基于人工神经网络（ANN）和遗传算法（GA）的特斯拉阀门热/水- diodic性能优化数值模拟

IF 6.4 2区工程技术 Q1 MECHANICS

International Communications in Heat and Mass Transfer Pub Date : 2025-09-27 DOI:10.1016/j.icheatmasstransfer.2025.109711

Gang Du , Ali Basem , Nashwan Adnan Othman , Zainab Ali Bu sinnah , Saleh Alhumaid , Mohamed Ayadi , Hind Albalawi , H. Elhosiny Ali , Salem Alkhalaf , Hakim AL Garalleh

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引用次数: 0

摘要

先进的冷却和流体控制技术使工程师能够定制流体动力学，以满足精确的需求，从而提高系统的可靠性，安全性和性能。流量管理的前沿发展，如创新的阀门设计，是通过加强流量调节来实现这些目标的关键。本研究通过三个不同的部分来解决特斯拉阀门整体效率的提高。首先，分析了传统两级特斯拉阀在不同进口速度下的流动特性和热行为。然后提出了一种新的矩形特斯拉阀设计，以提高二极管的性能。最后，开发了一个改进的矩形特斯拉阀，以进一步提高传热能力和二极管性能。该研究使用热二极管性能（TDP）和液压二极管性能（HDP）作为关键性能指标。其次，研究了在不同速度下，级数对特斯拉阀热二极管性能的影响。结果表明，在进口速度为1m /s的情况下，采用五级进化的矩形特斯拉阀，而不是两级特斯拉阀，在反向流动中，系统的热性能比正向流动提高了约80.6%，二极管性能提高了789.4%。第三，采用基于人工神经网络的预测模型，研究了改进型矩形特斯拉阀的通道构型参数对其热二极管性能的影响。以特斯拉阀正反向整体效率最大化为目标，引入OT1和OT2两种最优配置。在OT1中，与基本样品相比，正反方向的总体效率分别提高了71.1%和20.4%。而在OT2中，与基本样品相比，正向总效率提高了39.7%，反向总效率提高了47%。

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Optimizing thermo/hydro-Diodic performance (T/H-DP) of Tesla valve designs by artificial neural network (ANN) and genetic algorithm (GA); a numerical simulation

Advanced cooling and fluid control techniques empower engineers to tailor fluid dynamics to meet precise needs, thereby boosting system reliability, safety, and performance. Cutting-edge developments in flow management, such as innovative valve designs, are pivotal in achieving these aims by enhancing flow regulation. The present study addresses the improvement of the overall efficiency of Tesla valves through three different sections. First, the flow properties and thermal behavior of a conventional two-stage Tesla valve were analyzed at varying inlet velocities. A novel rectangular Tesla valve design was then introduced to improve the diodic performance. Finally, an evolved rectangular Tesla valve was developed to further boost heat transfer capabilities alongside the diodic performance. The study used Thermal Diode Performance (TDP) and Hydraulic Diode Performance (HDP) as key performance metrics. Second, the impact of the number of stages on the thermo-diodic performance of the Tesla valve was examined at various velocities. The results showed that using a five-stage evolved rectangular Tesla valve at an inlet velocity of 1 m/s, instead of a two-stage one, enhanced the system's thermal performance by approximately 80.6 % and diodic performance by 789.4 % in reverse flow compared to forward flow. Third, the impact of configurational parameters of the valve's channel on the thermo-diodic performance of the evolved rectangular Tesla valve was examined by artificial neural network-based anticipation models. Two optimal configurations of OT1 and OT2 were introduced with the aim of maximizing the overall efficiency of the Tesla valve in forward and reverse directions, respectively. In the OT1, the overall efficiency in the forward and reverse directions has improved by 71.1 % and 20.4 %, respectively compared to the basic sample. Whereas in the OT2, the overall efficiency in the forward direction increased by 39.7 % and in the reverse direction by 47 % compared to the basic sample.

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来源期刊

International Communications in Heat and Mass Transfer 工程技术-力学

CiteScore

11.00

自引率

10.00%

发文量

648

审稿时长

32 days

期刊介绍： International Communications in Heat and Mass Transfer serves as a world forum for the rapid dissemination of new ideas, new measurement techniques, preliminary findings of ongoing investigations, discussions, and criticisms in the field of heat and mass transfer. Two types of manuscript will be considered for publication: communications (short reports of new work or discussions of work which has already been published) and summaries (abstracts of reports, theses or manuscripts which are too long for publication in full). Together with its companion publication, International Journal of Heat and Mass Transfer, with which it shares the same Board of Editors, this journal is read by research workers and engineers throughout the world.