Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes

• Published in: IEEE access 2019, Vol.7, p.55064-55072
• Main Authors: Deng, Hongying, Zhang, Yang, Chen, Bocheng, Liu, Yi, Zhang, Shengchang
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Computational modeling; Conditional probability; Data models; Discharge; Gaussian process; gaussian process model; Mathematical models; Multiphase; multiphase pump; Numerical models; Prediction models; Predictions; Predictive models; Probabilistic modeling; Pumps; Statistical analysis; Temperature; Transportation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2912977

Accurately predicting the temperature characteristics of a dynamic discharge process in different transportation conditions can improve the performance of reciprocating multiphase pumps in practice. However, an accurate model for the description of the complicated behavior is not available because of the unknown interphase interaction mechanisms and infeasible experiments. A probabilistic modeling method of automatically selecting prediction models is proposed for the dynamic discharge process. First, candidate computational fluid dynamics (CFD) models are empirically utilized to provide the training data for candidate Gaussian process models (GPMs). Then, a posterior probability index is proposed to assess the uncertainty of trained GPMs when the actual values are not available. With this information, the most suitable GPM and CFD models are selected sequentially for each new sample. Consequently, the developed special GPM (SGPM) can capture the main temperature characteristics. Moreover, the selection results of prediction models can provide useful information for the recognition of complicated flow patterns. The advantages of the proposed SGPM are demonstrated using a reciprocating multiphase pump under different transportation conditions.