Книги: "Time dependent solution"

1

1946-, Verwer J. G., ed. Numerical solution of time-dependent advection-diffusion-reaction equations. Berlin: Springer, 2003.

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2

Jan, Verwer, ed. Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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3

Hundsdorfer, Willem, and Jan Verwer. Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-09017-6.

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4

Kreiss, Heinz-Otto, and Hedwig Ulmer Busenhart. Time-dependent Partial Differential Equations and Their Numerical Solution. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8229-3.

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5

Eliasson, Peter. A solution method for the time-dependent Navier-Stokes equations for laminar, incompressible flow. Stockholm: Aeronautical Research Institute of Sweden, 1989.

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6

Baumeister, Kenneth J. Time-dependent parabolic finite difference formulation for harmonic sound propagation in a two-dimensional duct with flow. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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7

Baumeister, Kenneth J. Time-dependent parabolic finite difference formulation for harmonic sound propagation in a two-dimensional duct with flow. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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8

Richard, Laliberte, and Petit William A, eds. The natural solution to diabetes: [lower your blood sugar 25% simply, safely, without drugs : lose weight, beat your disease--one step at a time]. Pleasantville, N.Y: Reader's Digest, 2004.

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9

Gustafsson, Bertil. Time dependent problems and difference methods. New York: Wiley, 1995.

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10

Bertil, Gustafsson. Time dependent problems and difference methods. New York: Wiley, 1995.

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11

High order difference methods for time dependent PDE. Berlin: Springer, 2008.

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12

Zegeling, P. A. Moving-grid methods for time-dependent partial differential equations. Amsterdam: Centrum voor Wiskunde en Informatica, 1993.

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13

M, Webb Gary, and Zakharian Aramais R, eds. Numerical time-dependent partial differential equations for scientists and engineers. Amsterdam: Elsevier, 2010.

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14

Toro, E. F. A linearised Reimann solver for the time-dependent Euler equations of gas dynamics. Cranfield, Bedford, England: Dept. Aerodynamics and Fluid Mechanics, College of Aeronautics, Cranfield Institute of Technology, 1991.

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15

Trompert, R. A. Local uniform grid refinement for time-dependent partial differential equations. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1995.

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16

Aral, M. M. Analytical solutions for two-dimensional transport equation with time-dependent dispersion coefficients. Atlanta, Ga: Multimedia Environmental Simulations Laboratory, School of Civil and Environmental Engineering, Georgia Institute of Technology, 1996.

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17

Habib, Ammari, Capdeboscq Yves 1971-, and Kang Hyeonbae, eds. Multi-scale and high-contrast PDE: From modelling, to mathematical analysis, to inversion : Conference on Multi-scale and High-contrast PDE:from Modelling, to Mathematical Analysis, to Inversion, June 28-July 1, 2011, University of Oxford, United Kingdom. Providence, R.I: American Mathematical Society, 2010.

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18

Hersh, Reuben. Peter Lax, mathematician: An illustrated memoir. Providence, Rhode Island: American Mathematical Society, 2015.

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19

Dzhamay, Anton, Christopher W. Curtis, Willy A. Hereman, and B. Prinari. Nonlinear wave equations: Analytic and computational techniques : AMS Special Session, Nonlinear Waves and Integrable Systems : April 13-14, 2013, University of Colorado, Boulder, CO. Providence, Rhode Island: American Mathematical Society, 2015.

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20

K, Taylor Lafayette, and United States. National Aeronautics and Space Administration., eds. Numerical solution of the two-dimensional time-dependent incompressible Euler equations. Mississippi State, MS: Mississippi State University, Computational Fluid Dynamics Laboratory, NSF Engineering Research Center for Computational Field Simulation, 1994.

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21

K, Taylor Lafayette, and United States. National Aeronautics and Space Administration., eds. Numerical solution of the two-dimensional time-dependent incompressible Euler equations. Mississippi State, MS: Mississippi State University, Computational Fluid Dynamics Laboratory, NSF Engineering Research Center for Computational Field Simulation, 1994.

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22

Time-Dependent Partial Differential Equations and Their Numerical Solution (Lectures in Mathematics Eth Zurich). Birkhauser, 2001.

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23

R, Spall John, and Langley Research Center, eds. Time-dependent solution for axisymmetric flow over a blunt body with ideal gas, CF,□ or equilibrium air chemistry. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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24

R, Spall John, and Langley Research Center, eds. Time-dependent solution for axisymmetric flow over a blunt body with ideal gas, CF, or equilibrium air chemistry. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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25

Time-dependent solution for axisymmetric flow over a blunt body with ideal gas, CF, or equilibrium air chemistry. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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26

Time-dependent parabolic finite difference formulation for harmonic sound propagation in a two-dimensional duct with flow. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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27

Gustafsson, Bertil, Heinz-Otto Kreiss, and Joseph Oliger. Time-Dependent Problems and Difference Methods. Wiley & Sons, Incorporated, John, 2013.

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28

Gustafsson, Bertil, Heinz-Otto Kreiss, and Joseph Oliger. Time-Dependent Problems and Difference Methods. Wiley & Sons, Incorporated, John, 2013.

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29

Gustafsson, Bertil, Heinz-Otto Kreiss, and Joseph Oliger. Time-Dependent Problems and Difference Methods. Wiley & Sons, Incorporated, John, 2013.

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30

Gustafsson, Bertil, Heinz-Otto Kreiss, and Joseph Oliger. Time-Dependent Problems and Difference Methods. Wiley, 2013.

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31

Kreiss, Heinz-Otto, and Omar Eduardo Ortiz. Introduction to Numerical Methods for Time Dependent Differential Equations. Wiley & Sons, Incorporated, John, 2014.

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32

Introduction To Numerical Methods For Time Dependent Differential Equations. John Wiley & Sons Inc, 2014.

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33

Kreiss, Heinz-Otto, and Omar Eduardo Ortiz. Introduction to Numerical Methods for Time Dependent Differential Equations. Wiley & Sons, Incorporated, John, 2014.

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34

Hundsdorfer, Willem, and Jan G. Verwer. Numerical Solutions of Time-Dependent Advection-Diffusion-Reaction Equations. Springer, 2003.

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35

Yousuff, Hussaini M., Langley Research Center, and Institute for Computer Applications in Science and Engineering., eds. On spectral multigrid methods for the time-dependent Navier-Stokes equations. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1985.

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36

Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method. San Jose, Calif: MCAT Institute, 1990.

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37

Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method. San Jose, Calif: MCAT Institute, 1990.

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38

United States. National Aeronautics and Space Administration., ed. Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method. San Jose, Calif: MCAT Institute, 1990.

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39

United States. National Aeronautics and Space Administration., ed. Development of a time-dependent incompressible Navier-Stokes solver based on a fractional-step method. San Jose, Calif: MCAT Institute, 1990.

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40

Time-Dependant Partial Differential Equations and Their Numerical Solution (Lectures in Mathematics. ETH Zürich). Birkhäuser Basel, 2001.

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41

Morawetz, Klaus. Relaxation-Time Approximation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797241.003.0018.

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A conserving relaxation time approximation is presented resulting into a Mermin-type of polarisation functions. The transport properties are calculated for the relaxation time approximation and an arbitrary band structure. The results for metals and gases are discussed and the shortcoming of relaxation time approximation to describe experimental values is outlined. As improvement, the exact solution of the linearised quantum Boltzmann equation is presented leading to momentum-depended relaxation times specific for each observable. Explicit expressions are given for the electric and thermal conductivity as well as the shear viscosity.

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42

Henriksen, Niels Engholm, and Flemming Yssing Hansen. Dynamic Solvent Effects: Kramers Theory and Beyond. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805014.003.0011.

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This chapter discusses dynamical solvent effects on the rate constants for chemical reactions in solution. The effect is described by stochastic dynamics, where the influence of the solvent on the reaction dynamics is included by describing the motion along the reaction coordinate as Brownian motion. Two theoretical approaches are discussed: Kramers theory with a constant time-independent solvent friction coefficient and Grote–Hynes theory, a generalization of Kramers theory, based on the generalized Langevin equation with a time-dependent solvent friction coefficient. The expressions for the rate constants have the same form as in transition-state theory, but are multiplied by transmission coefficients that incorporate the dynamical solvent effect. In the limit of fast motion along the reaction coordinate, the solvent molecules can be considered as “frozen,” and the predictions of the Grote–Hynes theory can differ from the Kramers theory by several orders of magnitude.

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43

Boudreau, Joseph F., and Eric S. Swanson. Continuum dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0019.

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The theory and application of a variety of methods to solve partial differential equations are introduced in this chapter. These methods rely on representing continuous quantities with discrete approximations. The resulting finite difference equations are solved using algorithms that stress different traits, such as stability or accuracy. The Crank-Nicolson method is described and extended to multidimensional partial differential equations via the technique of operator splitting. An application to the time-dependent Schrödinger equation, via scattering from a barrier, follows. Methods for solving boundary value problems are explored next. One of these is the ubiquitous fast Fourier transform which permits the accurate solution of problems with simple boundary conditions. Lastly, the finite element method that is central to modern engineering is developed. Methods for generating finite element meshes and estimating errors are also discussed.

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44

Teresia, Teaiwa, and Greenpeace Australia Pacific, eds. Turning the tide: Towards a Pacific solution to conditional aid. Suva, Fiji: Greenpeace Australia Pacific, 2002.

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45

G, Collins F., Aumalis A. E, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. An exact solution for the solidification of a liquid slab of binary mixture. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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46

Walker, Ralph C. S. Objective Imperatives. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192857064.001.0001.

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Abstract Kant sees the moral law as an objective imperative in its own right, inherently prescriptive and not dependent on anything or anyone else. This book argues in defence of his position. That has often been misunderstood, largely because of the obscurities in his presentation. The book seeks to clarify the account of the Categorical Imperative in the light of its standing as an objective imperative, exploring the centrality of ‘autonomy’ and the several ways in which feeling is essential to morality. He commits himself to a form of determinism that apparently leaves no place for an objective imperative to motivate anyone. He never provides a clear solution of this apparently fundamental objection to his position. The simplest way would be to reject his determinism. He never explicitly does that, but it turns out that the determinism to which he commits himself is not really complete. Though he never seems fully to recognize it, the transcendental idealism of his first Critique is actually incompatible with the thesis that every event has a cause. This is because his account of the spatio-temporal world is an anti-realist one, open-ended in that it leaves no place for ‘every event’: there is no such thing as ‘every event’. In addition, Kant’s whole position could have been made clearer if he had accepted our conception of a theory. He comes closer to that, however, as time goes on, recognizing that rationality warrants belief in the continuing order of the world and of a designer.

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47

Maysinger, Dusica, P. Kujawa, and Jasmina Lovrić. Nanoparticles in medicine. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.14.

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This article examines the applications of nanoparticles in medicine. Nanomedicine is a promising field that can make available different nanosystems whose novel, usually size-dependent, physical, chemical and/or biological properties are exploited to combat the disease of interest. One kind of particulate systems represents a vast array of either metallic,semiconductor, polymeric, protein or lipid nanoparticles that can be exploited for diagnosis and treatment of various diseases. This article first provides an overview of general issues related to physicochemical and biological properties of different nanoparticles. It then considers the current problems associated with the use of nanoparticles in medicine and suggests some solutions. It also discusses the interaction of nanoparticles with cells and factors that determine these interactions and concludes with some examples of new approaches for real-time imaging of experimental animals that could be useful, complementary methods for evaluations of effectiveness (or toxicity) of novel nanomaterials andnanomedicines.

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48

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

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49

Araújo, Ana Cláudia Vaz de. Síntese de nanopartículas de óxido de ferro e nanocompósitos com polianilina. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-120-2.

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In this work magnetic Fe3O4 nanoparticles were synthesized through the precipitation method from an aqueous ferrous sulfate solution under ultrasound. A 23 factorial design in duplicate was carried out to determine the best synthesis conditions and to obtain the smallest crystallite sizes. Selected conditions were ultrasound frequency of 593 kHz for 40 min in 1.0 mol L-1 NaOH medium. Average crystallite sizes were of the order of 25 nm. The phase obtained was identified by X-ray diffractometry (XRD) as magnetite. Scanning electron microscopy (SEM) showed polydisperse particles with dimensions around 57 nm, while transmission electron microscopy (TEM) revealed average particle diameters around 29 nm, in the same order of magnitude of the crystallite size determined with Scherrer’s equation. These magnetic nanoparticles were used to obtain nanocomposites with polyaniline (PAni). The material was prepared under exposure to ultraviolet light (UV) or under heating, from dispersions of the nanoparticles in an acidic solution of aniline. Unlike other synthetic routes reported elsewhere, this new route does not utilize any additional oxidizing agent. XRD analysis showed the appearance of a second crystalline phase in all the PAni-Fe3O4 composites, which was indexed as goethite. Furthermore, the crystallite size decreases nearly 50 % with the increase in the synthesis time. This size decrease suggests that the nanoparticles are consumed during the synthesis. Thermogravimetric analysis showed that the amount of polyaniline increases with synthesis time. The nanocomposite electric conductivity was around 10-5 S cm-1, nearly one order of magnitude higher than for pure magnetite. Conductivity varied with the amount of PAni in the system, suggesting that the electric properties of the nanocomposites can be tuned according to their composition. Under an external magnetic field the nanocomposites showed hysteresis behavior at room temperature, characteristic of ferromagnetic materials. Saturation magnetization (MS) for pure magnetite was ~ 74 emu g-1. For the PAni-Fe3O4 nanocomposites, MS ranged from ~ 2 to 70 emu g-1, depending on the synthesis conditions. This suggests that composition can also be used to control the magnetic properties of the material.

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50

Fox, Michael H. Why We Need Nuclear Power. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199344574.001.0001.

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Nuclear power may just be the most important solution to our search for clean, sustainable energy sources. Although wind and solar can contribute to our energy mix, we need a reliable source to meet large-scale energy demands and break our dependence on fossil fuels. However, most people are wary, if not downright afraid, of nuclear power. Given nuclear disasters such as Chernobyl and Fukushima, it's not difficult to see why. In the wake of these events, fear has clouded the public's understanding of the facts. It's time to clear up those misconceptions and examine the science behind nuclear power, in order to determine what role it could and should play in our future. In Why We Need Power: The Environmental Case, radiation biologist Michael H. Fox argues that nuclear power is essential to slowing down the impact of global warming. He examines the issue from every angle, relying on thirty-five years of research spent studying the biological effects of radiation. Fox begins with the problem, carefully laying out how our current energy uses and projections for the future will affect greenhouse gases and global warming. The book then evaluates each major energy source and demonstrates the limits of renewable energy sources, concluding that nuclear power is the best solution to our environmental crisis. Fox then delves into nuclear power, looking at the effects of radiation, the potential for nuclear accidents, and the best methods to dispose of nuclear waste. By systematically analyzing each aspect of the nuclear issue, Fox clarifies which concerns have a scientific basis and which remain unsupported. His in-depth exploration of the facts persuasively demonstrates that nuclear power is critical to reducing the effects of energy production on the global climate. Written in an engaging and accessible style, Why We Need Nuclear Power is an invaluable resource for both general readers and scientists interested in the facts behind nuclear energy.

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