LAPSE:2023.23133
Published Article
LAPSE:2023.23133
Analysis of Algorithm Efficiency for Heat Diffusion at Nanoscale Based on a MEMS Structure Investigation
March 27, 2023
Abstract
This paper presents an analysis of the time complexity of algorithms prepared for solving heat transfer problems at nanoscale. The first algorithm uses the classic Dual-Phase-Lag model, whereas the second algorithm employs a reduced version of the model obtained using a Krylov subspace method. This manuscript includes a description of the finite difference method approximation prepared for analysis of the real microelectromechanical system (MEMS) structure manufactured by the Polish Institute of Electron Technology. In addition, an approximation scheme of the model, as well as the Krylov subspace-based model order reduction technique are also described. The paper considers simulation results obtained using both investigated versions of the Dual-Phase-Lag model. Moreover, the relative error generated by the reduced model, as well as the computational complexity of both algorithms, and a convergence of the proposed approach are analyzed. Finally, all analyses are discussed in detail.
Keywords
algorithm convergence analysis, algorithm efficiency analysis, computational complexity analysis, Dual-Phase-Lag heat transfer model, finite difference method scheme, Grünwald–Letnikov fractional derivative, Krylov subspace-based model order reduction, relative error analysis, thermal simulation algorithm
Suggested Citation
Raszkowski T, Zubert M. Analysis of Algorithm Efficiency for Heat Diffusion at Nanoscale Based on a MEMS Structure Investigation. (2023). LAPSE:2023.23133
Author Affiliations
Raszkowski T: Department of Microelectronics and Computer Science, Lodz University of Technology, 90-924 Lodz, Poland [ORCID]
Zubert M: Department of Microelectronics and Computer Science, Lodz University of Technology, 90-924 Lodz, Poland [ORCID]
Journal Name
Energies
Volume
13
Issue
10
Article Number
E2520
Year
2020
Publication Date
2020-05-15
ISSN
1996-1073
Version Comments
Original Submission
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PII: en13102520, Publication Type: Journal Article
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LAPSE:2023.23133
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https://doi.org/10.3390/en13102520
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