Data and code corresponding to the research paper: Simulator-based surrogate optimisation employing adaptive uncertainty-aware sampling (https://doi.org/10.1016/j.compchemeng.2025.109243).
Two case studies are included to demonstrate the method:
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Case 1: Having access to the simulator - Blade design
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Objective: Optimise the thermal design of a jet engine turbine blade
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Simulator: Thermal analysis using Finite Element Analysis (FEA) via MATLAB's Partial Differential Equation Toolbox
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Reference: Based on MATLAB's Thermal Stress Analysis of Jet Engine Turbine Blade example
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Case 2: Being limited to pre-acquired datasets - Auto-thermal reformer
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Objective: Optimise the operating conditions of an auto-thermal reformer
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Data source: 2,800 pre-simulated data points from the OMLT repository
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Reference: Optimisation setup follows the OMLT implementation
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