Topology optimization is a systematic method of generating designs to meet specific engineering requirements. It is exploited today in several industries including aircraft, automobile, and machinery, and it strongly complements the emerging field of additive manufacturing. Yet, the wide-spread use of topology optimization has been deterred due to high computational cost and significant software/hardware investment.
In this paper, we propose a cloud based topology optimization (CTO) framework to overcome these challenges, thereby promoting the wider use of topology optimization. CTO requires a confluence of several methods and technologies, each of which is discussed in this paper.
First and foremost, CTO requires a fast 3D topology optimization method that can respond rapidly to multiple clients. Here, PareTO, a topological sensitivity based method is used as the backbone of the framework. PareTO relies on limited-memory finite element analysis with a deflated linear solver that is designed to exploit multi-core and many-core architectures. At the client-end, the framework relies on JavaScript based WebGL and ThreeJS technologies to display 3D geometry and formulate structural problems within a browser. Finally, Ajax, php and HTML5 technologies are exploited to achieve asynchronous and robust user experience. An implementation of this framework is available at www.cloudtopopt.com; to use this free service, JavaScript must be enabled within the browser.
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FEniTop: a simple FEniCSx implementation for 2D and 3D topology optimization supporting parallel computing
Topology optimization has emerged as a versatile design tool embraced across diverse domains. This popularity has led to great efforts in the development of education-centric topology optimization codes with various focuses, such as targeting beginners seeking user-friendliness and catering to experienced users emphasizing computational efficiency. In this study, we introduce , a novel 2D and 3D topology optimization software developed in Python and built upon the open-source library, designed to harmonize usability with computational efficiency and post-processing for fabrication. employs a modular architecture, offering a unified input script for defining topology optimization problems and six replaceable modules to streamline subsequent optimization tasks. By enabling users to express problems in the weak form, eliminates the need for matrix manipulations, thereby simplifying the modeling process. The software also integrates automatic differentiation to mitigate the intricacies associated with chain rules in finite element analysis and sensitivity analysis. Furthermore, provides access to a comprehensive array of readily available solvers and preconditioners, bolstering flexibility in problem-solving. is designed for scalability, furnishing robust support for parallel computing that seamlessly adapts to diverse computing platforms, spanning from laptops to distributed computing clusters. It also facilitates effortless transitions for various spatial dimensions, mesh geometries, element types and orders, and quadrature degrees. Apart from the computational benefits, facilitates the automated exportation of optimized designs, compatible with open-source software for post-processing. This functionality allows for visualizing optimized designs across diverse mesh geometries and element shapes, automatically smoothing 3D designs, and converting smoothed designs into STereoLithography (STL) files for 3D printing. To illustrate the capabilities of , we present five representative examples showcasing topology optimization across 2D and 3D geometries, structured and unstructured meshes, solver switching, and complex boundary conditions. We also assess the parallel computational efficiency of by examining its performance across diverse computing platforms, process counts, problem sizes, and solver configurations. Finally, we demonstrate a physical 3D-printed model utilizing the STL file derived from the design optimized by . These examples showcase not only ’s rich functionality but also its parallel computing performance. The open-source is given in Appendix B and will be available to download at
- PAR ID:
- 10530195
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Structural and Multidisciplinary Optimization
- Volume:
- 67
- Issue:
- 8
- ISSN:
- 1615-147X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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