首页> 外文会议>ASME Turbo Expo: Turbomachinery Technical Conference and Exposition >A MULTI-DIMENSIONAL EXTENSION OF BALJE CHART FOR AXIAL FLOW TURBOMACHINERY USING ARTIFICIAL INTELLIGENCE BASED META-MODELS
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A MULTI-DIMENSIONAL EXTENSION OF BALJE CHART FOR AXIAL FLOW TURBOMACHINERY USING ARTIFICIAL INTELLIGENCE BASED META-MODELS

机译:基于人工智能的荟萃模型的轴流涡轮机的Balje图表的多维延伸

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The main intent of this work is the exploration of the rotor-only fan design-space to identify correlations between fan performance and enriched geometric and kinematic parameters. In particular, the aim is to derive a multidimensional "Balje chart", where the main geometric and operational parameters are taken into account in addition to the specific speed and diameter, to guide a fan designer towards the correct choice of parameters such as hub solidity, blade number, hub-to-tip ratio. This multidimensional chart was built using performance data derived from a quasi-3D in-house software for axisymmetric blade analysis and then explored by means of machine learning techniques suitable for big data analysis. Principal Component Analysis (PCA) and Projection to Latent Structure (PLS) allowed finding optimal values of the main geometric parameters required by each specific speed/specific diameter pair.
机译:这项工作的主要目的是探索转子的风扇设计空间,以识别风扇性能与富集几何和运动参数之间的相关性。特别地,目的是推导出多维“BALJE图表”,其中除了特定速度和直径之外,还考虑了主要的几何和操作参数,以引导风扇设计师朝向诸如集线器稳定性的正确选择,刀片号,集线器到尖端比例。该多维图表是使用从AcaMMetric刀片分析的准3D内部软件衍生的性能数据建立的,然后通过适合大数据分析的机器学习技术探索。主成分分析(PCA)和潜在结构的投影(PLS)允许找到每个特定速度/特定直径对所需的主要几何参数的最佳值。

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