水田动力底盘逆向建模与质心验证——基于Geomagic Design X

针对目前农业机械仿真和优化模型精度不高的问题,提出了一种构建精确模型的逆向建模方法。应用光学扫描仪采集点云数据,基于Geomagic Design X处理点云数据并重构零部件模型,在SolidWorks中完成水田动力底盘的装配。以VP6D-CQ型水田动力底盘为例,将测得的实际质心位置与所建的模型质心位置进行比较,并对提出的逆向建模方法进行了试验验证以及误差分析。结果表明:总质量、x坐标、y坐标、z坐标的误差值分别为4.55%、3.62%、2.23%、4.81%,误差值均在5%内。此方法可构建准确的三维模型,为后续仿真优化数据精确性奠定了基础,与传统的研发相比较,可缩短农业机械研发周期、降低设计成本。

基于“Design Thinking”理念的职业院校创新创业人才培养模式

随着我国经济不断发展和社会进步,创新创业已经成为推动经济增长的关键驱动力,在2015年全国人民代表大会上,李克强总理将“大众创业、万众创新”明确列为我国经济增长的“双引擎”之一,彰显了创新创业对于推动社会发展的重要地位。为响应国家政策,各高校积极开展创新创业教育,将其纳入高等教育综合改革的重要议程,成为培养未来人才的重要途径。本文分析了“Design Thinking”理念下职业院校创新创业人才培养的必要性,围绕新时期专业院校创新创业人才培养的要求,提出了基于“Design Thinking”理念的职业院校创新创业人才培养模式的有效策略,为职业院校人才培养效率的强化提供了参考性意见。

基于Design-Expert的电链锯锯齿结构参数优化

为提高电链锯的锯切效率,对电链锯锯齿的齿形结构参数进行研究,以单位锯切功为电链锯锯切效率的衡量指标,通过对电链锯锯切过程的仿真研究,得出不同结构锯齿的单位锯切功。以电链锯锯齿中的外形前角、侧刃楔角和顶刃楔角等结构参数作为影响因子,采用Box-Benhnken中心组合试验方法设计多因素正交试验,使用Design-expert软件进行数据分析得出最优齿形结构参数组合。研究结果表明,所选取的齿形结构参数对单位锯切功影响程度由大到小顺序依次为外形前角、侧刃楔角、顶刃楔角,并获得最优齿形结构参数组合,外形前角为10.92°、侧刃楔角为45.7°、顶刃楔角为45.41°。

Study of the Ballistic Impact Behavior of Protective Multi-Layer Composite Armor

The abalone shell,a composite material whose cross-section is composed of inorganic and organic layers,has high strength and toughness.Inspired by the abalone shell,several multi-layer composite plates with different layer sequences and thicknesses are studied as bullet-proof material in this paper.To investigate the ballistic performance of this multi-layer structure,the complete characterization model and related material parameters of large deformation,failure and fracture ofAl_(2)O_(3)ceramics andCarbon Fiber Reinforced Polymer(CFRP)are studied.Then,3D finite element models of the proposed composite plates with different layer sequences and thicknesses impacted by a 12.7 mm armor-piercing incendiary(API)are built using Abaqus to predict failure.The simulation results show that the CFRP/Al2O3 ceramic/Ultrahigh Molecular Weight Polyethylene(UHMWPE)/CFRP(1 mm/4 mm/4 mm/1 mm)composite is the optimized stack of layers.The simulation results under specified layer sequence and thickness have a reasonable correlation with the experimental results and reflect the failure and fracture of the multi-layer composite protective armor.

基于Cast Designer的壳体铸件铸造工艺设计及智能优化

使用Cast Designer铸造仿真分析软件,通过可铸性评估系统DFM,完成浇冒系统的辅助设计,然后使用DOE技术和基于遗传算法GA的智能优化技术,评估不同工艺设计方案与铸造过程参数对壳体铸件质量的影响,分析在不同的冒口、冷铁和浇注温度条件下得料率和缩孔量的关系。根据对DOE模拟结果的分析,发现顶冒口和侧冒口尺寸是影响得料率和缩孔量的关键因数,再利用遗传算法GA选择最佳设计方案和参数。通过帕累托曲线得到了生产壳体铸件的最优工艺方案。

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

荷兰代尔夫特理工大学Urban Design工作坊教学模式对我国城市设计课程教学的启示

荷兰代尔夫特理工大学的Urban Design工作坊小组教学模式,为我国城市设计教育提供了创新路径。该模式强化学生跨学科思维与问题解决能力,通过项目实践培育评审、模拟、谈判、决策及冲突管理等技能,教师角色转变为引导者和促进者,激励创新并深化团队协作与沟通。针对我国城市设计教育现状,建议加强综合能力的培养,促进理论教学与设计实践的深度结合,并构建均衡的课程评估框架。此模式的本土化将有力推动我国城市设计教育的创新与发展。

基于Smart Design的校园光伏电站仿真研究

基于智能光伏设计软件Smart Design,对株洲市某高校图书馆屋顶的工商业光伏电站进行了设计与仿真。通过评估当地太阳能资源和气象参数,确定了光伏电站的最佳选址和倾角。利用软件的3D建模和电气设计功能,完成了光伏板排布和电气连接,采用隆基绿能双面光伏组件和华为光伏优化器及逆变器,提高了电站效率。仿真结果显示,该电站具有良好的发电量、经济效益和社会贡献。

Machine learning for membrane design and discovery

Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.

Optimization of Biofuel Formulation by Mixture Design

With the full growth of energy needs in the world, several studies are now focused on finding renewable sources. The aim of this work is to optimise biofuel formulation from a mixture design by studying physical properties, such as specific gravity and kinematic viscosity of various formulated mixtures. Optimization from the mixture plan revealed that in the chosen experimental domain, the optimal conditions are: 40% for used frying oil (UFO), 50% for bioethanol and 10% for diesel. These experimental conditions lead to a biofuel with a density of 0.84 and a kinematic viscosity of 2.97 cSt. These parameters are compliant with the diesel quality certificate in tropical areas. These density and viscosity values were determined according to respective desirability values of 0.68 and 0.75.

Review on the design of high-strength and hydrogen-embrittlement-resistant steels

Given the carbon peak and carbon neutrality era,there is an urgent need to develop high-strength steel with remarkable hydrogen embrittlement resistance.This is crucial in enhancing toughness and ensuring the utilization of hydrogen in emerging iron and steel materials.Simultaneously,the pursuit of enhanced metallic materials presents a cross-disciplinary scientific and engineering challenge.Developing high-strength,toughened steel with both enhanced strength and hydrogen embrittlement(HE)resistance holds significant theoretical and practical implications.This ensures secure hydrogen utilization and further carbon neutrality objectives within the iron and steel sector.Based on the design principles of high-strength steel HE resistance,this review provides a comprehensive overview of research on designing surface HE resistance and employing nanosized precipitates as intragranular hydrogen traps.It also proposes feasible recommendations and prospects for designing high-strength steel with enhanced HE resistance.

Quantum algorithm for minimum dominating set problem with circuit design

Using quantum algorithms to solve various problems has attracted widespread attention with the development of quantum computing.Researchers are particularly interested in using the acceleration properties of quantum algorithms to solve NP-complete problems.This paper focuses on the well-known NP-complete problem of finding the minimum dominating set in undirected graphs.To expedite the search process,a quantum algorithm employing Grover’s search is proposed.However,a challenge arises from the unknown number of solutions for the minimum dominating set,rendering direct usage of original Grover’s search impossible.Thus,a swap test method is introduced to ascertain the number of iterations required.The oracle,diffusion operators,and swap test are designed with achievable quantum gates.The query complexity is O(1.414^(n))and the space complexity is O(n).To validate the proposed approach,qiskit software package is employed to simulate the quantum circuit,yielding the anticipated results.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Deep Insight of Design,Mechanism,and Cancer Theranostic Strategy of Nanozymes

Since the discovery of enzyme-like activity of Fe3O4 nanoparticles in 2007,nanozymes are becoming the promising substitutes for natural enzymes due to their advantages of high catalytic activity,low cost,mild reaction conditions,good stability,and suitable for large-scale production.Recently,with the cross fusion of nanomedicine and nanocatalysis,nanozyme-based theranostic strategies attract great attention,since the enzymatic reactions can be triggered in the tumor microenvironment to achieve good curative effect with substrate specificity and low side effects.Thus,various nanozymes have been developed and used for tumor therapy.In this review,more than 270 research articles are discussed systematically to present progress in the past five years.First,the discovery and development of nanozymes are summarized.Second,classification and catalytic mechanism of nanozymes are discussed.Third,activity prediction and rational design of nanozymes are focused by highlighting the methods of density functional theory,machine learning,biomimetic and chemical design.Then,synergistic theranostic strategy of nanozymes are introduced.Finally,current challenges and future prospects of nanozymes used for tumor theranostic are outlined,including selectivity,biosafety,repeatability and stability,in-depth catalytic mechanism,predicting and evaluating activities.

Additive manufactured osseointegrated screws with hierarchical design

Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,screws are evolving from solid and smooth to porous and rough.Additive manufacturing(AM)offers a high degree of manufacturing freedom,enabling the preparation of predesigned screws that are porous and rough.This paper provides an overview of the problems currently faced by bone screws:long-term loosening and screw breakage.Next,advances in osseointegrated screws are summarized hierarchically(sub-micro,micro,and macro).At the sub-microscale level,we describe surface-modification techniques for enhancing osseointegration.At the micro level,we summarize the micro-design parameters that affect the mechanical and biological properties of porous osseointegrated screws,including porosity,pore size,and pore shape.In addition,we highlight three promising pore shapes:triply periodic minimal surface,auxetic structure with negative Poisson ratio,and the Voronoi structure.At the macro level,we outline the strategies of graded design,gradient design,and topology optimization design to improve the mechanical strength of porous osseointegrated screws.Simultaneously,this paper outlines advances in AM technology for enhancing the mechanical properties of porous osseointegrated screws.AM osseointegrated screws with hierarchical design are expected to provide excellent long-term fixation and the required mechanical strength.

Mid-term outcomes of a kinematically designed cruciate retaining total knee arthroplasty

BACKGROUND Advances in implant material and design have allowed for improvements in total knee arthroplasty(TKA)outcomes.A cruciate retaining(CR)TKA provides the least constraint of TKA designs by preserving the native posterior cruciate ligament.Limited research exists that has examined clinical outcomes or patient reported outcome measures(PROMs)of a large cohort of patients undergoing a CR TKA utilizing a kinematically designed implant.It was hypothesized that the studied CR Knee System would demonstrate favorable outcomes and a clinically significant improvement in pain and functional scores.AIM To assess both short-term and mid-term clinical outcomes and PROMs of a novel CR TKA design.METHODS A retrospective,multi-surgeon study identified 255 knees undergoing a TKA utilizing a kinematically designed CR Knee System(JOURNEY™II CR;Smith and Nephew,Inc.,Memphis,TN)at an urban,academic medical institution between March 2015 and July 2021 with a minimum of two-years of clinical follow-up with an orthopedic surgeon.Patient demographics,surgical information,clinical outcomes,and PROMs data were collected via query of electronic medical records.The PROMs collected in the present study included the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement(KOOS JR)and Patient-Reported Outcomes Measurement Information System(PROMIS■)scores.The significance of improvements in mean PROM scores from preoperative scores to scores collected at six months and two-years postoperatively was analyzed using Independent Samples t-tests.RESULTS Of the 255 patients,65.5%were female,43.8%were White,and patients had an average age of 60.6 years.Primary osteoarthritis(96.9%)was the most common primary diagnosis.The mean surgical time was 105.3 minutes and mean length of stay was 2.1 d with most patients discharged home(92.5%).There were 18 emergency department(ED)visits within 90 d of surgery resulting in a 90 d ED visit rate of 7.1%,including a 2.4%orthopedic-related ED visit rate and a 4.7%non-orthopedic-related ED visit rate.There were three(1.2%)hospital readmissions within 90 d postoperatively.With a mean time to latest follow-up of 3.3 years,four patients(1.6%)required revision,two for arthrofibrosis,one for aseptic femoral loosening,and one for peri-prosthetic joint infection.There were significant improvements in KOOS JR,PROMIS Pain Intensity,PROMIS Pain Interference,PROMIS Mobility,and PROMIS Physical Health from preoperative scores to six month and two-year postoperative scores.CONCLUSION The evaluated implant is an effective,novel design offering excellent outcomes and low complication rates.At a mean follow up of 3.3 years,four patients required revisions,three aseptic and one septic,resulting in an overall implant survival rate of 98.4%and an aseptic survival rate of 98.8%.The results of our study demonstrate the utility of this kinematically designed implant in the setting of primary TKA.

A Blade Altering Toolbox for Automating Rotor Design Optimization

The Blade Altering Toolbox(BAT)described in this paper is a tool designed for fast reconstruction of an altered blade geometry for design optimization purposes.The BAT algorithm is capable of twisting a given rotor’s angle of attack and stretching the chord length along the span of the rotor.Several test cases were run using the BAT’s algorithm.The BAT code’s twisting,stretching,and mesh reconstruction capabilities proved to be able to handle reasonably large geometric alterations to a provided input rotor geometry.The test examples showed that the toolbox’s algorithm could handle any stretching of the blade’s chord as long as the blade remained within the original bounds of the unaltered mesh.The algorithm appears to fail when the net twist angle applied the geometry exceeds approximately 30 degrees,however this limitation is dependent on the initial geometry and other input parameters.Overall,the algorithm is a very powerful tool for automating a design optimization procedure.

Electrolyte Design for Low‑Temperature Li‑Metal Batteries:Challenges and Prospects

Electrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation.To get the most energy storage out of the battery at low temperatures,improvements in electrolyte chemistry need to be coupled with optimized electrode materials and tailored electrolyte/electrode interphases.Herein,this review critically outlines electrolytes’limiting factors,including reduced ionic conductivity,large de-solvation energy,sluggish charge transfer,and slow Li-ion transportation across the electrolyte/electrode interphases,which affect the low-temperature performance of Li-metal batteries.Detailed theoretical derivations that explain the explicit influence of temperature on battery performance are presented to deepen understanding.Emerging improvement strategies from the aspects of electrolyte design and electrolyte/electrode interphase engineering are summarized and rigorously compared.Perspectives on future research are proposed to guide the ongoing exploration for better low-temperature Li-metal batteries.