Hybrid vehicle engines modified for high exhaust gas recirculation (EGR) are a good choice for high efficiency and low NOx emissions. Such operation can result in an HEV when a downsized engine is used at high load for a large fraction of its run time to recharge the battery or provide acceleration assist. However, high EGR will dilute the engine charge and may cause serious performance problems such as incomplete combustion, torque fluctuation, and engine misfire. An efficient way to overcome these drawbacks is to intensify tumble leading to increased turbulent intensity at the time of ignition. The enhancement of turbulent intensity will increase flame velocity and improve combustion quality, therefore increasing engine tolerance to higher EGR.It is accepted that the detailed experimental characterization of flow field near top dead center (TDC) in an engine environment is no longer practical and cost effective. Instead, CFD is more convenient, more economical, and more versatile to study the in-cylinder flow physics if its accuracy is validated with experimental results. To achieve the goal of increasing tolerance to EGR, this work reports investigations of intake port design simulation.Part 1 of this two-part paper presents a CFD simulation methodology. It includes a preliminary study of software selection and a systematic validation study to verify the accuracy of the CFD tool. The validations were performed through the comparison with PIV experimental tests. An assessment of the standard k-ε (SKE), renormalization group k-ε (RNG), and Reynolds stress model (RSM) turbulence models were performed for a series of intake valve lift and intake pressure combination. The results indicate that SKE is the best suited over RNG and RSM models as it is most accurate, at least for the current test conditions. The investigation of grid independence and parameter sensitivity study is also presented. The developed CFD methodology is applied, in Part 2, for a new intake port design with a transient study of real engine operation conditions to determine the effectiveness of the optimized shape.