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Michał Mika, M.Sc.

Ph.D. candidate, Computational methods for cavitating flows

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Education

  • school M.Sc. Computational Methods in Engineering, Leibniz Universität Hannover
  • school B.Sc. Computergestützte Ingenieurwissenschaften, Leibniz Universität Hannover

Research interests

isogeometric analysis, efficient numerical methods, software development

About

I've studied computational engineering science and I'm currently a Ph.D. student at the Institute of Mechanics and Computational Mechanics, Leibniz University Hannover, where I work on efficient isogeometric methods with application to cavitation problems. Here I host my projects and student works. If you've found any mistakes, have questions or suggestions, please let me know at ydL7N734vsoNV@mika.sh.

Publications

A matrix-free isogeometric Galerkin method for Karhunen-Loève approximation of random fields using tensor product splines, tensor contraction and interpolation based quadrature

M.L. Mika, T.J.R. Hughes, D. Schillinger, P. Wriggers and R.R. Hiemstra

February 9th, 2021;

The Karhunen-Loève series expansion decomposes a Gaussian stochastic process into an infinite series of pairwise uncorrelated random variables and pairwise L2 orthogonal functions. The computational complexity of standard Galerkin finite element formation and assembly techniques, as well as memory requirements of direct solution techniques become quickly computationally intractable with increasing polynomial degree and the number of elements. In this work we present a novel matrix-free solution strategy that scales optimally with the problem size, is indepedent of the polynomial degree and is emberassingly parallel. A high-order three-dimensional benchmark illustrates exceptional computational performance combined with high accuracy and robustness.

A comparison of matrix-free isogeometric Galerkin and collocation methods for Karhunen–Loève expansion

M.L. Mika, R.R. Hiemstra, T.J.R. Hughes and D. Schillinger

January 2nd, 2021;

Numerical computation of the Karhunen–Loève expansion is computationally challenging in terms of both memory requirements and computing time. We compare two state-of-the-art methods that claim to efficiently solve for the K–L expansion: (1) the matrix-free isogeometric Galerkin method using interpolation based quadrature proposed by the authors in [1] and (2) our new matrix-free implementation of the isogeometric collocation method proposed in [2]. Two three-dimensional benchmark problems indicate that the Galerkin method performs significantly better for smooth covariance kernels, while the collocation method performs slightly better for rough covariance kernels.

Student projects

Electrostatic sensor modeling for torque measurements

M.L. Mika, M. Dannert, F. Mett, H. Weber, W. Mathis and U. Nackenhorst

September 21st, 2017;

Based on a simple draft of an exemplary measurement setup, this paper discusses the general idea behind an electrostatic capacitive torque sensor. For better understanding of the working principle of the sensor the electrostatics, the geometry and the mechanics of the considered measurement setup are modeled.

Visualization of finite element analysis results on 3D printed models

M.L. Mika, K. Dees and U. Nackenhorst

April 14th, 2017;

This project explores the possibilities of visualization of finite element analysis results using 3D printed models. By outer-surface extraction and implementation of a VRML interface the interpolated datasets are prepared for a 3D print.