The bivariate two dimensional empirical mode decomposition (Bivariate 2D-EMD) is extended to estimate the turbulent fluctuations and to identify cycle-to-cycle variations (CCV) of in-cylinder flow. The Bivariate 2D-EMD is an adaptive approach that is not restricted by statistical convergence criterion, hence it can be used for analyzing the nonlinear and non-stationary phenomena. The methodology is applied to a high-speed PIV dataset that measures the velocity field within the tumble symmetry plane of an optically accessible engine. The instantaneous velocity field is decomposed into a finite number of 2D spatial modes. Based on energy considerations, the in-cylinder flow large-scale organized motion is separated from turbulent fluctuations. This study is focused on the second half of the compression stroke. For most of the cycles, the maximum of turbulent fluctuations is located between 50 and 30 crank angle degrees before top dead center (TDC). In regards to the phase-averaged velocity field, the contribution of CCV to the fluctuating kinetic energy is approximately 55% near TDC.

中文翻译：

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双变量 2D-EMD 在缸内流动瞬时流动结构和周期变化分析中的开发和应用

双变量二维经验模态分解 (Bivariate 2D-EMD) 被扩展到估计湍流波动和识别缸内流动的循环到循环变化 (CCV)。Bivariate 2D-EMD是一种不受统计收敛准则限制的自适应方法，因此可用于分析非线性和非平稳现象。该方法应用于高速 PIV 数据集，该数据集测量光学可访问发动机的翻滚对称平面内的速度场。瞬时速度场被分解为有限数量的二维空间模式。基于能量的考虑，将缸内流动大尺度有组织的运动与湍流波动分开。这项研究的重点是压缩冲程的后半部分。对于大多数周期，最大湍流波动位于上止点 (TDC) 前 50 到 30 度曲柄角之间。对于相均速度场，CCV 对波动动能的贡献在 TDC 附近约为 55%。