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Applying multi-objective optimization to a ventilation duct for an automotive vehicle

Optimization is the task of finding solutions to a problem until no better ones can be found. The search process is aimed at finding the extremes of one or more objectives. Often the objectives are restricted by one or more constraints. In multi-objective optimization the number of objective functions is more than one, making the task of finding the optimum a difficult one especially if objectives are conflicting.
In this study we have applied optimization techniques to an automotive ventilation duct. The problem originates from the real-world demand of determining an optimal duct shape during the early stages of car development. Traditionally, optimization is driven by engineering decisions and applied late in the development process. We have followed the different approach of employing automatic process-based optimization and considering a simplified geometry suitable for the early phases of a project. The ultimate goal was to provide feasibility and design indications.
During the study we have developed a simplified geometrical model of right ventilation duct. This was used as the starting point for building the optimization process in terms of design variables. The objectives were set to minimize the pressure losses and the noise levels in the duct. Constraints were limited to the project’s geometrical bounds and extremes.
However, the initial fluid dynamic objectives have been changed throughout the study to geometrical only principles in order to avoid the computational expense of CFD simulations. To correlate with the initial objectives a CFD analysis was performed on the results at the end of the process. The new objectives were set to achieve maximal duct uniformity and minimal surface curvature.
To optimize the duct geometry a multi-level approach was chosen. The significant number of parameters involved and the discontinuities in the search space demanded for an incremental approach rather than a direct optimization. We successfully optimized the geometry by performing an initial Design Of Experiments to seek for feasible solutions followed by three optimization runs with evolutionary algorithms to achieve uniformity and minimal curvature.
Results have proven how the geometrical principles can replace successfully the fluid dynamic objectives and still obtain optimal solutions. Pressure losses and noise levels decreased constantly from the initial geometrical setup onwards to final results.

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Motivations 1 1. Introduction 1.1 Motivations Optimization is the process of searching for one or more solutions to a problem until no better one can be found. The results are considered optimal in terms of superior characteristics with respect to one or more objectives compared to all other solutions. The need to find optimal solutions comes mostly from extreme purposes of finding, for example, the minimum cost of fabrication or the maximum possible reliability or others. Multi-objective optimization is strictly the task of finding the optimal solution in presence of more than one objective. As the objectives are more than one, the optimum cannot be simply found for only one objective when the rest of the objectives are also important. Different solutions can represent trade-offs in conflicting scenarios among different objectives. A solution with extreme characteristics with respect to one objective might not be (and usually is not) an extreme for the rest of the objectives. It is commonly a compromise in other objectives. For this reason we cannot simply choose a solution which is optimal with respect to only one objective. Interestingly, most real world problems naturally involve multiple objectives. This aim of this work is to apply optimization techniques to a real world problem: a ventilation duct of an automotive vehicle. Automotive projects commonly pose several problems to engineers. Among them, ducts of the ventilation system are required to provide sufficient airflow to the outlets limiting the noise detectable in the cockpit. Airflow is required to heat (or refresh) sufficiently a portion of the cockpit, while loudness should be avoided mainly for passenger comfort. These qualitative requirements are translated into quantitative objectives, namely minimal pressure losses (better airflow) and minimal noise. Project prescriptions often require values of pressure loss and noise levels to be in a predetermined target range. In this context, we seek to find one or more optimal solutions to the multi-objective problem of minimizing the pressure loss and reducing the noise levels in the duct we were given to study. Traditionally, optimization from the engineering perspective is driven by human knowledge and applied when detail design is already performed or inherited from previous projects. Another approach can be followed by introducing automated optimization in the early stages of the engineering process to envision feasibility and obtain design indications. This approach will inevitably be different. Traditional approaches to optimization involve human factors as experience and insight. Engineers foresee how a design can be improved and experiment different solutions. This approach involves a “trial and error” iterative process where new designs are repetitively verified until a satisfactory solution is found. Moreover, the approach does not ultimately guarantee for a successful outcome as problems could be extremely difficult to solve. Generally, an acceptable trade-off design is considered to be sufficient. This methodology is affected by a couple of drawbacks: • The engineer experiments only a limited number of designs whereas combinations and possibilities could be much more; • The search process is limited by human decision of which designs should be verified.

Laurea liv.I

Facoltà: Ingegneria

Autore: Eugenio Giulio Rizzi Contatta »

Composta da 99 pagine.


Questa tesi ha raggiunto 58 click dal 05/03/2012.

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