The effect of magnetohydrodynamic (MHD) plasma actuators on the control of hypersonic shock wave/turbulent boundary layer interactions is investigated here using Reynolds-averaged Navier-Stokes calculations with low magnetic Reynolds number approximation. A Mach 5 oblique shock/turbulent boundary layer interaction was adopted as the basic configuration in this numerical study in order to assess the effects of flow control using different combinations of magnetic field and plasma. Results show that just the thermal effect of plasma under experimental actuator parameters has no significant impact on the flow field and can therefore be neglected. On the basis of the relative position of control area and separation point, MHD control can be divided into four types and so effects and mechanisms might be different. Amongst these, D-type control leads to the largest reduction in separation length using magnetically-accelerated plasma inside an isobaric dead-air region. A novel parameter for predicting the shock wave/turbulent boundary layer interaction control based on Lorentz force acceleration is then proposed and the controllability of MHD plasma actuators under different MHD interaction parameters is studied. The results of this study will be insightful for the further design of MHD control in hypersonic vehicle inlets.
Because of the complexity and irregularity of the internal structure of aluminum foam, it is difficult to establish a three-dimensional model that can accurately reflect this structure. In this study, an algorithm, named spherical core stratification algorithm, for three-dimensional modeling of spherical aluminum foam was proposed, and by using this algorithm, a three-dimensional model for sphere aluminum foam with random pores has been successfully constructed. The constructed model not only has a high similarity with the real structure of spherical open cell aluminum foam, but also can match its pore size and thickness by adjusting the size and number of holes in the random pore. In order to verify the feasibility of the modeling method, firstly, the three-dimensional model of the cylindrical spherical aluminum foam with a size of Φ35 mm × 20 mm and pore diameter of 5 mm has been generated by using the new algorithm. Secondly, taking the influence of relative density and shape function on the compressive properties of spherical open cell aluminum foams into consideration, a quasi-static constitutive model suitable for the material has been established based on the Sherwood-Frost classical compression constitutive model, which provides material parameters for quasi-static compression simulation. The comparison results show that the established constitutive equation has a good fit with the experiment, with a fitting correlation coefficient of above 0.99. Finally, the quasi-static compression simulation was carried out by ABAQUS, and the simulated nominal stress-strain curve was obtained. The simulation results indicate that the simulated stressstrain curve had the same trend with the one obtained by the quasi-static compression experiment with a small deviation.
Machine learning methods such as Adaptive Network-Based Fuzzy Inference System (ANFIS) have been widely employed in intelligent urban storm water disaster warning for the purpose of smart city. However, there exists lack of research proposed for applying ANFIS and mobile application (App) to reach the purpose of smart city. In order to accomplish the goal, the study integrates ANFIS and Qt Framework to develop a Typhoon Rainfall Forecasting System to real-time typhoon rainfall forecast via a mobile device. The Service is first built by applying cluster analysis to typhoon data (Tamsui Weather Station of Taiwan) during June 1967 and November 2020 to classify the data into four groups and then applying the ANFIS to construct the Service with data in each group. The fuzzy rule of ANFIS is established by grid partition method. Both the Service and App employ Qt Framework as the cross-operating development tool, and the App is transformed to a smart mobile device App of different platforms. The simulated results show the following: (1) Taking the example of typhoon Nakri in group 1, the lowest root mean square error (7.898 mm) and the lowest computation time (178 sec) were obtained for training with 1000 steps and three membership functions. (2) Using the optimal parameters of the typhoon belonging to that group can obtain better prediction results. The developed typhoon rainfall forecasting system App in the supplementary information demonstrates that the user can use the smart mobile device for real-time typhoon forecasting at the most three hours ahead easily.
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