Multi-condition optimization of a cross-flow fan based on the maximum entropy method

Ding, Yawen; Wang, Jun; Wang, Wei; Jiang, Boyan; Xiao, Qianhao; Ye, Tao

doi:10.1177/0957650921993996

Cited by 5 publications

(3 citation statements)

References 26 publications

(24 reference statements)

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“…Therefore, the optimization of blade geometry is one of the most effective ways to improve fan performance. Compared with a single arc, the spline curve has a more flexible and complex shape, one that is widely used in fan blade design [26]. Concerning the parametric description of the blades in the axial flow fan [27], we used the B-spline curve to control the shape of the blades.…”

Section: Geometric Modelmentioning

confidence: 99%

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

Xiao

Shi²,

Wu³

et al. 2021

Processes

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In this study, the blade shape of the squirrel-cage fan system inside the range hood was optimized using the surrogate model to improve the maximum volume flow rate. The influence of computational fluid dynamics (CFD) noise was concerned. The regression Kriging model (RKM) was used as a surrogate model to reflect the relationship between the design parameters of the blade and the volume flow rate. The parallel filling criterion after re-interpolation was used to improve the optimization efficiency further and ensure global optimization. Through experimental verification, we found that the relative error between the volume flow rate of the optimal sample of RKM and the experiment was only 0.4%. Compared with the prototype, the maximum volume flow rate of the optimal sample of RKM was increased by 2.9%, and the efficiency under the corresponding working conditions was increased by 2%. RKM was used to predict the velocity field of the volute and impeller exit section to explore the feasibility of the RKM in the flow field prediction. Research shows that the RKM cannot accurately predict the velocity of each grid on the cross-section. Still, it can accurately predict the changing trend of the velocity.

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Section: Geometric Modelmentioning

confidence: 99%

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

Xiao

Shi²,

Wu³

et al. 2021

Processes

Self Cite

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“…Recent studies proved that the aerodynamic profile of the blade considerably influences the performance. Yanyan et al [20] parameterized the camber line with a Bezier curve and optimized the internal flow field with a 2D simulation under different operating conditions; they found out that the best performance is when the eccentric vortex moves towards the volute tongue.…”

Section: Introductionmentioning

confidence: 99%

Towards an Accurate Aerodynamic Performance Analysis Methodology of Cross-Flow Fans

et al. 2022

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Cross-flow fans (CFFs) have become increasingly popular in recent years. This is due to their use in several domains such as air conditioning and aircraft propulsion. They also show their utility in the ventilation system of hybrid electric cars. Their high efficiency and performance significantly rely on the design parameters. Up to now, there is no general approach that predicts the CFFs’ performance. This work describes a new methodology that helps deduce the performance of CFFs in turbomachinery, using both analytical modeling and experimental data. Two different loss models are detailed and compared to determine the performance–pressure curves of this type of fan. The efficiency evaluation is achieved by realizing a multidisciplinary study, computational fluid dynamics (CFD) simulations, and an optimization algorithm combined to explore the internal flow field and obtain additional information about the eccentric vortex, to finally obtain the ultimate formulation of the Eck/Laing CFF efficiency, which is validated by the experimental results with good agreement. This approach can be an efficient tool to speed up the cross-flow fans’ design cycle and to predict their global performance.

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“…Their optimized model increased the flow path efficiency by 4.2% compared to the original prototype. 1 Similar approaches to apply geometrical modification to conventional fans like blade trimming in squirrel-cage fan 11 or camber line parameterization in cross-flow fan 12 are common techniques to improve the fan performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a vacuum cleaner fan suction and shaft power using Kriging surrogate model and MIGA

Almasi

Ghorani

Haghighi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part A:

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Optimization of vacuum cleaner fan components is a low-cost and time-saving solution to satisfy the increasing requirement for compact energy-efficient cleaners. In this study, surrogate-based optimization technique is used and for the first time it is focused on maximization of Airwatt parameter, which describes the fan suction power, as an objective function (Case II). Besides, the shaft power is minimized (Case I) as another optimization target in order to reduce the power consumption of the vacuum cleaner. 11 geometrical variables of 3 fan components including impeller, diffuser and return channel are selected as the optimization design variables. 80 training points are distributed in the sample space using Advanced Latin Hypercube Sampling (ALHS) technique and the outputs of sample points are calculated by means of CFD simulations. Kriging and RSA surrogate models have been fitted to the outputs of the sample space. Through coupling of constructed Kriging models and Multi-Island Genetic Algorithm (MIGA), the optimal design for each of the optimization cases is presented and evaluated using numerical simulations. A 20.22% reduction in shaft power in Case I and an improvement of 27.73% in Airwatt in Case II have been achieved as the overall results of this study. Despite achieving goals in both optimization cases, a slight decrease in Airwatt in Case I (−6.20%) and a slight increase in shaft power in Case II (+4.82%) are observed relative to primary fan. Furthermore, the Analysis of Variance (ANOVA) determines the importance level of design variables and their 2-way interactions on the objective functions. It was concluded that geometrical parameters related to all of the fan components must be considered simultaneously to conduct a comprehensive optimization. The reasons of enhancement in optimal cases compared with the reference design have been further investigated by analysis of the fan internal flow field. Post-processing of the CFD results demonstrates that the applied geometrical modifications cause a more uniform flow through the flow passages of the optimal fan components.

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Multi-condition optimization of a cross-flow fan based on the maximum entropy method

Cited by 5 publications

References 26 publications

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

Towards an Accurate Aerodynamic Performance Analysis Methodology of Cross-Flow Fans

Optimization of a vacuum cleaner fan suction and shaft power using Kriging surrogate model and MIGA

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