With the increasing noise pollution, low noise optimization of centrifugal pimps has become a hot topic. However, experimental study on this problem is unacceptable for industrial applications due to unsustainable cos...With the increasing noise pollution, low noise optimization of centrifugal pimps has become a hot topic. However, experimental study on this problem is unacceptable for industrial applications due to unsustainable cost. A hybrid method that couples computational fluid dynamics （CFD） with computational aeroacoustic software is used to predict the flow-induced noise of pumps in order to minimize the noise of centrifugal pumps in actual projects. Under Langthjem＇s assumption that the blade surface pressure is the main flow-induced acoustic source in centrifugal pumps, the blade surface pressure pulsation is considered in terms of the acoustical sources and simulated using CFX software. The pressure pulsation and noise distribution in the near-cutoff region are examined for the blade-passing frequency （BPF） noise, and the sound pressure level （SPL） reached peaks near the cutoff that corresponded with the pressure pulsation in this region. An experiment is performed to validate this prediction. Four hydrophones are fixed to the inlet and outlet ports of the test pump to measure the flow-induced noise from the four-port model. The simulation results for the noise are analyzed and compared with the experimental results. The variation in the calculated noise with changes in the flow agreed well with the experimental results. When the flow rate was increased, the SPL first decreased and reached the minimum near the best efficient point （BEP）; it then increased when the flow rate was further increased. The numerical and experimental results confirmed that the BPF noise generated by a blade-rotating dipole roughly reflects the acoustic features of centrifugal pumps. The noise simulation method in current study has a good feasibility and suitability, which could be adopted in engineering design to predict and optimize the hydroacoustic behavior of centrifugal pumps.展开更多
多级高压提水泵属于复杂旋转机械,受设备自身结构影响,其故障特征频率易被强噪声淹没,且叶轮、轴承、齿轮等转子部件的故障振动信号相似度极高,导致现有故障辨识方法无法快速准确辨识其故障模式。针对上述问题,该研究提出一种改进的一...多级高压提水泵属于复杂旋转机械,受设备自身结构影响,其故障特征频率易被强噪声淹没,且叶轮、轴承、齿轮等转子部件的故障振动信号相似度极高,导致现有故障辨识方法无法快速准确辨识其故障模式。针对上述问题,该研究提出一种改进的一维卷积长短期记忆神经网络(One-Dimensional Convolution and Long Short-Term Memory Neural Network,1D-CNN-LSTM)自适应故障辨识模型。首先通过贝叶斯优化算法获得给定模型超参数,再输入经互补集合经验模态分解降噪后的振动数据集,通过1D-CNN层自适应提取样本特征并作为LSTM层输入;利用LSTM层学习具有识别性的深层特征并训练模型,最后由输出层Softmax函数完成故障辨识与分类。多级高压提水泵试验台实测数据集对模型进行验证的结果表明:提出的1D-CNN-LSTM智慧故障辨识模型能够快速辨识关键转子部件的故障模式,且准确率可达97%,具有更好的抗噪能力和鲁棒性能,可为智慧应急供水与净水一体化系统的可靠运维技术研发奠定理论基础。展开更多
In order to explore the unforced unsteadiness of centrifugal pumps,a 2-D frequency domain imaging display technology was used to study the development of these unsteady flow structures at partial flow conditions.The r...In order to explore the unforced unsteadiness of centrifugal pumps,a 2-D frequency domain imaging display technology was used to study the development of these unsteady flow structures at partial flow conditions.The results showed that,the unsteady flow field was not only affected by rotor and stator interaction,but also appeared an unforced unsteadiness with fundamental frequency of St≈0.23 around the impeller throat area.Moreover,as the flow rates decreased,this unsteady flow structure gradually weakened and disappeared.When the flow rate was reduced to 0.6 times of design flow rate,another two unforced unsteady flow structures with characteristic frequencies of St≈0.0714 and St≈0.12 began to appear in the same area.Therefore,with the operating condition smaller than design flow rate,the internal flow became more and more complex.In addition to the forced unsteadiness,the unforced unsteadiness which is not connected with the blade passage frequency became more and more obvious.展开更多
基金Supported by Research and Innovation Project for College Graduates of Jiangsu Province of China(Grant No.CXZZ13_0673)National Natural Science Foundation of China(Grant No.51009072)+1 种基金National Science&Technology Pillar Program of China(Grant No.2011BAF14B04)State Key Program of National Natural Science Foundation of China(Grant No.51239005)
文摘With the increasing noise pollution, low noise optimization of centrifugal pimps has become a hot topic. However, experimental study on this problem is unacceptable for industrial applications due to unsustainable cost. A hybrid method that couples computational fluid dynamics （CFD） with computational aeroacoustic software is used to predict the flow-induced noise of pumps in order to minimize the noise of centrifugal pumps in actual projects. Under Langthjem＇s assumption that the blade surface pressure is the main flow-induced acoustic source in centrifugal pumps, the blade surface pressure pulsation is considered in terms of the acoustical sources and simulated using CFX software. The pressure pulsation and noise distribution in the near-cutoff region are examined for the blade-passing frequency （BPF） noise, and the sound pressure level （SPL） reached peaks near the cutoff that corresponded with the pressure pulsation in this region. An experiment is performed to validate this prediction. Four hydrophones are fixed to the inlet and outlet ports of the test pump to measure the flow-induced noise from the four-port model. The simulation results for the noise are analyzed and compared with the experimental results. The variation in the calculated noise with changes in the flow agreed well with the experimental results. When the flow rate was increased, the SPL first decreased and reached the minimum near the best efficient point （BEP）; it then increased when the flow rate was further increased. The numerical and experimental results confirmed that the BPF noise generated by a blade-rotating dipole roughly reflects the acoustic features of centrifugal pumps. The noise simulation method in current study has a good feasibility and suitability, which could be adopted in engineering design to predict and optimize the hydroacoustic behavior of centrifugal pumps.
文摘多级高压提水泵属于复杂旋转机械,受设备自身结构影响,其故障特征频率易被强噪声淹没,且叶轮、轴承、齿轮等转子部件的故障振动信号相似度极高,导致现有故障辨识方法无法快速准确辨识其故障模式。针对上述问题,该研究提出一种改进的一维卷积长短期记忆神经网络(One-Dimensional Convolution and Long Short-Term Memory Neural Network,1D-CNN-LSTM)自适应故障辨识模型。首先通过贝叶斯优化算法获得给定模型超参数,再输入经互补集合经验模态分解降噪后的振动数据集,通过1D-CNN层自适应提取样本特征并作为LSTM层输入;利用LSTM层学习具有识别性的深层特征并训练模型,最后由输出层Softmax函数完成故障辨识与分类。多级高压提水泵试验台实测数据集对模型进行验证的结果表明:提出的1D-CNN-LSTM智慧故障辨识模型能够快速辨识关键转子部件的故障模式,且准确率可达97%,具有更好的抗噪能力和鲁棒性能,可为智慧应急供水与净水一体化系统的可靠运维技术研发奠定理论基础。
基金supported by the National Natural Science Foundation of China(Grant No.51976125)Open Research Subject of Key Laboratory of Fluid and Power Machinery(Xihua University),Ministry of Education(Grant number zj2015-024)Natural Science Fund of Shanghai(Grant No.19ZR1425900)。
文摘In order to explore the unforced unsteadiness of centrifugal pumps,a 2-D frequency domain imaging display technology was used to study the development of these unsteady flow structures at partial flow conditions.The results showed that,the unsteady flow field was not only affected by rotor and stator interaction,but also appeared an unforced unsteadiness with fundamental frequency of St≈0.23 around the impeller throat area.Moreover,as the flow rates decreased,this unsteady flow structure gradually weakened and disappeared.When the flow rate was reduced to 0.6 times of design flow rate,another two unforced unsteady flow structures with characteristic frequencies of St≈0.0714 and St≈0.12 began to appear in the same area.Therefore,with the operating condition smaller than design flow rate,the internal flow became more and more complex.In addition to the forced unsteadiness,the unforced unsteadiness which is not connected with the blade passage frequency became more and more obvious.
