A 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 µm 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 (Tvib) and rotational (Trot) temperatures were extracted from the recorded infrared spectrum, leading to Tvib = 1346 ± 52 K and Trot = 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±47 K, while the population distribution within a given polyad (namely, the dyad or the pentad) was modeled correctly by TvibII=54±4 K, testifying to a more rapid vibrational relaxation between the vibrational energy levels constituting a polyad.
The interface between fluid mechanics and machine learning has ushered in a new avenue of scientific inquiry for complex fluid flow problems. This paper presents the development of a reduced-order predictive framework for the fast and accurate estimation of internal flowfields in two classes of scramjet intakes for hypersonic airbreathing propulsion. Proper orthogonal decomposition is employed as a reduced-order model while the moving least squares-based regression model and the multilayer perceptron-based neural network technique are employed. The samples required for the training process are generated using a sampling strategy, such as Latin hypercube sampling, or obtained as an outcome of multi-objective optimization. The study explores the flowfield estimation capability of this framework for the two test cases, each representing a unique type of scramjet intake. The importance of tuning the user-defined parameters as well as the use of multiple reduced-order bases instead of a global basis are highlighted. It is also demonstrated that the bias involved in the generation of input samples in an optimization problem can potentially be utilized to build a reduced-order predictive framework while using only a moderate number of training samples. This offers the potential to significantly reduce the computational time involved in expensive optimization problems, especially those relying on a population-based approach to identify global optimal solutions.
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