Earticle

초록

한국어

This paper discusses the performance of linear regression, regression tree, support vector regression, and ensemble learners in modelling airflow between two spaces based on accuracy and training time. To obtain training data, different scenarios from an existing computational fluid dynamics (CFD) model are simulated via transient analysis using Cradle scSTREAM. The raw dataset is transformed to having time step sizes of 2.5s, 5.0s, and 50.0s. Feature scaling is also employed on the each data set using both min-max scaling and z-score methods for a total of 9 datasets. Hyperparameters according to machine learning (ML) algorithms are varied such that 15 ML models across the four algorithms are developed. The results show that the regression trees perform the best over all other algorithms, with all models maintaining R2 values above 0.95 at the different datasets. On the other hand, as expected, all linear models demonstrated poor performance compared to nonlinear models. Data resolution affects model accuracy and training time, with accuracy declining slightly as time step size increased. It is also found that there is no significant effect of feature scaling. Lastly, ML models yield substantially cheaper simulation costs than CFD to simulate airflow.

목차

Abstract
1. INTRODUCTION
2. MACHINE LEARNING ALGORITHMS
3. METHODOLOGY
1) Data Acquisition by CFD
2) ML Model Development
4. NUMERICAL RESULTS
1) CFD ML Prediction Results
2) Model Performance Considering Feature Scaling
3) Computational expense
5. CONCLUSION
REFERENCES

저자

• Ana Claudinne Olivas [ Department of ICT Integrated Safe Ocean Smart Cities Engineering, Dong-A University, Busan, Republic of Korea ]
• Changkye Lee [ University Core Research Center for Disaster-free & Safe Ocean City Construction, Dong-A University, Busan, Republic of Korea ]
• Jurng-Jae Yee [ Department of ICT Integrated Safe Ocean Smart Cities Engineering, Dong-A University, Busan, Republic of Korea ] Corresponding Author

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