CFD Modelling and Optimization Procedure of an Adhesive System for a Modular Climbing Robot.
CFD Modelling and Optimization Procedure of an Adhesive System for a Modular Climbing Robot.
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用于模块化机器人的粘合系统的CFD建模和优化过程。
DOI：
10.3390/s21041117
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发表时间：
2021-02-05
期刊：
影响因子：
--
通讯作者：
Gambao E
中科院分区：
文献类型：
--
作者：
Hernando M;Gómez V;Brunete A;Gambao E
粘附系统在用于基础设施检查的机器人中非常重要（特别是在垂直墙壁中）。他们提出了优化电池供电机器人的真空/功耗比的挑战。在本文中，CFD（计算机流体动力学）建模和优化过程中的机器人粘附系统进行，以确定最佳的执行配置的真空和功耗。开发了分析和数值模型，以预测不同配置的系统的行为。通过校准模拟泄漏流的任意定义的入口高度，使用试验台测量对模型进行验证。然后，改变不同的几何参数以基于真空/功率消耗比值确定最佳性能配置。本文提出的模型能够预测不同配置的系统的行为，真空预测的误差幅度为15%，电机功率计算的误差幅度为25%。最后，利用该模型对系统的参数进行了优化，如叶轮的叶片数。附着系统是为模块化自主爬腿机器人ROMERIN设计的。
Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper, a CFD (computer fluid dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.
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DOI：
10.1243/09544062jmes1196
发表时间：
2009-04-01
影响因子：
2
作者：
Epple, P.;Karic, B.;Delgado, A.
通讯作者：
Delgado, A.
影响因子：
10.3
作者：
Menendez, Elisabeth;Victores, Juan G.;Balaguer, Carlos
通讯作者：
Balaguer, Carlos
影响因子：
3.6
作者：
Phares, Brent M.;Washer, Glenn A.;Moore, Mark
通讯作者：
Moore, Mark
影响因子：
2.5
作者：
MENTER, FR
通讯作者：
MENTER, FR
影响因子：
4.3
作者：
Schmidt, Daniel;Berns, Karsten
通讯作者：
Berns, Karsten