Konrad Gizynski | Searcharxiv

arXiv2021

Internal energy in compressible Poiseuille flow

We analyse a compressible Poiseuille flow of ideal gas in a plane channel. We provide the form of internal energy U for a non-equilibrium stationary state (NESS) that includes viscous dissipation and pressure work. We demonstrate that U depends strongly on the ratio Δp/p_0, where Δp is the pressure difference between inlet and outlet and p_0 is the outlet's pressure. In addition, U depends on two other variables: the channel aspect ratio and the parameter equivalent to Reynolds number. The stored internal energy, ΔU=U-U0, is small compared to the internal energy U0 of the equilibrium state (ES) for a moderate range of values of Δp/p_0. However, ΔU can become large for big Δp or close to vacuum conditions at the outlet (p_0~0 Pa).

physics.flu-dyn↗

arXiv2020

On the Optimum Geometry and Training Strategy for Chemical Classifiers that Recognize the Shape of a Sphere

In this paper, we continue the discussion on database classifiers constructed with networks of interacting chemical oscillators. In our previous papers we demonstrated that a small, regular network of oscillators can predict if three random numbers in the range $[0,1]$ describe a point located inside a sphere inscribed within the unit cube $[0,1] \times [0,1] \times [0,1]$ with the accuracy exceeding $80 \%$. The parameters of the network were determined using evolutionary optimization. Here we apply the same technique to investigate if the classifier accuracy for this problem can be improved by selecting a specific geometry of interacting oscillators. We also address questions on the optimum size of the training database for evolutionary optimization and on the minimum size of the testing dataset for objective evaluation of classifier accuracy.

nlin.AO↗