Experimental and numerical investigations were performed to determine the pressure distributions and the drag forces on a passenger car model. Experiments were carried out with 1/5th scale model FIAT Linea for 20% and ~ 1% blockage ratios in the Uludag University Wind Tunnel (UURT) and in the Ankara Wind Tunnel (ART), respectively. Computational fluid dynamics (CFD) analysis for 1/5th scale model with 0%, 5%, and 20% blockage ratios was performed to validate various blockage correction methods supplementary to the experimental results. Three-dimensional, incompressible, and steady governing equations were solved by STAR-CCM+ code with realizable k–ε two-layer turbulence model. The calculated drag coefficients were in good agreement with the experimental results within 6%. Pressure coefficients on the model surfaces have shown similar trends in the experimental and numerical studies. Some of the existing blockage correction methods were successfully compared in this study and predicted drag coefficients were within ± 5%. The authors propose the continuity and the Sykes blockage correction methods for passenger car models because they are very simple and practical and they can be used economically for engineering applications.
Experimental and computational studies were performed to study the drag forces and the pressure distributions of a one-fifth scale model FIAT Linea at increasing yaw angle and two-vehicle platoon. Experiments were performed in the Uludag University Wind Tunnel (UURT) only for the yaw angles of 0 deg, 5 deg, and 10 deg due to the test section dimensional restriction. Supplementary tests were performed in the Ankara Wind Tunnel (ART) to evaluate the aerodynamic coefficients up to yaw angle of 40 deg. The test section blockage ratios were 20% and 1%, respectively, in the UURT and ART tunnels. The blockage effects for the yaw angles up to 10 deg were studied by the comparison of two wind tunnel results. The aerodynamic tests of two-vehicle platoon were performed in the ART tunnel at spacings of “x/L” 0, 0.5, and 1. Static pressure distributions were obtained from the model centerline and three vertical sections. In the numerical study, three-dimensional, incompressible, and steady governing equations were solved by star-ccm+ code with realizable k-ɛ two-layer turbulence model. Experimental and numerical Cp distributions and Cd values were found in good agreement for considered yaw angles and two-vehicle platoon. Maximum drag coefficient was obtained at yaw angle of 35 deg for both experimental and numerical calculations. The two-vehicle platoon analysis resulted with the significant drag coefficient improvement for the leading car at spacings of x/L = 0 and 0.5, while for the tail car drag coefficient remained slightly above the vehicle in isolation.
PurposeIn the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.Design/methodology/approachInitially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.FindingsTo the best of authors’ knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.Originality/valueHigh level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.
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