ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Performance evaluation of air ejectors using artificial neural network approach

Gupta, P and Rao, SMV and Kumar, P (2023) Performance evaluation of air ejectors using artificial neural network approach. In: Sadhana - Academy Proceedings in Engineering Sciences, 48 (2).

[img] PDF
sad_aca_pro_48-2_2023.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1007/s12046-023-02087-2


Ejectors are implemented in energy conservation applications such as power and refrigeration systems and fuel cell stacks due to their passive nature and geometrical simplicity. Ejectors work on the principle of thermal compression that utilizes low-grade energy to produce a compression effect. A low-enthalpy secondary fluid gets compressed through gasdynamic interactions with a co-flowing high-enthalpy primary flow in ejectors. The aerodynamic choking of the secondary flow within an ejector leads to two modes of ejector operation: (a) critical flow regime and (b) mixed flow regime. Several low-fidelity models for analyzing ejectors have been proposed in the literature. These models are adequate for the rapid design of specific categories of ejectors. However, they yield notable deviations of 20-25 with experimental measurements for different operating conditions. On the other hand, computational fluid dynamics (CFD) simulations for ejectors are computationally expensive. Furthermore, they require a careful selection of numerical solvers and turbulence models to predict performance and flow characteristics accurately. This paper presents a data-driven artificial neural network (ANN) model to predict the critical parameters of supersonic ejectors. The model is trained using experimental measurement for air ejectors at various operating and geometrical parameters. The ANN model consists of five input parameters, representing operating and geometrical parameters of ejectors to estimate two output parameters: (a) entrainment ratio and (b) operating regime. The trained ANN model predicts the entrainment ratio with a maximum deviation of about 7 and classifies the ejector operational mode with an accuracy of 100. © 2023, Indian Academy of Sciences.

Item Type: Journal Article
Publication: Sadhana - Academy Proceedings in Engineering Sciences
Publisher: Springer
Additional Information: The copyright for this article belongs to Springer.
Keywords: Air ejectors; Computational fluid dynamics; Ejectors (pumps); Energy conservation; Enthalpy; Fuel cells; Geometry; Turbulence models, Artificial neural network approach; Artificial neural network modeling; Critical flow; Critical flow regime; Ejector performance; Entrainment ratio; Flow regimes; Performance; Performances evaluation; Supersonic ejector, Neural networks
Department/Centre: Division of Interdisciplinary Sciences > Interdisciplinary Centre for Energy Research
Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Division of Mechanical Sciences > Mechanical Engineering
Date Deposited: 21 Apr 2023 10:05
Last Modified: 21 Apr 2023 10:05
URI: https://eprints.iisc.ac.in/id/eprint/81359

Actions (login required)

View Item View Item