Load Sharing Behavior of Star Gearing Reducer for Geared Turbofan Engine

Load sharing behavior is very important for power-split gearing system, star gearing reducer as a new type and special transmission system can be used in many industry fields. However, there is few literature regarding the key multiple-split load sharing issue in main gearbox used in new type geared turbofan engine. Further mechanism anal- ysis are made on load sharing behavior among star gears of star gearing reducer for geared turbofan engine. Compre- hensive meshing error analysis are conducted on eccentricity error, gear thickness error, base pitch error, assembly error, and bearing error of star gearing reducer respectively. Floating meshing error resulting from meshing clearance variation caused by the simultaneous floating of sun gear and annular gear are taken into account. A refined mathematical model for load sharing coefficient calculation is established in consideration of different meshing stiffness and support- ing stiffness for components. The regular curves of load sharing coefficient under the influence of interactions, single action and single variation of various component errors are obtained. The accurate sensitivity of load sharing coefficienttoward different errors is mastered. The load sharing coef- ficient of star gearing reducer is 1.033 and the maximum meshing force in gear tooth is about 3010 N. This paper provides scientific theory evidences for optimal parameter design and proper tolerance distribution in advanced devel- opment and manufacturing process, so as to achieve optimal effects in economy and technology.

Study on the Influencing Factors of Stakeholders Knowledge Sharing Behavior in Aircraft Product Development

Based on the individual level,influencing factors of stakeholders knowledge sharing behavior in aircraft product development are discussed,which are participant degree,sharing intention,and sharing capability.And the relationship of these factors between explicit knowledge sharing behavior and tacit knowledge sharing behavior are analyzed theoretically,some assumptions are put forward,and a theorittcal study model is established.Then,using a structural equation model,the assumptions are validated through principal component analysis and model fitting for 215 sample data.Lastly,the conclusion is gained that participant degree,sharing intention,and sharing capability all have a significant positive effect on the explicit knowledge sharing behavior and tacit knowledge sharing behavior in aircraft product development.

Unpredictable Sharing Economy: Barbaric Growth, Deceptive Representation and Divergent Regulation

In the context of globalization and informatization, some traditionally lowtech and labor-intensive sectors have developed mobile Internet-based platforms and formed a new business format: the sharing economy. In the beginning, such a new business format lowered the threshold of participation, cut dealing costs, and enjoyed the relative advantage of being free from regulatory institutions. Therefore, it easily attracted investments and witnessed rapid development. Early-stage of the sharing economy features were "being shared, collaborative and on-demand." For a variety of reasons, the development of the sharing economy has already been accepted and recognized by current society. It is expected to become a new growth point and has the chance to replace the established business format of our industrial society and subsequently trigger many more "pain points." Regarding the regulation and governance of the sharing economy, there are divergences in its legality, platform nature, industrial impact and employment relationships. Identifying and resolving such regulatory divergences to the maximum degree is a prerequisite for the governance of the sharing economy and also a key to its smooth development.

A Shared Neural Node for Multiple Innate Behaviors in Drosophila

The nervous system orchestrates diverse behaviors such as reproduction, sleep, feeding, and aggression, and selects a single behavior for execution at any given time. This requires neural mechanisms for behavioral selection sensitive to both internal physiological states and external environmental conditions. For example, hungry animals