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Books on the topic 'Phase field modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Biner, S. Bulent. Programming Phase-Field Modeling. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41196-5.

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2

Mazo, Aleksandr, and Konstantin Potashev. The superelements. Modeling of oil fields development. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1043236.

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This monograph presents the basics of super-element modeling method of two-phase fluid flows occurring during the development of oil reservoir. The simulation is performed in two stages to reduce the spatial and temporal scales of the studied processes. In the first stage of modeling of development of oil deposits built long-term (for decades) the model of the global dynamics of the flooding on the super-element computational grid with a step equal to the average distance between wells (200-500 m). Local filtration flow, caused by the action of geological and technical methods of stimulation, are modeled in the second stage using a special mathematical models using computational grids with high resolution detail for the space of from 0.1 to 10 m and time — from 102 to 105 C. The results of application of the presented models to the solution of practical tasks of development of oil reservoir. Special attention is paid to the issue of value transfer in filtration-capacitive properties of the reservoir, with a detailed grid of the geological model on the larger grid reservoir models. Designed for professionals in the field of mathematical and numerical modeling of fluid flows occurring during the development of oil fields and using traditional commercial software packages, as well as developing their own software. May be of interest to undergraduate and graduate students studying in areas such as "Mechanics and mathematical modeling", "Applied mathematics", "Oil and gas".

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3

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24406-8.

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4

Nudo, Raffaele, ed. Lezioni dai terremoti: fonti di vulnerabilità, nuove strategie progettuali, sviluppi normativi. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-072-3.

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This book is a collection of the academic contributions presented at the conference entitled "Lessons from earthquakes: sources of vulnerability, new design strategies and regulatory developments" which was held at Chianciano Terme on 8 October 2010. The issues addressed are central to Seismic Engineering and comprise a wide range of arguments on both consolidated subjects and innovative aspects in the sector. Among these, appropriate attention is devoted to: analysis of the structural instability revealed on the occasion of seismic events and the lessons that can be drawn from the same; the procedures of assessment of the existing buildings, starting from the phase of monitoring and diagnostics through to the definition of the most opportune intervention techniques; the use of composite materials and alternative methods of seismic protection; non-linear field modelling relating to regular and non-regular structures; and finally, the development of the methods of calculation that have characterised the evolution of the regulatory codes.

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5

Biner, S. Bulent. Programming Phase-Field Modeling. Springer, 2017.

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6

Biner, S. Bulent. Programming Phase-Field Modeling. Springer, 2018.

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7

Biner, S. Bulent. Programming Phase-Field Modeling. Springer London, Limited, 2016.

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8

Wick, Thomas. Multiphysics Phase-Field Fracture: Modeling, Adaptive Discretizations, and Solvers. de Gruyter GmbH, Walter, 2020.

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9

Zhang, Tao. Phase-field Modeling of Phase Changes and Mechanical Stresses in Electrode Particles of Secondary Batteries. KIT Scientific Publishing, 2021.

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10

Provatas, Nikolas, Tatu Pinomaa, and Nana Ofori-Opoku. Quantitative Phase Field Modelling of Solidification. Taylor & Francis Group, 2021.

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11

Quantitative Phase Field Modelling of Solidification. Taylor & Francis Group, 2021.

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12

Quantitative Phase Field Modelling of Solidification. Taylor & Francis Group, 2021.

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13

Provatas, Nikolas, Tatu Pinomaa, and Nana Ofori-Opoku. Quantitative Phase Field Modelling of Solidification. Taylor & Francis Group, 2021.

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14

Lenhard, Johannes. Computation and Simulation. Edited by Robert Frodeman. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198733522.013.36.

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Simulation modeling makes use of computational resources in fields that are not of a mathematical nature. The first part of this chapter introduces four phases of the historical development of computation and simulation (C&S) and discusses their relationship to interdisciplinarity. Starting with a pioneering phase, C&S went through disciplinary specialization, ubiquitous diffusion, and a recent infrastructural turn. The second part of the chapter explores aspects of the interdisciplinary dynamics of C&S, especially the interplay between complexity, experimentation, and visualization. Then, C&S is put in relationship to Shinn’s “research technology” and Galison’s “trading zones.” Finally, the chapter observes a recent turn toward network-like interdisciplinary integration.

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15

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer London, Limited, 2015.

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16

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer, 2015.

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17

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer International Publishing AG, 2016.

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18

Barrat, J. L., and J. J. de Pablo. Introduction to molecular simulations in soft matter. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198789352.003.0011.

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We describe the main features of the coarse-grained models that are typically useful in modelling soft interfaces, from force fields to the continuum descriptions involving density fields. We explain the theoretical basis of the main numerical methods that are used to explore the phase space associated with these models. Finally, three recent examples, illustrating the spirit in which relatively simple simulations can contribute to solving pending problems in soft matter physics, are briefly described. Clearly, a short series of lectures can offer, at best, a biased and restricted view of the available approaches. Our aim here will be to provide the reader with such an overview, with a focus on methods and descriptions that ‘bridge the scale’ between the molecular scale and the continuum or quasi-continuum one. The objective to present a guide to the relevant literature—which has now to a large extent appeared in the form of textbooks.

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19

Megerdoomian, Karine. Computational Linguistics. Edited by Anousha Sedighi and Pouneh Shabani-Jadidi. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198736745.013.19.

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This chapter introduces the fields of Computational Linguistics (CL)—the computational modelling of linguistic representations and theories—and Natural Language Processing (NLP)—the design and implementation of tools for automated language understanding and production—and discusses some of the existing tensions between the formal approach to linguistics and the current state of the research and development in CL and NLP. The paper goes on to explain the specific challenges faced by CL and NLP for Persian, much of it derived from the intricacies presented by the Perso-Arabic script in automatically identifying word and phrase boundaries in text, as well as difficulties in automatic processing of compound words and light verb constructions. The chapter then provides an overview of the state of the art in current and recent CL and NLP for Persian. It concludes with areas for improvement and suggestions for future directions.

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