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Research Article | Open Access

Primary resonance analysis of a novel fractional-order coronary artery model

Guanghua Wei1Xiaorong Zhang2Zhoujin Cui3( )
School of Science, Jinling Institute of Technology, Nanjing, 211169, China
School of Economics and Management, Jiangsu Maritime Institute, Nanjing, 211170, China
School of Mathematical Sciences, Jiangsu Second Normal University, Nanjing, 210013, China
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Abstract

This paper analyzed the primary resonance of a novel fractional-order coronary artery model, exploring cardiovascular diseases related to vascular behavior from the nonlinear dynamics perspective. By applying the averaging method, approximate analytical solutions and the amplitude-frequency equation were derived, whereas Lyapunov stability theory was utilized to analyze the steady-state behavior. Numerical simulations validated the accuracy of the analytical approach, demonstrating close agreement between theoretical predictions and computational results. Key findings include the identification of parameter-driven bifurcations that modulate resonance amplitude and stability. Specifically, a lower fractional order q and reduced damping μ amplify resonant responses, while increased pulse pressure F correlates with elevated spasm risk. These results provide a theoretical framework for understanding how blood vessel viscoelasticity and external stimuli influence cardiovascular dynamics, offering insights for developing diagnostic tools and therapeutic strategies to mitigate coronary artery spasm and related cardiovascular diseases.

CLC number: 34A08, 34K37, 37N99

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AIMS Mathematics
Pages 14996-15011

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Cite this article:
Wei G, Zhang X, Cui Z. Primary resonance analysis of a novel fractional-order coronary artery model. AIMS Mathematics, 2025, 10(6): 14996-15011. https://doi.org/10.3934/math.2025672

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Received: 19 April 2025
Revised: 14 June 2025
Accepted: 24 June 2025
Published: 30 June 2025
©2025 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)