Algorithmically-consistent deep learning frameworks for structural topology optimization

2021 ◽  
Vol 106 ◽  
pp. 104483
Author(s):  
Jaydeep Rade ◽  
Aditya Balu ◽  
Ethan Herron ◽  
Jay Pathak ◽  
Rishikesh Ranade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Learning Frameworks

Non-iterative structural topology optimization using deep learning

2019 ◽  
Vol 115 ◽  
pp. 172-180 ◽  
Author(s):  
Baotong Li ◽  
Congjia Huang ◽  
Xin Li ◽  
Shuai Zheng ◽  
Jun Hong
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology

Explicit structural topology optimization using BEM ‐based MMV approach

2021 ◽  
Author(s):  
Weisheng Zhang ◽  
Qingqing Jiang ◽  
Weizhe Feng ◽  
Sung‐Kie Youn ◽  
Xu Guo
Keyword(s):  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology

Structural topology optimization based on system condensation

2014 ◽  
Vol 92 ◽  
pp. 26-35 ◽  
Author(s):  
Hyun-Gi Kim ◽  
Soo-hyun Park ◽  
Maenghyo Cho
Keyword(s):  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology

Structural topology optimization without ersatz material approach using the adaptive mesh method

2020 ◽  
Vol 2020.30 (0) ◽  
pp. 2105
Author(s):  
Yoshinori KOIKE ◽  
Takayuki YAMADA ◽  
Benliang ZHU ◽  
Kazuhiro IZUI ◽  
Shinji NISHIWAKI
Keyword(s):  
Topology Optimization ◽  
Adaptive Mesh ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Adaptive Mesh Method ◽  
Mesh Method

Innovative Design, Structural Optimization and Additive Manufacturing of New-Generation Turbine Blades

2021 ◽  
pp. 1-31
Author(s):  
Lorenzo Pinelli ◽  
Andrea Amedei ◽  
Enrico Meli ◽  
Federico Vanti ◽  
Benedetta Romani ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Structural Optimization ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Topological Optimization ◽  
Mode Shapes ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
New Generation

Abstract The need for high performances is pushing the complexity of mechanical design at very high levels, especially for turbomachinery components. Structural topology optimization methods together with additive manufacturing techniques for high resistant alloys are considered very promising tools, but their potentialities have not been deeply investigated yet for critical rotating components like new-generation turbine blades. This research work proposes a methodology for the design, the optimization and the additive manufacturing of extremely stressed turbomachinery components like turbine blade-rows. The presented procedure pays particular attention to important aspects of the problems as fluid-structure interactions and fatigue of materials, going beyond the standard structural optimization approaches found in the literature. The numerical procedure shows robustness and efficiency, making the proposed methodology a good tool for rapid design and prototyping, and for reducing the design costs and the time-to-market typical of these mechanical elements. The procedure has been applied to a low-pressure turbine rotor to improve the aeromechanical behavior while keeping the aerodynamic performance. From the original geometry, mode-shapes, forcing functions and aerodynamic damping have been numerically evaluated and are used as input data for the following topological optimization. Finally, the optimized geometry has been verified in order to confirm the improved aeromechanical design. After the structural topology optimization, the final geometries provided by the procedure have been then properly rendered to make them suitable for additive manufacturing. Some prototypes of the new optimized turbine blade have been manufactured to be tested in terms of fatigue.

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