Conformally Mapped Polynomial Chaos Expansions for Maxwell's Source Problem with Random Input Data

Generalized Polynomial Chaos (gPC) expansions are well established for forward uncertainty propagation in many application areas. Although the associated computational effort may be reduced in comparison to Monte Carlo techniques, for instance, further convergence acceleration may be important to tackle problems with high parametric sensitivities. In this work, we propose the use of conformal maps to construct a transformed gPC basis, in order to enhance the convergence order. The proposed basis still features orthogonality properties and hence, facilitates the computation of many statistical properties such as sensitivities and moments. The corresponding surrogate models are computed by pseudo-spectral projection using mapped quadrature rules, which leads to an improved cost accuracy ratio. We apply the methodology to Maxwell's source problem with random input data. In particular, numerical results for a parametric finite element model of an optical grating coupler are given

Determination of Bond Wire Failure Probabilities in Microelectronic Packages

This work deals with the computation of industry-relevant bond wire failure probabilities in microelectronic packages. Under operating conditions, a package is subject to Joule heating that can lead to electrothermally induced failures. Manufacturing tolerances result, e.g., in uncertain bond wire geometries that often induce very small failure probabilities requiring a high number of Monte Carlo (MC) samples to be computed. Therefore, a hybrid MC sampling scheme that combines the use of an expensive computer model with a cheap surrogate is used. The fraction of surrogate evaluations is maximized using an iterative procedure, yielding accurate results at reduced cost. Moreover, the scheme is non-intrusive, i.e., existing code can be reused. The algorithm is used to compute the failure probability for an example package and the computational savings are assessed by performing a surrogate efficiency study.Comment: submitted to Therminic 2016, available at http://ieeexplore.ieee.org/document/7748645

Modeling of Spatial Uncertainties in the Magnetic Reluctivity

In this paper a computationally efficient approach is suggested for the stochastic modeling of an inhomogeneous reluctivity of magnetic materials. These materials can be part of electrical machines, such as a single phase transformer (a benchmark example that is considered in this paper). The approach is based on the Karhunen-Lo\`{e}ve expansion. The stochastic model is further used to study the statistics of the self inductance of the primary coil as a quantity of interest.Comment: submitted to COMPE

Collision detection for rigid superellipsoids using the normal parameterization

The normal parameterization as an approach to describe geometries is introduced. The advantages of this description – as compared to other parameterizations or implicit functions – in the context of collision detection are: the possibility to explicitly calculate axis aligned bounding boxes for any convex geometry, an efficient iterative algorithm for collision detection between objects with arbitrary geometry that does not require any (analytical) derivatives. A system of several rigid superellipsoids is used to demonstrate the application and performance of the proposed approach in a multibody simulation

Coupled Simulation of Transient Heat Flow and Electric Currents in Thin Wires: Application to Bond Wires in Microelectronic Chip Packaging

This work addresses the simulation of heat flow and electric currents in thin wires. An important application is the use of bond wires in microelectronic chip packaging. The heat distribution is modeled by an electrothermal coupled problem, which poses numerical challenges due to the presence of different geometric scales. The necessity of very fine grids is relaxed by solving and embedding a 1D sub-problem along the wire into the surrounding 3D geometry. The arising singularities are described using de Rham currents. It is shown that the problem is related to fluid flow in porous 3D media with 1D fractures [C. D'Angelo, SIAM Journal on Numerical Analysis 50.1, pp. 194-215, 2012]. A careful formulation of the 1D-3D coupling condition is essential to obtain a stable scheme that yields a physical solution. Elliptic model problems are used to investigate the numerical errors and the corresponding convergence rates. Additionally, the transient electrothermal simulation of a simplified microelectronic chip package as used in industrial applications is presented.Comment: all numerical results can be reproduced by the Matlab code openly available at https://github.com/tc88/ETwireSi

Low-Dimensional Stochastic Modeling of the Electrical Properties of Biological Tissues

Uncertainty quantification plays an important role in biomedical engineering as measurement data is often unavailable and literature data shows a wide variability. Using state-of-the-art methods one encounters difficulties when the number of random inputs is large. This is the case, e.g., when using composite Cole-Cole equations to model random electrical properties. It is shown how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion. The low-dimensional random model is used to quantify uncertainties in the axon activation during deep brain stimulation. Numerical results for a Medtronic 3387 electrode design are given.Comment: 4 pages, 5 figure

Energy-optimized bipedal running of a simple humanoid robot

A method to optimize energy efficiency for bipedal running robots is presented. A running model of a simple bipedal robot consisting of five rigid bodies connected by actuated revolute joints is introduced. The actuators’ torques are generated by a trajectory tracking controller to produce periodic running gaits. The controller’s reference trajectories are parameterized by Bézier polynomials. A numerical optimization is used to employ reference trajectories with optimal energy efficiency for average velocities in the range of 1.5 to 5.5 m/s

A Brief Survey on Non-standard Constraints: Simulation and Optimal Control

In terms of simulation and control holonomic constraints are well documented and thus termed standard. As non-standard constraints, we understand non-holonomic and unilateral constraints. We limit this survey to mechanical systems with a finite number of degrees of freedom. The long-term behavior of non- holonomic integrators as compared to structure-preserving integrators for holonomically constrained systems is briefly discussed. Some recent research regarding the treatment of unilaterally constrained systems by event-driven or time-stepping schemes for time integration and in the context of optimal control problems is outlined

Investigation of optimal bipedal walking gaits subject to different energy-based objective functions

Optimal bipedal walking gaits subject to different energy-based objective functions are investigated using a simple planar rigid body model of a bipedal robot with upper body, thighs and shanks. The robot’s segments are connected by revolute joints actuated by electric motors. The actuators’ torques are generated by a trajectory tracking controller to produce periodic walking gaits. A numerical optimization routine is used to find optimal reference trajectories for average speeds in the range of 0.3 – 2.3 m/s to investigate the influence of different objective functions