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Andreas Müller

Publications and source records attributed to Andreas Müller.

45 records · Page 3Linked to original sources
arXiv2020

A Comparative Study on 2-DOF Variable Stiffness Mechanisms

Based on the idea of variable stiffness mechanisms, a variety of such mechanisms is shown in this work. Specifically, 2-DOF parallel kinematic machines equipped with redundant actuators and non-linear springs in the actuated joints are presented and a comparative overview is drawn. Accordingly, a general stiffness formulation in task space of all mechanisms is given. Under fixed geometric parameters, optimization of task space stiffness is carried out on the designs comprising all kinematic solutions. Finally, a stiffness metric is introduced that allows a quantitative comparison of the given mechanism designs. This gives rise to design guidelines for engineers but also shows an interesting outline for future applications of variable stiffness mechanisms.

cs.RO↗
arXiv2020

ATHENA -- a pre-university study programme at the university of Geneva

Athena (named after the ancient Greek goddess of wisdom) is a pre-university study programme for mathematics and physics organised by the Faculty of Science at the University of Geneva. It targets pupils enrolled in the final or penultimate year of Secondary II (high school), giving them an opportunity to explore and discover university-level studies in mathematics and physics. The programme aims to enhance pupils interest for the physical and mathematical sciences by introducing them to new topics, all while giving them a taste for student life. It also seeks to promote scientific careers to young pupils, especially to young women, as well as improving the transition between Secondary II and university

physics.ed-ph↗
arXiv2019

Mechanism Singularities Revisited from an Algebraic Viewpoint

It has become obvious that certain singular phenomena cannot be explained by a mere investigation of the configuration space, defined as the solution set of the loop closure equations. For example, it was observed that a particular 6R linkage, constructed by a combination of two Goldberg 5R linkages, exhibits kinematic singularities at a smooth point in its configuration space. Such problems are addressed in this paper. To this end, an algebraic framework is used in which the constraints are formulated as polynomial equations using Study parameters. The algebraic object of study is the ideal generated by the constraint equations (the constraint ideal). Using basic tools from commutative algebra and algebraic geometry (primary decomposition, Hilbert's Nullstellensatz), the special phenomenon is related to the fact that the constraint ideal is not a radical ideal. With a primary decomposition of the constraint ideal, the associated prime ideal of one primary ideal contains strictly into the associated prime ideal of another primary ideal which also gives the smooth configuration curve. This analysis is extended to shaky and kinematotropic linkages, for which examples are presented.

cs.RO↗
arXiv2019

Batch 2: Definition of novel Weather & Climate Dwarfs

This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying weather & climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. This deliverable contains the description of the characteristics of a second set of so-called numerical weather & climate prediction dwarfs that form key functional components of prediction models in terms of the science that they encapsulate and in terms of computational cost they impose on the forecast production. The ESCAPE work flow between work packages centres on these dwarfs and hence their selection, their performance assessment, code adaptation and optimisation is crucial for the success of the project. These new dwarfs have been chosen with the purpose of extending the range of computational characteristic represented by the dwarfs previously selected in batch 1 (see Deliverable D1.1). The dwarfs have been made, their documentation has been compiled and the software has been made available on the software exchange platform. The dwarfs in this deliverable include a multigrid elliptic solver, a novel advection scheme for unstructured meshes, an advection scheme for structured meshes and a radiation scheme. This deliverable includes their scientific description and the guidance for installation, execution and testing.

cs.DC↗
arXiv2018

Scikit-learn: Machine Learning in Python

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.

cs.LG↗
arXiv2017

Optimized thermoelectric sensitivity measurement for differential thermometry with thermopiles

A novel approach to calibrate the sensitivity of a differential thermometer, consisting of several thermocouples connected in series (thermopile), has been developed. The goal of this method is to increase the accuracy of small temperature difference measurements ($ΔT \leq 1\:\textrm{K}$), without invoking higher sensor complexity. To this end, a method to determine the optimal temperature difference employed during the differential measurement of thermoelectric sensitivities has been developed. This calibration temperature difference is found at the minimum of combined measurement and linearization error for a given mean temperature. The developed procedure is demonstrated in an illustrative example calibration of a nine-junction thermopile. For mean temperatures between $-10\:^{o}\textrm{C}$ and $15\:^{o}\textrm{C}$, the thermoelectric sensitivity was measured with an uncertainty of less than $\pm 2\:\%$. Subsequently, temperature differences as low as $10^{-2}\:\textrm{K}$ can be resolved, while the thermometer used for the example calibration was accurate only to $\pm 0.3\:\textrm{K}$. This and higher degrees of accuracy are required in certain research applications, for example to detect heat flux modulations in bifurcating fluidic systems.

physics.ins-det↗
arXiv2016

Strong Scaling for Numerical Weather Prediction at Petascale with the Atmospheric Model NUMA

Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution NWP models use a horizontal resolution of about 10km. In order to increase the resolution of NWP models highly scalable atmospheric models are needed. The Non-hydrostatic Unified Model of the Atmosphere (NUMA), developed by the authors at the Naval Postgraduate School, was designed to achieve this purpose. NUMA is used by the Naval Research Laboratory, Monterey as the engine inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier-Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). Mesh generation is done using the p4est library. NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation. This paper presents the performance analysis and optimization of the spectral element version of NUMA. The performance at different optimization stages is analyzed using a theoretical performance model as well as measurements via hardware counters. Machine independent optimization is compared to machine specific optimization using BG/Q vector intrinsics. By using vector intrinsics the main computations reach 1.2 PFlops on the entire machine Mira (12% of the theoretical peak performance). The paper also presents scalability studies for two idealized test cases that are relevant for NWP applications. The atmospheric model NUMA delivers an excellent strong scaling efficiency of 99% on the entire supercomputer Mira using a mesh with 1.8 billion grid points. This allows to run a global forecast of a baroclinic wave test case at 3km uniform horizontal resolution and double precision within the time frame required for operational weather prediction.

cs.DC↗
arXiv2012

Experimental tests of pseudo-complex General Relativity

Based on previous publications exploring pseudo-complex General Relativity (pc-GR) we present a selection of observable consequences of pc-GR and possible ways to experimentally access them. Whenever possible we compare the results to Einstein's GR and differences are worked out in detail. We propose experimental tests to check the predictions of pc-GR for the orbital frequency of test particles, the gravitational redshift effect and the last stable orbit. We will show that the orbital frequency of test particles at a given radius in pc-GR is in general lower compared to standard GR. Also the effect of frame dragging is modified (weakened) in pc-GR. Concerning the gravitational redshift of a radiation emitting object we find that it is also lower in pc-GR than in standard GR. Eventually the classical concept of a last stable orbit has to be modified in pc-GR.

gr-qc↗