design should facilitate the prediction of the impact of architectural changes and potential failures due to SoS behavior. However, existing approaches do not support such evaluation. Hence, these systems have been usually built without a proper evaluation of their architecture. This article presents Dynamic-SoS, an approach to predict/anticipate at design time the SoS architectural behavior at runtime to evaluate whether the SoS can sustain their operation. The main contributions of this approach comprise: (i) characterization of the dynamic architecture changes via a set of well-defined operators; (ii) a strategy to automatically include a reconfiguration controller for SoS simulation; and (iii) a means to evaluate architectural configurations that an SoS could assume at runtime, assessing their impact on the viability of the SoS operation. Results of our case study reveal Dynamic-SoS is a promising approach that could contribute to the quality of SoS by enabling prior assessment of its dynamic architecture.

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18

Djitog, Ignace, Hamzat Olanrewaju Aliyu, and Mamadou Kaba Traoré. "Multi-Perspective Modeling of Healthcare Systems." International Journal of Privacy and Health Information Management 5, no. 2 (July 2017): 1–20. http://dx.doi.org/10.4018/ijphim.2017070101.

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This paper presents a multi-perspective approach to Modeling and Simulation (M&S) of Healthcare Systems (HS) such that different perspectives are defined and integrated together. The interactions between the isolated perspectives are done through dynamic update of models output-to-parameter integration during concurrent simulations. Most often, simulation-based studies of HS in the literature focus on specific problem like allocation of resources, disease propagation, and population dynamics that are studied with constant parameters from their respective experimental frames throughout the simulation. The proposed idea provides a closer representation of the real situation and helps to capture the interactions between seemingly independent concerns - and the effects of such interactions - in simulation results. The article provides a DEVS (Discrete Event System Specification)-based formalization of the loose integration of the different perspectives, an Object-Oriented framework for its realization and a case study as illustration and proof of concept.

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19

Ranky, Paul G. "Dynamic Simulation of Flexible Manufacturing Systems (FMS)." Applied Mechanics Reviews 39, no. 9 (September 1, 1986): 1339–44. http://dx.doi.org/10.1115/1.3149523.

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The simulation method to be used in FMS should be multilevel and dynamic and should incorporate solid modeling techniques. This means that operation control simulation in FMS should rely on information sources provided from different levels of the organization; thus there should be an overall planning level and a dynamic, or real-time, level. One should also conclude from this article that, without understanding the design principles and operating rules of FMS, the simulation model created will be inadequate and in most cases misleading. Because of this, FMS simulation should be performed by a team, incorporating the manufacturing system designers as well as the simulation experts.

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20

Yuan, Qiong. "Efficient Simulation for Dynamic Systems with Discontinuities." Advanced Materials Research 989-994 (July 2014): 2515–18. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2515.

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In many techneques for handling discontinuities, the presence of a discontinuity is detected by a change of sign in the value of a discontinuity function. This paper discusses the problems caused by this sign rule of detecting discontinuities for some engineering applications, and describes an alternative which uses a change of the state marker value .The modified program with Runge-Kutta-Merson and Gear integration subroutines have been successfully applied to the simulation for mechanical, electrical and other dynamic systems with discontinuities for which the original program is inefficient.

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21

Shevtsov, Alexandr Nikolayevich. "SOME QUESTIONS SIMULATION OF INTERACTIVE DYNAMIC SYSTEMS." Theoretical & Applied Science 9, no. 01 (January 30, 2014): 5–22. http://dx.doi.org/10.15863/tas.2014.01.9.2.

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22

Pan, W., and E. J. Haug. "Dynamic Simulation of General Flexible Multibody Systems∗." Mechanics of Structures and Machines 27, no. 2 (January 1999): 217–51. http://dx.doi.org/10.1080/08905459908915697.

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23

Paiva, H. M., and R. K. H. Galvao. "Simulation of Dynamic Systems With Output Saturation." IEEE Transactions on Education 47, no. 3 (August 2004): 385–88. http://dx.doi.org/10.1109/te.2004.825533.

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24

Sacks, Elisha. "A dynamic systems perspective on qualitative simulation." Artificial Intelligence 42, no. 2-3 (March 1990): 349–62. http://dx.doi.org/10.1016/0004-3702(90)90058-8.

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25

Leonov, A. S. "Optimal simulation of nonlinear deterministic dynamic systems." Computational Mathematics and Modeling 7, no. 3 (1996): 333–37. http://dx.doi.org/10.1007/bf01128165.

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26

Dalle Molle, D. T., B. J. Kuipers, and T. F. Edgar. "Qualitative modeling and simulation of dynamic systems." Computers & Chemical Engineering 12, no. 9-10 (September 1988): 853–66. http://dx.doi.org/10.1016/0098-1354(88)87013-3.

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27

Mollazadeh, Shirin, Amirhossein Sahebkar, Mohsen Shahlaei, and Sajad Moradi. "Nano drug delivery systems: Molecular dynamic simulation." Journal of Molecular Liquids 332 (June 2021): 115823. http://dx.doi.org/10.1016/j.molliq.2021.115823.

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28

Toyama, Shigeki, and Yasuo Murakuki. "Dynamic Autonomous Car Mobile Analysis Simulating Mechanical Systems Analysis – First Dynamic Characteristics of Running Mouse –." Journal of Robotics and Mechatronics 10, no. 6 (December 20, 1998): 488–93. http://dx.doi.org/10.20965/jrm.1998.p0488.

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This paper dynamically simulates a small running 2DW2C automobile (mouse) and simulates path tracking control. Our purpose was to optimize mouse design using simulation results. We added tire force and DC motor force to A1 Motion, a simulator for analyzing mechanical systems developed in our laboratory, and improved the simulator simulating a running automobile. Experiments with a small 2DW2C automobile compared experimental and simulation results involving dynamic characteristics of an actual mouse. We got correct simulation results using this model and simulator. We studied its running performance, affected by its wheelbase and caster length, and evaluated path tracking control using closoidal curves.

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29

Yoon, Sugjoon, and Hyounjoo Kang. "Dynamic-window search for real-time simulation of dynamic systems." Communications in Numerical Methods in Engineering 19, no. 11 (September 26, 2003): 877–86. http://dx.doi.org/10.1002/cnm.637.

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30

Park, Gwangmin, Seonghun Lee, Sung Ho Jin, and Sangshin Kwak. "Modeling and Analysis for Powertrain Dynamics of Electric Vehicle Systems." Applied Mechanics and Materials 110-116 (October 2011): 2426–31. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2426.

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This paper provides presents the dynamic analysis and computer simulation results of electric vehicle (EV) powertrain performance systems. The generic simulation platform of an electric vehicle is developed using based on the SimPowerSystems/SimDriveline of MATLAB. Individual components of the model are constructed based on real vehicle data and mathematical dynamic model equations. The analytic results obtained from the mathematical modeling are verified with electric vehicle dynamics using generic simulation platform.

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31

Balakrishna, Ramachandran, Haris N. Koutsopoulos, Moshe Ben-Akiva, Bruno M. Fernandez Ruiz, and Manish Mehta. "Simulation-Based Evaluation of Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1910, no. 1 (January 2005): 90–98. http://dx.doi.org/10.1177/0361198105191000111.

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Traveler information has the potential to reduce travel times and improve their reliability. Studies have verified that driver overreaction from the dissemination of information can be eliminated through prediction-based route guidance that uses short-term forecasts of network state. Critical off-line tests of advanced dynamic traffic assignment–based prediction systems have been limited, since the system being evaluated has also been used as the test bed. This paper outlines a detailed simulation-based laboratory for the objective and independent evaluation of advanced traveler information systems, a laboratory with the flexibility to analyze the impacts of various design parameters and modeling errors on the quality of the generated guidance. MITSIMLab, a system for the evaluation of advanced traffic management systems, is integrated with Dynamic Network Assignment for the Management of Information to Travelers (DynaMIT), a simulation-based decision support system designed to generate prediction-based route guidance. Evaluation criteria and requirements for the closed-loop integration of MITSIMLab and DynaMIT are discussed. Detailed case studies demonstrating the evaluation methodology and sensitivity of DynaMIT's guidance are presented.

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32

Felez, J., C. Vera, I. San Jose, and R. Cacho. "BONDYN: A Bond Graph Based Simulation Program for Multibody Systems." Journal of Dynamic Systems, Measurement, and Control 112, no. 4 (December 1, 1990): 717–27. http://dx.doi.org/10.1115/1.2896200.

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This paper presents the BONDYN program (BONd graph DYNamics) as a procedure for simulating dynamic systems. It is based on bond graph theory and provides a means for treating dynamic systems that simultaneously include various physical domains. The program makes use of the bond graph module handling facility in order to build a general model starting from simple submodels. Although the latter can be defined by the user, a library has been appended to the preprocessor which includes some of these submodels. Special developments for simulating multibody systems can be found among them. Once the overall bond graph has been assembled the program builds the state equations of the system in the form of a subroutine that can be accepted by a high level language compiler, which is FORTRAN 77 in this case. Simulation outputs can be shown either graphically or in a table.

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33

Schindlerová, Vladimíra, Ivana Šajdlerová, and Dominika Lehocká. "Dynamic simulation for optimisation solution of manufacturing processes." MATEC Web of Conferences 244 (2018): 01010. http://dx.doi.org/10.1051/matecconf/201824401010.

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One way how to study the real behaviour of industrial processes or systems in practice is to use computer simulations. We can simulate different conditions and find optimal parameters without increased risk. The right application of these parameters in practice can produce the desired results. The advantage is not only the safe verification of various variants of the simulated parameters, but also the possibility of their use in different areas of industrial practice. This article deals with an example of the use of simulation in the production of the selected automobile cooling system component. The simulation model was created to design the correct number of Kanban circuits to shorten production lead time and to reduce inter-operational supplies. The suitability of using computer simulations to optimize the production processes and systems in practice can be confirmed based on comparison of the results from the computer simulation with results achieved in practice.

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34

Tian, Fu Yang, Yu Dao Li, Zhen Wang, and Fa De Li. "Efficient Recursive Dynamics and Real Time Simulation of Flexible Space Robots System." Advanced Materials Research 842 (November 2013): 546–52. http://dx.doi.org/10.4028/www.scientific.net/amr.842.546.

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As space robots gain more and more importance in space operations, it was becoming imperative to understand their distinctive dynamics. The dynamics model of the space flexible robots is very complex, and the differential equation derived from dynamics was solved difficultly and slowly. In this paper the efficient recursive O(n) dynamics algorithm of space flexible robots systems with rigid base and flexible manipulators was discussed, and the fast efficient integration method was used to solve this dynamics equation for real time simulation. Simulations results show that the dynamic modeling and fast efficient numerical integration techniques of flexible space robot proposed in this paper are very useful. Keyword: space robots;efficient dynamic; recursive; real time simulation

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35

Richard, M. J. "Dynamic Simulation of Multibody Mechanical Systems Using the Vector-Network Model." Transactions of the Canadian Society for Mechanical Engineering 12, no. 1 (March 1988): 21–30. http://dx.doi.org/10.1139/tcsme-1988-0004.

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Pressing technological problems have created a growing interest in the development of dynamic models for the digital simulation of multibody systems. This paper describes a new approach to the problem of motion prediction. An extension of the “vector-network” method to rigid body systems in three-dimensional space is introduced. The entire procedure is a basic application of concepts of graph theory in which laws of vector dynamics are combined. The analytical procedure was successfully implemented within a general-purpose digital simulation program since, from a minimal definition of the mechanism, it will automatically predict the behavior of the system as output, thereby giving the impression that the equations governing the motion of the mechanical system have been completely formulated and solved by the computer. Simulations of the response of a rail vehicle which demonstrate the validity, applicability and self-formulating aspect of the automated model are provided.

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36

BUTLER, ALUN, MOH IBRAHIM, KEITH RENNOLLS, and LIZ BACON. "Composing simulation architectures for autonomic systems." Knowledge Engineering Review 21, no. 3 (September 2006): 249–59. http://dx.doi.org/10.1017/s0269888906000919.

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Simulation has long played a part in testing new configurations and new functionality in a diverse range of software. Through such simulations, the boundaries of the system state are explored and the relationship of that state to other applications tested — sometimes to destruction. A critical differentiator between a simulation and a live, deployed application is that simulations are allowed to fail. As truly autonomous applications evolve, this capacity for simulation must be built in from the ground up or the benefits of experience — including the ability to tolerate failure — will be lost. This must be achieved without undermining the global correctness of visible application behaviour. We suggest an engineering approach to enable the introduction of such simulation with minimal or no recoding and we propose a composition architecture to allow for safe dynamic deployment in substantial autonomic systems. We have identified our approach as application Dreaming.

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37

Mills, J. K., and C. V. Nguyen. "Robotic Manipulator Collisions: Modeling and Simulation." Journal of Dynamic Systems, Measurement, and Control 114, no. 4 (December 1, 1992): 650–59. http://dx.doi.org/10.1115/1.2897737.

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In this paper, a new formulation of the dynamics of a robotic manipulator work environment is presented. The work environment is modeled in a way that permits the robot transition to and from contact with the work environment to be effectively simulated. This method circumvents the discontinuities inherent in previously proposed models of work environment dynamic models that have, until now, prevented researchers from considering that phase of manipulation. Combined with an existing model of the manipulator dynamics, the overall model of the manipulator-work environment system is such that the system states evolve continuously in time, as is the case in reality. Specifically, a continuous dynamics model is presented which models dynamic behavior of an n degree of freedom rigid link robotic manipulator during the transition to and from frictionless point contact with a work environment. The dynamic model of the work environment is sufficiently general to encompass, as limiting cases, both constrained motion and compliant motion contacts. The general properties of the work environment dynamics model are readily altered with only two parameters. A singular perturbation analysis provides an analytical approach to verification of the properties of the model of the work environment known to be true from an intuitive perspective. Results concerning the behavior of the impact force during a collision between the manipulator and work environment are also obtained using a singular perturbation theory approach. Detailed dynamic simulation results are given to illustrate the behavior of the proposed model. Simulation results of a two-degree-of-freedom manipulator with proportional and derivative control applied during the transition from noncontact to contact motion are given. Comparison of simulation results to experimentally obtained results reported in the robotics literature reveal a remarkable similarity in the time responses, given the simplicity of the work environment dynamic model.

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38

Karnopp, Dean. "Computer Simulation of Stick-Slip Friction in Mechanical Dynamic Systems." Journal of Dynamic Systems, Measurement, and Control 107, no. 1 (March 1, 1985): 100–103. http://dx.doi.org/10.1115/1.3140698.

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Stick-slip friction is present to some degree in almost all actuators and mechanisms and is often responsible for performance limitations. Simulation of stick-slip friction is difficult because of strongly nonlinear behavior in the vicinity of zero velocity. A straightforward method for representing and simulating friction effects is presented. True zero velocity sticking is represented without equation reformulation or the introduction of numerical stiffness problems.

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39

Smith, Jeremy C., Pan Tan, Loukas Petridis, and Liang Hong. "Dynamic Neutron Scattering by Biological Systems." Annual Review of Biophysics 47, no. 1 (May 20, 2018): 335–54. http://dx.doi.org/10.1146/annurev-biophys-070317-033358.

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Dynamic neutron scattering directly probes motions in biological systems on femtosecond to microsecond timescales. When combined with molecular dynamics simulation and normal mode analysis, detailed descriptions of the forms and frequencies of motions can be derived. We examine vibrations in proteins, the temperature dependence of protein motions, and concepts describing the rich variety of motions detectable using neutrons in biological systems at physiological temperatures. New techniques for deriving information on collective motions using coherent scattering are also reviewed.

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40

Xu, Zhenlong, Michael Accorsi, and John Leonard. "Simulation of Dynamic Contact Problems in Parachute Systems." Journal of Aerospace Computing, Information, and Communication 1, no. 7 (July 2004): 288–307. http://dx.doi.org/10.2514/1.7787.

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41

Regan, Amelia C., Hani S. Mahmassani, and Patrick Jaillet. "Evaluation of Dynamic Fleet Management Systems: Simulation Framework." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 176–84. http://dx.doi.org/10.3141/1645-22.

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The problem of dynamic fleet management for truckload carrier fleet operations is introduced, and the principal elements of a simulation framework for the evaluation of dynamic fleet management systems are described. The application of the simulated framework to the investigation of the performance of a family of real-time fleet operational strategies, which include load acceptance, assignment, and reassignment strategies, also is described. The simulation framework described is an example of a first-generation tool for the evaluation of dynamic fleet management systems. Selected experimental results are highlighted. These are intended to illustrate some of the issues encountered in real-time fleet management and the role of the simulation modeling environment in investigating them.

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Yang, Qi, Haris N. Koutsopoulos, and Moshe E. Ben-Akiva. "Simulation Laboratory for Evaluating Dynamic Traffic Management Systems." Transportation Research Record: Journal of the Transportation Research Board 1710, no. 1 (January 2000): 122–30. http://dx.doi.org/10.3141/1710-14.

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Advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) are promising technologies for achieving efficiency in the operation of transportation systems. A simulation-based laboratory environment, MITSIMLab, is presented that is designed for testing and evaluation of dynamic traffic management systems. The core of MITSIMLab is a microscopic traffic simulator (MITSIM) and a traffic management simulator (TMS). MITSIM represents traffic flows in the network, and the TMS represents the traffic management system under evaluation. An important feature of MITSIMLab is its ability to model ATMS or ATIS that generate traffic controls and route guidance based on predicted traffic conditions. A graphical user interface allows visualization of the simulation, including animation of vehicle movements. An ATIS case study with a realistic network is also presented to demonstrate the functionality of MITSIMLab.

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Polack, Fiona, and Kieran Alden. "On Developing and Validating Dynamic Systems: Simulation Engineering." Journal of Object Technology 19, no. 3 (2020): 3:1. http://dx.doi.org/10.5381/jot.2020.19.3.a6.

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Lee, Chun-Woo, Ju-Hee Lee, Bong-Jin Cha, Hyun-Young Kim, and Ji-Hoon Lee. "Physical modeling for underwater flexible systems dynamic simulation." Ocean Engineering 32, no. 3-4 (March 2005): 331–47. http://dx.doi.org/10.1016/j.oceaneng.2004.08.007.

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Ding, Shuiting, Tian Qiu, Xiaofeng Liu, and Shuguang Zhang. "Dynamic Coupled Systems FHA: A Simulation-aided Approach." Procedia Engineering 80 (2014): 479–93. http://dx.doi.org/10.1016/j.proeng.2014.09.106.

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Schülke, L., and B. Zheng. "Monte Carlo simulation of critical dynamic spin systems." Nuclear Physics B - Proceedings Supplements 53, no. 1-3 (February 1997): 712–14. http://dx.doi.org/10.1016/s0920-5632(96)00762-1.

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Lu, S. "Dynamic modelling and simulation of power plant systems." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 213, no. 1 (February 1999): 7–22. http://dx.doi.org/10.1243/0957650991537392.

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Ben-Akiva, Moshe E., Haris N. Koutsopoulos, Rabi G. Mishalani, and Qi Yang. "Simulation Laboratory for Evaluating Dynamic Traffic Management Systems." Journal of Transportation Engineering 123, no. 4 (July 1997): 283–89. http://dx.doi.org/10.1061/(asce)0733-947x(1997)123:4(283).

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Wilke, David, and Carsten Obenaus. "Design of Pneumatic Brake Systems by Dynamic Simulation." ATZ worldwide eMagazine 113, no. 5 (April 29, 2011): 24–29. http://dx.doi.org/10.1365/s38311-011-0052-1.

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Mankala, Kalyan K., and Sunil K. Agrawal. "Dynamic Modeling and Simulation of Satellite Tethered Systems." Journal of Vibration and Acoustics 127, no. 2 (June 28, 2004): 144–56. http://dx.doi.org/10.1115/1.1891811.

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The objective of this paper is to study the dynamic simulation of a tether as it is deployed or retrieved by a winch on a satellite orbiting around earth. In an effort to understand the problem incrementally, the following three models were developed: (a) Model 1: A tether with constant length moves on earth in the plane of constant gravity; (b) Model 2: A tether is deployed from a drum on earth in the plane of constant gravity, i.e., length of the cable changes during deployment; (c) Model 3: A tether is deployed from a drum on an orbiting satellite. These models have been chosen to bring different aspects as well as levels of difficulty in the analysis. For example, in Model 1, the length of cable is fixed and the gravity direction is constant during motion. The equations of motion for this model are derived using Newton’s laws and Hamilton’s principle to show the equivalence of the two methods. In Model 2, free length of the cable changes during deployment. The changing length of the cable introduces coupled nonlinearities into the motion. Model 3 includes the orbital effect on the motion of deployed cable. Each of these three dynamic models characterized by partial differential equations are first converted to a finite number of ordinary differential equations using Ritz’s procedure and are then numerically integrated using Matlab ODE solvers.

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