Predicting Centrifugal Pumps’ Complete Characteristics Using Machine Learning
Processes, cilt.11, sa.2, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 11 Sayı: 2
- Basım Tarihi: 2023
- Doi Numarası: 10.3390/pr11020524
- Dergi Adı: Processes
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
- Anahtar Kelimeler: Suter curves, pump hydraulic transient, pump specific speed, dimensionless torque curve, dimensionless head curve, performance curve
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Erciyes Üniversitesi Adresli: Evet
Özet
The complete characteristics of centrifugal pumps are crucial for the modeling of hydraulic transient phenomena occurring in pipe systems. However, due to the effort required to obtain these curves, pump manufacturers typically only provide basic information, particularly when the pump operates under normal conditions. To acquire the full characteristic curves based on the manufacturer’s normal performance curve, a machine learning (ML) model is proposed to predict full, complete Suter curves using a pump’s specific speed with the known parts of the Suter curve. The training data for the model are sourced from the available Suter curves from laboratory experiments. Subsequently, the proposed ML model combines several types of regression models in an attempt to find the most accurate prediction in terms of the root mean square error (RMSE). The result proved highly efficient, as the experiments attained a maximum RMSE value of 0.032 across the three categories of centrifugal pumps based on their specific speeds, hence demonstrating the potential of machine learning in the study of pump characteristic curves.