Artificial intelligence aided CFD analysis regime validation and selection in feature-based cyclic CAD/CFD interaction process

ABSTRACT

Multiple-view feature modeling is supposed to keep the information consistency during product development. However, for products involving fluid flow, the information consistency is difficult to keep because the application of CFD (Computational Fluid Dynamics) requires specific knowledge and rich experience. To conquer this deficiency, intelligent CFD solver functions toward an expert system are proposed to update the CFD analysis view in response to the changes in the design view which is embedded in the CAD fluid functional features. The CAE interface protocol is used to convert the features in the design view into the CAE boundary features in the CFD analysis view. The CFD analysis view also includes the fluid physics features and dynamic physics features which support the intelligent CFD solver functions. The intelligent CFD solver is enhanced with the capability to model complex turbulent phenomena and estimate the discretization error. A case study of contracted pipe is illustrated to show the effectiveness of the proposed multiple-view feature modeling method by comparing with empirical results.

GRAPHICAL ABSTRACT

Keywords:

• CAD
• CFD analysis view
• multiple-view feature modeling
• expert system

Acknowledgements

The authors would like to acknowledge Natural Sciences and Engineering Research Council of Canada (NSERC), RGL Reservoir Management, China Scholarship Council (CSC), University of Alberta and Alberta Innovates Technology Futures (AITF) for the financial support.

ORCID

Lei Li http://orcid.org/0000-0002-5259-407X

Carlos F. Lange http://orcid.org/0000-0001-9390-8728

Yongsheng Ma http://orcid.org/0000-0002-6155-0167