Lialia Nikitina

Real-time simulation of elastic objects in virtual environments using finite element method and precomputed Green's functions

Simulation of an objects elastic deformation is an important feature in applications where three-dimensional object behavior is explored. In addition, the benefits of user-object interactions are best realized in interactive environments which require the rapid computation of deformations. In this paper we present a prototype of a system for the simulation of elastic objects in Virtual Environments (VE) under real-time conditions. The approach makes use of the method of finite elements and precomputed Greens functions. The simulation is interactively visualized in fully immersive rear-projection based Virtual Environments such as the CyberStage and semi-immersive ones such as the Responsive Workbench. Using pick-ray interaction techniques the user can interactively apply forces to the object causing its deformation. Our interactive visualization module, embedded in VE system Avango, supports real time deformations of high-resolution 3D model (10,000 nodes) at a speed >20 stereoimages/sec.

Nonlinear Metamodeling of Bulky Data and Applications in Automotive Design

We describe and discuss methods for nonlinear metamodeling of simulation databases featuring continuous exploration of simulation results, tolerance prediction, sensitivity analysis, and rapid interpolation of bulky FEMdata. Themethods have been implemented in the design-parameter optimization tool DesParO. Reallife applications from the automotive industry show their efficiency.

Aspects of adaptive hierarchical RBF metamodels for optimization

Radial basis functions (RBFs), among other techniques, are used to construct metamodels that approximate multiobjective expensive high-fidelity functions from a finite number of function evaluations (design of experiments, DoE). Radial basis functions can be applied if the DoE covers the parameter space in an arbitrary though uniform manner. Leave-one-out strategies allow for computing tolerance limits. The approximated value and a certain tolerance can be interpreted as expectation and variance ofa random experiment. Thus, model improvement as described for Kriging models in the literature can in principal be applied to RBF-based metamodels, too. We describe our adaptive and hierarchical metamodelling approach that deals with the specific problems that such metamodel adaptions pose to RBF-based models. We also briefly discuss implementation details and first industrial test cases.

Collaborative Visualization of Tang Chang'an over the Internet

In this paper we introduce Simulated Reality (SR) as a new concept for the interplay between simulation, optimization and interactive visualization. We see SR as a new metaphor for the interactive visual exploration of simulation and optimization results. The vision of Simulated Reality implies interactive behavior of simulations. Fact is today that simulations might still need hours of computation time, especially in crash worthiness. This paper shows approaches to come closer to the vision of SR. Combining design of experiments methods, metamodeling, new interpolation schemes and innovative graphics methods, we enable to user to interact with simulation parameters, optimization criteria and come to a new interpolated crash result within seconds. The approaches have been successfully applied for solution of real life car design optimization problems.

Analysis of Bulky Crash Simulation Results: Deterministic and Stochastic Aspects

Crash simulation results show both deterministic and stochastic behavior. For optimization in automotive design it is very important to distinguish between effects caused by variation of simulation parameters and effects triggered, for example, by buckling phenomena. We propose novel methods for the exploration of a simulation database featuring non-linear multidimensional interpolation, tolerance prediction, sensitivity analysis, robust multiobjective optimization as well as reliability and causal analysis. The methods are highly optimized for handling bulky data produced by modern crash simulators. The efficiency of these methods is demonstrated for industrially relevant benchmark cases.

Multi-objective optimisation on the basis of random models for ethylene oxide

This paper is part of our pursuit to develop an efficient procedure for optimising parameters that provide a reliable foundation for highly predictive molecular simulations. We tested whether DesParO, a mathematical tool originally used in automotive design, is suitable for creating Lennard-Jones (LJ) parameters that accurately reproduce the experimental phase behaviour for our test compound ethylene oxide (EO). So, we created a multitude of diverse random parameter sets, performed Gibbs ensemble Monte Carlo simulations and collected the resulting physical properties. On that data basis, DesParO derived a meta-model through a multidimensional interpolation. We then explored, in an interactive fashion unique to DesParO, the LJ parameter space and selected some suitable parameter sets, which were then tested by simulations. For EO, the selected parameter sets were indeed superior to the initial parameters. Furthermore, the new parameters can be reliably used as input for further optimisation by other methods, resulting in extremely robust LJ parameters. Beyond the prediction of parameter sets, DesParO enabled us to examine the underlying parameter–property relationships that help us solve future optimisation problems by creating subordinate parameter optimisation tasks in a systematic manner; this ability makes DesParO a valuable tool in the overall optimisation process.

MYNTS: Multi-physics network simulator

We present a generic approach for the simulation of transport networks, where the steps of physical modeling and numerical simulation are effectively separated. The model is described by a list of physical equations and inequalities as problem constraints for non-linear programming (NLP). This list is translated to the language of expression trees and is made accessible for the numerical solution by standard NLP solvers. Various problem types can be solved in this way, including stationary and transient network simulation, feasibility analysis and energy-saving optimization. The simulation is provided for different disciplines, such as gas transport, water supply and electric power networks. We demonstrate the implementation of this approach in our multiphysics network simulator.

A globally convergent method for generalized resistive systems and its application to stationary problems in gas transport networks

We consider generalized resistive systems, comprising linear Kirchhoff equations and non-linear element equations, depending on the flow through the element and on two adjacent nodal variables. The derivatives of the element equation should possess a special signature. For such systems we prove the global non-degeneracy of the Jacobi matrix and the applicability of globally convergent solution tracing algorithms. We show that the stationary problems in gas transport networks belong to this generalized resistive type. We apply the tracing algorithm to several realistic networks and compare its performance with a generic Newton solver.

Quasi-Monte Carlo and RBF Metamodeling for Quantile Estimation in River Bed Morphodynamics

Four generic methods for quantile estimation have been compared: Monte Carlo (MC), Monte Carlo with Harrel-Davis weighting (WMC), quasi-Monte Carlo with Sobol sequence (QMC) and quasi-random splines (QRS). The methods are combined with RBF metamodel and applied to the analysis of morphodynamic—hydrodynamic simulations of the river bed evolution. The following results have been obtained. Harrel-Davis weighting gives a moderate 10–20 % improvement of precision at small number of samples N ~ 100. Quasi-Monte Carlo methods provide significant improvement of quantile precision, e.g. the number of function evaluations necessary to achieve rms ~ 10−4 precision is reduced from 1,000,000 for MC to 100,000 for QMC and to 6,000 for QRS. On the other hand, RBF metamodeling of bulky data allows to speed up the computation of one complete result in the considered problem from 45 min (on 32CPU) to 20 s (on 1CPU), providing rapid quantile estimation for the whole set of bulky data.

Focused ultrasonic therapy planning: Metamodeling, optimization, visualization

Abstract We present a generic approach for focused ultrasonic therapy planning on the basis of numerical simulation, multi-objective optimization, stochastic analysis and visualization in virtual environments. A realistic test case is used to demonstrate the approach. RBF metamodeling of simulation results is performed for continuous representation of two optimization objectives. The non-convex Pareto front of the objectives is determined by means of non-dominated set and local improvement algorithms. Uncertainties of metamodeling are estimated by means of a cross-validation procedure. The 3D visualization in virtual environment framework Avango allows detailed inspection of MRT images, the corresponding material model and spatial distribution of the resulting thermal dose.