Abstract
In this study, a novel simulation-based algorithm for CO2 ejector design and performance evaluation is presented. The algorithm is based on an automated Computational Fluid Dynamics (CFD) workflow that can account for different ejector geometries and operating conditions. The CFD data points are used to train a Gaussian Process Regression (GPR) machine learning model to predict the ejector performance indicators; efficiency, mass flow rates, outflow uniformity, and entropy generation. Three use cases are investigated using this methodology: 1) performance mapping for off-design operating conditions of a given ejector, 2) design mapping of ejector performance with 5 geometry variables investigated, and 3) flow structure prediction between different ejector mixing chamber geometries. The results show that this algorithm can be used to efficiently explore ejector designs with mean average errors between 0.07 and 0.1 [–] in entrainment ratio. Furthermore, the method can to look for optimized geometries using gradient descent methods, as well as produce ejector performance maps. Additionally, the method is able to predict local flow structures of velocity and pressure inside the ejector with varied ejector geometries. The databases and GPR method implementation from this work is made available open-source for further development and research.
Highlights
The urgent need to rapidly reduce greenhouse gas emissions calls for unprecedented action from individuals, industry, and governments
Any computable and physical combination of geometry variables, operating condition parameters, or CFD model parameters can be used for the mapping
In addition the Gaussian Process Regression (GPR) model can be applied for the prediction of flow structures such as pressure and velocity dis tributions, entropy production, and flow separation
Summary
The urgent need to rapidly reduce greenhouse gas emissions calls for unprecedented action from individuals, industry, and governments. The Heating, Ventilation, Air-conditioning, and Refrigeration (HVAC & R) industry is accountable for a significant part of global greenhouse gas emissions. Large-scale adoption of high GWP synthetic working fluids like hydrofluorocarbons (HFCs) is expected to be a major contributor to global emissions within 2050 [1]. A promising alternative to the use of synthetic refrigerants is natural working fluids such as CO2 (R744). R744 has a GWP of 1 and is a non-toxic, non-flammable, and natural refrigerant that can be efficiently implemented for many applications [3]. Expansion-loss recovery devices are often implemented to achieve higher system efficiency
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