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Capturing the Random Changes in Process Parameters in the Stochastic Grey-box Model of the Flow-Front Dynamics

机译:捕获流前动力学随机灰箱模型中过程参数的随机变化

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With the continuously increasing size of the wind turbine blades, the complexity of the casting process and the risk of failures has also increased. TheIntegralBlades?vacuum assisted resin transfer moulding (VATRM) production process at theSiemens Gamesa Renewable Energyfacility in Aalborg, Denmark, does not permit the visual inspection of the process. Hence a sensor system (possibly virtual) for process control and monitoring is highly prized. Furthermore, the effect of material handling, variations in permeability of the casting media and the material (epoxy) properties affect the outcome of the casting process. Therefore, it is necessary to analyse the effect of such variations at an early stage of the design process (e.g. during the simulations) of such a sensor system. Therefore, in this paper, we first describe an effective method to simulate the random changes in the permeability and viscosity in high-dimensional partial differential equations based model of the fluid flow. Next, a low-dimensional grey-box (cyber-physical) spatiotemporal model is proposed to capture the effect of random change in permeability and viscosity during the progression of the flow-front. Finally, a numerical case-study is presented demonstrating the effectiveness of the proposed methodology.
机译:随着风力涡轮机叶片尺寸的不断增加,铸造工艺的复杂性和故障风险也增加了。位于丹麦奥尔堡的Siemens Gamesa可再生能源工厂的IntegralBlades真空辅助树脂传递模塑(VATRM)生产工艺不允许对工艺进行目视检查。因此,高度重视用于过程控制和监视的传感器系统(可能是虚拟的)。此外,材料处理的影响,铸造介质渗透性的变化以及材料(环氧树脂)的性能也会影响铸造工艺的结果。因此,有必要在这种传感器系统的设计过程的早期(例如,在仿真期间)分析这种变化的影响。因此,在本文中,我们首先介绍一种有效的方法,以基于流体流动模型的高维偏微分方程,模拟渗透率和粘度的随机变化。接下来,提出了一个低维灰盒(网络物理)时空模型,以捕获流动前沿发展过程中渗透率和粘度随机变化的影响。最后,进行了数值案例研究,证明了所提出方法的有效性。

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