UTFacultiesETDepartmentsMS3Research ChairsApplied Mechanics and Data Analysis

Applied Mechanics and Data Analysis (AMDA)

The research group “Applied Mechanics and Data Analysis” (AMDA) is recently founded with a focus on building an interplay between the physics supported models and data analysis in a context of improved predictive analysis of engineering systems described by nonlinear material behaviour.

Motivation

Incapability to extricate model parameter values or even a model form in some cases given experimental data, the absence of available data sets at sufficiently large space and/or time scales, and the never-ending issue of model validation are some of the main reasons for uncertainty quantification and data analysis of real-world phenomena. Today it is desired to quantitatively characterize and reduce uncertainties in both computational and real-world applications in either probabilistic or polymorphic forms. Therefore,  proper methodologies and tools have to be developed in order to make such an analysis of real-scale structures possible.

Research

With the recent advancement of uncertainty quantification techniques and machine/deep learning, the research group is focusing on incorporating stochastic descriptions into applied mechanics problems. In particular special attention is paid to the analysis of the mechanics of materials and systems, for small and large deformations, in quasi-static and dynamic conditions, as well as to the multiscale modelling. The research is based on an interdisciplinary approach combining experiments, mathematical modelling and numerical approaches to the quantification of uncertainty, its prediction and data assimilation. The main goal is to develop efficient and robust learning and uncertainty quantification numerical algorithms of wide range purposes that can be used for solving practical problems starting from material aging up to the design of controllers for manufacturing processes. Having in mind that the real applications are often time-dependent and of a large-scale and nonlinear nature, the ongoing research is also trying to address this problem.

  • Active and vibration control

    The main research interest is active sound and vibration control with the primary objective to control the acoustic field. This comprises development of control algorithms, which can be adaptive, non-adaptive or semi-active, sensors and actuators, and signal processing techniques such as virtual sensors, beamformers, and model reduction methods. A further research interest is active and semi-active metamaterials that are effective in a broad frequency range, as well as passive methods combined with active control methods.

    Figure 1: Flexure mechanism of a rotary subwoofer (Julian Mulder and Beinte Groen’s MSc projects)

    Figure 1: Flexure mechanism of a rotary subwoofer (Julian Mulder and Beinte Groen’s MSc projects)

  • Machine and deep learning

    To be added.

  • Material Characterization

    The mechanical response of any structure made of composite materials is dependent on the design, mechanical properties of the materials used, the processing method and the imposed load case (see Figure 1). In terms of mechanical response (vibrations) and failure of structures, the anisotropic nature of the material and the ease of crack propagation through the material are important characteristics to be studied. Additionally, the non-homogeneity and random defects that are present in the material microstructure causes materials to behave unpredictably. As an extreme example, a discontinuously reinforced composite (with flakes) and its local microstructure can be seen in Figure 1. The discrete and anisotropic nature of the material imposes several challenges in defining a stiffness and strength parameter for them.

    Figure 1: The interrelationship between different domains concerning material science of composite materials 

  • Robust Modelling for Design and Control

    To be added.

  • Flexible Multibody Dynamics

    Research in the field of Flexible Multibody Dynamics aims to incorporate flexible bodies in a dynamic simulation on a system level.New methods are developed that enable more realistic coupled simulation of large system motions and elastic deformations. Reuse of Finite Element models of individual bodies allows for the application of powerful model order reduction techniques and efficient computations of internal stresses during motion. In this way, realistic and reliable strength and durability analyses can be performed.

    Von Mises stress during Motion by Jurjen Blaauw

  • Uncertainty Quantification

    With the recent advancement of uncertainty quantification techniques and machine/deep learning, the research group is focusing on incorporating stochastic descriptions into applied mechanics problems. In particular special attention is paid to the analysis of the mechanics of materials and systems, for small and large deformations, in quasi-static and dynamic conditions, as well as to the multiscale modelling. The research is based on an interdisciplinary approach combining experiments, mathematical modelling and numerical approaches to the quantification of uncertainty, its prediction and data assimilation. The main goal is to develop efficient and robust learning and uncertainty quantification numerical algorithms of wide range purposes that can be used for solving practical problems starting from material aging up to the design of controllers for manufacturing processes. Having in mind that the real applications are often time-dependent and of a large-scale and nonlinear nature, the ongoing research is also trying to address this problem. 

Academic Partners

Collaborations with international research groups are embodied by participating in the research frameworks DFG SPP1748 Reliable Simulation Techniques in Solid Mechanics. Development of Non-standard Discretization Methods, Mechanical and Mathematical Analysis, DFG SPP 1886 Polymorphic uncertainty modelling for the numerical design of structure, the International Research and Training Group IRTG 1627 at Leibniz University Hanover, Germany, and Graduate school 2075. Modelling the constitutive evolution of building materials and structures with respect to aging at Technische Universität Braunschweig, Germany.

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