Cooling of rocket plume using aqueous jets during launching

Zhou, Zhitan; Bao, Yiying; Sun, Peijie; Li, Yiqing

doi:10.1080/19942060.2021.2004926

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…As an example of shadowgraph applied for the characterization of rocket plumes, Figure 16 shows a Mach shock cell of the experimental shadowgraphs used to validate analytical methods for simulating inclined impinging exhaust jet from a rocket engine. Flow fields resulting from the under-expanded jet that hits the plate were compared with the experimental results at different impact points on the plate (h), impinging angle (θ) and the ratio between the nozzle outlet pressure and ambient pressure, NPR [39]. As an example of shadowgraph applied for the characterization of rocket plumes, Figure 16 shows a Mach shock cell of the experimental shadowgraphs used to validate analytical methods for simulating inclined impinging exhaust jet from a rocket engine.…”

Section: 𝑛1 Sin(𝜗1) = 𝑛2 Sin (𝜗2)mentioning

confidence: 99%

“…Flow fields resulting from the under-expanded jet that hits the plate were compared with the experimental results at different impact points on the plate (h), impinging angle (θ) and the ratio between the nozzle outlet pressure and ambient pressure, NPR [39].…”

Section: 𝑛1 Sin(𝜗1) = 𝑛2 Sin (𝜗2)mentioning

confidence: 99%

“…Hence, the color Schlieren was implemented in the literature. The color Schlieren was experimented by Toepler [39], substituting the knife edge with color bands in a way that the deflected beam, instead of being cut by the knife edge, would be colored with two different colors, based on the sign of the density gradient (positive or negative). In Figure 27, an example of color Schlieren applied to a supersonic nozzle, for an air-to-air jet, is shown.…”

Section: Schlieren Techniquementioning

confidence: 99%

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Optical Diagnostics for Solid Rocket Plumes Characterization: A Review

et al. 2022

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In recent decades, solid fuel combustion propulsion of spacecraft has become one of the most popular choices for rocket propulsion systems. The reasons for this success are a wide range of applications, lower production costs, simplicity, and safety. The rocket’s plumes leave the nozzle at high temperatures; hence, the knowledge of produced infrared (IR) emissions is a crucial aspect during the design and tests of the rocket motors. Furthermore, rocket plume composition is given by N2, H2, H2O, CO and CO2, while solid rocket motors (SRM) additionally inject some solid particles, given by metal fuel additives in the propellant grain, i.e., aluminum oxide (Al2O3) particles. The main issue is the detection of the particles remaining in the atmosphere due to the exhaust gas of the solid rocket propulsion system that could have effects on ozone depletion. The experimental characterization of SRM plumes in the presence of alumina particles can be conducted using different optical techniques. The present study aims to review the most promising ones with a description of the optics system and their potential applications for SRM plume measurements. The most common measurement techniques are infrared spectroscopy imaging, IR imaging. UV–VIS measurements, shadowgraph, and Schlieren optical methods. The choice of these techniques among many others is due to the ability to study the plume without influencing the physical conditions existing in and around the study object. This paper presents technical results concerning the study of rocket engines plumes with the above-mentioned methods and reveals the feasibility of the measurement techniques applied.

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Section: 𝑛1 Sin(𝜗1) = 𝑛2 Sin (𝜗2)mentioning

confidence: 99%

Section: 𝑛1 Sin(𝜗1) = 𝑛2 Sin (𝜗2)mentioning

confidence: 99%

Section: Schlieren Techniquementioning

confidence: 99%

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Optical Diagnostics for Solid Rocket Plumes Characterization: A Review

et al. 2022

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“…Such a simulation where the smallest scales of the flame are not solved but simply modeled is called a Large Eddy Simulation (LES). When an LES is performed, the smallest scales are not resolved but only modeled, which smooths the flame and thus greatly reduces its total flame area [61][62][63]. Most turbulent modeling approaches are therefore based on a reconstruction of this flame wrinkling to obtain a correct flame surface by making so-called "flammelette" assumptions, in which the average turbulent reaction rate is expressed as terms of flame area.…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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