arXiv subjects

Hermann Fasel

Publications and source records attributed to Hermann Fasel.

3 recordsLinked to original sources

arXiv2023

Mori-Zwanzig mode decomposition: Comparison with time-delay embeddings

We introduce the Mori-Zwanzig Mode Decomposition (MZMD), a novel data-driven technique for efficient modal analysis of and reduced-order modeling of large-scale spatio-temporal dynamical systems. MZMD represents an extension of Dynamic Mode Decomposition (DMD) by providing an approximate closure term with MZ memory kernels accounting for how the unresolved modes of DMD interact with the resolved modes, thus addressing limitations when the state-space observables do not form a Koopman-invariant subspace. Leveraging the Mori-Zwanzig (MZ) formalism, MZMD identifies the modes and spectrum of the discrete-time Generalized Langevin Equation (GLE); an integro-differential equation that governs the dynamics of selected observables and their memory-dependent coupling with the unresolved degrees of freedom. This feature fundamentally distinguishes MZMD from time-delay embedding methods, such as Higher-Order DMD (HODMD). In this work, we derive and analyze MZMD and compare it with DMD and HODMD, using two exemplary Direct Numerical Simulation (DNS) datasets: a 2D flow over a cylinder (as validation) and laminar-turbulent boundary-layer transition over a flared cone at Mach 6. We demonstrate that MZMD, via the addition of MZ memory terms, improves the resolution of spatio-temporal structures within the transitional/turbulent regime by the introduction of transient and periodic modes (not captured by DMD), which contain features that arise due to nonlinear mechanisms, such as the generation of the so-called hot streaks on the surface of the flared cone. Our results demonstrate that MZMD serves as an efficient generalization of DMD (reducing to DMD in the absence of memory), improves stability, and exhibits greater robustness and resistance to overfitting compared to HODMD.

physics.flu-dyn↗

arXiv2023

Data-Driven Mori-Zwanzig: Reduced Order Modeling of Sparse Sensors Measurements for Boundary Layer Transition

Understanding, predicting and controlling laminar-turbulent boundary-layer transition is crucial for the next generation aircraft design. However, in real flight experiments, or wind tunnel tests, often only sparse sensor measurements can be collected at fixed locations. Thus, in developing reduced models for predicting and controlling the flow at the sensor locations, the main challenge is in accounting for how the surrounding field of unobserved variables interacts with the observed variables at the fixed sensor locations. This makes the Mori-Zwanzig (MZ) formalism a natural choice, as it results in the Generalized Langevin Equations which provides a framework for constructing non-Markovian reduced-order models that includes the effects the unresolved variables have on the resolved variables. These effects are captured in the so called memory kernel and orthogonal dynamics. In this work, we explore the data-driven MZ formulations to two boundary layer flows obtained from DNS data; a low speed incompressible flow; and a high speed compressible flow over a flared cone at Mach 6. An array of "sensors" are placed near the surface of the solid boundary, and the MZ operators are learned and the predictions are compared to the Extended Dynamic Mode Decomposition (EDMD), both using delay embedded coordinates. Further comparisons are made with Long Short-Term Memory (LSTM) and a regression based projection framework using neural networks for the MZ operators. First we compare the effects of including delay embedded coordinates with EDMD and Mori based MZ and provide evidence that using both memory and delay embedded coordinates minimizes generalization errors on the relevant time scales. Next, we provide numerical evidence that the data-driven regression based projection MZ model performs best with respect to the prediction accuracy (minimum generalization error) on the relevant time scales.

physics.flu-dyn↗

arXiv2023

Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition

In this work, we apply, for the first time to spatially inhomogeneous flows, a recently developed data-driven learning algorithm of Mori-Zwanzig (MZ) operators, which is based on a generalized Koopman's description of dynamical systems. The MZ formalism provides a mathematically exact procedure for constructing non-Markovian reduced-order models of resolved variables from high-dimensional dynamical systems, where the effects due to the unresolved dynamics are captured in the memory kernel and orthogonal dynamics. The algorithm developed in this work applies Mori's linear projection operator and an SVD based compression to the selection of the resolved variables (equivalently, a low rank approximation of the two time covariance matrices). We show that this MZ decomposition not only identifies the same spatio-temporal structures found by DMD, but it can also be used to extract spatio-temporal structures of the hysteresis effects present in the memory kernels. We perform an analysis of these structures in the context of a laminar-turbulent boundary-layer transition flow over a flared cone at Mach 6, and show the dynamical relevance of the memory kernels. Additionally, by including these memory terms learned in our data-driven MZ approach, we show improvement in prediction accuracy over DMD at the same level of truncation and at a similar computational cost. Furthermore, an analysis of the spatio-temporal structures of the MZ operators shows identifiable structures associated with the nonlinear generation of the so-called "hot" streaks on the surface of the flared code, which have previously been observed in experiments and direct numerical simulations.

physics.flu-dyn↗