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The loading mechanism of textures considering turbulence has not been fully covered. This paper aims to investigate the effect of turbulence on the textured loading capacity under water lubrication and to analyze the causes of the turbulence effect.

Computational fluid dynamic models with different textured shapes are established after validation. The transition shear stress transport (SST) model, which is suitable for predicting the transition process of fluid from laminar state to turbulent state, is adopted in the present study. To illustrate the effect of turbulence, the loading capacity of textures predicted by transition SST model and laminar model is compared.

The loading capacity is higher after considering turbulence because more lubricant enters into textures and the flow rate of lubricant to textured outlet increases. There exists an optimal textured depth ratio and density for loading capacity and the change of flow state would not affect the optimal values. The degree of fluid blockage at textured outlet has a dominant influence on loading capacity. As the textured shape changes to triangle or ellipse from rectangle, the vortices at the textured bottom move forward and the blockage at a textured outlet is enhanced, which makes loading capacity improved under the action of blocking effect.

The enhancement of the blocking effect is found to be crucial to the improvement of textured loading capacity after considering turbulence. Present research provides references to understand the loading mechanism of textures under turbulent conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0149/

A city is a spatial carrier of innovation activities. Improving the level of urban innovation can play a significant supporting role in building an innovative country. China began to implement the innovative city pilot policy in 2008 and continued to expand the policy into more areas for exploring the path of innovative urban development with Chinese characteristics and improving urban innovation.

Based on mechanism analysis, this paper used the panel data of 269 cities from 2003 to 2016 to empirically test the effect of the pilot policy on the level of urban innovation by using different methods, such as the difference-in-differences model.

The results show that the innovative city pilot policy significantly improves the level of urban innovation. However, according to the findings of the heterogeneity analysis, the effect of the pilot policy on improving the innovation level in direct-controlled municipalities, provincial capitals and sub-provincial cities is weaker than that in ordinary cities, and the effect of the pilot policy on improving the innovation level in cities with a higher quality of science and education resources is weaker than that in cities with lower quality of science and education resources.

Moreover, as the level of urban innovation increases, the effect of the pilot policy on improving the level of urban innovation is an asymmetric inverted V shape, which means the effect is first strengthened and then weakened. The research also finds that the locational heterogeneity of the pilot policy for improving the level of urban innovation is not notable. In addition, the innovative city pilot policy can strengthen the government's strategic guidance, promote the concentration of talent, incentivize corporate investment and optimize the innovation environment, having a positive impact on urban innovation. Moreover, the effect of concentration of talent and the effect of corporate investment incentive are the important reasons for the pilot policy to promote the improvement of the level of urban innovation.