Real-time thermoacoustic data assimilation
| Autor: | A. Nóvoa, L. Magri |
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| Přispěvatelé: | Nóvoa, A [0000-0003-0597-8326], Magri, L [0000-0002-0657-2611], Apollo - University of Cambridge Repository |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
instability control Mechanical Engineering Applied Mathematics Fluid Dynamics (physics.flu-dyn) FOS: Physical sciences Physics - Fluid Dynamics Computational Physics (physics.comp-ph) Condensed Matter Physics Computational Engineering Finance and Science (cs.CE) Mechanics of Materials nonlinear dynamical systems Computer Science - Computational Engineering Finance and Science acoustics Physics - Computational Physics |
| DOI: | 10.17863/cam.87229 |
| Popis: | Low-order thermoacoustic models are qualitatively correct, but typically, they are quantitatively inaccurate. We propose a time-domain bias-aware method to make qualitatively low-order models quantitatively (more) accurate. First, we develop a Bayesian ensemble data assimilation method for a low-order model to self-adapt and self-correct any time that reference data become available. Second, we apply the methodology to infer the thermoacoustic states and heat-release parameters on the fly without storing data (real time). We perform twin experiments using synthetic acoustic pressure measurements to analyse the performance of data assimilation in all nonlinear thermoacoustic regimes, from limit cycles to chaos, and interpret the results physically. Third, we propose practical rules for thermoacoustic data assimilation. An increase, reject, inflate strategy is proposed to deal with the rich nonlinear behaviour; and physical time scales for assimilation are proposed in non-chaotic regimes (with the Nyquist–Shannon criterion) and in chaotic regimes (with the Lyapunov time). Fourth, we perform data assimilation using data from a higher-fidelity model. We introduce an echo state network to estimate in real time the forecast bias, which is the model error of the low-fidelity model. We show that: (i) the correct acoustic pressure, parameters, and model bias can be inferred accurately; (ii) the learning is robust as it can tackle large uncertainties in the observations (up to 50 % of the mean values); (iii) the uncertainty of the prediction and parameters is naturally part of the output; and (iv) both the time-accurate solution and statistics can be inferred successfully. Data assimilation opens up new possibility for real-time prediction of thermoacoustics by combining physical knowledge and experimental data synergistically. Cambridge Trust |
| Databáze: | OpenAIRE |
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