Search

This paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD). • Innovative Fusion: Introduces a pioneering Computational Fluid Dynamics-Artificial Neural Network (CFD-ANN) approach for optimizing plunger valves, achieving a unique synergy between hydraulic flow and geometric configuration. • Optimized ANN Architecture: Develops a refined ANN architecture with an impressive R2 value of 0.987, demonstrating accuracy in predicting plunger valve performance. Key parameters are modified, leading to an 88% reduction in time expenditure compared to traditional CFD studies. • Geometric Parameter Impact: Analyzes the effects of valve slot number, diameter, and length on plunger valve performance, providing valuable insights into the interplay between geometric factors and hydraulic flow. • Efficient Optimization Process: Validates the enhanced CFD method through experimental work, ensuring a minimal 8.9% error. The study showcases cost-efficiency and superior performance in optimizing plunger valves through the ANN approach. [ABSTRACT FROM AUTHOR]

As the "respiratory tract" of the air breathing engine, the hypersonic inlet plays the role of compressing, decelerating, and pressurizing flow. However, research on the traditional inlet design is mainly based on gas dynamics theory and experience, which not only leads to a long design period but also fails to ensure the performance of the inlet at non-design points under broad working conditions. In recent years, with the further development of flow physics, a variety of advanced inlet design methods have been proposed, and a large number of valuable data have been accumulated by applying high-fidelity computational fluid dynamics numerical simulation and ground wind tunnel tests. A new generation of machine learning technologies, typically represented by deep learning, is developing vigorously. By building a deep neural network structure and using data-driven methods to carry out model training, it can realize typical feature extraction automatically, efficiently, and accurately, which is helpful to deeply explore the hidden flow mechanism between data and establish a fast prediction model of inlet performance. The optimal inlet design scheme can be obtained by applying the performance intelligent prediction model and the multi-objective intelligent evolution algorithm. This paper provides a detailed overview of the latest progress in inlet design by applying traditional ideas, expounds the basic principles and typical applications of some representative and prospective machine learning methods, and especially reports the current research status of the combination of machine learning and inlet. Finally, the future development trends and potential applications of several research directions are summarized. [ABSTRACT FROM AUTHOR]