Готові списки джерел за темами / Physical modeling and simulation

Добірка наукової літератури з теми "Physical modeling and simulation"

Автор: Grafiati
Опубліковано: 4 травня 2024

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Статті в журналах з теми "Physical modeling and simulation"

1

Kebch, A. El, N. Dlimi, D. Saifaoui, A. Dezairi, and M. El Mouden. "Modeling and simulation of physical sputtering." Molecular Crystals and Liquid Crystals 627, no. 1 (March 3, 2016): 183–89. http://dx.doi.org/10.1080/15421406.2015.1137676.

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2

Wang, Haosheng, and 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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3

Bora, Tanujjal, Adrien Dousse, Kunal Sharma, Kaushik Sarma, Alexander Baev, G. Louis Hornyak, and Guatam Dasgupta. "Modeling nanomaterial physical properties: theory and simulation." International Journal of Smart and Nano Materials 10, no. 2 (November 3, 2018): 116–43. http://dx.doi.org/10.1080/19475411.2018.1541935.

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4

Thompson, Bradley, and Hwan-Sik Yoon. "Internal Combustion Engine Modeling Framework in Simulink: Gas Dynamics Modeling." Modelling and Simulation in Engineering 2020 (September 3, 2020): 1–16. http://dx.doi.org/10.1155/2020/6787408.

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Анотація:
With advancements in computer-aided design, simulation of internal combustion engines has become a vital tool for product development and design innovation. Among the simulation software packages currently available, MATLAB/Simulink is widely used for automotive system simulations, but does not contain a comprehensive engine modeling toolbox. To leverage MATLAB/Simulink’s capabilities, a Simulink-based 1D flow engine modeling framework has been developed. The framework allows engine component blocks to be connected in a physically representative manner in the Simulink environment, reducing model build time. Each component block, derived from physical laws, interacts with other blocks according to block connection. In this Part 1 of series papers, a comprehensive gas dynamics model is presented and integrated in the engine modeling framework based on MATLAB/Simulink. Then, the gas dynamics model is validated with commercial engine simulation software by conducting a simple 1D flow simulation.
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5

Zhou, Hao, Mengyao Zhao, Linbo Wu, and Xiaohong Chen. "Simulating Timing Behaviors for Cyber-Physical Systems Using Modelica." International Journal of Software Science and Computational Intelligence 11, no. 3 (July 2019): 44–67. http://dx.doi.org/10.4018/ijssci.2019070103.

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Cyber-physical systems (CPSs) connect the cyber world with the physical world through a network of interrelated elements, such as sensors and actuators, robots, and other computing devices. Timing constraints on the interactions (timing behaviors) should be modelled and verified as cyber-physical systems are becoming more and more complex. This article proposes modeling the typical timing behaviors according to their time characteristics, periodicity, multiform time, and synchronization, and verifies them against properties using simulations. Sequence diagrams are presented for the modeling, and modelica is used for simulation. In the simulation, the time dependence relations are defined, and used for simulation parameter data automatic generation, in addition to the paths from the sequence diagrams. Finally, a Parachute System is used as an example to show the feasibility and effectiveness of the approach.
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6

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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7

Formigoni, A., E. F. Rodrigues, J. R. Maiellaro, L. T. Kawamoto Junior, M. A. Cipriano, and R. S. Lira. "Physical Distribution Routing Using Computational Modeling and Simulation." Journal of Mechatronics 2, no. 4 (December 1, 2014): 329–33. http://dx.doi.org/10.1166/jom.2014.1078.

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8

Zhang, Shi Hong, Hong Wu Song, Ming Cheng, and Zhong Tang Wang. "A Mathmatical Approach for Modeling Real Hot Forming Process Using Physical Simulation Results." Materials Science Forum 575-578 (April 2008): 502–7. http://dx.doi.org/10.4028/www.scientific.net/msf.575-578.502.

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Анотація:
Recently, physical simulation has played a more and more important role in modeling hot forming process. However, difficulty still existed in simulating real hot forming process using physical simulation results directly for obvious difference in deformation history between physical simulation condition and real hot forming process. In this work, difference between physical simulation and real hot forming process was discussed and a mathmatical approach was proposed to model real hot forming process using physical simulation results. The main consideration of the method was to put physical simulation results into differential forms in order to take count in the contribution of deformation history (temperature and strain rate) at each incremental step. For the application of the approach, modeling of material flow stress, dynamical recrystallization including critical condition and recrystallziaton fraction, damage evolution and fracture criteria during real hot forming process were presented as examples, although experimental support was still needed for validation and further application.
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9

Jeffrey, Jeffrey, Didi Widya Utama, and Gatot Soeharsono. "RANCANG BANGUN KONTRUKSI DAN SISTEM GERAK SUMBU PADA MESIN FUSED DEPOSITION MODELLING." POROS 14, no. 2 (September 20, 2017): 99. http://dx.doi.org/10.24912/poros.v14i2.842.

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Abstract: Fused Deposition Modelling (FDM) is a technology additive manufacture for modelling, prototyping, and production. This technology is one of the techniques used for 3D printers. Our focus is on studying, design machines fused deposition with 3D modeling and simulation with autodesk inventor and other design tools. Design is done by simulating the strength of the construction and then determine the components needed. We are making fused deposition modeling is intended as a prototype in order to understand how it works and how to innovate in the development of fused deposition modeling. The results of the design in the form of a fused depositon modeling that is able to create physical models
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10

Wagner, Neal. "Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis." Systems 12, no. 3 (March 14, 2024): 96. http://dx.doi.org/10.3390/systems12030096.

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Анотація:
Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.
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Більше джерел

Дисертації з теми "Physical modeling and simulation"

1

Latorre, Malcolm. "The Physical Axon : Modeling, Simulation and Electrode Evaluation." Doctoral thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138587.

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Electrodes are used in medicine for detection of biological signals and for stimulating tissue, e.g. in deep brain stimulation (DBS). For both applications, an understanding of the functioning of the electrode, and its interface and interaction with the target tissue involved is necessary. To date, there is no standardized method for medical electrode evaluation that allows transferability of acquired data. In this thesis, a physical axon (Paxon) potential generator was developed as a device to facilitate standardized comparisons of different electrodes. The Paxon generates repeatable, tuneable and physiological-like action potentials from a peripheral nerve. It consists of a testbed comprising 40 software controlled 20 μm gold wires embedded in resin, each wire mimicking a node of Ranvier. ECG surface Ag-AgCl electrodes were systematically tested with the Paxon. The results showed small variations in orientation (rotation) and position (relative to axon position) which directly impact the acquired signal. Other electrode types including DBS electrodes can also be evaluated with the Paxon. A theoretical comparison of a single cable neuronal model with an alternative established double cable neuron model was completed. The output with regards to DBS was implemented to comparing the models. These models were configured to investigate electrode stimulation activity, and in turn to assess the activation distance by DBS for changes in axon diameter (1.5-10 μm), pulse shape (rectangular biphasic and rectangular, triangular and sinus monophasic) and drive strength (1-5 V or mA). As both models present similar activation distances, sensitivity to input shape and computational time, the neuron model selection for DBS could be based on model complexity and axon diameter flexibility. An application of the in-house neuron model for multiple DBS lead designs, in a patient-specific simulation study, was completed. Assessments based on the electric field along multiple sample planes of axons support previous findings that a fixed electric field isolevel is sufficient for assessments of tissue activation distances for a predefined axon diameter and pulse width in DBS.
Elektroder används inom sjukvården, både för att mäta biologiska signaler, t.ex. hjärtats aktivitet med EKG, eller för att stimulera vävnad, t.ex. vid djup hjärnstimulering (DBS). För båda användningsområdena är det viktigt med en grundläggande förståelse av elektrodens interaktion med vävnaden. Det finns ingen standardiserad metod för att utvärdera medicinsk elektroders dataöverföringsfunktion. I den här avhandlingen presenteras en metod för att underlätta elektrodtestning. En hårdvarumodell av ett axon (Paxon) har utvecklats. Paxon kan programmeras för att efterlikna repeterbara aktionspotentialer från en perifer nerv. Längs axonet finns 40 noder, vilka var och en består av en tunn (20 μm) guldtråd inbäddad i harts och därefter kopplad till elektronik. Denna testbädd har använts för att undersöka EKG elektroders egenskaper. EKG elektroderna visade på variationer i orientering och position i relation till Paxon. Detta har en direkt inverkan på den registrerade signalen. Även andra elektrotyper kan testas i Paxon, t.ex. DBS elektroder. En teoretisk jämförelse mellan två neuronmodeller med olika komplexitet, anpassade för användning vid DBS studier, har utförts. Modellerna konfigurerades för att studera inverkan på aktiveringsavstånd från olika axondiametrar, stimulationspuls och stimulationsstyrka. Då båda modellerna visade likvärdiga aktiveringsavstånd och beräkningstid så förordas den enklare neuronmodellen för DBS simuleringar. En enklare modell kan lättare introduceras i klinisk verksamhet. Simuleringarna stöder tidigare resultat som visat att det elektriska fältet är en bra parameter för presentation av resultat vid simulering av DBS. Metoden exemplifieras vid simulering av aktiveringsavstånd och elektriska fältets utbredning för olika typer av DBS elektroder i en patient-specifik studie.
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2

Sjöstedt, Carl-Johan. "Modeling and Simulation of Physical Systems in a Mechatronic Context." Doctoral thesis, KTH, Maskinkonstruktion (Avd.), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10522.

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Анотація:
This thesis gives different views on the modeling and simulation of physical systems, especially together with embedded systems, forming mechatronic systems. The main considered application domain is automotive. One motivation behind the work is to find suitable representations of physical systems to be used in an architectural description language for automotive embedded systems, EAST-ADL2, which is implemented as a UML2 profile, and uses concepts from both UML and SysML. As a part of the thesis, several languages and tools are investigated, including bond graphs, MATLAB/Simulink, Ptolemy II, Modelica, MATLAB/Simscape and SysML. For SysML, the modeling of continuous-time systems and how it relates to MATLAB/Simulink and Modelica is evaluated. A case study of an electric power assisted steering is modeled to show the differences, the similarities and the usage of the above mentioned languages and tools. To be able to classify the tools and languages, five realization levels were developed: Physical modeling models Constraint models Continuous causal models Discretized models Discretized models with solver and platform implementation By using these realization levels, models, tools and modeling languages can be classified, and transformations between them can be set up and analyzed. As a result, a method to describe the simulation behavior of a MATLAB/Simulink model has been developed using SysML activity diagrams as an approach to achieve integrated system models. Another result is an evaluation of the parametric diagrams of SysML for continuous-time modeling, which shows that they do not enable “physical modeling”, i.e. modeling the topology of the system and getting the underlying equations out of this topology. By including physical ports and physical connectors to SysML internal block diagrams, this could be solved. The comparison also shows many similarities between the languages. The results led to a more detailed investigation on conjugate variables, such as force and velocity, and electric current and voltage, and how these are treated in various languages. The thesis also includes two industrial case studies: one of a twin-screw compressor, and one of a simulation environment for automotive fuel-cell systems. Conclusions are drawn from these models, referring to the realization levels.
QC 20100810
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3

Esmael, Muzeyen Hassen. "Modeling Basic Physical Links in Acumen." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-18119.

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Анотація:
Simulation is the process of computing a behavior determined by a given model of a system of interest.  Modeling is the process of creating a model that formally describes a given class of system.  Modeling and simulation can be used to quickly and cheaply study and understand new technologies.  Today, a wide range of systems are simulated using different tools.  However, converting models into simulation codes can still be difficult and time consuming. In this thesis, we study how a new modeling and simulation language called Acumen can be used to model basic physical links.  This language is aimed at bridging the gap between modeling and simulation.  We focus on basic physical links as an interesting type of system to model and simulate. We also focus on comparing Acumen to MATLAB and Simulink.  The types of links we consider include models of an RC low-pass filter, Amplitude Modulation, Frequency Modulation, Amplitude Shift Keying, Phase Shift Keying and Frequency Shift Keying systems. Each of these examples is modeled in Acumen, MATLAB and Simulink. We find that, for the most part, Acumen allows us to naturally express a wide range of modulation techniques mentioned above. When compared to MATLAB ad Simulink, we find that Acumen is simple language to understand. Acumen codes are described in a more natural way. Simplicity is the biggest advantage of Acumen.
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4

Cozza, Dario. "Modeling and physical studies of kesterite solar cells." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4302.

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Анотація:
Ce travail de thèse porte sur la modélisation et la simulation numérique de cellules solaires à base de kësterite (CZTSé, CZTS) dans le but d’étudier leurs mécanismes physiques et d’améliorer la conception de ces dispositifs. Les kësterites sont une classe de matériaux que l’on peut déposer en couches minces et qui sont constitués d’éléments abondants sur Terre et donc à faible coût. Deux modèles numériques pour les cellules solaires CZTSe et CZTS sont proposés. Des simulations 1D et 2D sont réalisées: le logiciel SCAPS est utilisé pour étudier l’impact des couches de molybdène et de MoSe2, présents au contact arrière des cellules solaires CZTSe. Nous étudions également les propriétés idéales de couches d’interface alternatives qui pourraient remplacer le MoSe2 pour améliorer les performances des cellules solaires. La méthode des matrices de transfert (TMM) et le logiciel SCAPS sont utilisés conjointement pour effectuer des simulations optoélectroniques dans le but d’optimiser l’épaisseur du buffer (CdS) et le TCO (Transparent Conductive Oxide) afin de maximiser le courant de court-circuit (JSC ) des cellules solaires. Enfin Silvaco est utilisé pour réaliser des simulations 2D des joints de grains (GBs) du CZTSe présents à l’intérieur des absorbeurs polycristallins de la kësterite. Pour ce faire, des caractérisations KPFM sont effectuées dans le but de trouver des corrélations possibles entre les pertes de rendement et l'activité électrique des GBs
This thesis deals with modeling and simulations of kesterite solar cells with the aim of studying their physical mechanisms and improving the design of the devices. Synthetic kesterites are thin film materials made of cheap/earth-abundant elements. Two numerical models for a Cu2ZnSnSe4 (CZTSe) and a Cu2ZnSnS4 (CZTS) solar cell are proposed. The provided values of the material parameters, for all the layers of the solar cell, are obtained either from comparisons/analysis of data found in literature or, in some cases, from direct measurements. 1D and 2D simulations are performed: the software SCAPS is used to study the impact of the Molybdenum and the MoSe2 layers, present at the back contact of CZTSe solar cells. We investigate also the ideal properties of alternative interfacial layers that could replace the MoSe2 layer to improve the device performances. The transfer matrix method (TMM) and SCAPS are employed together to perform optoelectronic simulations with the aim of optimizing the thickness of the buffer (CdS) and the window (ITO) layers in order to maximize the short circuit current (JSC ) of the device. Finally Silvaco is used to perform 2D simulations of the CZTSe grain boundaries (GBs) present inside the polycrystalline kesterite absorbers. For the latter work, experimental Kelvin probe force microscopy (KPFM) characterizations are performed in order to find possible correlations between the performance losses and the electrical activity of the GBs
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5

Sjöstedt, Carl-Johan. "Modeling and simulation of physical systems in a mechatronic context /." Stockholm : Skolan för indutstriell teknik och managemnet, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10522.

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6

Du, Dongping. "Physical-Statistical Modeling and Optimization of Cardiovascular Systems." Scholar Commons, 2002. http://scholarcommons.usf.edu/etd/5875.

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Анотація:
Heart disease remains the No.1 leading cause of death in U.S. and in the world. To improve cardiac care services, there is an urgent need of developing early diagnosis of heart diseases and optimal intervention strategies. As such, it calls upon a better understanding of the pathology of heart diseases. Computer simulation and modeling have been widely applied to overcome many practical and ethical limitations in in-vivo, ex-vivo, and whole-animal experiments. Computer experiments provide physiologists and cardiologists an indispensable tool to characterize, model and analyze cardiac function both in healthy and in diseased heart. Most importantly, simulation modeling empowers the analysis of causal relationships of cardiac dysfunction from ion channels to the whole heart, which physical experiments alone cannot achieve. Growing evidences show that aberrant glycosylation have dramatic influence on cardiac and neuronal function. Variable but modest reduction in glycosylation among congenital disorders of glycosylation (CDG) subtypes has multi-system effects leading to a high infant mortality rate. In addition, CDG in all young patients tends to cause Atrial Fibrillation (AF), i.e., the most common sustained cardiac arrhythmia. The mortality rate from AF has been increasing in the past two decades. Due to the increasing healthcare burden of AF, studying the AF mechanisms and developing optimal ablation strategies are now urgently needed. Very little is known about how glycosylation modulates cardiac electrical signaling. It is also a significant challenge to experimentally connect the changes at one organizational level (e.g.,electrical conduction among cardiac tissue) to measured changes at another organizational level (e.g., ion channels). In this study, we integrate the data from in vitro experiments with in-silico models to simulate the effects of reduced glycosylation on the gating kinetics of cardiac ion channel, i.e., hERG channels, Na+ channels, K+ channels, and to predict the glycosylation modulation dynamics in individual cardiac cells and tissues. The complex gating kinetics of Na+ channels is modeled with a 9-state Markov model that have voltage-dependent transition rates of exponential forms. The model calibration is quite a challenge as the Markov model is non-linear, non-convex, ill-posed, and has a large parametric space. We developed a new metamodel-based simulation optimization approach for calibrating the model with the in-vitro experimental data. This proposed algorithm is shown to be efficient in learning the Markov model of Na+ model. Moreover, it can be easily transformed and applied to many other optimization problems in computer modeling. In addition, the understanding of AF initiation and maintenance has remained sketchy at best. One salient problem is the inability to interpret intracardiac recordings, which prevents us from reconstructing the rhythmic mechanisms for AF, due to multiple wavelets' circulating, clashing and continuously changing direction in the atria. We are designing computer experiments to simulate the single/multiple activations on atrial tissues and the corresponding intra-cardiac signals. This research will create a novel computer-aided decision support tool to optimize AF ablation procedures.
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7

Sadeghi, Reineh Maryam. "Physical Modeling and Simulation Analysis of an Advanced Automotive Racing Shock Absorber using the 1D Simulation Tool AMESim." Thesis, Linköpings universitet, Fluida och mekatroniska system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92146.

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Анотація:
Shock absorbers are crucial components of a vehicle’s chassis responsible for the trade-off between stability, handling, and passenger comfort. The aim of the thesis is to investigate the physical behavior of an advanced automotive racing shock absorber, known as TTR, developed by Öhlins Racing AB. This goal is achieved by developing a detailed lumped parameter numerical model of the entire TTR suspension in the advanced 1D simulation tool, AMESim. The shock absorber is mainly composed of the main cylinder with through-rod piston design and the gas reservoir located at the low pressure hydraulic line, which connects the compression and rebound sides. The mentioned sides are identical in terms of the components which are a High Speed Adjuster, a Low Speed Adjuster, and a check valve mounted in parallel. The adjusters are special hydraulic valves, which can be modified in terms of flow metering characteristics by means of external accessible screws. Adjustment is done in a series of discrete numbers called ‘clicks’. A fixed orifice and a spring-loaded poppet valve are responsible for controlling the piston low and high speed regions respectively. The developed AMESim numerical model is capable of capturing the physics behind the real shock absorber damping characteristics, under both static and dynamic conditions. The model is developed mainly using the standard AMESim mechanical, hydraulic and hydraulic component design libraries and allows discovering the impact of each single hydraulic component on the TTR overall behavior. In particular, the 1D model is presented in two levels of progressive physical complexity in order to improve the dynamic damping characteristics. Several physical phenomena are considered, such as the hydraulics volumes pressure dynamics, the contribution of external spring and pressure forces to the dynamic balance of the moving elements, the static and viscous frictions, and the elastic deformations induced by solid boundaries pressure. In this thesis, progressive model validation with different types of measurements is as well presented, covering the individual hydraulic components models as well as the entire shock absorber model. The measurements have been performed on the flow benches and dynamometers available at the Öhlins Racing measurements laboratory. These comparisons, deeply discussed in the thesis, allow discovering the impact of specific physical effects on the low and high speed hydraulic valves static performance and on the shock absorber dynamic behavior. Numerical results show good agreement, especially at low and medium frequencies and symmetric ‘click’ adjustments on compression and rebound sides. Further model development is necessary in the other areas, for example by considering more complex models of the valve dynamics and fluid flow patterns, i.e. flow forces, together with more advanced models of the sealing elements viscous friction, and thermal effects. Finally, the AMESim environments offered a good level of flexibility in designing the TTR hydro-mechanical system, by allowing the user to choose between different levels of model complexity.
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8

San, Omer. "Multiscale Modeling and Simulation of Turbulent Geophysical Flows." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28031.

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Анотація:
The accurate and efficient numerical simulation of geophysical flows is of great interest in numerical weather prediction and climate modeling as well as in numerous critical areas and industries, such as agriculture, construction, tourism, transportation, weather-related disaster management, and sustainable energy technologies. Oceanic and atmospheric flows display an enormous range of temporal and spatial scales, from seconds to decades and from centimeters to thousands of kilometers, respectively. Scale interactions, both spatial and temporal, are the dominant feature of all aspects of general circulation models in geophysical fluid dynamics. In this thesis, to decrease the cost for these geophysical flow computations, several types of multiscale methods were systematically developed and tested for a variety of physical settings including barotropic and stratified wind-driven large scale ocean circulation models, decaying and forced two-dimensional turbulence simulations, as well as several benchmark incompressible flow problems in two and three dimensions. The new models proposed here are based on two classes of modern multiscale methods: (i) interpolation based approaches in the context of the multigrid/multiresolution methodologies, and (ii) deconvolution based spatial filtering approaches in the context of large eddy simulation techniques. In the first case, we developed a coarse-grid projection method that uses simple interpolation schemes to go between the two components of the problem, in which the solution algorithms have different levels of complexity. In the second case, the use of approximate deconvolution closure modeling strategies was implemented for large eddy simulations of large-scale turbulent geophysical flows. The numerical assessment of these approaches showed that both the coarse-grid projection and approximate deconvolution methods could represent viable tools for computing more realistic turbulent geophysical flows that provide significant increases in accuracy and computational efficiency over conventional methods.
Ph. D.
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9

Shen, Wensheng. "Computer Simulation and Modeling of Physical and Biological Processes using Partial Differential Equations." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_diss/501.

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Scientific research in areas of physics, chemistry, and biology traditionally depends purely on experimental and theoretical methods. Recently numerical simulation is emerging as the third way of science discovery beyond the experimental and theoretical approaches. This work describes some general procedures in numerical computation, and presents several applications of numerical modeling in bioheat transfer and biomechanics, jet diffusion flame, and bio-molecular interactions of proteins in blood circulation. A three-dimensional (3D) multilayer model based on the skin physical structure is developed to investigate the transient thermal response of human skin subject to external heating. The temperature distribution of the skin is modeled by a bioheat transfer equation. Different from existing models, the current model includes water evaporation and diffusion, where the rate of water evaporation is determined based on the theory of laminar boundary layer. The time-dependent equation is discretized using the Crank-Nicolson scheme. The large sparse linear system resulted from discretizing the governing partial differential equation is solved by GMRES solver. The jet diffusion flame is simulated by fluid flow and chemical reaction. The second-order backward Euler scheme is applied for the time dependent Navier-Stokes equation. Central difference is used for diffusion terms to achieve better accuracy, and a monotonicity-preserving upwind difference is used for convective ones. The coupled nonlinear system is solved via the damped Newton's method. The Newton Jacobian matrix is formed numerically, and resulting linear system is ill-conditioned and is solved by Bi-CGSTAB with the Gauss-Seidel preconditioner. A novel convection-diffusion-reaction model is introduced to simulate fibroblast growth factor (FGF-2) binding to cell surface molecules of receptor and heparan sulfate proteoglycan and MAP kinase signaling under flow condition. The model includes three parts: the flow of media using compressible Navier-Stokes equation, the transport of FGF-2 using convection-diffusion transport equation, and the local binding and signaling by chemical kinetics. The whole model consists of a set of coupled nonlinear partial differential equations (PDEs) and a set of coupled nonlinear ordinary differential equations (ODEs). To solve the time-dependent PDE system we use second order implicit Euler method by finite volume discretization. The ODE system is stiff and is solved by an ODE solver VODE using backward differencing formulation (BDF). Findings from this study have implications with regard to regulation of heparin-binding growth factors in circulation.
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10

REN, QIANGGUO. "A BDI AGENT BASED FRAMEWORK FOR MODELING AND SIMULATION OF CYBER PHYSICAL SYSTEMS." Master's thesis, Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/213130.

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Electrical and Computer Engineering
M.S.E.E.
Cyber-physical systems refer to a new generation of synergy systems with integrated computational and physical processes which interact with one other. The development and simulation of cyber-physical systems (CPSs) are obstructed by the complexity of the subsystems of which they are comprised, fundamental differences in the operation of cyber and physical elements, significant correlative dependencies among the elements, and operation in dynamic and open environments. The Multiple Belief-Desire-Intention (BDI) agent system (BDI multi-agent system) is a promising choice for overcoming these challenges, since it offers a natural way to decompose complex systems or large scale problems into decentralized, autonomous, interacting, more or less intelligent entities. In particular, BDI agents have the ability to interact with, and expand the capabilities of, the physical world through computation, communication, and control. A BDI agent has its philosophical grounds on intentionality and practical reasoning, and it is natural to combine a philosophical model of human practical reasoning with the physical operation and any cyber infrastructure. In this thesis, we introduce the BDI Model, discuss implementations of BDI agents from an ideal theoretical perspective as well as from a more practical perspective, and show how they can be used to bridge the cyber infrastructure and the physical operation using the framework. We then strengthen the framework's performance using the state-of-the-art parallel computing architecture and eventually propose a BDI agent based software framework to enable the efficient modeling and simulation of heterogeneous CPS systems in an integrated manner.
Temple University--Theses
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Книги з теми "Physical modeling and simulation"

1

1957-, Ebrom Daniel A., and McDonald John A. 1931-, eds. Seismic physical modeling. Tulsa, Okla: Society of Exploration Geophysicists, 1994.

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2

A, Ebrom Daniel, and McDonald John A, eds. Seismic physical modeling. Tulsa, Okla: Society of Exploration Geophysicists, 1994.

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3

Automated modeling of physical systems. Berlin: Springer, 1995.

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4

Michael, Tiller. Introduction to physical modeling with Modelica. Boston: Kluwer Academic Publishers, 2001.

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5

Lisle, Curtis. Physical modeling for interaction in real-time simulation. Orlando, FL: Institute for Simulation and Training, University of Central Florida, 1996.

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6

Fritzson, Peter. Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118094259.

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7

Introduction to modeling and simulation of technical and physical systems with Modelica. Hoboken, N.J: Wiley, 2011.

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8

Warnatz, Jürgen. Combustion: Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999.

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9

Dar, S. M. Physical and computer modeling of roof bolt systems. Washington, DC: Bureau of Mines, U.S. Dept. of the Interior, 1989.

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J, Kirkby M., ed. Computer simulation in physical geography. 2nd ed. Chichester: J. Wiley, 1993.

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Частини книг з теми "Physical modeling and simulation"

1

Ringleb, Stacie I. "Physical Modeling." In Modeling and Simulation in the Medical and Health Sciences, 65–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118003206.ch4.

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de Baynast, A., M. Bohge, D. Willkomm, and J. Gross. "Physical Layer Modeling." In Modeling and Tools for Network Simulation, 135–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12331-3_9.

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3

Pal, Snehanshu, and K. Vijay Reddy. "Physical Property Evaluation by MD Simulation." In Molecular Dynamics for Materials Modeling, 23–33. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003323495-2.

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4

Wu, Yizhi, Yongsheng Ding, and Hongan Xu. "Comprehensive Fuzzy Evaluation Model for Body Physical Exercise Risk." In Life System Modeling and Simulation, 227–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74771-0_26.

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5

Weitnauer, Erik, Robert Haschke, and Helge Ritter. "Evaluating a Physics Engine as an Ingredient for Physical Reasoning." In Simulation, Modeling, and Programming for Autonomous Robots, 144–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17319-6_16.

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6

El Hefni, Baligh, and Daniel Bouskela. "Averaged Physical Quantities." In Modeling and Simulation of Thermal Power Plants with ThermoSysPro, 43–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05105-1_3.

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7

Kryzhanovsky, Georgy Alekseevich, Anatoly Ivanovich Kozlov, Oleg Ivanovich Sauta, Yuri Grigoryevich Shatrakov, and Ivan Nikolaevich Shestakov. "Physical Modeling of Transport Processes—Simulation Modeling, Training Complexes." In Modeling of Transportation Aviation Processes, 133–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7607-0_7.

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8

Traoré, Mamadou K. "Multi-Perspective Modeling and Holistic Simulation." In Complexity Challenges in Cyber Physical Systems, 81–110. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119552482.ch4.

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9

Hojny, Marcin. "Integration of Physical and Computer Simulation." In Modeling Steel Deformation in the Semi-Solid State, 25–39. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40863-7_4.

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10

Hojny, Marcin. "Integration of Physical and Computer Simulation." In Modeling Steel Deformation in the Semi-Solid State, 31–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67976-1_4.

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Тези доповідей конференцій з теми "Physical modeling and simulation"

1

Henriksson, Dan, and Hilding Elmqvist. "Cyber-Physical Systems Modeling and Simulation with Modelica." In The 8th International Modelica Conference, Technical Univeristy, Dresden, Germany. Linköping University Electronic Press, 2011. http://dx.doi.org/10.3384/ecp11063502.

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2

Larkin, Dale, Kevin J. Lynch, George Ball, Kyle Collins, Matt Schmit, Ted A. Bapty, and Justin B. Knight. "Ontology-Driven Metamodel Validation in Cyber-Physical Systems." In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4005.

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3

"Physical Display for Visualization of Three-Dimensional Surfaces." In The 34th European Modeling & Simulation Symposium. CAL-TEK srl, 2022. http://dx.doi.org/10.46354/i3m.2022.emss.049.

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4

"Comparative Analysis of Digital Twin and Cyber-Physical System Concepts." In The 35th European Modeling & Simulation Symposium. CAL-TEK srl, 2023. http://dx.doi.org/10.46354/i3m.2023.emss.016.

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5

ROBERT, Sylvain, Benoit DELINCHANT, Bruno HILAIRE, and Tanguy YANN. "Plumes: Towards A Unified Approach To Building Physical Modeling." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.2039.

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6

Luo, Shiying, Yu Jian, and Qiang Gao. "Synchronous generator modeling and semi - physical simulation." In 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2019. http://dx.doi.org/10.1109/icems.2019.8921721.

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7

Mezghanni, Mariem, Theo Bodrito, Malika Boulkenafed, and Maks Ovsjanikov. "Physical Simulation Layer for Accurate 3D Modeling." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01315.

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8

Grosswindhager, Stefan, Andreas Voigt, and Martin Kozek. "Efficient Physical Modelling of District Heating Networks." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.735-094.

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9

Poursoltan, Milad, Nathalie Pinede, Bruno Vallespir, and Mamadou Kaba Traore. "A New Modeling Framework For Cyber-Physical And Human Systems." In 2022 Annual Modeling and Simulation Conference (ANNSIM). IEEE, 2022. http://dx.doi.org/10.23919/annsim55834.2022.9859402.

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10

Dourado, E., Lev Sarkisov, Joaquín Marro, Pedro L. Garrido, and Pablo I. Hurtado. "Physical adsorption in porous materials: Molecular modelling, theory and applications." In MODELING AND SIMULATION OF NEW MATERIALS: Proceedings of Modeling and Simulation of New Materials: Tenth Granada Lectures. AIP, 2009. http://dx.doi.org/10.1063/1.3082306.

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Звіти організацій з теми "Physical modeling and simulation"

1

Svobodny, Thomas P. Mathematical Modeling, Simulation, and Control of Physical Processes. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada455803.

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2

Manion, Charles. Physical Component Libraries for SysPhS Modeling and Simulation in Manufacturing. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8490.

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3

Zhu, Minjie, and Michael Scott. Two-Dimensional Debris-Fluid-Structure Interaction with the Particle Finite Element Method. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, April 2024. http://dx.doi.org/10.55461/gsfh8371.

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In addition to tsunami wave loading, tsunami-driven debris can cause significant damage to coastal infrastructure and critical bridge lifelines. Using numerical simulations to predict loads imparted by debris on structures is necessary to supplement the limited number of physical experiments of in-water debris loading. To supplement SPH-FEM (Smoothed Particle Hydrodynamics-Finite Element Method) simulations described in a companion PEER report, fluid-structure-debris simulations using the Particle Finite Element Method (PFEM) show the debris modeling capabilities in OpenSees. A new contact element simulates solid to solid interaction with the PFEM. Two-dimensional simulations are compared to physical experiments conducted in the Oregon State University Large Wave Flume by other researchers and the formulations are extended to three-dimensional analysis. Computational times are reported to compare the PFEM simulations with other numerical methods of modeling fluid-structure interaction (FSI) with debris. The FSI and debris simulation capabilities complement the widely used structural and geotechnical earthquake simulation capabilities of OpenSees and establish the foundation for multi-hazard earthquake and tsunami simulation to include debris.
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4

Pollock, Guylaine M., William Dee Atkins, Moses Daniel Schwartz, Adrian R. Chavez, Jorge Mario Urrea, Nicholas Pattengale, Michael James McDonald, et al. Modeling and simulation for cyber-physical system security research, development and applications. Office of Scientific and Technical Information (OSTI), February 2010. http://dx.doi.org/10.2172/1028942.

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5

Collins, Joseph B. Standardizing an Ontology of Physics for Modeling and Simulation. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada610086.

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6

Sabharwall, Piyush, Ching-Sheng Lin, Joshua E. Hansel, Vincent Laboure, David Andrs, William M. Hoffman, Stephen R. Novascone, Andrew E. Slaughter, and Richard C. Martineau. Integrated Modeling and Simulation Capability For Full Scale Multi-Physics Simulation and Visualization of MicroReactor Concept. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1643493.

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7

Rohmer, Damien, Arkadiusz Sitek, and Grant T. Gullberg. Simulation of the Beating Heart Based on Physically Modeling aDeformable Balloon. Office of Scientific and Technical Information (OSTI), July 2006. http://dx.doi.org/10.2172/908496.

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Tackett, Gregory B. Distributed Virtual Newtonian Physics as a Modeling and Simulation Grand Challenge. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada422094.

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9

Aldemir, Tunc, Richard Denning, Umit Catalyurek, and Stephen Unwin. Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1214664.

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10

Levine, Edward R., and Louis Goodman. Modeling Improved Parameterizations of Shallow Water Ocean Physics into Simulation Models for AUVs. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612403.

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