Optimal Time-Step for Coupled CFD-DEM Model in Sand Production

Daniyar Kazidenov; Sagyn Omirbekov; Yerlan Amanbek

doi:10.1007/978-3-031-37111-0_9

Optimal Time-Step for Coupled CFD-DEM Model in Sand Production

Daniyar Kazidenov
, Sagyn Omirbekov
, Yerlan Amanbek

Department of Mathematics

Nazarbayev University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

3 Downloads (Pure)

Abstract

The coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) is a useful tool for modeling the dynamics of sand production that occurs in oil and gas reservoirs. To perform accurate, physically relevant and efficient calculations, the optimal size of the simulation time-step should be selected. In this study, we investigate the selection of an appropriate time-step interval between CFD and DEM models in sand production simulations. The CPU time, speedup and root mean squared relative error of the obtained results are examined to compare the sand production phenomenon at different coupling numbers. Most of the results including the final sand production rate, bond number and bond ratio indicate that the simulations with coupling numbers of N = 10 and N = 100 produce more accurate results. Moreover, these outcomes demonstrate significant improvements in terms of acceleration of the modeling process.

Original language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings
Editors	Osvaldo Gervasi, Beniamino Murgante, Francesco Scorza, Ana Maria A. C. Rocha, Chiara Garau, Yeliz Karaca, Carmelo M. Torre
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	116-130
Number of pages	15
ISBN (Print)	9783031371103
DOIs	https://doi.org/10.1007/978-3-031-37111-0_9
Publication status	Published - 2023
Event	23rd International Conference on Computational Science and Its Applications, ICCSA 2023 - Athens, Greece Duration: Jul 3 2023 → Jul 6 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14106 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Computational Science and Its Applications, ICCSA 2023
Country/Territory	Greece
City	Athens
Period	7/3/23 → 7/6/23

Funding

Vadim Lisitsa and Kseniia Gadylshina developed frequency domain NDM-net approach and performed numerical experiments under the support of RSF grant no. 22-11-00004. Dmitry Vishnevsky performed seismic modeling using NKS-30T cluster of the Siberian Supercomputer Center under the support of basic research project FWZZ-2022-0022. Kirill Gadylshin optimized the NDM-net hyperparameters. Acknowledgements. The authors wish to acknowledge the support of the research grant, no. AP19575428, from the Ministry of Science and Higher Education of the Republic of Kazakhstan. Authors gratefully acknowledge the support of the Nazarbayev University Faculty Development Competitive Research Grant (NUFDCRG), Grant No. 20122022FD4141. The research was done under the support of RSF grant no. 22-21-00738. Acknowledgement. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5C2A03093531). This work was supported by Samsung Electronics Co., Ltd(IO201208-07839-01). Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2022-00166529) and the Gachon University research fund of 2022 (GCU-202208860001). The EDA tool was supported by the IC Design Education Center(IDEC), Korea. Acknowledgements. This work was supported by the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023, the Inter-University Semiconductor Research Center (ISRC), the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01840, Analysis on technique of accessing and acquiring user data in smartphone), the National Research Foundation of ICCSA 2023 was organized by the National Technical University of Athens (Greece), the University of the Aegean (Greece), the University of Perugia (Italy), the University of Basilicata (Italy), Monash University (Australia), Kyushu Sangyo University (Japan), the University of Minho (Portugal). The conference was supported by two NTUA Schools, namely the School of Rural, Surveying and Geoinformatics Engineering and the School of Electrical and Computer Engineering. Keywords: Restoration Ecology · Land Use/Land Cover Change · Ecosystem services This article has been inspired by the first, preliminary, analytical assessment we developed in the “National Biodiversity Future Center - NBFC” – SPOKE 5 Urban Biodiversity - CUP: D43C22001250001 – funded by the European Union - Next Generation EU under the PNRR MUR Program - “Mission 4, Component 2, Investment 1.4” - Project Code CN_000033. T. Khachkova developed the algorithms to solve C-H and B-L equations within the FNI project FWZZ-2022-0022. V. Lisitsa performed numerical simulation using the Supercomputer of the Siant-Petersburg Polytechnical University under the support of Russian Science Foundation grant no. 21-71-20003. V.L. developed the algorithm of optimal dataset construction, D.V. performed seismic modeling, E.G. performed numerical experiments on NDM-net training under the support of RSF grant no. 22-11-00004, K.G. optimized the NDM-net hyperparameters under the support of RSF grant no. 22-11-00104 The mathematical model was developed by E. Romenski within the framework of the state contract of the Sobolev Institute of Mathematics (project no. FWNF-2022-0008). Numerical method was developed by G. Reshetova and supported by the Russian Science Foundation grant no. 22-21-00759. E. Romenski’s contribution to numerical modeling was supported by the Russian Science Foundation grant no. 22-11-00104. University of Perugia, Italy Universidade Nova de Lisboa, Portugal University of Beira Interior, Portugal University of Almeria, Spain University of Salerno, Italy Erciyes University, Turkey University of Naples “Federico II”, Italy Sungkyunkwan University, Korea Sunway University, Malaysia Sungkyunkwan University, Korea Polytechnic Institute of Viana do Castelo, Portugal Federal University of Bahia, Brazil INFN, Italy Universidade Federal do Rio Grande do Sul, Brazil The Council for Scientific and Industrial Research (CSIR), South Africa Instituto Tecnológico de Informática, Spain Kausan University of Technology, Lithuania London South Bank University, UK Memorial University of Newfoundland, Canada University of Coimbra, Portugal University of L’Aquila, Italy NetApp, India/USA University of Perugia, Italy University of Minho, Portugal U.S. DOE Ames Laboratory, USA Polytechnic Institute of Bragança, Portugal National Centre for Biotechnology, CSIS, Spain Polytechnic Institute of Bragança, Portugal University of Aveiro, Portugal

Funders	Funder number
Canada University of Coimbra
DOE Ames Laboratory
European Union - Next Generation EU
India/USA University of Perugia
Instituto Tecnológico de Informática, Spain Kausan University of Technology, Lithuania London South Bank University, UK Memorial University of Newfoundland
Inter-University Semiconductor Research Center
Italy Erciyes University
Italy Sungkyunkwan University
Italy University of Minho
Korea Polytechnic Institute of Viana do Castelo
Korea Sunway University, Malaysia Sungkyunkwan University
National Biodiversity Future Center	D43C22001250001
Portugal Federal University of Bahia
Portugal National Centre for Biotechnology
Portugal University of Almeria, Spain University of Salerno
Portugal University of Aveiro
Portugal University of Beira Interior
Portugal University of L’Aquila
School of Rural, Surveying and Geoinformatics Engineering
Siant-Petersburg Polytechnical University
Siberian Supercomputer Center	FWZZ-2022-0022
Turkey University of Naples
Samsung	IO201208-07839-01
European Defence Agency
Center for Strategic and International Studies
National Research Foundation
Council for Scientific and Industrial Research, South Africa
Seoul National University
Gachon University	GCU-202208860001
Ministry of Education
Ministry of Science, ICT and Future Planning	2020-0-01840, RS-2022-00166529
National Research Foundation of Korea	NRF-2022S1A5C2A03093531
Ministry of Education and Science of the Republic of Kazakhstan
Universidade Nova de Lisboa
Russian Science Foundation	22-21-00738, 22-21-00759, 21-71-20003, 22-11-00004, 22-11-00104
Institute for Information and Communications Technology Promotion
Università degli Studi di Perugia
Nazarbayev University	20122022FD4141
School of Electrical and Computer Engineering,University of Tehran
Instituto Politécnico de Bragança
National Technical University of Athens

Keywords

CFD-DEM coupling
Sand production
Time-step

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-37111-0_9

Optimal Time-Step for Coupled CFD-DEM Model in Sand Production ed3f94afFinal published version, 1.97 MB

Cite this

Kazidenov, D., Omirbekov, S., & Amanbek, Y. (2023). Optimal Time-Step for Coupled CFD-DEM Model in Sand Production. In O. Gervasi, B. Murgante, F. Scorza, A. M. A. C. Rocha, C. Garau, Y. Karaca, & C. M. Torre (Eds.), Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings (pp. 116-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14106 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37111-0_9

Optimal Time-Step for Coupled CFD-DEM Model in Sand Production. / Kazidenov, Daniyar; Omirbekov, Sagyn; Amanbek, Yerlan.
Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. ed. / Osvaldo Gervasi; Beniamino Murgante; Francesco Scorza; Ana Maria A. C. Rocha; Chiara Garau; Yeliz Karaca; Carmelo M. Torre. Springer Science and Business Media Deutschland GmbH, 2023. p. 116-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14106 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kazidenov, D, Omirbekov, S & Amanbek, Y 2023, Optimal Time-Step for Coupled CFD-DEM Model in Sand Production. in O Gervasi, B Murgante, F Scorza, AMAC Rocha, C Garau, Y Karaca & CM Torre (eds), Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14106 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 116-130, 23rd International Conference on Computational Science and Its Applications, ICCSA 2023, Athens, Greece, 7/3/23. https://doi.org/10.1007/978-3-031-37111-0_9

Kazidenov D, Omirbekov S, Amanbek Y. Optimal Time-Step for Coupled CFD-DEM Model in Sand Production. In Gervasi O, Murgante B, Scorza F, Rocha AMAC, Garau C, Karaca Y, Torre CM, editors, Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 116-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-37111-0_9

Kazidenov, Daniyar ; Omirbekov, Sagyn ; Amanbek, Yerlan. / Optimal Time-Step for Coupled CFD-DEM Model in Sand Production. Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. editor / Osvaldo Gervasi ; Beniamino Murgante ; Francesco Scorza ; Ana Maria A. C. Rocha ; Chiara Garau ; Yeliz Karaca ; Carmelo M. Torre. Springer Science and Business Media Deutschland GmbH, 2023. pp. 116-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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