In many simulations of turbulent flows the viscous forces ν∇ 2 u are replaced by a hyper-viscous term −νp(−∇ 2 ) p u in order to suppress the effect of viscosity at the large scales. In this work we examine the effect of hyper-viscosity on decaying turbulence for values of p ranging from p = 1 (regular viscosity) up to p = 100. Our study is based on direct numerical simulations of the Taylor-Green vortex for resolutions from 512 3 to 2048 3 . Our results demonstrate that the evolution of the total energy E and the energy dissipation remain almost unaffected by the order of the hyper-viscosity used. However, as the order of the hyper-viscosity is increased, the energy spectrum develops a more pronounced bottleneck that contaminates the inertial range. At the largest values of p examined, the spectrum at the bottleneck range has a positive power-law behavior E(k) ∝ k α with the power-law exponent α approaching the value obtained in flows at thermal equilibrium α = 2. This agrees with the prediction of Frisch et al. [Phys. Rev. Lett. 101, 144501 (2008)] who suggested that at high values of p, the flow should behave like the truncated Euler equations (TEE). Nonetheless, despite the thermalization of the spectrum, the flow retains a finite dissipation rate up to the examined order, which disagrees with the predictions of the TEE system implying suppression of energy dissipation. We reconcile the two apparently contradictory results, predicting the value of p for which the hyper-viscous Navier-Stokes goes over to the TEE system and we discuss why thermalization appears at smaller values of p.
The charge plasma-based tunnel field-effect transistor (TFET) has been seen as the potential candidate to replace the conventional TFET as it offers fabrication simplicity and its proficiency to be used for ultra-low-power applications. A charge plasma TFET (CPTFET) with hetero materials for enhancement of device performance is presented. For this, a narrow bandgap material (InAs) is used instead of silicon in source region for reducing the lateral tunnelling distance at the source/channel interface. The reduced tunnelling width at the source/ channel junction enables higher band-to-band tunnelling generation rate, thus the device offers higher ON-state current. In this context, a comparative study of CPTFET and hetero junction charge plasma TFET (H-CPTFET) has been performed in terms of transfer characteristic (I ds-V gs), transconductance (g m), gate-to-drain capacitance (C gd), cutoff frequency (f T) and gain-bandwidth product. In addition to this, the effect of variation in channel length (L g) and drain to source voltage (V ds) on the DC and analogue/radio frequency performance of H-CPTFET is also analysed.
Conversations on polarization are increasingly central to discussions of politics and society, but the schisms between parties and states can be hard to identify systematically in what politicians say. In this paper, we demonstrate the use of representation learning as a window into political dialogue on social media through the tweets authored by politicians on Twitter. We propose to embed politicians in a space such that their output embedding vectors represent the content similarity between the two politicians based on their tweets. We further propose a short-text based embedding technique to overcome some of the shortcomings of the previous methods. The learnt embeddings for politicians of India and the United States show two trends. First, that in the US case, we find a clear distinction between Democrats and Republicans, which is also reflected in the coalescing of the states that lean towards each party placing likewise in a graphical space. However, in the Indian case, the federal structure, multiparty system, and linguistic differences clearly manifest in the coalescing political discourse in the largely monolingual north and the scattered regional states. Our work shows ways in which machine learning methods can offer a window into thinking about how polarized political discourses manifest in what politicians say online.
We have analyzed the nature of the transition metal (Ti, V, or Mn) defects in β-silicon nitride using first-principles calculations. Three supercells consisting of 168 atoms, termed as Si71N96Ti, Si71N96V, and Si71N96Mn, were derived from the supercell Si72N96 by substituting a Si atom with a transition metal atom. The density of states (DOS) were calculated for all the supercells. In Si71N96Mn, several defect levels appeared in bandgap. Some of the defect levels are energetically deep. Atomic Layer DOS (ALDOS) calculations have confirmed the localization of the defect levels around the Mn atom. Some of the defect levels would be able to behave as deep electron trap sites. Several defect levels also appeared in the supercell Si71N96V. On the other hand, in Si71N96Ti, no deep defect levels appeared in the bandgap.
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