Data assimilation, ie, upgrading a numerical model by using experimental observations, is
applied to adapt the performances of a simulation-based digital twin (DT) of a semi-industrial …

We highlight the critical role of data in developing sustainable combustion technologies for
industries requiring high-density and localized energy sources. Combustion systems are …

Accurate and detailed data are vital for fundamental understanding of turbulent combustion.
However, studies of turbulent combustion often suffer from measurement sparsity or high …

A data-driven investigation of the flow around a high-rise building is performed by combining
heterogeneous experimental samples and numerical models based on the Reynolds …

Low-order thermoacoustic models are qualitatively correct, but typically, they are
quantitatively inaccurate. We propose a time-domain bias-aware method to make …

A sequential estimator based on the Ensemble Kalman Filter for Data Assimilation of fluid
flows is presented in this research work. The main feature of this estimator is that the Kalman …

The influence of correlated scalar fluctuations on time-averaged laser absorption
tomography measurements of temperature and species in a piloted turbulent premixed …

Reduced-order models of flame dynamics can be used to predict and mitigate the
emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines …

Many important chemically reacting systems are inherently multi-dimensional with spatial
and temporal variations in the thermochemical state, which can be strongly coupled to …

We propose an on-the-fly statistical learning method to take a qualitative reduced-order
model of the dynamics of a premixed flame and make it quantitatively accurate. This physics …