An Artificial Neural Network for the Prediction of Penetration Height of Aerated Elliptical Liquid Jets in Gaseous Crossflows

Ali Dolatabadi,Mehdi Jadidi,Sana Shaghaghian,Seth B Dworkin

doi:10.2218/iclass.2021.6035

Abstract

An Artificial Neural Network for the Prediction of Penetration Height of Aerated Elliptical Liquid Jets in Gaseous Crossflows

Highlights

Keywords Liquid jet in crossflow, Penetration height, Artificial neural network, Aerated liquid, Elliptic jet Introduction Liquid jet atomization in a subsonic gaseous crossflow (LJIC) is an important phenomenon happening in gas turbines, augmentors, and thermal sprays [1]–[3]
Several parameters like column and surface breakup, droplet size and velocity distributions, Sauter mean diameter (SMD), etc. have been used to study the LJIC comprehensively, the penetration height has been considered as one of the most important parameters since it indicates the location of droplets in the field and shows how well the liquid is mixed with the gas flow [4], [5]
Different experiments have been conducted under various operating conditions and several empirical correlations have been developed to estimate the liquid penetration height in crossflows [4]

Summary

Introduction

Liquid jet atomization in a subsonic gaseous crossflow (LJIC) is an important phenomenon happening in gas turbines, augmentors, and thermal sprays [1]–[3]. To gather validated data, aerated elliptical and circular liquid jets in gaseous crossflows have been studied experimentally in a wind tunnel using a high-speed camera and shadowgraph technique. In these experiments, the nozzle ellipticity, orientation, gas-to-liquid mass flow rate ratio (GLR), and q (which is defined as the liquid-to-crossflow momentum flux ratio at GLR=0) have been changed and their effects on the penetration height have been obtained. In the field of LJIC, x and y are typically in the crossflow and liquid jet directions, respectively (see Figure 3) These parameters are normalized by the equivalent diameter of the orifices, d. 1, 2, 3 10, 20, 30, ..., 180, 190, 200 constant and adaptive 0.0005, 0.0008, 0.001, 0.003, 0.005, 0.008, 0.01, 0.03, 0.05, 0.08, 0.1, 0.5 50, 75, 100, 125, 150, 175, 200, 300, 500 identity, tanh, relu stochastic gradient descent (sgd), adam 0.0001, 0.0005, 0.0008, 0.001, 0.005, 0.008, 0.01, 0.05, 0.08, 0.1, 0.5

Results

Conclusions