Five new species of Eoptychopteridae (Diptera) from the Mesozoic of Asia

Five new species of the extinct family Eoptychopteridae are described from Asia. They are Eoptychoptera shurabica n.sp. (Lower or Middle Jurassic, Kyrgyzstan), E. elevata n.sp., Propty chopterina immensa n.sp., P. tenera n.sp. (last three from the Upper Jurassic or Lower Cretaceous, Siberia), and P. makarov a n.sp. (Lower Jurassic, Siberia).Five new species of the extinct family Eoptychopteridae are described from Asia. They are Eoptychoptera shurabica n.sp. (Lower or Middle Jurassic, Kyrgyzstan), E. elevata n.sp., Propty chopterina immensa n.sp., P. tenera n.sp. (last three from the Upper Jurassic or Lower Cretaceous, Siberia), and P. makarov a n.sp. (Lower Jurassic, Siberia)

Experimental investigation of the influence of the passive porous coating on laminar-turbulent transition of the hypersonic boundary layer of the sharp cone at angles of attack

The problem of the delaying laminar to turbulent transition by the passive porous coating on the sharp cone at the angle of attack has been studied for the first time. Experiments are conducted in a Transit-M hypersonic short-duration wind tunnel at the Mach number 5.9. The boundary layer laminar-turbulent transition is determined by heat flux distribution on the surface of the cone. It is found that the transition on the porous surface in compare to smooth surface has been delaying on the windward side at the 0.5°, 1° and on leeward side at the 0.5°. There is no effect of the porous coating on the location of the laminar to turbulent transition at angle of attack 1°

Knowledge-Informed Neuro-Integrators for Aggregation Kinetics

We report a novel approach for the efficient computation of solutions of a broad class of large-scale systems of non-linear ordinary differential equations, describing aggregation kinetics. The method is based on a new take on the dimensionality reduction for this class of equations which can be naturally implemented by a cascade of small feed-forward artificial neural networks. We show that this cascade, of otherwise static models, is capable of predicting solutions of the original large-scale system over large intervals of time, using the information about the solution computed over much smaller intervals. The computational cost of the method depends very mildly on the temporal horizon, which is a major improvement over the current state-of-the-art methods, whose complexity increases super-linearly with the system's size and proportionally to the simulation time. In cases when prior information about the values of solutions over a relatively small interval of time is already available, the method's computational complexity does not depend explicitly on the system's size. The successful application of the new method is illustrated for spatially-homogeneous systems, with a source of monomers, for a number of the most representative reaction rates kernels