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Explicit Reconstruction and Shape Optimization of Topology Optimization Results with Mechanical Performance Preservation 认领 引用
1
作者 Yuting Tang Yu Li +3 位作者 Xingyu Xiang Jiaxiang Luo Weien Zhou Wen Yao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期314-342,共29页
Topology optimization is widely used in lightweight structural design to determine optimal material distributions.However,density-based results are represented in an implicit pixel-wise form with blurred boundaries an... Topology optimization is widely used in lightweight structural design to determine optimal material distributions.However,density-based results are represented in an implicit pixel-wise form with blurred boundaries and jagged contours,which limits their direct use in engineering design and manufacturing.This study proposes a two-stage post-processing framework to reconstruct topology optimization results into explicit parametric geometries while preserving structural performance.The framework first extracts and processes contour points from the optimized density field and reconstructs the geometry using Non-Uniform Rational B-Splines(NURBS).A subsequent shape optimization step based on the fixed-grid finite element method(FG-FEM)adjusts boundary control points to reduce performance deviation introduced during reconstruction while satisfying volume and topological homeomorphism constraints.Numerical examples,including the cantilever beam,Michell beam,half-MBB beam,and a quadcopter frame,validate the effectiveness of the framework.The results show that the proposed method enables explicit geometric reconstruction while maintaining structural performance,with compliance deviations within 0.5%-2.6%in benchmark cases. 展开更多
关键词 Topology optimization post processing explicit reconstruction shape optimization topological homeomorphism performance preservation
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A satellite layout-structure integrated optimization method based on thermal metamaterials 认领 引用
2
作者 Senlin HUO Bingxiao DU +2 位作者 Wei CONG Yong ZHAO Xianqi CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期328-340,共13页
In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirement... In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites. 展开更多
关键词 Layout optimization Metamaterials Satellites Structure design Thermal management Topology optimization
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Intention Recognition of Space Noncooperative Targets Using Large Language Models 认领 引用
3
作者 Heng Jing Qinbo Sun +1 位作者 Zhaohui Dang Hua Wang 《Space(Science & Technology)》 EI CSCD 2025年第1期779-799,共21页
This study proposes a novel method for intention recognition of space noncooperative targets using large language models(LLMs).Traditional methods rely on motion data to assess orbital motion intentions but cannot inf... This study proposes a novel method for intention recognition of space noncooperative targets using large language models(LLMs).Traditional methods rely on motion data to assess orbital motion intentions but cannot infer operation and task intentions from multi-source information like images.LLMs,with their logical reasoning capabilities,can address this limitation.The intentions are categorized into 3 types and 23 subtypes based on multi-source information and their characteristics:motion intentions(e.g.,“hovering”,“flyby”,and“rendezvous”),operation intentions(e.g.,“docking”,“refueling”,and“repair”),and task intentions(e.g.,“detection”,“surveillance”,and“attack”).The proposed method constructs LLMs for spacecraft intention recognition,involving prompt classification,template design,and test sample generation.The use of prompt tuning V2(P-tuning V2)and low-rank adaptation(LoRA)fine-tuning enhances the models’performance.A dataset of 50,688 nominal samples and 8,448 perturbed samples was created through computer simulation based on expert knowledge,focusing on intention recognition of approaching targets in space station on-orbit operation and surveillance scenarios.The models were tested under 3 prompt conditions:basic,instruction,and chain-of-thought(CoT).The performance of 6 models(ChatGLM2-6B and ChatGLM3-6B base and fine-tuned models)was analyzed.Notably,the LoRA finetuned ChatGLM3-6B model on instruction prompts achieved 99.9%accuracy,with improved robustness compared to the base model.This work presents a pioneering application of LLMs for spacecraft intention recognition,offering valuable insights for future research and applications. 展开更多
关键词 space noncooperative targets logical reasoning orbital motion intentions large language models motion data large language models llms traditional intention recognition
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