Virtual Design Optimization of a Valve Train Actuator Using Computer Based Optimization (Multi-Objective Genetic) Algorithms

This presentation was made at the NAFEMS Americas Seminar - Confidence in Engineering Simulation: The Next 10 Years of CAE in Mexico.

What is the future for engineering analysis and simulation in Mexico? Discover innovative engineering simulation processes and tools which are helping companies in Mexico improve production capabilities. Engage with domain experts, industry leaders, and peers in a focused, comprehensive one-day event that covers topics on engineering analysis, simulation, and systems modeling and simulation that every engineer in Mexico should know.

Resource Abstract

When optimizing the performance of a component or engineering system, traditional methods suffer several drawbacks Although traditional methods can offer an “optimal” solution, it heavily depends on the decision maker’s prior knowledge. That is because they are mainly based on a trial and error strategy. Also, traditional methods are lengthy and costly processes. Thus, they are limited by the available time and budget resources.

Typical engineering systems are described by a large amount of variables and they require the simultaneous optimization of more than one objective. The design engineer’s task is to specify the appropriate values for these variables. Yet, because of the size and complexity of this task, even the most skilled designer is unable to consider all of the involved variables simultaneously. In addition to that, the designer is frequently faced with a trade-off between objectives.

Combining computer-aided-engineering (CAE) analysis and simulation tools with computer based optimization algorithms allows for a more realistic approach to tackle nontrivial problems. As more design parameters are considered, conflicting objectives are likely to arise. Then, the need for having a set of solutions instead of a single solution becomes natural. Obtaining a set of solutions is possible by implementing a multi-objective optimization. As such, Mutli-objective genetic algorithms (MOGAs) enable the designer to obtain a set of pareto-optimal designs. With a set of solutions, the engineer can compare and make better decisions. In essence, a MOGA will make the objective conflicts come out in a clear way to the decision maker.

The purpose of this work is to apply these techniques to the study of a valve train actuator. The goal is to optimize the magnetic force which includes several design variables. The objectives concerned with the magnetic package analysis are to increase the average force while minimizing the force variation through all the actuator stroke. This is achieved by means of a Genetic Algorithm (GA) technique. In addition, to evaluate the objective response function, S/N ratio is calculated similar to the robust engineering methodology. Another aspect that this work addresses is the automatization of the process by the use of modern simulation tools.

The Isight software was used as the Process Integration and Design Optimization (PIDO) tool. Isight integrates engineering CAD/CAE tools (NX & Maxwell). The resulting solution Pareto surface is then analyzed using the postprocessing tools within Isight. This allows individual designs that best meet all criteria to be chosen.

Document Details

AuthorBuendia. R
Date 8th November 2018
OrganisationDelphi Diesel Systems S. de R.L. de C.V.


Back to Search Results