Low-order thermoacoustic models are qualitatively correct, but they are typically quantitatively inaccurate. We propose a time-domain method to make qualitatively low-order models quantitatively (more) accurate. First, we develop a Bayesian data assimilation method for a low-order model to self-adapt and self-correct any time that reference data, for example from experiments, becomes available. Second, we apply the methodology to infer the thermoacoustic states, heat release parameters, and model errors on the fly without storing data (real-time). Third, we analyse the performance of the data assimilation with synthetic data and interpret the results physically. We apply the data assimilation algorithm to all nonlinear thermoacoustic regimes, from limit cycles to chaos, in which acoustic pressure measurements from microphones are assimilated. Fourth, we propose practical rules for thermoacoustic data assimilation based on physical observations on the dynamics. An increase, reject, inflate strategy is proposed to deal with the rich nonlinear behaviour, the bifurcations of which are sensitive to small perturbations to the parameters. We show that (i) the correct acoustic pressure and parameters can be accurately inferred; (ii) the learning is robust because it can tackle large uncertainties in the observations (up to 50% 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 successfully inferred. Physical time scales for assimilation are proposed in non-chaotic regimes (with the Nyquist-Shannon criterion) and in chaotic regimes (with the Lyapunov time). Data assimilation opens up new possibility for real--time prediction of thermoacoustics by synergistically combining physical knowledge and data.
翻译:低顺序的温度感应模型在质量上是正确的,但通常在数量上是不准确的。 我们提出一个时间- 域方法, 使质量低顺序模型在数量上( 更多) 准确。 首先, 我们为低序模型开发一种巴伊西亚数据同化方法, 到自适应和自纠正, 任何参考数据( 例如实验数据) 出现时, 我们用这个方法来推断热声状态、 热释放参数和飞行模型错误, 而不存储数据( 实时) 。 第三, 我们用合成数据分析数据同化的性能, 并用物理解释结果。 我们用数据同值计算数据, 从限量周期到混杂状态, 将数据同化。 第四, 我们根据对动态的物理观察, 提出热感应数据同化的实用规则。 提议增加、 拒绝、 冷淡化战略, 以应对丰富的非线性行为, 其内值的比值与参数的细度值比较。 我们用数据与非线性值的数值一起, 显示( ) 准确的压压压压值和精确的数值是时间( ), 和直数级的数值可以进行, 。我们用测测算, 和直调的数值是时间, 。