June 10th, 2021, 12:15 PM EDT - 1:45 PM EDT
Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification
Deep Dive:
12:15 - 12:45 PM EDT, Paper No. GT2021-59717, “Structurally Constrained Aerodynamic Adjoint Optimisation of Highly Loaded Compressor Blades”
Deep Dive:
12:45 - 1:15 PM EDT, Paper No. GT2021-60158, “Using Autoencoders and Output Consolidation to Improve Machine Learning Models for Turbomachinery Applications”
Rapid Talk:
1:15 - 1:25 PM EDT, Paper No. GT2021-58469, “Automatically Designed Deep Gaussian Process for High Dimensional Turbomachinery Application”
Rapid Talk:
1:25 - 1:35 PM EDT, Paper No. GT2021-58562, “Constraint Handling in Bayesian Optimization -- a Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement”
Rapid Talk:
1:35 - 1:45 PM EDT, Paper No. GT2021-59580, “Adjoint-Based Optimization of Rocket Engine Turbine Blades”
Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Structurally Constrained Aerodynamic Adjoint Optimisation of Highly Loaded Compressor Blades | View |
| Submission | Using Autoencoders and Output Consolidation to Improve Machine Learning Models for Turbomachinery Applications | View |
| Submission | Automatically Designed Deep Gaussian Process for High Dimensional Turbomachinery Application | View |
| Submission | Constraint Handling in Bayesian Optimization -- a Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement | View |
| Submission | Adjoint-Based Optimization of Rocket Engine Turbine Blades | View |