A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Multiscale design of stiffening and ROS scavenging hydrogels for the augmentation of mandibular bone regeneration

Although biomimetic hydrogels play an essential role in guiding bone remodeling,reconstructing large bone defects is still a significant challenge since bioinspired gels often lack osteoconductive capacity,robust mechanical properties and suitable antioxidant ability for bone regeneration.To address these challenges,we first engineered molecular design of hydrogels(gelatin/polyethylene glycol diacrylate/2-(dimethylamino)ethyl methacrylate,GPEGD),where their mechanical properties were significantly enhanced via introducing trace amounts of additives(0.5 wt%).The novel hybrid hydrogels show high compressive strength(>700 kPa),stiff modulus(>170 kPa)and strong ROS-scavenging ability.Furthermore,to endow the GPEGD hydrogels excellent osteoinductions,novel biocompatible,antioxidant and BMP-2 loaded polydopamine/heparin nanoparticles(BPDAH)were developed for functionalization of the GPEGD gels(BPDAH-GPEGD).In vitro results indicate that the antioxidant BPDAH-GPEGD is able to deplete elevated ROS levels to protect cells viability against ROS damage.More importantly,the BPDAH-GPEGD hydrogels have good biocompatibility and promote the osteo differentiation of preosteoblasts and bone regenerations.At 4 and 8 weeks after implantation of the hydrogels in a mandibular bone defect,Micro-computed tomography and histology results show greater bone volume and enhancements in the quality and rate of bone regeneration in the BPDAH-GPEGD hydrogels.Thus,the multiscale design of stiffening and ROS scavenging hydrogels could serve as a promising material for bone regeneration applications.

Functionally Graded Cellular Structure Design Using the Subdomain Level Set Method with Local Volume Constraints

Functional graded cellular structure(FGCS)usually shows superiormechanical behaviorwith lowdensity and high stiffness.With the development of additivemanufacturing,functional graded cellular structure gains its popularity in industries.In this paper,a novel approach for designing functionally graded cellular structure is proposed based on a subdomain parameterized level set method(PLSM)under local volume constraints(LVC).In this method,a subdomain level set function is defined,parameterized and updated on each subdomain independently making the proposed approach much faster and more cost-effective.Additionally,the microstructures on arbitrary two adjacent subdomains can be connected perfectly without any additional constraint.Furthermore,the local volume constraint for each subdomain is applied by virtue of the augmented Lagrange multiplier method.Finally,several numerical examples are given to verify the correctness and effectiveness of the proposed approach in designing the functionally graded cellular structure.From the optimized results,it is also found that the number of local volume constraints has little influence on the convergence speed of the developed approach.

Size effect of lattice material and minimum weight design

The size effects of microstructure of lattice materials on structural analysis and minimum weight design are studied with extented multiscale finite element method（EMsFEM） in the paper. With the same volume of base material and configuration, the structural displacement and maximum axial stress of micro-rod of lattice structures with different sizes of microstructure are analyzed and compared.It is pointed out that different from the traditional mathematical homogenization method, EMsFEM is suitable for analyzing the structures which is constituted with lattice materials and composed of quantities of finite-sized micro-rods.The minimum weight design of structures composed of lattice material is studied with downscaling calculation of EMsFEM under stress constraints of micro-rods. The optimal design results show that the weight of the structure increases with the decrease of the size of basic sub-unit cells. The paper presents a new approach for analysis and optimization of lattice materials in complex engineering constructions.

Self-consistent Clustering Analysis-Based Moving Morphable Component(SMMC)Method for Multiscale Topology Optimization

Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined,and thus lack the ability for structural concerns like buckling strength,robustness,and multi-functionality.Therefore,in this paper,a new multiscale concurrent topology optimization design,referred to as the self-consistent analysis-based moving morphable component(SMMC)method,is proposed.Compared with the conventional moving morphable component method,the proposed method seeks to optimize both material and structure simultaneously by explicitly designing both macrostructure and representative volume element(RVE)-level microstructures.Numerical examples with transducer design requirements are provided to demonstrate the superiority of the SMMC method in comparison to traditional methods.The proposed method has broad impact in areas of integrated industrial manufacturing design:to solve for the optimized macro and microstructures under the objective function and constraints,to calculate the structural response efficiently using a reduced-order model:self-consistent analysis,and to link the SMMC method to manufacturing(industrial manufacturing or additive manufacturing)based on the design requirements and application areas.

超算与机器学习辅助高渗透性海水反渗透膜系统优化设计

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)的多尺度优化设计新方法.在进料含盐度35,000 ppm,回收率50%典型工况下,对标目前国际先进海水反渗透淡化工艺,本文提出的优化方案能使淡水制备比能耗(1.66 k Wh/m^(3))降低27.5%,所需膜面积减少约37.2%,系统最大浓差极化因子控制在工程允许范围以内(<1.20),可有效缓解高渗透膜系统中膜污染问题,为高性能膜材料精准设计提供理论依据、计算工具和大数据支撑,有重要的应用潜力.本文提出的机器学习结合超算的多尺度设计新研究范式,突破了基于“试错法”的传统单一尺度组件设计限制,高通量并行计算规模可扩展至93,120核以上,较串行算法计算效率提升3000倍以上,可大幅度缩短高性能膜组件的设计周期.