Luca Magri

Labahn J, Nastac G, Magri L, Ihme Met al., Determining dynamic content of turbulent flow LES using the Lyapunov exponent, Center for Turbulence Research Annual Research Briefs

Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES) have been em- ployed for computing turbulent flows . While DNS resolves all turbulent scales involved in the dynamics with no physical modeling, LES represents the energy contained in the large scales, and effects of the smaller scales are taken into account either explicitly through a subgrid scale model or implicitly through the numerical dissipation of the nu- merical method. Besides numerical algorithms, two factors determine the quality of LES: the physical model or dissipation of the subgrid scales (SGS), which are filtered out in the governing equations, and the filter width, which describes the numerical resolution of the resolved scales.

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Juniper MP, Magri L, Application of receptivity and sensitivity analysis to thermoacoustic instability, VKI Lecture Series, Progress in flow instability analysis and laminar-turbulent transition modeling

Receptivity and sensitivity analysis is a branch of linear stability analysis. In stability analysis, one typically calculates a linear system’s eigenmodes. These encapsulate the frequency, growth rate, and mode shape of each natural mode of the system. Receptivity analysis then quantifies the receptivity of each mode to external (open loop) forcing. Sensitivity analysis quantifies the sensitivity of each mode either to internal feedback, which is known as the structural sensitivity, or to changes in the base state, which is known as the base state sensitivity. Sensitivity analysis can be performed by finite difference - e.g. by computing the system’s eigenvalues at two slightly different base states and then calculating the gradient with respect to the change between the two states - but this is computationally expensive and prone to numerical error. A more efficient and more accurate method is to use adjoint equations, which is the subject of this lecture.

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Sashittal P, Sayadi T, Schmid P, Jang I, Magri Let al., Adjoint-based sensitivity analysis of a reactive jet in crossflow, Center for Turbulence Research Proceedings of the Summer Program

With current advances in computational resources, high- delity simulations of reactive ows are increasingly being used as predictive tools in various industrial applications. In order to accurately capture the combustion process, detailed/reduced chemical mechanisms are employed, which in turn rely on various model parameters. Therefore, it would be of great interest to quantify the sensitivities of the predictions with respect to the introduced models. Due to the high dimensionality of the parameter space, methods such as nite di erences which rely on multiple forward simulations prove to be very costly, and adjoint-based techniques are a suitable alternative. The complex nature of the governing equations, however, renders an e cient strategy in nding the adjoint equations a challenging task. In this study, we employ the modular approach of Fosas de Pando et al. (2012) to build a discrete adjoint framework applied to a reacting jet in cross ow. The developed framework is then used to extract the sensitivity of the integrated heat release with respect to the existing combustion parameters. Analyzing the sensitivities in the three-dimensional domain provides insight toward the speci c regions of the ow that are more susceptible to the choice of the model. Masking functions are also employed in order to isolate speci c regions within the computational domain for analysis.

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Magri L, Ihme M, Recent advances in linear methods for sensitivity, passive control and modeling of thermo-acoustic instabilities, Center for Turbulence Research Annual Research Briefs

Thermo-acoustic instabilities are a challenging problem that a ects aircraft gas tur- bines, rockets, furnaces, and other con ned combustion systems (Lieuwen & Yang 2005). For these instabilities to occur, the uctuating heat release has to be su ciently in phase with the pressure. This leads to a thermo-acoustic oscillation, which usually displays itself as noise, vibrations, ame extinction and structural failure. It is, therefore, paramount for engineers to understand, predict and control these oscillations, and suppress them when they occur.

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