Artificial neural network modeling for variable area ratio ejector

Abstract

In this article, machine learning method is applied to model ejectors. Three-layer feed-forward neural network with sigmoid active functions was employed to estimate the outlet pressure of ejector given states of primary and secondary inlets. Well prediction results were achieved within the boundary of training dataset in experiment on ejector based multi-evaporator refrigeration system. The number of hidden layer neurons is optimized by minimizing validation error. Moreover, this research lays the foundation of optimizing system parameters and building control strategies for ejector based refrigeration system based on the machine learning methods.

Thermodynamic investigation on low GWP drop‐in alternatives to HFC‐245fa in ejector refrigeration cycle for air condition application

“…The ML model, as a computationally inexpensive model, predicts the performance based on existing data, which is different from the above models. ML has realized its extensive application in several engineering cases, such as the prediction of heat transfer coefficients, 23,24 the modeling and performance prediction of ejector and the cycle with the ejector, 25,26 and the optimal design of the structure of ejector. 27 These applications have indicated that a welltrained ML model is able to accurately predict output parameters for regimes beyond the validation dataset.…”

The Digital Twin Realization of an Ejector for Multiphase Flows

“…In particular, the authors have calculated the energy losses in the system, avoiding the classical thermodynamic analysis that uses complex differential equations and complex simulation programs. Reference [29] developed an ANN to estimate the output pressure from an ejector, given various input states. The authors then proposed an alternative method of ejector modeling compared Energies 2021, 14, 5533 4 of 23 to traditional models (thermodynamic and CFD models).…”