Numerical performance predictions of artificial intelligence-driven centrifugal compressor designs
Manuel F, Philipp E, Boris K, Stefan G, Delgado A, Valentyn B (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: American Society of Mechanical Engineers (ASME)
Book Volume: 1
Conference Proceedings Title: American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM
ISBN: 9780791883716
Abstract
This paper demonstrates the application of artificial intelligence-driven turbomachinery design, its numerical performance predictions and their numerical validation. A common problem in the industrial application of turbomachinery is that readily available turbomachines are not necessarily matching the desired performance targets (performance characteristics) required for a specific application. Many machines operate under off-design conditions and hence are not operating at maximum efficiency. Traditional numerical analysis and response-driven optimization methods are ineffective and still too time-consuming and are particularly sensitive to changing performance targets. Most commercially available optimization algorithms are based on maximizing or minimizing a response function, for instance the standard error from a desired target performance characteristic of a turbomachine, by changing design variables. This work uses a newly developed artificial intelligence-based approach that is not dependent on the specific design target using the turbomachinery design software AxSTREAM from SoftInWay. Here a neural network was trained within a constraint design space by many samples of design variables and their respective numerical performance predictions. For the numerical verification of the designs the solver Simcenter STAR-CCM+ from Siemens was used. Subsequently the trained neural network was applied to generate a set of design parameters that satisfied the physically feasible desired target performance characteristics very fast. This trained neural network enabled an effective reversal of the traditional iterative design process where now the desired target performance characteristics became the input and the geometry became the output, turning it into a generative inverse design process. This method was applied to generate a centrifugal compressor design within a given geometrically and physically constraint design space. A specific desired target performance characteristic was chosen. The generated designs and results are presented in detail.
Authors with CRIS profile
Involved external institutions
SoftInWay Inc.
United States (USA) (US)
Hochschule für angewandte Wissenschaften Coburg
Germany (DE)
United States (USA) (US)
Hochschule für angewandte Wissenschaften Coburg
Germany (DE)
How to cite
APA:
Manuel, F., Philipp, E., Boris, K., Stefan, G., Delgado, A., & Valentyn, B. (2020). Numerical performance predictions of artificial intelligence-driven centrifugal compressor designs. In American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM. American Society of Mechanical Engineers (ASME).
MLA:
Manuel, Fritsche, et al. "Numerical performance predictions of artificial intelligence-driven centrifugal compressor designs." Proceedings of the ASME 2020 Fluids Engineering Division Summer Meeting, FEDSM 2020, collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels American Society of Mechanical Engineers (ASME), 2020.
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