Optimization of a High Pressure Industrial Fan

E. Rivera, Fanny Besem-Cordova, J. Bonaccorsi
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Abstract

Fans are used in industrial refineries, power generation, petrochemistry, pollution control, etc. These fans can perform in sometimes extreme, mission-critical conditions. The design of fans has historically relied on turbomachinery affinity laws, resulting in oversized machines that are expensive to manufacture and transport. With the increasingly lower CPU cost of fluid modeling, designers can now turn to CFD optimization to produce the necessary machine performance and flow conditions while respecting manufacturing constraints. The objective of this study is to maximize the pressure rise across an industrial fan while respecting manufacturing constraints. First, a 3D scan of the baseline impeller is used to create the CFD model and validated against experimental data. The baseline impeller geometry is then parameterized with 21 free parameters driving the shape of the hub, shroud, blade lean and camber. A fully automated optimization process is conducted using Numeca’s Fine™/Design3D software, allowing for a CPU-efficient Design Of Experiment (DOE) database generation and a surrogate model using the powerful Minamo optimization kernel and data-mining tool. The optimized impeller coupled with a CFD-aided redesigned volute showed an increase in overall pressure rise over the whole performance line, up to 24% at higher mass flow rates compared to the baseline geometry.
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高压工业风机的优化设计
风机广泛应用于工业炼化、发电、石油化工、污染治理等领域。这些风扇有时可以在极端的关键任务条件下工作。从历史上看,风扇的设计依赖于涡轮机械的亲和规律,导致制造和运输成本高昂的超大机器。随着流体建模的CPU成本越来越低,设计人员现在可以转向CFD优化,以在尊重制造限制的情况下产生必要的机器性能和流动条件。本研究的目的是在尊重制造限制的情况下最大化工业风扇的压力上升。首先,使用基线叶轮的3D扫描来创建CFD模型,并根据实验数据进行验证。然后用驱动轮毂、叶冠、叶片倾斜度和弧度形状的21个自由参数参数化基准叶轮几何形状。使用Numeca的Fine™/Design3D软件进行全自动优化过程,允许cpu高效的实验设计(DOE)数据库生成和使用功能强大的Minamo优化内核和数据挖掘工具的代理模型。经过优化的叶轮与cfd辅助的重新设计的蜗壳相结合,与基线几何形状相比,在更高的质量流量下,整个性能线的总压力上升幅度增加了24%。
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