A coupled thermo-mechanical model for the simulation of discrete particle systems in advanced manufacturing (2020)
- Authors:
- USP affiliated authors: CAMPELLO, EDUARDO DE MORAIS BARRETO - EP ; RUIZ, OSVALDO DARIO QUINTANA - EP
- Unidade: EP
- DOI: 10.1007/s40430-020-02465-5
- Subjects: GEOMETRIA E MODELAGEM COMPUTACIONAL; TRANSFERÊNCIA DE CALOR
- Keywords: Método dos Elementos Discretos
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2020
- Source:
- Título: Journal of the Brazilian Society of Mechanical Sciences and Engineering
- ISSN: 1806-3691
- Volume/Número/Paginação/Ano: v. 42, Article 387, 21 p., 2020
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
QUINTANA RUIZ, Osvaldo Dario e CAMPELLO, Eduardo de Morais Barreto. A coupled thermo-mechanical model for the simulation of discrete particle systems in advanced manufacturing. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, p. 21 , 2020Tradução . . Disponível em: https://doi.org/10.1007/s40430-020-02465-5. Acesso em: 28 mar. 2026. -
APA
Quintana Ruiz, O. D., & Campello, E. de M. B. (2020). A coupled thermo-mechanical model for the simulation of discrete particle systems in advanced manufacturing. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42, 21 . doi:10.1007/s40430-020-02465-5 -
NLM
Quintana Ruiz OD, Campello E de MB. A coupled thermo-mechanical model for the simulation of discrete particle systems in advanced manufacturing [Internet]. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2020 ; 42 21 .[citado 2026 mar. 28 ] Available from: https://doi.org/10.1007/s40430-020-02465-5 -
Vancouver
Quintana Ruiz OD, Campello E de MB. A coupled thermo-mechanical model for the simulation of discrete particle systems in advanced manufacturing [Internet]. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2020 ; 42 21 .[citado 2026 mar. 28 ] Available from: https://doi.org/10.1007/s40430-020-02465-5 - A methodology to predict the effective thermal conductivity of a granular assembly using deep learning
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