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  • Open Access

    ARTICLE

    Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

    Hongsheng Su, Lixia Dong*, Xiaoying Yu, Kai Liu

    Energy Engineering, Vol.121, No.4, pp. 973-986, 2024, DOI:10.32604/ee.2023.043497 - 26 March 2024

    Abstract Time based maintenance (TBM) and condition based maintenance (CBM) are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes, however, these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions issues. Hence, this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the issue. Based on the change of the gearbox state during operation and the influence of external random factors on the gearbox state, a stochastic differential equation model (SDE) and More > Graphic Abstract

    Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

  • Open Access

    ARTICLE

    Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

    Kazem Nouri1,*, Milad Fahimi1, Leila Torkzadeh1, Dumitru Baleanu2,3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1495-1514, 2022, DOI:10.32604/cmc.2022.024406 - 24 February 2022

    Abstract The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics… More >

  • Open Access

    ARTICLE

    A Numerical Efficient Technique for the Solution of Susceptible Infected Recovered Epidemic Model

    Muhammad Shoaib Arif1,*, Ali Raza1,2, Kamaleldin Abodayeh3, Muhammad Rafiq4, Mairaj Bibi5, Amna Nazeer5

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 477-491, 2020, DOI:10.32604/cmes.2020.011121 - 20 July 2020

    Abstract The essential features of the nonlinear stochastic models are positivity, dynamical consistency and boundedness. These features have a significant role in different fields of computational biology and many more. The aim of our paper, to achieve the comparison analysis of the stochastic susceptible, infected recovered epidemic model. The stochastic modelling is a realistic way to study the dynamics of compartmental modelling as compared to deterministic modelling. The effect of reproduction number has also observed in the stochastic susceptible, infected recovered epidemic model. For comparison analysis, we developed some explicit stochastic techniques, but they are the More >

  • Open Access

    ARTICLE

    A Reliable Stochastic Numerical Analysis for Typhoid Fever Incorporating With Protection Against Infection

    Muhammad Shoaib Arif1,*, Ali Raza1, Muhammad Rafiq2, Mairaj Bibi3, Rabia Fayyaz3, Mehvish Naz3, Umer Javed4

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 787-804, 2019, DOI:10.32604/cmc.2019.04655

    Abstract In this paper, a reliable stochastic numerical analysis for typhoid fever incorporating with protection against infection has been considered. We have compared the solutions of stochastic and deterministic typhoid fever model. It has been shown that the stochastic typhoid fever model is more realistic as compared to the deterministic typhoid fever model. The effect of threshold number T* hold in stochastic typhoid fever model. The proposed framework of the stochastic non-standard finite difference scheme (SNSFD) preserves all dynamical properties like positivity, bounded-ness and dynamical consistency defined by Mickens, R. E. The stochastic numerical simulation of More >

  • Open Access

    ARTICLE

    Numerical Treatment for Stochastic Computer Virus Model

    Ali Raza1, Muhammad Shoaib Arif1,*, Muhammad Rafiq2, Mairaj Bibi3, Muhammad Naveed1, Muhammad Usman Iqbal4, Zubair Butt4, Hafiza Anum Naseem4, Javeria Nawaz Abbasi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.2, pp. 445-465, 2019, DOI:10.32604/cmes.2019.06454

    Abstract This writing is an attempt to explain a reliable numerical treatment for stochastic computer virus model. We are comparing the solutions of stochastic and deterministic computer virus models. This paper reveals that a stochastic computer virus paradigm is pragmatic in contrast to the deterministic computer virus model. Outcomes of threshold number C hold in stochastic computer virus model. If C < 1 then in such a condition virus controlled in the computer population while C > 1 shows virus persists in the computer population. Unfortunately, stochastic numerical methods fail to cope with large step sizes of time. More >

  • Open Access

    ARTICLE

    The Stochastic α Method: A Numerical Method for Simulation of Noisy Second Order Dynamical Systems

    Nagalinga Rajan, Soumyendu Raha1

    CMES-Computer Modeling in Engineering & Sciences, Vol.23, No.2, pp. 91-116, 2008, DOI:10.3970/cmes.2008.023.091

    Abstract The article describes a numerical method for time domain integration of noisy dynamical systems originating from engineering applications. The models are second order stochastic differential equations (SDE). The stochastic process forcing the dynamics is treated mainly as multiplicative noise involving a Wiener Process in the Itô sense. The developed numerical integration method is a drift implicit strong order 2.0 method. The method has user-selectable numerical dissipation properties that can be useful in dealing with both multiplicative noise and stiffness in a computationally efficient way. A generalized analysis of the method including the multiplicative noise is More >

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