This paper presents a numerical investigation of bird attitude angles affecting the soft-impact damage of a full fan assembly. Firstly, considering the geometry of a mallard, a real bird model is established by the Smoothed Particle Hydrodynamics (SPH) method and calibrated with available test data. Then, complying with airworthiness requirements, simulations of a full-bladed fan assembly subjected to a real bird were conducted to determine the critical ingestion parameters (CIP). Furthermore, a real bird with different attitude angles aimed at a full fan assembly was simulated. Results show that attitude angles of the bird produce a significant impact on the effect of the bird strike on rotating blades and would increase the possibility of blade failures, especially for the yaw angle of -45° and the pitch angle of −60°. It is invaluable for commercial airlines and engine manufactures to provide safe flight and landing by adopting the real bird model with critical yaw and pitch angles in the design for resistance to bird ingestion.
This study proposes a new uncertain optimization algorithm to suppress vibration of the crankshaft system. In this new algorithm, the interval expression with random-interval hybrid variables is obtained by the confidence level. In addition, the interval order relation, interval probability, radial basis function neural network technology, and multi-objective genetic algorithm are applied to construct uncertain optimization algorithm with random-interval hybrid variables. Moreover, typical examples are used to demonstrate the effectiveness of the proposed algorithm. To suppress vibration of the crankshaft system, the optimization-Latin hypercube sampling design is used to obtain the experimental scheme and the data sampling is performed by multi-body system simulation of the vibration performance. Then, the radial basis function neural network is built considering the torsional displacement and transient stress of the crankshaft. Finally, the uncertain optimization algorithm is operated on the crankshaft structure design of the high-power reciprocating compressor. The results demonstrate that the robustness of the vibration performance and strength property is improved through the uncertain optimization algorithm, compared with that through deterministic optimization. The uncertain optimization algorithm to suppress vibration of the crankshaft system with random-interval hybrid variables is an efficient and effective approach, which is finally proved by the prototype test.
Smoothed Particle Hydrodynamics (SPH) is widely adopted to predict bird strike events. To improve the parallel computing efficiency of the SPH approach, parallel computing was performed on the process of a bird striking the fan assembly. Since the cube-shaped domains aligned along the coordinate axes that are inherent in the decomposition algorithm may result in low computational efficiency, the effect of customized data partitioning on the efficiency is investigated. The results show that customized decompositions can minimize communication between processors and ensure the load balance during the simulation process. Besides, distributed computing with domain decompositions can present reasonable predictions at soft-impact damage, achieving consistent results within a range of less than 7% of the reference data derived from shared memory computing.
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