Sciact
  • EN
  • RU

Sintering Simulation Using GPU-Based Algorithm for the Samples with a Large Number of Grains Научная публикация

Конференция Международная конференция "Суперкомпьютерные дни в России"
26-27 сент. 2022 , Москва
Сборник 8th Russian Supercomputing Days, RuSCDays 2022
Сборник, 2022. 713 c. ISBN 9783031229404.
Журнал Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349
Вых. Данные Год: 2022, Страницы: 313-327 Страниц : 15 DOI: 10.1007/978-3-031-22941-1_23
Ключевые слова Sintering ·Porous materials ·Phase-field
Авторы Prokhorov Dmitriy 1 , Bazaikin Yaroslav 1 , Lisitsa Vadim 1
Организации
1 Sobolev Institute of Mathematics

Информация о финансировании (1)

1 Российский научный фонд 21-71-20003

Реферат: The sintering simulation is an actual problem in computational mathematics since computer simulation allows performing much more experiments than can be performed using chemical or physical techniques, especially in the case of studying the material’s intrinsic structure. The most perspective approach for the sintering simulation is a phase-field method. Usually, this approach requires solving the system of the Cahn-Hilliard and Allen-Cahn equation. The main difficulty is that number of Allen-Cahn equations is equal to the number of different grains in the sample. It causes requirements in computational resources to increase not only with increasing the grid size but with increasing the number of grains in the sample; if finite differences are used for solving the system. The paper presents the sintering simulation algorithm, which tracks the individual grains. This feature allows solving each of the Allen-Cahn equations only in a small subdomain corresponding to the current grain. The algorithm is implemented using Graphic Processor Units.
Библиографическая ссылка: Prokhorov D. , Bazaikin Y. , Lisitsa V.
Sintering Simulation Using GPU-Based Algorithm for the Samples with a Large Number of Grains
В сборнике 8th Russian Supercomputing Days, RuSCDays 2022. 2022. – Т.13708. – C.313-327. – ISBN 9783031229404. DOI: 10.1007/978-3-031-22941-1_23 Scopus OpenAlex
Даты:
Опубликована online: 16 дек. 2022 г.
Идентификаторы БД:
Scopus: 2-s2.0-85144816595
OpenAlex: W4313065584
Цитирование в БД:
БД Цитирований
Scopus 1
OpenAlex 1
Альметрики: