Abstract
Check valves are responsible for regulating and controlling the direction of flow in various systems. The Tesla-type check valve (TTCV) is one kind of passive-type check valve with a regulating performance influenced by its fixed geometry. The main evaluation criterion to quantify the regulating performance is diodicity (Di). In this article, aiming for improving the Di, a surrogate-model based methodology is presented for optimizing the geometric parameters of the TTCV. The length of the straight segment of the side-channel, the angle between the side-channel and the main-channel, the angle between the tangent of the inner curve and the main-channel, and channel width are selected as design variables for searching an optimum design. To obtain a suitable surrogate model for this case, different surrogate models, such as polynomial response surface (PRS), Kriging (KRG), support vector regression (SVR), and radial basis function (RBF), which have been widely used for a variety of engineering problems, are compared in this study. A derivative-free global optimum algorithm, the Genetic Algorithm (GA), is adopted for achieving a global optimum. The improvement in TTCV is analyzed and the optimization results are validated to confirm the effectiveness and feasibility of the proposed methodology. It is found that compared with the existing optimum design, the Di of the predicted optimum design still has an improvement of 4.32%. The proposed methodology may facilitate improvements in the design and optimization of the TTCV, thus benefiting the development of fluid transport techniques in micro- or mini-channel systems.
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Acknowledgements
This project is supported by National Key R&D Program of China (Grant No. 2018YFB1700704), National Natural Science Foundation of China (Grant No. 52075068).
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Li, K., Wang, S., Zong, C., Liu, Y., Song, X. (2022). Diodicity Optimization of Tesla-Type Check Valve Based on Surrogate Modeling Techniques. In: Tan, J. (eds) Advances in Mechanical Design. ICMD 2021. Mechanisms and Machine Science, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-7381-8_76
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DOI: https://doi.org/10.1007/978-981-16-7381-8_76
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