The experimental analysis is conducted for jet noise reduction of separate flow chevron pylon-based nozzles at takeoff condition. The experimental results indicate that the pylon makes a noise reduction at low-frequency but produces an increase at high-frequency, together with an overall sound pressure reduction below the pylon. Compared to chevron nozzle without pylon, the adding of a pylon reduces noise benefit of chevron nozzles found in the isolated nozzle without a pylon. The best low-frequency noise reduction is located below the pylon where peak noise reduction is as high as about 1.3dB on frequency spectrum.

The jet noise reduction of chevron nozzles was investigated on high bypass ratio turbofan engine separated exhaust system using both computational predictions and scale model experiments. Six different exhaust nozzles are designed including one baseline nozzle and five different chevron nozzles. The jet noise experiments were carried out in the anechoic chamber. Tam and Auriault’s jet noise prediction theory and MGBK theory were used to predict the noise spectra of different exhaust nozzles. The results show that the far-field noise spectra as well as the noise reduction benefits of chevrons are predicted correctly by the two theories although some discrepancies occur at the high frequency range, and Tam and Auriault’s jet noise theory can give relatively more accurate prediction results. chevron nozzles reduce jet noise at the low frequencies, but increase it at high frequencies.

A new hyperbolic function discretization equation for two dimensional Navier-Stokes equation in the stream function vorticity from is derived. The basic idea of this method is to integrat the total flux of the general variable ϕ in the differential equations, then incorporate the local analytic solutions in hyperbolic function for the one-dimensional linearized transport equation. The hyperbolic discretization (HD) scheme can more accurately represent the conservation and transport properties of the governing equation. The method is tested in a range of Reynolds number (Re=100~2000) using the viscous incompressible flow in a square cavity. It is proved that the HD scheme is stable for moderately high Reynolds number and accurate even for coarse grids. After some proper extension, the method is applied to predict the flow field in a new type combustor with air blast double-vortex and obtained some useful results.

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This paper proposes a novel modeling concept, the “public opinion digital twin,” for public opinion analysis. The public opinion digital twin can be regarded as an experimental sandbox for social science. By digitalizing public data acquired from cyberspace into digital models, the modeling enables practical simulation, data analytics, scenario reflection, and decision support in a digital space with fine controllability, so that all possible evolutions of the research target can be analyzed. By simply inputting or filtering variables, any number of future scenarios are simulated, the effect models of each strategy for coping with public opinion are presented, and the optimized solution can be derived from continuous deep learning. If a robust digital twin is established and the required digital replicas are constantly updated, the system can perform risk assessments and trend predictions for social events. In this case, public opinion information can provide intelligent decision support for governments or enterprises and significantly facilitate social loss aversion, which will greatly advance the revolution in production, dissemination, and guidance.