An ANN-based data-predictive approach for comparative study between CFD finite difference and finite volume method

Virendra Talele and Yuvraj Vadaje
Additional contact information
Virendra Talele: Department of Mechanical Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra 412201, India†U V Knowledge Link PVT LTD, Nashik, Maharashtra 422007, India
Yuvraj Vadaje: ��U V Knowledge Link PVT LTD, Nashik, Maharashtra 422007, India‡Department of Mechanical Engineering, MET Institute of Technology, Nashik, Maharashtra 423101, India

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 10, 1-20

Abstract: In computational fluid dynamics (CFD), there is a transformation of methods over the years for building commercially coded software. Each method has predicted its own set of importance, but the exportation and prediction of data are some of the crucial elements for post-processing and validating results. In the present investigation, a detailed comparative analysis is performed over finite difference method (FDM) and finite volume method (FVM) method for the 1D steady-state heat conduction problem over a 1-m-long plate. The comparison was made between solution creation and validation between FDM and FVM for the analytical and computational scheme. The convergence-dependent study is performed as multi-objective optimization to predict how artificial neural network (ANN) can be used to verify and validate the solution of CFD.

Keywords: FDM; FVM; CFD; ANN; 1D; steady state; validation (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S012918312250139X
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:33:y:2022:i:10:n:s012918312250139x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S012918312250139X

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().