[논문]머신 러닝 알고리즘을 이용한 역방향 깃발의 에너지 하베스팅 효율 예측

임세환; 박성군

doi:10.5407/jksv.2021.19.3.031

머신 러닝 알고리즘을 이용한 역방향 깃발의 에너지 하베스팅 효율 예측
Prediction of Energy Harvesting Efficiency of an Inverted Flag Using Machine Learning Algorithms 원문보기

한국가시화정보학회지= Journal of the Korean society of visualization, v.19 no.3, 2021년, pp.31 - 38

임세환 (Department of Mechanical Engineering, Seoul National University of Science and Technology) , 박성군 (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology)

Abstract ▼ AI-Helper

The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R2 value of the artificial neural network and random forest algorithms is observed to be more than 0.9.

주제어

표/그림 (10)

그림 Fig. 1. Schematic diagram of an inclined inverted flag. The red line indicates the initial position of the flag. The two green lines indicate the position when the flag reaches the maximum and minimum y-position, respectively.
그림 Fig. 2. Schematic diagram of perceptron model
그림 Fig. 3. Schematic diagram of the random forest algorithm.
그림 Fig. 4. Schematic diagram of the support vector regression algorithm.
그림 Fig. 5. Superimposed inverted flag at the bending rigidity of 0.8. The red line indicates the initial position of the inverted flag. The green line indicates the limit of the flutter motion in the converging state. (a) straight, (b) biased, (c) biased, (d) over flapping mode.
그림 Fig. 6. The flapping frequency depending on the bending rigidity and inclination angle.
그림 Fig. 7. The energy harvesting efficiency depending on the bending rigidity and inclination angle.
그림 Fig. 8. Schematic diagram of K-fold cross validation method.
그림 Fig. 9. The predicted result (y-axis) versus the numerical result (x-axis) for the three different machine learning algorithms;(a) artificial neural network, (b) random forest, (c) support vector regression.
표 Table 1. Prediction accuracy according to each machine learning algorithm.

참고문헌 (13)

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