arXiv subjects

Guillaume Daviller

Publications and source records attributed to Guillaume Daviller.

4 recordsLinked to original sources

arXiv2026

Acoustic radiation of thermodiffusively unstable turbulent lean premixed hydrogen-air flames

The impact of thermodiffusive effects on combustion noise in turbulent premixed slot jet flames is investigated using Direct Numerical Simulations. Two thermodiffusively unstable lean hydrogen-air flames are compared with a thermodiffusively stable stoichiometric methane-air flame with comparable laminar properties and same turbulence intensity. The hydrogen cases differ in bulk velocity, chosen to match either the turbulent flame brush length or the bulk velocity of the methane case. Thermodiffusive effects are found to strongly alter both the heat release rate fluctuations, which dominate the far-field acoustic radiation, and the flame surface dynamics. A theoretical framework extending the classical flamelet theory to thermodiffusively unstable flames is proposed and validated, relating the flame-generated sound to the time derivative of the flame surface area and to the stretch factor $I_0$. The analysis identifies flame stretch as a key mechanism promoting noise radiation in thermodiffusively unstable flames. Spectral analyses further show that hydrogen flames exhibit stronger low-frequency heat release rate fluctuations and reduced high-frequency content relative to the methane flame. This is shown to be related to the coupled action of turbulence and thermodiffusive instabilities, which enhance large-scale flame motions while attenuating small-scale flame annihilation events. Consequently, hydrogen flames radiate more strongly at low frequencies, near the acoustic peak, and exhibit a steeper high-frequency spectral roll-off. Finally, Spectral Proper Orthogonal Decomposition reveals that hydrogen non-equidiffusion intensifies shear layer instabilities between combustion products and ambient air. These results indicate that thermodiffusive effects influence both direct and indirect combustion noise generation mechanisms in hydrogen flames.

physics.flu-dyn↗

arXiv2016

Sparsity enabled cluster reduced-order models for control

Characterizing and controlling nonlinear, multi-scale phenomena play important roles in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying low-dimensional dynamics of complex systems. CROM builds a data-driven discretization of the Perron-Frobenius operator, resulting in a probabilistic model for ensembles of trajectories. A key advantage of CROM is that it embeds nonlinear dynamics in a linear framework, and uncertainty can be managed with data assimilation. CROM is typically computed on high-dimensional data, however, access to and computations on this full-state data limit the online implementation of CROM for prediction and control. Here, we address this key challenge by identifying a small subset of critical measurements to learn an efficient CROM, referred to as sparsity-enabled CROM. In particular, we leverage compressive measurements to faithfully embed the cluster geometry and preserve the probabilistic dynamics. Further, we show how to identify fewer optimized sensor locations tailored to a specific problem that outperform random measurements. Both of these sparsity-enabled sensing strategies significantly reduce the burden of data acquisition and processing for low-latency in-time estimation and control. We illustrate this unsupervised learning approach on three different high-dimensional nonlinear dynamical systems from fluids with increasing complexity, with one application in flow control. Sparsity-enabled CROM is a critical facilitator for real-time implementation on high-dimensional systems where full-state information may be inaccessible.

physics.data-an↗

arXiv2014

Cluster-based reduced-order modelling of a mixing layer

We propose a novel cluster-based reduced-order modelling (CROM) strategy of unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt et al. 2006) and and transition matrix models introduced in fluid dynamics in Eckhardt's group (Schneider et al. 2007). CROM constitutes a potential alternative to POD models and generalises the Ulam-Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron-Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Secondly, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e.g. using finite-time Lyapunov exponent and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative example), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these examples, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics.

physics.flu-dyn↗

arXiv2014

Acceleration feature points of unsteady shear flows

In this paper, we propose a novel framework to extract features such as vortex cores and saddle points in two-dimensional unsteady flows. This feature extraction strategy generalizes critical points of snapshot topology in a Galilean-invariant manner, allows to prioritize features according to their strength and longevity, enables to track the temporal evolution of features, is robust against noise and has no subjective parameters. These characteristics are realized via several constitutive elements. First, acceleration is employed as a feature identifier following Goto and Vassilicos (2006), thus ensuring Galilean invariance. Second, the acceleration magnitude is used as basis for a mathematically well-developed scalar field topology. The minima of this field are called acceleration feature points, a superset of the acceleration zeros. These points are discriminated into vortices and saddle points depending the spectral properties of the velocity Jacobian. Third, all operations are based on discrete topology for the scalar field with combinatorial algorithms. This parameter-free foundation allows (1) to use persistence as a physically meaningful importance measure to prioritize feature points, (2) ensures robustness since no differentiation and interpolation need to be performed with the data, and (3) enables a natural and robust tracking algorithm for the temporal feature evolution. In particular, we can track vortex merging events in an unsupervised manner. Data based analyses are presented for an incompressible periodic cylinder wake, an incompressible planar mixing layer and a weakly compressible planar jet. They demonstrate the power of the tracking approach, which provides a spatiotemporal hierarchy of the minima.

physics.flu-dyn↗