Search results

The purpose of this study is to investigate the entropy generation in different nanofluids flow over a vertically moving rotating disk. Unlike the classical Karman flow, water-based nanofluids have various suspended nanoparticles, namely, Cu, Ag, Al₂O₃ and TiO₂, and the disk is also moving vertically with time-dependent velocity.

The Keller box technique numerically solves the governing equations after reduction by suitable similarity transformations. The shear stress and heat transport features, along with flow and temperature fields, are numerically computed for different concentrations of the nanoparticles.

This study is done comparatively in between different nanofluids and for the cases of vertical movement of the disk. It is found that heat transfer characteristics rely not only on considered nanofluid but also on disk movement. Moreover, the upward movement of the disk diminishes the heat-transfer characteristics of the fluid for considered nanoparticles. In addition, for the same group of nanoparticles, an entropy generation study is also performed, and an increasing trend is found for all nanoparticles, with alumina nanoparticles dominating the others.

This research is a novel work on a vertically moving rotating surface for the water-conveying nanoparticle fluid flow with entropy generation analysis. The results were found to be in good agreement in the case of pure fluid.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.