Browsing by Author "Venter, G."

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    Experimentally determined material parameters for temperature prediction of an automobile tire using finite element analysis
    (SAIMechE, 2019) Van Blommestein, W. B.; Venter, G.; Venter, M. P.
    The material parameters of an automotive truck tire were experimentally determined and validated for use in a thermal finite element analysis to determine the temperature distribution in the tire due to different operating conditions. Uni-axial tensile tests were performed on tire samples. The force displacement response of each was used to determine material properties by means of direct curve-fitting and iterative numerical procedures. Equivalent finite element simulation models were used to validate the properties. Hysteresis behaviour of the rubber regions were identified by dynamic mechanical analysis. Material definitions were incorporated into a finite element model to predict the steady-state heat generation and temperature distribution within a tire due to hysteresis. Experimental rolling tire temperature measurements were taken on a test bench. A comparison of the results with those obtained from the equivalent thermal models was used to validate the numerical models.
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    Review of optimization techniques
    (John Wiley & Sons Ltd., 2010) Venter, G.
    A basic overview of optimization techniques is provided. The standard form of the general nonlinear, constrained optimization problem is presented, and various techniques for solving the resulting optimization problem are discussed. The techniques are classified as either local (typically gradient-based) or global (typically non-gradient based or evolutionary) algorithms. A great many optimization techniques exist and it is not possible to provide a complete review in the limited space available here. Instead, an effort is made to concentrate on techniques that are commonly used in engineering optimization applications. The review is kept general in nature, without considering special cases like linear programming, convex problems, multi-objective optimization, multi-disciplinary optimization, etc. The advantages and disadvantages of the different techniques are highlighted, and suggestions are made to aid the designer in selecting an appropriate technique for a specific problem at hand. Where possible, a short overview of a representative method is presented to aid the discussion of that particular class of algorithms.