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Aerospace Research Communications
About the Journal
Aerospace Research Communications (ARC) is an open access, peer-reviewed international journal covering all aspects of aeronautics and astronautics. The journal publishes original papers and review articles related to all fields of aerospace research, in both theory and practice. The scope is considerably wide, covering research achievements in flight vehicles, propulsion systems, and experimental equipment, including fluid mechanics, flight mechanics, solid mechanics, vehicle conceptual design, avionics, control, material engineering, and mechanical manufacturing.
We are very delighted to launch Aerospace Research Communications, as aerospace science and technology is an expanding research area. Open science has already come a long way, and I believe that Zhejiang University Press and Frontiers have a leading role to play in making all high-quality science universally, freely and immediately available.
Zhejiang University, China
Deep Reinforcement Learning: A New Beacon for Intelligent Active Flow Control
- Fangfang Xie
- Changdong Zheng
- Tingwei Ji
- Xinshuai Zhang
- Ran Bi
- Hongjie Zhou
- Yao Zheng
Review The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.
Published on Feb 16, 2023
Aerosp. Res. Commun.
Deep‐Learning-Based Uncertainty Analysis of Flat Plate Film Cooling With Application to Gas Turbine
- Yaning Wang
- Xubin Qiu
- Shuyang Qian
- Yangqing Sun
- Wen Wang
- Jiahuan Cui
Original Research Nowadays, gas turbines intake jet air at high temperatures to improve the power output as much as possible. However, the excessive temperature typically puts the blade in the face of unpredictable damage. Film cooling is one of the prevailing methods applied in engineering scenarios, with the advantages of a simple structure and high cooling efficiency. This study aims to assess the uncertain effect that the three major film cooling parameters exert on the global and fixed-cord-averaged film cooling effectiveness under low, medium, and high blowing ratios br. The three input parameters include coolant hole diameter d, coolant tube inclination angle θ, and density ratio dr. The training dataset is obtained by Computational Fluid Dynamics (CFD). Moreover, a seven-layer artificial neural network (ANN) algorithm is applied to explore the complex non-linear mapping between the input flat film cooling parameters and the output fixed-cord-averaged film cooling effectiveness on the external turbine blade surface. The sensitivity experiment conducted using Monte Carlo (MC) simulation shows that the d and θ are the two most sensitive parameters in the low-blowing-ratio cases. The θ comes to be the only leading factor of sensitivity in larger blowing ratio cases. As the blowing ratio rises, the uncertainty of the three parameters d, θ, and dr all decrease. The combined effect of the three parameters is also dissected and shows that it has a more significant influence on the general cooling effectiveness than any single effect. The d has the widest variation of uncertainty interval at three blowing ratios, while the θ has the largest uncertain influence on the general cooling effectiveness. With the aforementioned results, the cooling effectiveness of the gas turbine can be furthermore enhanced.
Published on Mar 30, 2023
Aerosp. Res. Commun.