A PARAMETRIC STUDY OF ARTIFICIAL NEURAL NETWORK AS A SURROGATE MODEL FOR HEEL-TOE RUNNING COMPUTATION

2007 ◽  
Vol 40 ◽  
pp. S565
Author(s):  
C-W Chan ◽  
N Wang ◽  
K-C Cha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parametric Study ◽  
Surrogate Model ◽  
Artificial Neural

An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

2019 ◽  
Author(s):  
James A. Tallman ◽  
Michal Osusky ◽  
Nick Magina ◽  
Evan Sewall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

scholarly journals Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

2021 ◽  
Vol 11 (22) ◽  
pp. 10834
Author(s):  
Seok Yoon ◽  
Dinh-Viet Le ◽  
Gyu-Hyun Go
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Parametric Study ◽  
Mechanical Analysis ◽  
Frozen Soil ◽  
Frost Heave ◽  
The Neural Network ◽  
Artificial Neural

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

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