Experimental validation of a three-dimensional through-flow model for high-speed compressor surge

Righi, Mauro; Pachidis, Vassilios; Könözsy, László; Giersch, Thomas; Schrape, Sven

doi:10.1016/j.ast.2022.107775

Cited by 6 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Turbulence is regarded as one of the challenging problems in classical physics [4], characterized by complex flow parameters, substantial temporal fluctuations, and marked features of non-uniformity and irregularity. Considerable effort has been devoted to the analysis of complex flow, particularly the internal flow through complicated thermodynamic machinery [5,6]. Unfortunately, it has not been possible thus far to simulate a three-dimensional full-field gas flow through the propulsion of an aircraft owing to the complexity of its structure and working process.…”

Section: Introductionmentioning

confidence: 99%

Physics-Embedded Deep Learning to Predict Real-Time Flow Parameters in Complex Thermodynamic Machinery

Lin,

Xiao,

Xiao

2024

Aerospace

View full text Add to dashboard Cite

Flow through complex thermodynamic machinery is intricate, incorporating turbulence, compressibility effects, combustion, and solid–fluid interactions, posing a challenge to classical physics. For example, it is not currently possible to simulate a three-dimensional full-field gas flow through the propulsion of an aircraft. In this study, a new approach is presented for predicting the real-time fluid properties of complex flow. This perspective is obtained from deep learning, but it is significant in that the physical context is embedded within the deep learning architecture. Cases of excessive working states are analyzed to validate the effectiveness of the given architecture, and the results align with the experimental data. This study introduces a new and appealing method for predicting real-time fluid properties using complex thermomechanical systems.

show abstract

Section: Introductionmentioning

confidence: 99%