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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.

One of the best tools to explore complicated in-cylinder physics is computational fluid dynamics (CFD). In order to assess the accuracy and reliability of the CFD simulations, it is critical to perform validation studies over different engine operating conditions. Simulation-based design of SI engines requires predictive capabilities, where results do not need to be tuned for each operating condition. This requires the models adopted to simulate their respective engine physics to be reliable under a broad range of conditions. A detailed set of experimental data was obtained to validate the CFD predictions of SI engine combustion.

Gasoline Direct Injection (GDI) is a key technology in the automotive industry for improving fuel economy and performance of gasoline internal combustion engines. GDI engine performance and emission characteristics are mainly determined by the complex interaction of in-cylinder flow, mixture formation and subsequent combustion processes. In a GDI engine, mixture formation depends on spray characteristics. Spray evolution and mixture formation is critical to GDI engine operation. In this work, a multi-component surrogate fuel blend was used to represent the chemical and physical properties of the gasoline employed in the experimental engine tests. Multi-component spray models were also validated in this study against experimental spray injection measurements in a chamber. The spray-chamber data include spray-penetration lengths, transient spray velocities and droplet Sauter mean diameter (SMD) at different axial and radial distances from the spray tip, obtained using a PDPA system.

Battery thermal management for high power applications such as electrical/hybrid vehicles is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While Computational Fluid Dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis especially for a battery module or pack. An accurate but much smaller battery thermal model using a state space representation is proposed. The parameters in the state space model are extracted from CFD results. The state space model is then shown to provide identical results as those from CFD under transient power inputs. While a CFD model may take hours to run depending on the size of the problem, the corresponding state space model runs in seconds.

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 paper presents the current state of a three-layer surface icing model for ice crystal icing risk assessment in aircraft engines, being developed jointly by Ansys and Honeywell to account for possible heat transfer from inside an engine into the flow path where ice accretion occurs. The bottom layer of the proposed model represents a thin metal sheet as a substrate surface to conductively transfer heat from an engine-internal reservoir to the ice layer. The middle layer is accretion ice with a porous structure able to hold a certain amount of liquid water. A shallow water film layer on the top receives impinged ice crystals. A mass and energy balance calculation for the film determines ice accretion rate. Water wicking and recovery is introduced to transfer liquid water between film layer and porous ice accretion layer.

Cylinder head design is one of the most involved disciplines in engine design. Whether designing an altogether new head or revamping an old one, several different coupled and inter-dependent technologies ranging from heat transfer, fluid flow, combustion, material non-linearity, structural and fatigue have to be accounted. Simultaneous designing of ports, jacket and combustion chamber leads to cylinder head design, which is then tested for its strength and durability. Traditionally a series of analytical, empirical, test-based and simulation based exercises are conducted to design cylinder heads. With increasing pressure on reducing cost and turnaround time, focus on moving towards a quasi-simulation based design and development of cylinder heads is gaining strength. This paper talks about how a simulation driven process for cylinder head design can be developed.

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.

In order to achieve the purpose of saving energy and reducing emission, the improvement of aerodynamic performance plays an increasingly crucial role for car manufacturers. Previous studies have confirmed the validity of gradient-based adjoint algorithm for its high efficiency in shape optimization. In this paper, two important aspects of adjoint approach were explored. One is vehicle aerodynamic optimization with multiple objectives, and the other is using time-averaged flow results as the primal solution, both are issues of high interest in recent applications. First, adjoint shape optimization with steady-state and time-averaged flow simulations were respectively calculated and comparatively discussed based on a production SUV. The shape modifications of the two cases indicated that the impact of primal solution on design change could not be neglected, due to the different intrinsic codes of steady and transient turbulence models.

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.