Flow Characterization and Parametric Analysis for Mechanical Kidney Stone Removal.
ORAL
Abstract
Kidney stones affect approximately 14% of individuals and are associated with significant morbidity, including severe pain due to urinary tract obstruction, renal injury, and potentially life-threatening infections (Khan et al., 2016). For larger and symptomatic stones, surgical intervention is often necessary. The most frequently employed surgical technique is ureteroscopy (URS), an endoscopic procedure wherein a small, flexible endoscope is inserted through the urethra into the bladder and subsequently advanced into the ureter to facilitate stone removal (Ho et al., 2017).
Despite its widespread adoption, URS is time-consuming and can leave residual fragments, which may cause recurrent symptomatic obstruction and serve as origins for stone regrowth (Chew et al., 2016; Iremashvili et al., 2019). Therefore, there is a critical need for a more efficient and safe method to clear stone fragments intraoperatively. In response to this need, a novel kidney stone extraction device has been developed (Zhao et al., 2023).
This work presents a characterization of the complex flow generated by the device. Wall-modeled Large Eddy Simulations (WMLES) on a moving mesh are used to model the system. Physical insights and parameters for optimizing the device's performance will be discussed.
Despite its widespread adoption, URS is time-consuming and can leave residual fragments, which may cause recurrent symptomatic obstruction and serve as origins for stone regrowth (Chew et al., 2016; Iremashvili et al., 2019). Therefore, there is a critical need for a more efficient and safe method to clear stone fragments intraoperatively. In response to this need, a novel kidney stone extraction device has been developed (Zhao et al., 2023).
This work presents a characterization of the complex flow generated by the device. Wall-modeled Large Eddy Simulations (WMLES) on a moving mesh are used to model the system. Physical insights and parameters for optimizing the device's performance will be discussed.
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Presenters
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Claire MacDougall
Stanford University
Authors
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Claire MacDougall
Stanford University
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Jasmine Vallejo
Stanford University
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Yilong Chang
Stanford University
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Renee Zhao
Stanford University
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Beverley J McKeon
Stanford University