Optimal shape design using machine learning for wind energy and pressure

Keyphrases

• Artificial Neural Network Algorithm 33%
• Computation Time 33%
• Computational Effort 33%
• Computational Fluid Dynamics 66%
• Computational Fluid Dynamics Simulation 33%
• Computational Load 33%
• Data-driven Framework 33%
• Design Generation 33%
• Energy Consumption 33%
• Energy Generation 66%
• Exponential Growth 33%
• Genetic Algorithm 33%
• Initial Design 33%
• Machine Learning 100%
• Machine Learning Techniques 33%
• Multiple Objective Optimization 33%
• Optimal Shape Design 100%
• Optimal Strategy 33%
• Optimization Algorithm 33%
• Parametric Design 33%
• Parametric Design Process 33%
• Pavilion 100%
• Performance Value 33%
• Pressure Calculation 33%
• Shell Thickness 33%
• Structural Behavior 33%
• Structural Integrity 66%
• Sustainable Design 33%
• Sustainable Energy 33%
• Thin Shell 33%
• Wind Energy 100%
• Wind Pressure 100%
• Wind Speed 100%
• Wind Turbine 33%

Engineering

• Artificial Neural Network 33%
• Compressed Air Motors 33%
• Computational Effort 33%
• Computational Fluid Dynamics 100%
• Computational Load 33%
• Computational Time 33%
• Design Process 33%
• Ecodesign 33%
• Exponential Growth 33%
• Genetic Algorithm 33%
• Initial Design 33%
• Learning System 100%
• Machine Learning Method 33%
• Objective Optimization 33%
• Optimal Design 66%
• Parametric Design 66%
• Power Generation 66%
• Renewable Energy 33%
• Shape Design 100%
• Shell Thickness 33%
• Structural Behavior 33%
• Structural Integrity 66%
• Turbine 33%
• Wind Power 100%

Material Science

• Computational Fluid Dynamics 100%
• Structural Behavior 33%