| Design of space propulsion components is extremely complex, expensive, and involves harsh environments. Coupling of computational fluid dynamics (CFD) and surrogate modeling to optimize performance of space propulsion components is becoming popular due to reduction in computational expense. However, there are uncertainties in predictions using this approach, like empiricism in computational models and surrogate model errors. We develop methods to estimate and to reduce such uncertainties.; We demonstrate the need to obtain experimental designs using multiple criteria by showing that using a single-criterion may lead to high errors. We propose using an ensemble of surrogates to reduce uncertainties in selecting the best surrogate and sampling strategy. We also develop an averaging technique for multiple surrogates that protects against poor surrogates and performed at par with best surrogate for many problems.; We assess the accuracy of different error estimation models, including an error estimation model based on multiple surrogates, used to quantify prediction errors. While no single error model performs well for all problems, we show possible advantage of combining multiple error models.; We apply these techniques to two problems relevant to space propulsion systems. First, we employ surrogate-based strategy to understand the role of empirical model parameters and uncertainties in material properties in a cryogenic cavitation model, and to calibrate the model. We also study the influence of thermal effects on predictions in cryogenic environment in detail. Second, we use surrogate models to improve the hydrodynamic performance of a diffuser by optimizing the shape of diffuser vanes. For both problems, we observed improvements using multiple surrogate models.; While we have demonstrated the approach using space propulsion components, the proposed techniques can be applied to any large-scale problem. |
