Physics- and engineering knowledge-based geometry repair system for robust parametric CAD geometries

Abstract

In modern multi-objective design optimisation, an effective geometry engine is becoming an essential tool and its performance has a significant impact on the entire process. Building a parametric geometry requires difficult compromises between the conflicting goals of robustness and flexibility. The work presents a solution for improving the robustness of parametric geometry models by capturing and modelling relative engineering knowledge into a surrogate model, and deploying it automatically for the search of a more robust design alternative while keeping the original design intent. Design engineers are given the opportunity to choose from a list of optimised designs to balance the robustness of the geometry and the original design intent. The prototype system is firstly tested on a 2D intake design repair example and shows the potential to reduce the reliance on human design experts in the conceptual design phase and improve the stability of the optimisation cycle. It also helps speed up the design process by reducing the time and computational power that could be wasted on flawed geometries or frequent human interferences. A case-study of the proposed repair system based on the design and analysis of a three-dimensional parametric turbine blade model has been set up. An automatic analysis workflow is set up and the results are summarised for setting up a repair database based on surrogate training methods. Positive repair results have been achieved and an automatic repair cycle for the blade model is being set up and tested. The proposed physics and engineering knowledge based geometry repair system for robust parametric geometries proves an effective tool for ensuring automation robustness and design flexibility.

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ORCID for Andras Sobester: orcid.org/0000-0002-8997-4375

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