Multi-objective optimization of a complex water distribution network
The activity presented in this proceeding has been divided into three phases: The first phase focuses on the position on the network of the minimum number of valves, in common with all the scenarios; the second phase deals with the determination of the optimum opening degree of the positioned valves, for every scenery and finally the third phase, which deals with the determination of the found solution robustness in respect of pipes roughness and node demands.
Multi Objective Robust Design Optimization of Vehicle Stability Control using CarSim and modeFRONTIER
In this presentation the integration of CarSim and Simulink into modeFRONTIER environment is shown. The use of robust design optimization applied to the tuning parameters of the controller allow the fast development of a system capable of controlling the the car stability even without the help of a viscous joint.
Test case showing how to deal with test scatter in simulation optimisation
The presentation deals with some test case showing how to deal with test scatter in simulation optimization
Innovative Vehicle Dynamic Simulation Solutions for Design and Test Engineers
The proceeding presents the innovative vehicle dynamic simulation solutions for design and test engineers.
Using simulation to gain competitive advantage
After the Multi Objective Robust Design Optimiszation (MORDO) an aggressive distribution was applied to the models by using UNIFORM distributions for each input variable as this maximizes the number of models with variables at the extremes of the tolerance ranges. Other distributions (normal, Cauchy, etc) would create a suite of models with a bias towards the nominal value. During the simulation many models (>200) were run with distribution around the nominal point. Of these runs, the number of models which pass all requirements (feasible) and the number of models which fail any (unfeasible) were used to create a pass/fail ratio. The pass/fail ratio is a simple measure of robustness where high ratio is desirable as this indicates a robust setup.
Advanced Design of Multibody Systems
The following, developed by VI Grade, presentation focuses on the advanced design of multibody systems.
Robust Design Optimisation using Linux Networx cluster
This presentation is a robust design example that proves the efficiency of Linux cluster and queuing system combined with the parallel capacities of modeFRONTIER. A problem that would have required months of computations using a single CPU machine, has been solved in three days using a cluster of 64 CPUs.
The use of race simulation models as an alternative to robust design optimisation
The idea of the Race Simulation model comes from the America’s Cup: The most challenging design is the one which wins most of the matches under stochastic perturbative conditions.The design which wins (maybe with a great gap) only one match under the most probable condition usually do not win the tournament.
Economic benefits and quality improvement by using MDO methods in aerospace product development
In the aerospace industry if part of the design process involves contributions from design teams in geographically disparate locations problems are more overbearing. Engineers under strict time and quality constraints have difficulty to meet the deadlines governed by product release cycles. The possible solutions of this problem are a framework that provides a solid foundation for overcoming the problems and so the objectives of the presentation are to continually improve the product by incorporating the latest technologies and to develop automated interaction tools in a robust computational environment.
Optimization and Robust Design with modeFRONTIER
Fiat’s latest success products, were developed using intensive use of virtual analysis in only 16-18 months.
The use of modeFRONTIER in Chassis Virtual Analysis as a DOE or/and optimization tool enabled to reduce time and calculation loops, to gain a deeper understanding of the system and the correlation between input variables and output parameters, to increase possibility to explore best solutions and to evaluate robust and stable solutions.