【摘要】 为了实现燃油离心泵汽蚀特性智能优化设计，提出一种基于Kriging的多阶段主动学习加点策略。重点是建立3个阶段的加点策略，并明确各阶段加点策略的切换判据。基于一维函数、多维函数等测试函数，与单加点策略对比以验证多阶段主动学习加点策略的有效性。进而，以某型燃油离心泵为例，完成所提出加点策略在其汽蚀特性的优化应用。测试函数算例结果表明：多阶段主动学习加点策略达到相同精度所需时间稍长于单MSE策略，优于单EI策略与单CV策略；但相比单MSE策略，全局最优解局部精度更高。离心泵汽蚀特性优化结果表明：优化泵进口处压力损失降低且进口处回流、旋涡等局部流动减弱，蜗壳内压力梯度增大且其出口处旋涡流动减弱；优化泵的汽蚀系数提高30.83%,汽蚀余量下降9.14%,汽蚀比转数下降7.17%,泵内气体含量下降17.27%,所提出的方法有效地提高了离心泵的抗汽蚀性能。更多还原

【Abstract】 In order to realize the intelligent optimization design of cavitation performance of fuel centrifugal pump, a multi-stage active learning adding strategy based on Kriging is proposed. The focus is to establish the three-stage adding strategy by using MSE, EI and CV, and clarify the switching criteria for each stage. Based on the test functions such as one-dimensional function and multi-dimensional function, it is compared with the single-stage adding strategy to verify the effectiveness of the proposed strategy. Furthermore, for a certain type of fuel centrifugal pump, the optimization application of the proposed strategy in its cavitation characteristics is completed. The result of the test function calculation shows that the proposed strategy takes slightly longer to achieve the same accuracy than MSE, which is better than EI and CV. However, compared with MSE, the local accuracy of the global optimal solution is higher. The result of centrifugal pump case shows that the pressure loss at the inlet of the optimized pump decreases, the local flow such as return flow and vortex at the inlet is weakened, the pressure gradient and the vortex flow in the volute weakens. Moreover, the cavitation coefficient of the optimized pump is increased by 30.83%, the NPSH allowance is reduced by 9.14%, the cavitation ratio is reduced by 7.17%, and the gas content in the pump is reduced by 17.27%. Thus, the proposed method improves the cavitation performance of the centrifugal pump effectively.更多还原