Archives : Évènements

This talk considers the inverse problem y=f(x), where x and y are observable parameters, in which we wish to recover the model f. Examples include dynamical systems and combat models with y=dx/dt and x=parameter(s), water catchments with y=streamflow and x=rainfall, and groundwater vulnerability with y=pollutant concentration(s) and x=hydrological parameter(s). Historically, these have been solved by many methods, including regression or sparse regularization for dynamical system models, and various empirical correlation methods for rainfall-runoff and groundwater vulnerability models. These can instead be analyzed within a Bayesian framework, using the maximum a posteriori (MAP) method to estimate the model parameters, and the Bayesian posterior distribution to estimate the parameter variances (uncertainty quantification). For systems with unknown covariance parameters, the joint maximum a-posteriori (JMAP) and variational Bayesian approximation (VBA) methods can be used for their estimation. These methods are demonstrated by the analysis of a number of dynamical and hydrological systems. 

The purpose of this study is to explore the replacement of conventional moving control surfaces on a typical tailless aircraft model at high subsonic cruise speeds. Since the efficacy of sweeping jet actuators was only demonstrated at low speeds, the current test considered their potential replacement by Supersonic Steady Jets (SSJs). It was shown that even a single jet properly located and oriented may outperform an array of actuators whose location and orientation did not take into consideration the changing local flow conditions. The test article chosen was the SWIFT model that represents a typical blended wing-body configuration of a tailless aircraft. It was selected because it was tested extensively using Sweeping Jet Actuators (SJAs). When a single supersonic jet designed for Mn=1.5 was used to control thepitch it was able to increase the trimmed lift by approximately a factor of 3. The power it consumed was not necessarily smaller than an array of 6 SJAs but it provided other advantages that are discussed in the paper. Large yawing moments could be provided by other SSJs that were not encumbered by large rolling moments. These tests proved that the momentum input is but one of many parameters controlling the flow. It was replaced by power coefficients that are unambiguously measured and are capable of comparing various modes of actuation.

Kekule structures are graphene-like lattices, with a modulation of the intersite coupling that preserves the hexagonal symmetry of the system. These structures possess very peculiar properties. In particular, they display topological phases manifested by the presence of edge waves propagating on the edge of a sample. We will discuss the extraordinary scattering properties of these edge waves across defects or disorder. We will also discuss how to realize Kekule structures in acoustic networks of waveguides

Computational fluid dynamics software have gained in popularity the past few decades. They use numerical analysis and data structures to analyze and solve problems that involve fluid flows. This remains a field under development, with ongoing research to improve the accuracy and speed of complex simulation scenarios.
New methods such as topology optimisation have been recently added to CFD software, paving the path to design highly efficient engineering components. The design of an engineering component is updated using a mathematical algorithm to maximise a performance value while being subject to specific boundary conditions. This seminar will explain the basis of topology optimisation and it will go through a specific application for valve and robust optimisation. CFD software enables a first prediction before relying on experimental data to confirm an engineering component performance. Discrepancies are a common occurrence, thus accuracy of experimental data and CFD software can be improved by the utilization of uncertainty quantification. This will be shown on a cantilever beam test case, where the exceedance probability is estimated while experimental data are subject to epistemic and aleatory uncertainties.  Accuracy of models prediction in CFD software can also be improved by using data from higher fidelity models. Machine learning algorithms have been gaining in popularity in the past couple years. They offer the opportunity to leverage data coming from high fidelity simulation to develop models for low fidelity simulation. This enables low fidelity to gain in accuracy while keeping a low computational cost. However, dealing with large data can be challenging in itself, which is going to be shown on an AI data driven turbulence model test case, where data from DNS and LES are used to build a turbulence model for RANS simulations.