261 research outputs found

    Clustering and energy spectra in two-dimensional dusty gas turbulence

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    We present Direct Numerical Simulation (DNS) of heavy inertial particles (dust) immersed in two-dimensional turbulent flow (gas). The dust are modeled as mono-dispersed heavy particles capable of modifying the flow through two-way coupling. By varying the Stokes number (St) and the mass-loading parameter (ϕm)({\phi}_{\rm m}), we study the clustering phenomenon and the gas phase kinetic energy spectra. We find that the dust-dust correlation dimension (d2)(d_2) also depends on ϕm{\phi}_{\rm m}. In particular, clustering decreases as mass-loading (ϕm)({\phi}_{\rm m}), is increased. In the kinetic energy spectra of gas we show: (i) emergence of a new scaling regime, (ii) the scaling exponent in this regime is not unique but rather a function of both St and (ϕm)({\phi}_{\rm m} ). Using a scale-by-scale enstrophy budget analysis we show in the new scaling regime, viscous dissipation due to the gas balances back-reaction from the dust

    Spatial dispersion of elastic waves in a bar characterized by tempered nonlocal elasticity

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    We apply the framework of tempered fractional calculus to investigate the spatial dispersion of elastic waves in a one-dimensional elastic bar characterized by range-dependent nonlocal interactions. The measure of the interaction is given by the attenuation kernel present in the constitutive stress-strain relation of the bar, which follows from the Kr\"oner-Eringen's model of nonlocal elasticity. We employ a fractional power-law attenuation kernel and spatially temper it, to make the model physically valid and mathematically consistent. The spatial dispersion relation is derived, but it turns out to be difficult to solve, both analytically and numerically. Consequently, we use numerical techniques to extract the real and imaginary parts of the complex wavenumber for a wide range of frequency values. From the dispersion plots, it is found that the phase velocity dispersion of elastic waves in the tempered nonlocal elastic bar is similar to that from the time-fractional Zener model. Further, we also examine the unusual attenuation pattern obtained for the elastic wave propagation in the bar.Comment: 16 pages, 4 EPS figures. The peer-reviewed version of this paper is now published in Fract. Calc. Appl. Anal. Vol. 19, No 2 (2016), pp. 498-515, DOI: 10.1515/fca-2016-0026. It is available at this http://www.degruyter.com/view/j/fca The current document is an e-print which differs in e.g. pagination, reference numbering, and typographic detai

    Comment on "Nonlinear charge--voltage relationship in constant phase element" [AEU-Int. J. Electron. Commun. 117, 153104 (2020)]

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    In this comment, we show a dimensional inconsistency that plagues one of the main founding equations, Eq. (5), of the manuscript, [Fouda et al., AEU-Int. J. Electron. Commun. 117, 153104 (2020)]. Also, a resolution of the inconsistency as well as a generalized yet a better version of the equation are suggested.Comment: 3 page

    Hidden jerk in universal creep and aftershocks

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    Most materials exhibit creep-memory under the action of a constant load. The memory behavior is governed by Andrade's power-law of creep. The creep law has an inherent connection with the Omori-Utsu law that describes the frequency of earthquake aftershocks. Both the laws are empirical and they lack a deterministic interpretation. Interestingly, the creep response of a fractional dashpot in anomalous viscoelastic modeling is given by Andrade's law. Consequently, fractional derivatives are invoked but they are plagued by curve-fits. Here we establish an analogous physical mechanism that underlies both the laws. In this Letter, we relate the parameters of the two laws with the macroscopic properties of the material. Surprisingly, the derivation necessitates the existence of a rheological property that relates strain with the first-order time-derivative of stress. The obtained results are validated in light of the established observations.Comment: 12 pages, 1 figur

    REMI: Constraint-based method for integrating relative expression and relative metabolite levels into a thermodynamically consistent metabolic model

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    Flux balance analysis (FBA) allows steady-state flux predictions using optimization principles and often does not result in a unique steady-state flux distribution. Therefore, integration of omics data, such transcriptomics, metabolomics has been employed as additional constraints to reduce the solution space of feasible flux phenotypes. Here, we present a computational method, termed REMI, which integrates relative expression along with relative metabolomics into genome-scale metabolic models (GEMs) to estimate the differential fluxes at GS level. First, we integrated relative expression data into an E.coli GEM using our approach and an existing GX-FBA method (Navid & Almaas, 2012; Orth et al, 2011). The results of our method are more robust and in better agreement with experiments as compared to GX-FBA, because our method facilitates alternative solution enumeration. High frequency solutions analysis between the alternatives may guide in understanding of a biological system physiology. Furthermore, to further reduce the flux space and obtain predictions closer to actual physiological state first we add thermodynamic constraints into models and then employed relative expression as well as relative metabolomics as additional constraints (Henry et al, 2007). The constraint model, resulted in reduced feasible flux space as one can expect, and predicts flux distributions that were in better agreement with experiments. References Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-Based Metabolic Flux Analysis. Biophysical Journal 92: 1792-1805 Navid A, Almaas E (2012) Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach. BMC Systems Biology 6: 150 Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology 7:
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