Cheng Wen

Bio: Cheng Wen is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Infrared signature & Nozzle. The author has an hindex of 4, co-authored 13 publications receiving 56 citations.

Rotary thrust vectoring nozzle for short-distance vertical take-off and landing engine

Abstract: The invention discloses a rotary thrust vectoring nozzle for a short-distance vertical take-off and landing engine. By the adoption of the rotary thrust vectoring nozzle for the short-distance vertical take-off and landing engine, mode conversion between a cruising state and a short-distance vertical take-off and landing state, according to the cruising state, a spray pipe is located at a horizontal position, and according to the short-distance vertical take-off and landing state, the spray pipe is in a vertical position or deviates downwards by other angles can be completed accurately and efficiently. Profile design and structural design are conducted on the rotary spray pipe based on the vector requirements of the engine used by a vertical take-off and landing aircraft, parts can be replaced, the structure is simple, and machining is convenient. A gear transmission scheme that three spray pipe sections are driven respectively is adopted, and the nonlinear variation tendency between the deflection angle of the first spray pipe section and the deflection angle of the second spray pipe section can be achieved easily; a bearing type transitional connection structure is adopted, abrasion between two cylinder sections which move relatively can be reduced, and thus the spray pipe can operate more quickly and more efficiently.

Investigation of Infrared Signature of Serpentine Nozzle for Turbofan

Abstract: Infrared stealth technology is one of the essential abilities for advanced stealth aircraft. The exhaust system is the main infrared radiation source of aircraft. The serpentine nozzle is a potenti...

Pressurization flow control device-containing SERN structure for TBCC

Abstract: The invention relates to a pressurization flow control device-containing SERN structure for a TBCC. The SERN structure comprises a lower wall surface of a single expansion ramp nozzle, a contraction section of the single expansion ramp nozzle and a single expansion ramp nozzle single-side expansion section, and further comprises the pressurization flow control device arranged on the single expansion ramp nozzle single-side expansion section, wherein the pressurization flow control device comprises a U-shaped communicating pipeline and a supercharger arranged on the U-shaped communicating pipeline; besides, a front port of the U-shaped communicating pipeline is positioned in front of an intersection point of an oblique shock wave and the single expansion ramp nozzle single-side expansion section; and a back port of the U-shaped communicating pipeline is arranged at the back of the intersection point of the oblique shock wave and the single expansion ramp nozzle single-side expansion section. According to the SERN structure, airflow flowing through the communicating pipeline is pressurized, extra airflow in a diverging zone is extracted and then is ejected from the front port at a relatively high speed, so that the separation zone range is greatly decreased and even eliminated, the shock wave is moved forwards, the static pressure distribution of a main flow is changed, and the working condition of overexpansion operation is improved.

Data-driven modelling and optimization of stiffeners on undevelopable curved surfaces

Abstract: Undevelopable stiffened curved shells have been widely used in engineering fields. The shape of the undevelopable curved surface is generally characterized with the non-straight generatrix and variable cross sections, which makes it challenging to automatically model and optimize stiffeners on the undevelopable curved surface. Therefore, the data-driven modelling and optimization framework are proposed for undevelopable stiffened curved shells in this paper. Firstly, a novel mesh deformation method is developed for the data-driven modelling of undevelopable stiffened curved shells based on RBF neural network machine learning method. Its main idea is to firstly define a developable curved shell (background mesh domain) having similar topological characteristics with the undevelopable curved shell (target mesh domain), and then train the mapping relationship between the background mesh domain and the target mesh domain by RBF neural network, and finally the complicated modelling problem of the undevelopable stiffened curved shell can be transformed into a simple modelling problem of developable stiffened curved shell by means of the mapping relationship. Moreover, based on the efficient global optimization (EGO) surrogate method, a data-driven layout optimization method is established for minimizing the structural weight of undevelopable stiffened curved shells. Finally, three representative optimization examples are carried out, including modelling and optimization of stiffeners on hyperbolic parabolic curved surfaces, blade-shaped curved surfaces and S-shaped variable cross-sectional curved surfaces. Optimal results indicate that the structural weight of undevelopable stiffened curved shells decreases significantly after the optimization, indicating the effectiveness of the proposed modelling and optimization framework.