Design and Output Performance Model of Turbodrill Blade Used in a Slim Borehole

Yu Wang, Bairu Xia, Zhiqiao Wang, Liguang Wang and Qin Zhou
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Yu Wang: School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Bairu Xia: School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Zhiqiao Wang: School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Liguang Wang: School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Qin Zhou: School of Engineering and Technology, China University of Geosciences, Beijing 100083, China

Energies, 2016, vol. 9, issue 12, 1-17

Abstract: Small-diameter turbodrills have great potential for use in slim boreholes because of their lower cost and higher efficiency when used in geothermal energy and other underground resource applications. Multistage hydraulic components consisting of stators and rotors are key aspects of turbodrills. This study aimed to develop a suitable blade that can be used under high temperature in granite formations. First, prediction models for single- and multi-stage blades were established based on Bernoulli’s Equation. The design requirement of the blade for high-temperature geothermal drilling in granite was proposed. A ?89 blade was developed based on the dimensionless parameter method and Bezier curve; the parameters of the blade, including its radial size, symotric parameters, and blade profiles, were input into ANASYS and CFX to establish a calculation model of the single-stage blade. The optimization of the blade structure of the small-diameter turbodrill enabled a multistage turbodrill model to be established and the turbodrill’s overall output performance to be predicted. The results demonstrate that the design can meet the turbodrill’s performance requirements and that the multistage model can effectively improve the accuracy of the prediction.

Keywords: slim borehole; granite section; turbodrill; multistage simulation models; output performances prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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