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In reliability-based topology optimization (RBTO), both quantification and maximization of structure reliability considering uncertainty of various quantities are essential. In this study, A novel gradient online learning and prediction (GoLap) method is proposed to accelerate the double-looped RBTO and reduce total computational time by using a one-hidden-layer neural networks (NN) and gradient and non-gradient based reliability quantification. The total computational time saving is achieved by (i) skipping the finite element analysis (FEA) runs and sensitivity analyses and (ii) updating the design and random variables using the gradients predicted by the NN model in selected inner and outer loops. 2D and 3D numerical examples, including an aircraft engine pylon, are presented to demonstrate the total computational time saving of up to 84.4%, and the prediction accuracy of the reliability of optimum structures for the GoLap implemented RBTO methods. [ABSTRACT FROM AUTHOR]

As a crucial component of aircraft engine combustors, the external-mixing prefilming atomizer exhibits significant advantages in enhancing combustion efficiency and reducing pollutant emissions. However, the intricate relationship between spray characteristics, droplet breakup mechanisms, flow fields, and geometric parameters of the external-mixing prefilming atomizer remains poorly understood. This paper presents a novel external-mixing prefilming atomizer, aiming to address this knowledge gap. Additionally, particle image velocimetry (PIV), high-speed Schlieren photography, and particle image analysis (PIA) measuring systems were employed to gain insights into the spray characteristics and droplet breakup mechanism of the external-mixing prefilming atomizer under various swirl flow fields. The obtained results reveal the formation of two distinct regions, namely a low-density area and a high-density area. Notably, the number of droplets in the low-density area, located within 0–2 cm from the central axis, is significantly lower compared to the high-density area situated further away from the central axis. The breakup of droplets in the low-density region is primarily attributed to the inner swirl airflow, while the outer swirl airflow induces droplet breakup in the high-density region. As the Reynolds number of the incoming flow increases, the air-liquid ratio (ALR) and Weber number also increase, thereby enhancing the fuel breakup effect. The Schlieren results reveal a two-stage spray development process. The initial stage is characterized by the dominant momentum of the dense spray, with the spray tip penetration (STP) exhibiting a relatively slow increase. The aerodynamic force of airflow plays a crucial role in governing the breakup and transport of droplets, leading to a linear variation of STP over time. As the swirl number increases, there is an enhanced momentum exchange between droplets and airflow, resulting in accelerated spray penetration and droplet breakup. [ABSTRACT FROM AUTHOR]