Power generation gas turbine performance enhancement in hot ambient temperature conditions through axial compressor design optimization

Shahrabi Farahani, Alireza; Kohandel, Hamed; Moradtabrizi, Hamid; Khosravi, Soudeh; Mohammadi, Esmaeil; Ramesh, Ali

doi:10.1016/j.applthermaleng.2023.121733

Cited by 4 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of artificial intelligence in turbomachinery aerodynamics: progresses and challenges

Zou,

Xu,

Chen

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Turbomachinery plays a vital role in energy conversion systems, with aerodynamic issues being integral to its entire lifecycle, spanning the period of design, validation, and maintenance. Conventionally, the reliance on skilled aerodynamic engineers has been pivotal in the successful development of turbomachines. However, in the current era of burgeoning artificial intelligence (AI) technology, researchers are increasingly turning to AI to replace human expertise and decision-making in these aerodynamic issues and to solve previously intractable aerodynamic problems. This paper presents a systematic literature review of the latest advancements in applying AI to turbomachinery aerodynamics, encompassing the design, validation, and maintenance of compressors and turbines. It underscores how AI is revolutionizing the research paradigm of turbomachinery aerodynamics. AI’s powerful learning capability facilitates more precise and convenient aerodynamic analyses and inspires innovative aerodynamic design ideas that go beyond the capabilities of classical design techniques. Additionally, AI’s autonomous decision-making capability can be employed for aerodynamic optimization and active flow control of turbomachines, generating optimal aerodynamic solutions and complex control strategies that surpass human brains. As a main contribution, we provide a detailed exposition of the future intelligent turbomachinery research and development (R &D) system, along with highlighting potential challenges such as physics embedding, interactive 3D design optimization, and real-time prognoses. It is anticipated that harnessing AI’s full potential will lead to a comprehensive AI-based turbomachinery R &D system in the future.

show abstract