Concurrent optimization of structural topology and infill properties with a CBF-based level set method

In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso scale.The concurrent optimization is achieved by a computational framework combining a new parametric level set approach with mathematical programming.Within the proposed framework,both the structural boundary evolution and the effective infill property optimization can be driven by mathematical programming,which is more advantageous compared with the conventional partial differential equatiodriven level set approach.Moreover,the proposed approach will be more efficient in handling nonlinear problems with multiple constraints.Instead of using radial basis functions(RBF),in this paper,we propose to construct a new type of cardinal basis functions(CBF)for the level set function parameterization.The proposed CBF parameterization ensures an explicit impose of the lower and upper bounds of the design variables.This overcomes the intrinsic disadvantage of the conventional RBF-based parametric level set method,where the lower and upper bounds of the design variables oftentimes have to be set by trial and error;A variational distance regularization method is utilized in this research to regularize the level set function to be a desired distanceregularized shape.With the distance information embedded in the level set model,the wrapping boundary layer and the interior infill region can be naturally defined.The isotropic infill achieved via the mesoscale topology optimization is conformally fit into the wrapping boundary layer using the shape-preserving conformal mapping method,which leads to a hierarchical physical structure with optimized overall topology and effective infill properties.The proposed method is expected to provide a timely solution to the increasing demand for multiscale and multifunctional structure design.

Mining Design Pattern Use Scenarios and Related Design Pattern Pairs:A Case Study on Online Posts

In common design pattern collections,e.g.,design pattern books,design patterns are documented with templates that consist of multiple attributes,such as intent,structure,and sample code.To adapt to modern developers,the depictions of design patterns,especially some specific attributes,should advance with the current programming technologies,for example,“known uses”,which exemplifies the use scenarios of design patterns in practice,and“related patterns”,which describes the relatedness between a design pattern and the others within a context.However,it is not easy to update the contents of these attributes manually due to the diversity of the programming technologies.To address this problem,in this work,we conducted a case study to mine design pattern use scenarios and related design pattern pairs from Stack Overflow posts to enrich the two attributes.We first extracted the question posts relevant to each design pattern by identifying the design pattern tags.Then,the topics of the posts were discovered by applying topic modeling techniques.Finally,by analyzing the topics specified for each design pattern,we detected 195 design pattern use scenarios and 70 related design pattern pairs,involving 61 design patterns totally.These findings are associated with a variety of popular software frameworks and programming techniques.They could complement the existing design pattern collections and help developers better acknowledge the usage and relatedness of design patterns in today's programming practice.