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A REDUCED-ORDER MODEL FOR PREDICTING THE PERFORMANCE OF A LIQUID-RING VACUUM PUMP

机译:预测液环真空泵性能的阶阶模型

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Vacuum and low pressures are needed in many applications, and the liquid-ring vacuum pump, which does not have any solid-solid contacts between moving and stationary parts, is widely used because of its low operational cost and long service life. Though progress has been made in advancing this pump, industry still has aggressive goals on improving its efficiency and performance. In this study, a reduced-order model was developed to predict the ability of liquid-ring pumps to ingest air and thereby create lower pressure as a function of pump design and operating parameters. The model developed is semi-empirical - constructed by first analyzing available experimental data to extract features and trends and then encapsulating them into a model through appropriate dimensionless parameters. This model by being in closed form shows the functional relationship between the pump's design and operating parameters and its ability to ingest air and create a vacuum. To make predictions, this model only requires the following inputs: suction pressure, impeller's rotational speed, and a few dimensions of the pump. The model developed was assessed by using it to predict the ability of the pump to ingest air for a wide range of suction pressures (cavitation pressure to 760 torr), rotor speeds (up to 1,750 rpm), and dimensions of the pump (radius and span of the impeller blade, hub radius) and then comparing predictions with experimental data not used in the creation of the model. The model developed was found to be accurate within 11% of the experimental data.
机译:在许多应用中,需要真空和低压,并且由于其低运营成本和长寿使用寿命,液环真空泵在移动和固定部件之间没有任何固体固体触点。虽然在推进这款泵方面取得了进展,但行业仍然有勇气地提高其效率和性能。在该研究中,开发了一种阶阶模型以预测液体环泵到摄取空气的能力,从而产生较低的压力作为泵设计和操作参数的函数。开发的模型是半经验的 - 通过首先分析可用的实验数据来提取特征和趋势,然后通过适当的无量纲参数将它们封装到模型中。通过封闭形式通过封闭形式显示泵的设计和操作参数之间的功能关系以及其摄取空气的能力并产生真空。为了预测,该模型仅需要以下输入:吸入压力,叶轮的转速和泵的几个尺寸。通过使用它来预测泵摄取空气的能力,用于摄取宽范围的吸入压力(空化压力至760 Torr),转子速度(高达1,750 rpm)和泵的尺寸(半径和叶轮叶片,集线器半径)的跨度,然后将预测与模型的创建未使用的实验数据进行比较。发现该模型在实验数据的11%内被准确。

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