Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment
| Autor: | Dimitri N. Mavris, Christian Perron, Dushhyanth Rajaram |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 477 |
| ISSN: | 1471-2946 1364-5021 |
| DOI: | 10.1098/rspa.2021.0495 |
| Popis: | This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance. |
| Databáze: | OpenAIRE |
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