A scale-based study of the Reynolds number scaling for the near-wall streamwise turbulence intensity in wall turbulence

Very recently, a defect model which depicts the growth tendency of the near-wall peak of the streamwise turbulence intensity has been developed (Chen $\&$ Sreenivasan, J. Fluid Mech. (2021), vol.908, R3). Based on the finiteness of the near-wall turbulence production, this model predicts that the magnitude of the peak will approach a finite limit as the Reynolds number increases. In the present study, we revisit the basic hypotheses of the model, such as the balance between the turbulence production and the wall dissipation in the region of peak production, the negligible effects of the logarithmic motions on the wall dissipation, and the typical time-scale that the outer-layer flow imposes on the inner layer. Our analyses show that some of them are not consistent with the characteristics of the wall-bounded turbulence. Moreover, based on the spectral stochastic estimation, we develop a framework to assess the wall dissipation contributed by the energy-containing eddies populating the logarithmic region, and uncover the linkage between its magnitude and the local Reynolds number. Our results demonstrate that these multi-scale eddies make a non-negligible contribution to the formation of the wall dissipation. Based on these observations, we verify that the classical logarithmic model, which suggests a logarithmic growth of the near-wall peak of the streamwise turbulence intensity with regard to the friction Reynolds number, is more physically consistent, and still holds even with the latest high-Reynolds-number database.Comment: 21 pages, 6 figures, accepted by International Journal of Heat and Fluid Flo

What Drives Workers to Learn Online during COVID 19 Pandemics?

One of the common practices during the COVID-19 pandemic is to work or study from home. This study aims to reexamine the factors affecting individual continuance intention of e-learning. During the pandemic, via a survey conducted in 2022, we assessed workers’ continuance intention of e-learning from different sectors in Taiwan. This research brought motivations as mediators in continuance intention to e-learning. Through the statistical analysis, we identified the mediation effect of motivations based on the self-determination theory. The results show that autonomous motivation facilitates the learners’ computer self-efficacy, the quality of the system and content toward continuance intention; controlled motivation could mediate the monetary award in influencing the continuance intention. The internalization of motivation is also an effective mediator. The obtained results not only add new knowledge of what affected the continuance intention of e-learning during the pandemic but also provide guidance for employers to allocate resources to boost e-learning after the pandemic

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks