Automatic modeling of aircraft external geometries for preliminary design workflows
Aerospace Science and Technology, ISSN: 1270-9638, Vol: 98, Page: 105667
2020
- 29Citations
- 57Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
This article introduces a high-fidelity geometry definition methodology enabling Multidisciplinary Design, Analysis and Optimization (MDAO) of aircraft configurations. All definitions and functional features have been implemented within the JPAD software, a Java-based computing library for aircraft designers, which provides a dedicated geometric modeling module called JPADCAD. The geometric module, that comes as an application programming interface (API) built on top of the OpenCASCADE Technology solid modeling kernel, is conceived for the automatic production of parametric aircraft CAD geometries. The tool allows the definition of input geometries for low-fidelity as well as high-fidelity aerodynamic analyses, hence proves to be a key factor in the entire MDAO process, particularly in conceptual or preliminary design analysis workflows. The main goal of such a geometric library remains ease of use and support for automation to minimize unnecessary or repetitive human effort. The backbone of the presented methodology is the parametric definition of a generic commercial transport aircraft configuration that translates into software data structures and functionalities of CAD surface modelers. These aspects are discussed in the first part of the article. The second part presents a use case example of the geometric modeling API, where an automated aerodynamic analysis workflow is used to construct a prediction model for canard-wing configurations.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1270963819316943; http://dx.doi.org/10.1016/j.ast.2019.105667; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85077915266&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1270963819316943; https://api.elsevier.com/content/article/PII:S1270963819316943?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S1270963819316943?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.ast.2019.105667
Elsevier BV
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