State of the Art of Modelling and Design Approaches for Ejectors in Proton Exchange Membrane Fuel Cell

Table 1

Literature survey on ejector simulation and geometric optimization.

Ref.	Year	Type	More details	Key findings
[38]	2019	Numerical	ANSYS Fluent, 2D axisymmetric steady flow, – SST, 1-phase 1-species	Increasing raises ER, after a certain value performance deteriorates
[39]	2020	Numerical	Thermodynamic model, annular mixing layer	Optimal depends on working condition
[40]	2022	Experimental	Ejectors with different nozzle and area ratios tested over wide ranges of operating conditions	Up to 4 “optimal” observed; these values were affected by both working condition and area ratios
[41]	2022	Mix	Experimental testing and validation of proposed model, ANSYS Fluent, 2D axisymmetric steady flow, – SST, 1-phase 1-species, compound choking criterion	Compound choking allowed to determine variation in secondary flow choking position under several suboptimal
[42]	2019	Numerical	ANSYS Fluent, 2D steady flow, –; model coupled with anodic pressure drop formula, 1-phase 2-species	Optimal range for (3.00–3.54) and (1–3)
[43]	2021	Numerical	3D steady flow, RNG –, 1-phase 2-species	is the first geometrical parameter to be optimized
[51]	2014	Numerical	Thermodynamic model of ejector coupled with semiempirical stack model	Definition of two dimensionless parameters to guide ejector design
[52]	2017	Experimental	Ejector designed and tested at constant load and in fast transient condition	Anode gas recirculation rate ranging from 40% fuel utilization per pass at 25 A stack current to 64% fuel utilization per pass at 160 A stack current
[53]	2022	Numerical	ANSYS Fluent, 2D steady flow, – SST, 1-phase 2-species	Determined order of influence of geometrical parameter: (i) Low current (110 A); (ii) Middle current (275 A); (iii) High current (412.5 A); Nozzle throat length
[54]	2013	Numerical	Thermodynamic model	Inlet primary flow temperature affects ejector entrainment ratio and component efficiency
[55]	2019	Mix	ANSYS Fluent, 2D axisymmetric steady flow, comparison between RNG – and – SST	RNG model shows higher accuracy than SST; optimal ° and  mm when stack works at its rated power
[56]	2020	Numerical	ANSYS Fluent, 2D axisymmetric steady flow, – SST, coupled with a pressure drop through anode model	ER can be influenced by anode inlet temperature, relative humidity, and differential pressure
[57]	2020	Numerical	OpenFOAM, 3D transient flow, RNG , 2-phase 3-species	Dynamic responses during power variations results from velocity differences between the primary and the secondary flow; increase of nitrogen mass fraction promotes total ER, while it reduces hydrogen ER
[58]	2020	Numerical	COMSOL, 3D steady flow, coupled with MATLAB/Simulink hydrogen recovery system model	A lower ejector temperature is disadvantageous in removing the moisture content of the recirculated hydrogen gas, thus in practical applications; the hydrogen inlet temperature/pressure must be carefully controlled
[59]	2022	Numerical	Thermodynamic model of a 2-phase CPM ejector	ER increase from 0.47 to 1.14 as mixing area ratios range from 1.0 to 1.2 under the given conditions
[60]	2015	Numerical	Integrated lumped parameter-CFD approach, ANSYS Fluent, 2D axisymmetric steady flow, – SST	The model can be used for studying off-design conditions, where ejector component efficiencies are not constant
[61]	2016	Numerical	ANSYS Fluent, 2D axisymmetric steady flow, RNG –, 2-phase 1-species, optimization through genetic and evolutionary algorithm	is the crucial parameter in ejector performance
[62]	2021	Numerical	ANSYS Fluent, 2D steady flow, RNG –, 1-phase 2-species	Humidity and temperature of the secondary flow have a noticeable influence on the performance of the ejector
[44]	2017	Numerical	Thermodynamic model, hybrid fish swarm algorithm	Optimization efficiency increased with respect to genetic algorithm
[45]	2018	Numerical	Multiobjective evolutionary algorithm coupled with a surrogate model based on CFD simulations	and are the most important geometrical variable; entrainment ratio can be increased up to 110% and 35%, for air and CO2, respectively
[46]	2023	Numerical	ANSYS Fluent, 2D axisymmetric steady flow, response surface methodology	Priority order in optimizing ejector geometry:
[47]	2014	Numerical	2D axisymmetric steady flow, RNG –, 1-phase 2-species, artificial neural network and genetic algorithm to obtain optimal geometry	Optimal ; optimal ; optimal °; optimal
[48]	2021	Numerical	Automated CFD workflow, ANSYS Fluent, RNG –, 2-phase 1-species, Gaussian process regression machine learning model	The algorithm can be used to efficiently explore ejector designs with mean average errors between 0.07 and 0.1
[49]	2022	Numerical	ANSYS Fluent, 2D steady flow, realizable –, optimization via adjoint method	ER increased by around 37%
[50]	2022	Numerical	MATLAB and experimental dataset of a steam-centered ejector are applied to train the ANN model of a steam ejector using three different algorithms	LM model yielded the best agreement; the effect of the outlet area ratio is less important with respect to throat area ratio