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Numerical Investigation on Detonation Wave through u-bend

Journal of Thermal Science 2010年第6期19卷 540-544页

作者： Shinobu Otsuka Masaya Suzuki Makoto Yamamoto Department of Mechanical Engineering Tokyo University of Science

Pulse detonation engine (PDE) is expected for a next-generation propulsion system. PDE is a promising engine that can generates power and thrust by using intermittent detonation. Promotion of deflagration to detonation transition (below DDT) is a key issue to realize this system. PDE has experimentally been investigated, and it was confirmed that detonation tubes with u-shaped bends are useful for fast DDT. However, the mechanism of DDT promotion due to u-bends has not been well clarified. In the present study, the influence of a u-bend on detona-tion wave propagation is researched with computational fluid dynamics (CFD). The numerical results show that detonation wave disappears once near the u-bend inlet and restarts after passing through it. In addition, it was found that the use of the u-bend with small channel width and curvature radius can induce fast DDT.

关键词： Detonation Deflagration to Detonation Transition (DDT) u-bend Computational Fluid Dynamics

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Comparison between conventional and deep learning-based surrogate models in predicting convective heat transfer performance of u-bend channels

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Energy and AI 2022年第2期8卷 13-29页

作者： Qi Wang Weiwei Zhou Li Yang Kang Huang School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of Aerodynamics MianyangSichuan621000China China Aerodynamics Research and Development Center MianyangSichuan621000China

Deep neural networks are efficient methods to achieve real-time visualization of physics *** main concerns that prevented deep learning from being implemented in the field of energy conversion were the risks of overfitting and the lack of ***,it is necessary to evaluate different kinds of surrogate modeling methods and provide guidelines for designers to choose *** this study,three conventional models(Artificial Neural Network,Radial Bias Function,and Kriging),and two deep learning-based models(Convolutional Neural Network and Conditional Generative Adversarial Neural Network)were established to predict the flow and heat transfer performance of a u-bend with variable *** models were detailly compared in terms of the single-point prediction accuracy,response accuracy,sensitivity to sample size,and other characteristics of *** showed that the conventional models had slightly higher single point accuracy and the relative error of pressure loss and heat transfer were within±6.6%and±5.7%respectively,while those of the deep learning-based models were within±8.0%and±6.3%***,the deep learning-based models had higher response accuracy and could reconstruct the distributions of surface pressure and wall heat flux with the pixel-wise absolute error within±2.0 Pa and±45 W/m^(2) *** results indicated that deep learning was a promising surrogate modeling approach due to its acceptable prediction error and ability to reconstruct physical *** effort was expected to serve as a guide for establishing more reliable data-driven surrogate models for energy conversion and heat transfer problems.

关键词： Surrogate modeling Deep learning Convective heat transfer u-bend

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Effect of Liquid and Gas Velocities on Magnitude and Location of Maximum Erosion in u-bend

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Open Journal of Fluid Dynamics 2012年第2期2卷 29-34页

作者： Quamrul H. Mazumder Department of Computer Science Engineering and Physics University of Michigan-Flint Flint USA

Solid particle erosion is a micromechanical process that is influenced by flow geometry, material of the impacting surface, impact angle, particle size and shape, particle velocity, flow condition and fluid properties. Among the various factors, particle size and velocity have been considered to be the most important parameters that cause erosion. Particle size and velocity are influenced by surrounding flow velocities and carrying fluid properties. Higher erosion rates have been observed in gas-solid flow in geometries where the flow direction changes rapidly, such as elbows, tees, valves, etc, due to local turbulence and unsteady flow behaviors. This paper presents the results of a Computational fluid dynamic (CFD) simulation of dilute gas-solid flow through a u-bend and the dynamics behavior of entrained solid particles in the flow. The effect of liquid and gas velocities on location of erosion were investigated for 50, 100, 150, 200, 250 and 300 microns sand particles. Three different fluid velocities of 15, 30.48 and 45 m/s were used in the CFD analysis. The magnitude and location of erosion presented in the paper can be used to determine the areas susceptible to maximum erosive wear in elbows and u-bends, along with corresponding rate of metal loss in these areas.

关键词： u-bend Erosion Multiphase Flow Wear CFD

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