ParaturboCAD: Automated Parametric Geometry Construction for Gas-Bearing Supported Turbocompressor Design With Python

Abstract This research introduces an automated methodology for visualizing gas-bearing supported turbocompressor designs utilizing CadQuery 2, a scripting Computer-Aided Design (CAD) platform. This innovative approach simplifies the traditional design process, which often involves the use of multiple software tools and extensive manual data manipulation, by consolidating it into a singular, efficient, and robust workflow. Moreover, CadQuery 2 enables the rapid visualization of abstract optimization results, enhancing the design phase’s efficiency. A pivotal element of this study is the adept transformation of design variables related to key components such as the rotor, compressor, and bearings into precise three-dimensional turbocompressor models. This intricate procedure is expedited by employing a structured Python dictionary, which serves to encapsulate the geometric parameters of each component comprehensively. The utility of this framework is demonstrated through the creation of turbocompressors featuring diverse geometries, highlighting the methodology’s capacity to produce models with high accuracy and within a reasonable generation timeframe of approximately seven minutes per turbocompressor. While there are minor constraints, notably in parametrization choices and the time efficiency of modeling with CadQuery 2, these do not significantly detract from the method’s overall value. Indeed, this approach represents a substantial advancement in the field of design and manufacturing, promising to refine and expedite the development process of these complex systems.

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Publication Details

Published
2024-08-25
DOI
https://doi.org/10.1115/detc2024-143696
Citations
1
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.37
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article

ParaturboCAD: Automated Parametric Geometry Construction for Gas-Bearing Supported Turbocompressor Design With Python

Jürg Schiffmann, Deniz Üstün, Soheyl Massoudi, István T. Horváth et al.
1 citations
Advanced Numerical Analysis Techniques
0.37
article

ParaturboCAD: Automated Parametric Geometry Construction for Gas-Bearing Supported Turbocompressor Design With Python

Jürg Schiffmann, Deniz Üstün, Soheyl Massoudi, István T. Horváth, Joseph Bejjani
article en
1 citations

Abstract

Abstract This research introduces an automated methodology for visualizing gas-bearing supported turbocompressor designs utilizing CadQuery 2, a scripting Computer-Aided Design (CAD) platform. This innovative approach simplifies the traditional design process, which often involves the use of multiple software tools and extensive manual data manipulation, by consolidating it into a singular, efficient, and robust workflow. Moreover, CadQuery 2 enables the rapid visualization of abstract optimization results, enhancing the design phase’s efficiency. A pivotal element of this study is the adept transformation of design variables related to key components such as the rotor, compressor, and bearings into precise three-dimensional turbocompressor models. This intricate procedure is expedited by employing a structured Python dictionary, which serves to encapsulate the geometric parameters of each component comprehensively. The utility of this framework is demonstrated through the creation of turbocompressors featuring diverse geometries, highlighting the methodology’s capacity to produce models with high accuracy and within a reasonable generation timeframe of approximately seven minutes per turbocompressor. While there are minor constraints, notably in parametrization choices and the time efficiency of modeling with CadQuery 2, these do not significantly detract from the method’s overall value. Indeed, this approach represents a substantial advancement in the field of design and manufacturing, promising to refine and expedite the development process of these complex systems.

École Polytechnique Fédérale de Lausanne (CH)
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Advanced Numerical Analysis Techniques
0.37
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