Structural strength analysis and optimization of converter lug based on Kriging model
The structural analysis of rail transit converters installed under the vehicle often involves time-consuming computer simulations to evaluate the strength of the structure for safety requirements. For typical rail transit converters, each static strength analysis with four working conditions costs a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2022-07-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.04.015 |
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| Summary: | The structural analysis of rail transit converters installed under the vehicle often involves time-consuming computer simulations to evaluate the strength of the structure for safety requirements. For typical rail transit converters, each static strength analysis with four working conditions costs at least 1 hour, and each random vibration analysis with one axis costs more than 8 hours. As a result, for using traditional engineering optimization methods to optimize the converter structure, the optimization efficiency will be severely limited, since these methods need to invoke a large amount of simulations as function evaluations. In this paper, machine learning methods were considered to approximate the functional relationships between design variables and responses. Specifically, the Latin Hypercube Sampling method was used to generate the samples and build Kriging surrogate models (also known as Gaussian Processes). With the information provided by the Kriging models, the sensitivity analysis, design space exploration and global optimization processes can all be facilitated so that the designers can avoid low safety allowance and over-redundant designs while the design circle is also shortened. A complete technical route of Kriging-based analysis and optimization was provided in this paper, and the key steps (design of experiments, surrogate model, adaptive sampling, sensitivity analysis, design space exploration, global optimization, etc.) were described in detail. Finally, the proposed technical route has been verified the validity by the analysis and optimization of a traction converter lug structure. |
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| ISSN: | 1000-128X |