Civil Engineering Section, University Polytechnic, AMU, Aligarh, UP 202001, India E-mail:
Civil Engineering Section, University Polytechnic, AMU, Aligarh, UP 202001, India.
Water Sci Technol. 2023 Oct;88(8):2136-2159. doi: 10.2166/wst.2023.319.
Triangular orifices are widely used in industrial and engineering applications, including fluid metering, flow control, and measurement. Predicting discharge through triangle orifices is critical for correct operation and design optimization in various industrial and engineering applications. Traditional approaches like empirical equations have accuracy and application restrictions, whereas computational fluid dynamics (CFD) simulations can be computationally costly. Alternatively, artificial neural networks (ANNs) have emerged as a successful solution for predicting discharge through orifices. They offer a dependable and efficient alternative to conventional techniques for estimating discharge coefficients, especially in intricate relationships between input parameters and discharge. In this study, ANN models were created to predict discharge through the triangle orifice and velocity at the downstream of the main channel, and their effectiveness was assessed by comparing the performance with the earlier models proposed by researchers. This paper also proposes a novel hybrid multi-objective optimization model (NSGA-II) that uses genetic algorithms to discover the best values for design parameters that maximize discharge and downstream velocity simultaneously.
三角形孔口在工业和工程应用中被广泛使用，包括流量计量、流量控制和测量。预测三角形孔口的流量对于各种工业和工程应用中的正确操作和设计优化至关重要。传统方法，如经验方程，具有精度和应用限制，而计算流体动力学（CFD）模拟可能计算成本高昂。相比之下，人工神经网络（ANN）已成为预测孔口流量的成功解决方案。它们为估算流量系数提供了一种可靠且高效的替代传统技术的方法，特别是在输入参数和流量之间存在复杂关系的情况下。在这项研究中，创建了人工神经网络模型来预测通过三角形孔口的流量和主通道下游的速度，并通过与研究人员之前提出的模型进行性能比较来评估其有效性。本文还提出了一种新的混合多目标优化模型（NSGA-II），该模型使用遗传算法来发现能够同时最大化流量和下游速度的设计参数的最佳值。
