Robby Haelterman

Non-stationary two-stage relaxation based on the principle of aggregation multi-grid

We propose an automatization procedure to find an optimal value of the parameter used in some relaxation schemes. The basis of the method is formed by an aggregation-type procedure into a single aggregate and is meant to be applicable as a patch for existing codes.

Stability Issues in Partitioned FSI Calculations

In this chapter a short review will be given on stability issues for fluid-structure interaction (FSI) problems we encountered and studied in the last decade. Based on this, the ideas behind two implicit coupling algorithms, developed in the department, will be explained. The first algorithm is the Interface Quasi-Newton coupling method and the second is the Interface Artificial Compressibility coupling method. Most of the applications that are shown are in the biomechanical field. These are representative for more general strongly coupled problems with incompressible fluids and flexible structures.

Bubble simulations with an interface tracking technique based on a partitioned fluid-structure interaction algorithm

Numerical techniques frequently used for the simulation of one bubble can be classified as interface tracking techniques and interface capturing techniques. Most of these techniques calculate both the flow around the bubble and the shape of the interface between the gas and the liquid with one code. In this paper, a rising axisymmetric bubble is simulated with an interface tracking technique that uses separate codes to determine the position of the gas-liquid interface and to calculate the flow around the bubble. The grid converged results correspond well with the experimental data. The gas-liquid interface is conceived as a zero-mass, zero-thickness structure whose position is determined by the liquid forces, a uniform gas pressure and surface tension. Iterations between the two codes are necessary to obtain the coupled solution of both problems and these iterations are stabilized with a fluid-structure interaction (FSI) algorithm. The flow around the bubble is calculated on a moving mesh in a reference frame that rises at the same speed as the bubble. The flow solver first updates the mesh throughout the liquid domain given a position of the gas-liquid interface and then calculates the flow around the bubble. It is considered as a black box with the position of the gas-liquid interface as input and the liquid forces on the interface as output. During the iterations, a reduced-order model of the flow solver is generated from the inputs and outputs of the solver. The solver that calculates the interface position uses this model to adapt the liquid forces on the gas-liquid interface during the calculation of the interface position.

Optimization of the Runge-Kutta iteration with residual smoothing

Iterative solvers in combination with multi-grid have been used extensively to solve large algebraic systems. One of the best known is the Runge-Kutta iteration. Previously (Haelterman et al. (2009) [3]) we reformulated the Runge-Kutta scheme and established a model of a complete V-cycle which was used to optimize the coefficients of the multi-stage scheme and resulted in a better overall performance. We now look into aspects of central and upwind residual smoothing within the same optimization framework. We consider explicit and implicit residual smoothing and either apply it within the Runge-Kutta time-steps, as a filter for restriction or as a preconditioner for the discretized equations. We also shed a different light on the very high CFL numbers obtained by upwind residual smoothing and point out that damping the high frequencies by residual smoothing is not necessarily a good idea.

Accelerating the convergence of a tokamak modeling code with Aitken’s method

Abstract Up till now, the approach to solve the coupled equations arising in modeling wave heating in tokamaks modeling has been to sequentially solve one equation after the other until convergence is reached, a method known as Iterative Substructuring. In this paper we use an elementary model of the physics inside a tokamak, consisting of a simplified wave equation, a simplified Fokker–Planck equation and a diffusion equation. Our aim is to accelerate the solution of the coupled equations using Aitken’s δ 2 method. Results show that a substantial reduction in CPU time can be obtained with this approach. It is hoped that results obtained with the simplified model serve as a proof-of-principle and carry over to more complicated systems of coupled equations used to model tokamaks.

Pedestrian Detection and Tracking in Thermal Images from Aerial MPEG Videos.

Video surveillance for security and intelligence purposes has been a precious tool as long as the technology has been available but is computationally heavy. In this paper, we present a fast and efficient framework for pedestrian detection and tracking using thermal images. It is designed for automatic surveillance applications in an outdoor environment like preventing border intrusions or attacks on sensitive facilities using image and video processing techniques implemented on-board Unmanned Aerial Vehicles (UAV)s. The proposed framework exploits raw H.264 compressed video streams with limited computational overhead. Our work is driven by the fact that Motion Vectors (MV) are an integral part of any video compression technique, by day and night capabilities of thermal sensors and the distinguished thermal signature of humans. Six different scenarios were carried out and filmed using a thermal camera in order to simulate suspicious events. The obtained results show the effectiveness of the proposed framework and its low computational requirements which make it adequate for on-board processing and real-time applications.

Automatic Generation of Statistical Shape Models in Motion

Statistical body shape modeling (SBSM) is a well-known technique to map out the variability of body shapes and is commonly used in 3D anthropometric analyses. In this paper, a new approach to integrate movement acquired by a motion capture system with a body shape is proposed. This was done by selecting landmarks on a body shape model, and predicting a body shape based on features. Then, a virtual skeleton was generated relative to those landmarks. This skeleton was parented to a body shape, allowing to modify its pose and to add pre-recorded motion to different body shapes in a realistic way.

Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine

The increasing risks of border intrusions or attacks on sensitive facilities and the growing availability of surveillance cameras lead to extensive research efforts for robust detection of pedestrians using images. However, the surveillance of borders or sensitive facilities poses many challenges including the need to set up many cameras to cover the whole area of interest, the high bandwidth requirements for data streaming and the high-processing requirements. Driven by day and night capabilities of the thermal sensors and the distinguished thermal signature of humans, the authors propose a novel and robust method for the detection of pedestrians using thermal images. The method is composed of three steps: a detection which is based on a saliency map in conjunction with a contrast-enhancement technique, a shape description based on discrete Chebyshev moments and a classification step using a support vector machine classifier. The performance of the method is tested using two different thermal datasets and is compared with the conventional maximally stable extremal regions detector. The obtained results prove the robustness and the superiority of the proposed framework in terms of true and false positives rates and computational costs which make it suitable for low-performance processing platforms and real-time applications.

Moving Statistical Body Shape Models Using Blender

In this paper, we present a new framework to integrate movement acquired by a motion capture system to a statistical body shape model using Blender. This provides a visualization of a digital human model based upon anthropometry and biomechanics of the subject. A moving statistical body shape model helps to visualize physical tasks with inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modeling approach is useful for reliable prediction and simulation of the body shape movement of a specific population with a few given predictors such as stature, body mass index and age.

Using 3D Statistical Shape Models for Designing Smart Clothing

In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR™ dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.