Multi-parameter optimization for the performance of the fuel cell air compressor based on computational fluid dynamics analysis at part load

Fu, Jianqin; Wang, Huailin; Bao, Huanhuan; Sun, Xilei; Liu, Jingping

doi:10.1016/j.tsep.2023.102057

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…Most of the optimizations were carried out by changing design parameters, such as trailing edge angles, polar angles, impeller shape factor, radius, blade height, and other design parameters. [11][12][13][14] Literature on theoretical methods The theoretical modeling of centrifugal compressors involves the creation of mathematical frameworks and computational simulations to elucidate their performance characteristics and behaviour. These models integrate fundamental principles of fluid dynamics, thermodynamics, and mechanical engineering to predict variables such as pressure ratios, flow rates, efficiency, and temperature distributions.…”

Section: Literature On Numerical Methodsmentioning

confidence: 99%

Aerodynamic performance enhancement of centrifugal compressor using numerical techniques

2024

F1000Res

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Background Centrifugal compressors are dynamic machines utilizing a rotating impeller, efficiently accelerate incoming gases, transforming kinetic energy into pressure energy for compression. They serve a wide range of industries, including air conditioning, refrigeration, gas turbines, industrial processes, and applications such as air compression, gas transportation, and petrochemicals, demonstrating their versatility. Designing a centrifugal compressor poses challenges related to achieving high aerodynamic efficiency, surge and choke control, material selection, rotor dynamics, cavitation, erosion, and addressing environmental considerations while balancing costs. Optimizing maintenance, reliability, and energy efficiency are essential aspects of the design process. Methods The primary objective of this research is to comprehensively investigate and improve the aerodynamic performance of centrifugal compressors. To accomplish this, a comprehensive investigation of variables such as blade number and hub diameter, along with various turbulence models will be conducted. This approach will leverage numerical techniques to fill the significant gaps in the current literature regarding centrifugal compressor design and optimization. The study encompasses the evaluation of two turbulence models, namely Shear Stress Transport and K-epsilon. Furthermore, it delves into the fine-tuning of blade geometry, including variations in blade number and hub diameter, aiming to refine the design for optimal performance. Extensive analyses using Ansys CFX encompass key variables such as Pressure, Mach Number, Density, Velocity, Turbulence Kinetic Energy, and Temperature. Results Notably, the optimized pressure profile yielded remarkable results, achieving a substantial 36% improvement, demonstrating the tangible benefits of these design enhancements. Conclusion The outcomes of this research hold significant utility for engineers, manufacturers, and regulatory bodies, offering invaluable insights and guidance to enhance compressor performance and efficiency.

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Section: Literature On Numerical Methodsmentioning

confidence: 99%

Aerodynamic performance enhancement of centrifugal compressor using numerical techniques

2024

F1000Res

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“…At present, individual nations are endeavoring to devise driving cycles tailored to the idiosyncrasies of their actual road networks during the development, testing and evaluation of automobiles 10 , 11 . As the most critical basic technology for the automotive industry, it is increasingly essential to develop actual driving cycles that fit the country or even a specific city 12 , 13 . Therefore, in order to save development costs and improve reusability, it is necessary to propose a method for facilitating the transition from original vehicle data to a final representative driving cycle 14 .…”

Section: Introductionmentioning

confidence: 99%

Construction and optimization of representative actual driving cycles based on the improved autoencoder

Zhao,

Sun,

Wang

et al. 2024

Sci Rep

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In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.

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A combinatorial prediction model for the performance of fuel cell air compressor with air bearings considering accelerated decay characteristics

Fu,

Zhang,

Sun

et al. 2024

Applied Thermal Engineering

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Multi-parameter optimization for the performance of the fuel cell air compressor based on computational fluid dynamics analysis at part load

Cited by 8 publications

References 34 publications

Aerodynamic performance enhancement of centrifugal compressor using numerical techniques

Aerodynamic performance enhancement of centrifugal compressor using numerical techniques

Construction and optimization of representative actual driving cycles based on the improved autoencoder

A combinatorial prediction model for the performance of fuel cell air compressor with air bearings considering accelerated decay characteristics

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