Abstract
From a statistical mechanics perspective, to describe the dynamics of a tracer, a phenomenological model has been established by a generalized Langevin equation (GLE) which includes a Basset force, a periodic perturbation force, a Stokes force, an external force and a thermal noise. Using the generalized Shapiro-Loginov formula, the iterative expressions of the first moments of the system are obtained. The time series of the first moments have been extensively investigated. By analyzing the time series of the first moments of the system with different system parameters, the irregular responses of the curves are revealed and tend to be stable for a long time. Significantly, the dynamics of average amplitudes of the first moments, influenced by various system parameters, have also been addressed in detail. Especially, the monotonic and non-monotonic properties of the average amplitudes of the first moments versus the memory exponent \(\alpha \) are discussed.
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09 May 2023
A Correction to this paper has been published: https://doi.org/10.1140/epjb/s10051-023-00523-0
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 11601450, 11961006, 11526172), Guangxi Natural Science Foundation (Nos. 2020GXNSFAA159100, AD21159013, 2021GXNSFAA220033), Natural Science Foundation of Guangxi Minzu University (No. 2019KJQD02), Sichuan Youth Science Project (No. 2022NSFSC1840), and Xiangsi Lake Young Scholars Innovation Team of Guangxi Minzu University (No. 2021RSCXSHQN05).
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LQ, GH: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. YP, HL, YT: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
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Appendices
Appendix A: Coefficients of Eqs. (29), (30), (31)
where \(d_1(s),d_1(s),d_3(s)\) and \(g_1(s),g_2(s),g_3(s),g_4(s)\) are given by Eqs. (27),(28), respectively.
Appendix B: The inverse Laplace transform of Eqs. (A.1)–(A.18)
From Eqs. (A.1)–(A.18), due to the following limit
where
therefore, one has
One assumes that \({h_n}\left( t \right) ,n = 1,2, \ldots ,6\), are the inverse Laplace transform of \({H_n}\left( s \right) ,n = 1,2, \ldots ,6\), respectively. Due to the fact that \({H_n}\left( s \right) ,n = 1,2, \ldots ,6\) are equivalent to \({Y_n}\left( s \right) ,n = 1,2, \ldots ,6\), taking the inverse Laplace transform of Eqs. (A.1)-(A.6), one can obtain
where involving two-parameter Mittag-Leffler function [56, 57] is given by \( E_{\mu , v}(z)=\sum _{n=0}^{\infty } \frac{z^{n}}{\varGamma (\mu n+v)}, \mathcal {R} e(\mu )>0, v \in \mathbb {C}, z \in \mathbb {C} \), and \(E_{\mu , 1}(z)=E_{\mu }(z)\) turns to Mittag-Leffler function [56, 57], \(E_{\mu }(z)=\sum _{n=0}^{\infty } \frac{z^{n}}{\varGamma (\mu n+1)}\), \(\alpha >0, z \in \mathbb {C}\).
Letting \({i_n}\left( t \right) ={L^{ - 1}}\left[ {I_n}\left( s \right) \right] ,n = 1,2, \ldots ,6\), Since \({I_n}\left( s \right) ,n = 1,2, \ldots ,6\), are equivalent to \({U_n}\left( s \right) ,n = 1,2, \ldots ,6\), taking the inverse Laplace transform of Eqs (A.7)–(A.12), one can obtain
Letting \({j_n}\left( t \right) ={L^{ - 1}}\left[ {J_n}\left( s \right) \right] ,n = 1,2, \ldots ,6\), using similar technique and taking the inverse Laplace transform of Eqs. (A.13)-(A.18), one can obtain
Appendix C: Relaxation functions
Using properties of Laplace trasform, from Eqs.(B.2)- (43), the relaxation functions \({{\hat{h}}_n}\left( t \right) \), \({{\hat{i}}_n}\left( t \right) \), \({{\hat{j}}_n}\left( t \right) (n=1,2,3)\) are given by
Appendix D: commutative property between average and differentiation
Supposed that x(t) is a random process with probability density function p(x, t). Its average or expectation is defined as \(\langle x\rangle =\int _{-\infty }^{+\infty }xp(x,t)\text {d}x\). Distinctly, the integral in the definition of average converges uniformly. Using differential properties and integral properties, one could yield
For \(\alpha \rightarrow 1\) one could find that \(\left\langle \frac{d}{dt}x(t)\right\rangle =\frac{d}{dt}\left\langle x(t)\right\rangle \). Analogously, for \(\alpha \rightarrow 2\), \(\left\langle \frac{d^2}{dt^2}x(t)\right\rangle =\frac{d^2}{dt^2}\left\langle x(t)\right\rangle \). For commutativity of integer order, one can also see Refs. [50,51,52,53,54].
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Qiu, L., He, G., Peng, Y. et al. Average amplitudes analysis for a phenomenological model under hydrodynamic interactions with periodic perturbation and multiplicative trichotomous noise. Eur. Phys. J. B 96, 43 (2023). https://doi.org/10.1140/epjb/s10051-023-00511-4
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DOI: https://doi.org/10.1140/epjb/s10051-023-00511-4