Unsteady Cylinder Wakes from Arbitrary Bodies with Differentiable Physics-Assisted Neural Network

This work delineates a hybrid predictive framework configured as a coarse-grained surrogate for reconstructing unsteady fluid flows around multiple cylinders of diverse configurations. The presence of cylinders of arbitrary nature causes abrupt changes in the local flow profile while globally exhibiting a wide spectrum of dynamical wakes fluctuating in either a periodic or chaotic manner. Consequently, the focal point of the present study is to establish predictive frameworks that accurately reconstruct the overall fluid velocity flowfield such that the local boundary layer profile, as well as the wake dynamics, are both preserved for long time horizons. The hybrid framework is realized using a base differentiable flow solver combined with a neural network, yielding a differentiable physics-assisted neural network (DPNN). The framework is trained using bodies with arbitrary shapes, and then it is tested and further assessed on out-of-distribution samples. Our results indicate that the neural network acts as a forcing function to correct the local boundary layer profile while also remarkably improving the dissipative nature of the flowfields. It is found that the DPNN framework clearly outperforms the supervised learning approach while respecting the reduced feature space dynamics. The model predictions for arbitrary bodies indicate that the Strouhal number distribution with respect to spacing ratio exhibits similar patterns with existing literature. In addition, our model predictions also enable us to discover similar wake categories for flow past arbitrary bodies. For the chaotic wakes, the present approach predicts the chaotic switch in gap flows up to the mid-time range.Comment: codes to follow shortly: https://github.com/tum-pbs/DiffPhys-CylinderWakeFlow

HIGH-TEMPERATURE HYPERSONIC LAVAL NOZZLE FOR NON-LTE CAVITY RINGDOWN SPECTROSCOPY

International audienceA small dimension Laval nozzle connected to a compact high enthalpy source equipped with cavity ringdown spectroscopy (CRDS) is used to produce vibrationally hot and rotationally cold high-resolution infrared spectra of polyatomic molecules in the 1.67 mu m region. The Laval nozzle was machined in isostatic graphite, which is capable of withstanding high stagnation temperatures. It is characterized by a throat diameter of 2 mm and an exit diameter of 24 mm. It was designed to operate with argon heated up to 2000 K and to produce a quasi-unidirectional flow to reduce the Doppler effect responsible for line broadening. The hypersonic flow was characterized using computational fluid dynamics simulations, Pitot measurements, and CRDS. A Mach number evolving from 10 at the nozzle exit up to 18.3 before the occurrence of a first oblique shock wave was measured. Two different gases, carbon monoxide (CO) and methane (CH4), were used as test molecules. Vibrational (T-vib) and rotational (T-rot) temperatures were extracted from the recorded infrared spectrum, leading to T-vib = 1346 +/- 52 K and T-rot = 12 +/- 1 K for CO. A rotational temperature of 30 +/- 3 K was measured for CH4, while two vibrational temperatures were necessary to reproduce the observed intensities. The population distribution between vibrational polyads was correctly described with TvibI=894 +/- 47K, while the population distribution within a given polyad (namely, the dyad or the pentad) was modeled correctly by TvibII=54 +/- 4K, testifying to a more rapid vibrational relaxation between the vibrational energy levels constituting a polyad