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Increased energy costs make optimal aerodynamic design even more critical today as even small improvements in aerodynamic performance can result in significant savings in fuel costs. Energy conscious industries like transportation (aviation and ground based) are particularly affected. There have been a number of different optimization methods, some of which require geometrically parameterized models. For non-parameterized models (as it is the case often in reality where models and shapes are very complex). Shape optimization and adjoin solvers are some of the latest approaches. In our study we are focusing on generating best practices and investigating different strategies of employing the commercially available shape optimizer tool from ANSYS'CFD solver Fluent. The shape optimizer is based on a polynomial mesh-morphing algorithm. The simple case of a low speed, airfoil/flap combination is used as a case study with the objective being the lift to drag ratio.

Thorough design exploration is essential for improving vehicle performance in various aspects such as aerodynamic drag. Shape optimization algorithms in combination with computational tools such as Computational Fluid Dynamics (CFD) play an important role in design exploration. The present work describes a Free-Form Deformation (FFD) approach implemented within a general purpose CFD code for parameterization and modification of the aerodynamic shape of real-life vehicle models. Various vehicle shape parameters are constructed and utilized to change the shape of a vehicle using a mesh morphing technique based on the FFD algorithm. Based on input and output parameters, a design of experiments (DOE) matrix is created. CFD simulations are run and a response surface is constructed to study the sensitivity of the output parameter (aerodynamic drag) to variations in each input parameter.

Computational Fluid Dynamics (CFD) is widely used in vehicle aerodynamics development today, but typically used to study one vehicle shape at a time. In order to be used for aerodynamic shape exploration and optimization the CFD simulation process has to be able to study a large set of design alternatives (vehicle shape variants) within the short period of time typically available in the overall aerodynamics development process. This paper reports the development and testing of a process, referred to as the 50:50:50 Method, which is developed to study a large set of design alternatives in a highly automated way, while ensuring that each design alternative is simulated with a high fidelity CFD simulation.

In this case study, the thermoforming of an automotive instrument panel is considered. The effect of different oven settings on the final material distribution is studied using structural FEA simulation. The variable thickness distribution of the thermoformed part is mapped onto a structural model using a new simple mapping algorithm, and a structural FEA simulation is carried out to examine the final warpage of the instrument panel. The simulation predicts that the minimum thickness of the formed part can be increased by 10% by optimizing the oven settings. Although the optimized process uses oven settings that are less uniform than the baseline settings, the model indicates that warpage experienced by the optimized part will be less than that of the baseline case.

This study presents an efficient process to optimize the transmission loss of a vehicle muffler by using both experimental and analytical methods. Two production mufflers were selected for this study. Both mufflers have complex partitions and one of them was filled with absorbent fiberglass. CAD files of the mufflers were established for developing FEA models in ANSYS and another commercial software program (CFEA). FEA models were validated by experimental measurements using a two-source method. After the models were verified, sensitivity studies of design parameters were performed to optimize the transmission loss (TL) of both mufflers. The sensitivity study includes the perforated hole variations, partition variations and absorbent material insertion. The experimental and sensitivity analysis results are included in the paper.

Computational Fluid Dynamics (CFD) is heavily used in automotive HVAC industry in order to reduce the time and cost in design, optimization, and development of different components [2]. Correct prediction of the aerodynamic characteristics of an HVAC blower is crucial in development of the accurate CFD models for the whole HVAC system. CFD models are extensively used in the optimization of both thermal and airflow characteristics of automotive HVAC [3, 4 and 5]. In this study we have performed CFD simulations for different blower operating conditions in order to assess the CFD results in prediction of the aerodynamic performance in an automotive HVAC forward curved (FC) centrifugal blower. The realizable k-ε turbulence model was used on the Reynolds Averaged Navier-Stokes approach to model complex flow field properly.

Due to the increasingly stringent environmental regulations all around the world confronted by exhaust emission and energy consumption, improving fuel economy has been the top priority for most automotive manufacturers. In this context, the basic process for vehicle shape development has evolved into optimizing the design to achieve better aerodynamic characteristics, especially drag reduction. Of all the optimization approaches, the gradient-based adjoint method has currently received extensive attention for its high efficiency in calculating the objective sensitivity with respect to geometry parameters, which is the first and foremost step for subsequent shape modification. In this work, the main goal is to explore the adjoint method through optimizing the vehicle shape for a lower drag based on a production SUV. Firstly, the influence of different mesh schemes was discussed on sensitivity prediction of aerodynamic drag.