Relevant bibliographies by topics / Brownian dynamics

Academic literature on the topic 'Brownian dynamics'

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Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Brownian dynamics"

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Sammüller, Florian, and Matthias Schmidt. "Adaptive Brownian Dynamics." Journal of Chemical Physics 155, no. 13 (October 7, 2021): 134107. http://dx.doi.org/10.1063/5.0062396.

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Ma, Lina, Xiantao Li, and Chun Liu. "From generalized Langevin equations to Brownian dynamics and embedded Brownian dynamics." Journal of Chemical Physics 145, no. 11 (September 21, 2016): 114102. http://dx.doi.org/10.1063/1.4962419.

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Bang, Jong-Geun, and Yoong-Sup Yoon. "Analysis of Filtration Performance by Brownian Dynamics." Transactions of the Korean Society of Mechanical Engineers B 33, no. 10 (October 1, 2009): 811–19. http://dx.doi.org/10.3795/ksme-b.2009.33.10.811.

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Erban, Radek. "From molecular dynamics to Brownian dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2167 (July 8, 2014): 20140036. http://dx.doi.org/10.1098/rspa.2014.0036.

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Three coarse-grained molecular dynamics (MD) models are investigated with the aim of developing and analysing multi-scale methods which use MD simulations in parts of the computational domain and (less detailed) Brownian dynamics (BD) simulations in the remainder of the domain. The first MD model is formulated in one spatial dimension. It is based on elastic collisions of heavy molecules (e.g. proteins) with light point particles (e.g. water molecules). Two three-dimensional MD models are then investigated. The obtained results are applied to a simplified model of protein binding to receptors on the cellular membrane. It is shown that modern BD simulators of intracellular processes can be used in the bulk and accurately coupled with a (more detailed) MD model of protein binding which is used close to the membrane.
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Geyer, T., C. Gorba, and V. Helms. "Interfacing Brownian dynamics simulations." Journal of Chemical Physics 120, no. 10 (March 8, 2004): 4573–80. http://dx.doi.org/10.1063/1.1647522.

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Donev, Aleksandar, Chiao-Yu Yang, and Changho Kim. "Efficient reactive Brownian dynamics." Journal of Chemical Physics 148, no. 3 (January 21, 2018): 034103. http://dx.doi.org/10.1063/1.5009464.

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Fradon, Myriam. "Brownian Dynamics of Globules." Electronic Journal of Probability 15 (2010): 142–61. http://dx.doi.org/10.1214/ejp.v15-739.

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Poelert, Sander L., Harrie Weinans, and Amir A. Zadpoor. "BROWNIAN DYNAMICS OF MICROTUBULES." Journal of Biomechanics 45 (July 2012): S440. http://dx.doi.org/10.1016/s0021-9290(12)70441-4.

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WHITE, TOBY O., GIOVANNI CICCOTT, and JEAN-PIERRE HANSEN. "Brownian dynamics with constraints." Molecular Physics 99, no. 24 (December 20, 2001): 2023–36. http://dx.doi.org/10.1080/00268970110090854.

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RICCI, ANDREA, and GIOVANNI CICCOTTI. "Algorithms for Brownian dynamics." Molecular Physics 101, no. 12 (June 20, 2003): 1927–31. http://dx.doi.org/10.1080/0026897031000108113.

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Dissertations / Theses on the topic "Brownian dynamics"

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Levitz, Pierre. "Intermittent brownian dynamics over strands." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-194192.

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Levitz, Pierre. "Intermittent brownian dynamics over strands." Diffusion fundamentals 6 (2007) 78, S. 1-2, 2007. https://ul.qucosa.de/id/qucosa%3A14258.

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Craig, Erin Michelle. "Models for Brownian and biomolecular motors /." Connect to title online (Scholars' Bank) Connect to title online (ProQuest), 2008. http://hdl.handle.net/1794/8565.

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Thesis (Ph. D.)--University of Oregon, 2008.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 164-171). Also available online in Scholars' Bank; and in ProQuest, free to University of Oregon users.
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Ansell, G. C. "A simulation of Brownian dynamics of colloidal dispersions." Thesis, University of Leeds, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373523.

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Lappala, Anna. "Molecular dynamics simulations : from Brownian ratchets to polymers." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709251.

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Burmenko, Irina. "Brownian dynamics simulations of fine-scale molecular models." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32330.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2005.
Includes bibliographical references (leaves 105-111).
One of the biggest challenges in non-Newtonian fluid mechanics is calculating the polymer contribution to the stress tensor, which is needed to calculate velocity and pressure fields as well as other quantities of interest. In the case of a Newtonian fluid, the stress tensor is linearly proportional to the velocity gradient and is given by the Newton's law of viscosity, but no such unique constitutive equation exists for non-Newtonian fluids. In order to predict accurately a polymer's rheological properties, it is important to have a good understanding of the molecular configurations in various flow situations. To obtain this information about molecular configurations and orientations, a micromechanical representation of a polymer molecule must be proposed. A micromechanical model may be fine scale, such as the Kramers chain model, which accurately predicts a real polymer's heological properties, but at the same time possesses too many degrees of freedom to be used in complex flow simulations, or it may be a coarse-grained model, such as the Hookean or the FENE dumbbell models, which can be used in complex flow analysis, but have too few degrees of freedom to adequately describe the rheology. The Adaptive Length Scale (ALS) model proposed by Ghosh et al. is only marginally more complicated than the FENE dumbbell model, yet it is able to capture the rapid stress growth in the start-up of uniaxial elongational flow, which is not predicted correctly by the simple dumbbell models. The ALS model is optimized in order to have its simulation time as close as possible to that of the FENE dumbbell.
(cont.) Subsequently, the ALS model is simulated in the start-up of the uniaxial elongational and shear flows as well as in steady extensional and shear flows, and the results are compared to those obtained with other competing rheological models such as the Kramers chain, FENE chain, and FENE dumbbell. While a 5-spring FENE chain predicts results that are in very good agreement with the Kramers chain, the required simulation time clearly makes it impossible to use this model in complex flow simulations. The ALS model agrees better with the Kramers chain than does the FENE dumbbell in the start-up of shear and elongational flows. However, the ALS model takes too long to achieve steady state, which is something that needs to be explored further before the model is used in complex flow calculations. Understanding of this phenomena may explain why the stress-birefringence hysteresis loop predicted by the ALS model is unexpectedly small. In general, if polymer stress is to be calculated using Brownian dynamics simulations, a large number of stochastic trajectories must be simulated in order to predict accurately the macroscopic quantities of interest, which makes the problem computationally expensive. However, recent technological advances as well as a new simulation algorithm called Brownian configuration fields make such problems much more tractable. The operation count in order to assess the feasibility of using the ALS model in complex flow situations yields very promising results if parallel computing is used to calculate polymer contribution to stress. In an attempt to capture polydispersity of real polymer solutions, the use of multi-mode models is explored.
(cont.) The model is fit to the linear viscoelastic spectrum to obtain relaxation times and individual modes' contributions to polymer viscosity. Then, data-fitting to the dimensionless extensional viscosity in the startup of the uniaxial elongational flow is performed for the ALS and the FENE dumbbell models to obtain the molecule's contour length, bmax. It is found that the results from the single-mode and the four-mode ALS models agree much better with the experimental data than do the corresponding single-mode and four-mode FENE dumbbell models. However, all four models resulted in a poor fit to the steady shear data, which may be explained by the fact that the zero-shear-rate viscosity obtained via a fit to the dynamic data by Rothstein and McKinley and used in present simulations, tends to be somewhat lower than the steady-state shear viscosity at very low shear rates, which may have caused a mismatch between the value of ... used in the simulation and the true ... of the polymer solution. As a motivation for using the ALS model in complex flow calculations, the results by Phillips, who simulated the closed-form version of the model in the benchmark 4:1:4 contraction- expansion problem are presented and compared to the experimental results by Rothstein and McKinley [49]. While the experimental observations show that there exists a large extra pres- sure drop, which increases monotonically with increasing De above the value observed for a Newtonian fluid subjected to the same flow conditions, the simulation results with a closed-form version of the FENE dumbbell model, called FENE-CR, exhibit the opposite trend.
(cont.) The ALS-C model, on the other hand, is able to predict the trend correctly. The use of the ALS-C model in another benchmark problem, namely the flow around an array of cylinders confined between two parallel plates, also shows very promising results, which are in much better agreement with experimental data by Liu as compared to the Oldroyd-B model. The simulation results for the ALS-C and the Oldroyd-B models are due to Joo, et al. [28] and Smith et al. [50], respectively. Overall, it is concluded that the ALS model is superior to the commonly used FENE dumb- bell model, although more work is needed to understand why it takes significantly longer than the FENE dumbbell to achieve steady state in uniaxial elongational flows, and why the stress birefringence hysteresis loop predicted by the ALS model is much smaller than that of the other rheological models.
by Irina Burmenko.
S.M.
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Mühle, Steffen [Verfasser]. "Nanoscale Brownian Dynamics of Semiflexible Biopolymers / Steffen Mühle." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2020. http://d-nb.info/1214887090/34.

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Madraki, Fatemeh. "Shear Thickening in Non-Brownian Suspensions." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1584354185678102.

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Sasai, Masaki, Masahiro Ueda, and Shin I. Nishimura. "Non-Brownian dynamics and strategy of amoeboid cell locomotion." American Physical Society, 2012. http://hdl.handle.net/2237/20623.

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Glaser, Jens, Masashi Degawa, Inka Lauter, Rudolf Merkel, and Klaus Kroy. "Tube geometry and brownian dynamics in semiflexible polymer networks." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-188856.

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Books on the topic "Brownian dynamics"

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Brownian motion: Fluctuations, dynamics, and applications. Oxford: Clarendon Press, 2002.

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Schuss, Zeev. Brownian Dynamics at Boundaries and Interfaces. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7687-0.

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Brownian Agents and Active Particles: Collective dynamics in the natural and social sciences. Berlin: Springer, 2003.

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Schuss, Zeev. Brownian dynamics at boundaries and interfaces: In physics, chemistry, and biology. New York: Springer, 2013.

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Satō, Akira. Introduction to practice of molecular simulation: Molecular dynamics, Monte Carlo, Brownian dynamics, Lattice Boltzmann, dissipative particle dynamics. Amsterdam: Elsevier, 2011.

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The Langevin and generalised Langevin approach to the dynamics of atomic, polymeric and colloidal systems. Amsterdam: Elsevier, 2005.

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Browning [sic] agents and active particles: Collective dynamics in the natural and social sciences. 2nd ed. Berlin: Springer, 2007.

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Browning [sic] agents and active particles: Collective dynamics in the natural and social sciences. 2nd ed. Berlin: Springer, 2007.

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author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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Srivastava, M. S. Dynamic sampling plan in CUSUM procedure for detecting a change in the drift of Brownian motion. Toronto: University of Toronto, Dept. of Statistics, 1991.

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Book chapters on the topic "Brownian dynamics"

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Evans, M. W., and D. M. Heyes. "Brownian Dynamics Simulation." In Modern Techniques in Computational Chemistry: MOTECC™-89, 425–33. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-010-9057-5_9.

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Rietman, Edward A. "Dynamics of Brownian Assembly." In Molecular Engineering of Nanosystems, 52–92. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3556-7_3.

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Bossis, G., and J. F. Brady. "Brownian and Stokesian Dynamics." In Microscopic Simulations of Complex Hydrodynamic Phenomena, 255–70. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-2314-1_19.

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Turq, Pierre, Jose Luis Fernandez-Abascal, and Giovanni Ciccotti. "Brownian Dynamics of Chemical Reactions." In Chemical Reactivity in Liquids, 287–96. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1023-5_25.

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Mourokh, L. G., and A. Yu Smirnov. "Brownian Motion in Submicron Rings." In Quantum Dynamics of Submicron Structures, 727–33. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0019-9_62.

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Cichocki, B. "Interacting Brownian Particles." In Dynamics: Models and Kinetic Methods for Non-equilibrium Many Body Systems, 65–71. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4365-3_5.

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Shen, Tongye, and Chung F. Wong. "Brownian Dynamics Simulation of Peptides with the University of Houston Brownian Dynamics (UHBD) Program." In Methods in Molecular Biology, 75–87. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2285-7_5.

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Ando, Tadashi, and Ichiro Yamato. "Brownian Dynamics Approach to Protein Folding." In Frontiers of Computational Science, 157–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-46375-7_19.

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Ebeling, Werner. "Nonlinear Dynamics of Active Brownian Particles." In Computational Statistical Physics, 141–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04804-7_9.

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Friedman, Avner. "Brownian dynamics simulations of colloidal dispersion." In Mathematics in Industrial Problems, 155–68. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4615-7405-7_15.

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Conference papers on the topic "Brownian dynamics"

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Feldorhof, B. U. "Brownian motion of suspensions." In Slow dynamics in condensed matter. AIP, 1992. http://dx.doi.org/10.1063/1.42332.

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Hauβler, Robert, Roland Bartussek, Peter Hanggi, James B. Kadtke, and Adi Bulsara. "Coupled Brownian rectifiers." In Applied nonlinear dynamics and stochastic systems near the millenium. AIP, 1997. http://dx.doi.org/10.1063/1.54225.

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ANDO, TADASHI, and YUJI SUGITA. "ALGORITHMS FOR BROWNIAN DYNAMICS SIMULATION." In The QBIC Workshop 2014. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811217838_0002.

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Golding, Brage. "Controlling Brownian particles with light." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302363.

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Edelstein, Arieh L., and Noam Agmon. "Brownian dynamics of reversible binding processes." In Ultrafast reaction dynamics and solvent effects. AIP, 1994. http://dx.doi.org/10.1063/1.45408.

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RANDRUP, JØRGEN, and PETER MÖLLER. "BROWNIAN SHAPE DYNAMICS IN NUCLEAR FISSION." In Proceedings of the Fifth International Conference on ICFN5. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814525435_0074.

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Acevedo, C. H., J. R. Guzman-Sepulveda, and A. Dogariu. "Brownian Dynamics Controlled by Phase Gradients." In CLEO: QELS_Fundamental Science. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/cleo_qels.2019.fth1c.8.

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Dice, Kevin, Cortez Gray, George Walker, Cody Baldwin, and Daniel Andresen. "CUDA-Accelerated Simulation of Brownian Dynamics." In PEARC '18: Practice and Experience in Advanced Research Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219104.3229260.

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SHIOKAWA, K. "ENTANGLEMENT DYNAMICS IN QUANTUM BROWNIAN MOTION." In Proceedings of the 9th International Symposium. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814282130_0059.

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Ivenso, Ikenna D., and Todd D. Lillian. "Brownian Dynamics Simulation of the Dynamics of Stretched DNA." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35487.

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DNA is a long flexible polymer and is involved in several fundamental cellular processes such as transcription, replication and chromosome packaging. These processes induce forces and torques in the DNA which deform it. These deformations in turn affect the structure and function of DNA. However, understanding of the dynamic response of DNA to the various forces that act on it is still far from complete. Several experiments have been carried out to study these responses most of which use a micron sized magnetic bead attached to the DNA molecule to both manipulate it and to observe its dynamics. One limitation of this approach is that the dynamics of the DNA molecule has mostly been characterized “indirectly” by observing the dynamics of the magnetic bead. It is also reasonable to expect that, because of the size of the bead relative to that of the DNA, the magnetic bead dynamics will obscure that of the DNA. We adapt existing coarse-grained Brownian dynamics models of DNA to develop a model capable of representing the dynamics of DNA without any of the artifacts inherent to the experiments. This model accounts for bending, torsion, extension, electrostatics, hydrodynamics and the random thermal forces acting on DNA in an electrolyte solution. We then carry out Brownian dynamics simulations with our model to benchmark with well established theoretical results of a stretched polymer in solution. Finally, we employ our model to predict the relaxation time scale for single molecule experiments which sets the framework for future studies in which we plan to further shed light on the dynamics of DNA over long length and time scales.
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Reports on the topic "Brownian dynamics"

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Соловйов, В. М., В. В. Соловйова, and Д. М. Чабаненко. Динаміка параметрів α-стійкого процесу Леві для розподілів прибутковостей фінансових часових рядів. ФО-П Ткачук О. В., 2014. http://dx.doi.org/10.31812/0564/1336.

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Modem market economy of any country cannot successfully behave without the existence of the effective financial market. In the conditions of growing financial market, it is necessary to use modern risk-management methods, which take non-gaussian distributions into consideration. It is known, that financial and economic time series return’s distributions demonstrate so-called «heavy tails», which interrupts the modeling o f these processes with classical statistical methods. One o f the models, that is able to describe processes with «heavy tails», are the а -stable Levi processes. They can slightly simulate the dynamics of the asset prices, because it consists o f two components: the Brownian motion component and jump component. In the current work the usage of model parameters estimation procedure is proposed, which is based on the characteristic functions and is applied for the moving window for the purpose of financial-economic system’ s state monitoring.
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