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。

App Designer辅助教学方法探索与实践——以软件无线电课程为例

软件无线电作为通信学科的必修课程,具有承上启下的重要作用。因课程原理抽象、理论公式复杂,学员在学习过程存在理解掌握难、实践应用难等问题。通过对当前软件无线电教学过程中存在的不足与困难进行分析,提出利用MATLAB App Designer辅助课程教学,从教学准备、课堂互动、自主实验、反馈提升四个方面开展课程教学改革。实践表明,该方法对于提升学员的工程思维、系统思维具有显著效果,同时可以增强学员理论联系实际的能力。

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.

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.

Inverse design of mechanical metamaterial achieving a prescribedconstitutive curve

Besides exhibiting excellent capabilities such as energy absorption,phase-transforming metamaterials offer a vast design space for achieving nonlinear constitutive relations.This is facilitated by switching between different patterns under deformation.However,the related inverse design problem is quite challenging,due to the lack of appropriate mathematical formulation and the convergence issue in the post-buckling analysis of intermediate designs.In this work,periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by eliminating the degrees of freedom in void regions.Furthermore,by exploring the Pareto frontiers between error and cost,an inverse design formulation is proposed for unit cells.This formulation aims to achieve a prescribed constitutive curve and is validated through numerical examples and experimental results.The design approach presented here can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.

Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

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.

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.

Alloy design for laser powder bed fusion additive manufacturing:a critical review

Metal additive manufacturing(AM)has been extensively studied in recent decades.Despite the significant progress achieved in manufacturing complex shapes and structures,challenges such as severe cracking when using existing alloys for laser powder bed fusion(L-PBF)AM have persisted.These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes,overlooking the fast cooling rates,steep temperature gradients and multiple thermal cycles of L-PBF.To address this,there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies.This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF.It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys.The review begins by discussing the features of the L-PBF processes,focusing on rapid solidification and intrinsic heat treatment.Next,the printability of the four main existing alloys(Fe-,Ni-,Al-and Ti-based alloys)is critically assessed,with a comparison of their conventional weldability.It was found that the weldability criteria are not always applicable in estimating printability.Furthermore,the review presents recent advances in alloy development and associated strategies,categorizing them into crack mitigation-oriented,microstructure manipulation-oriented and machine learning-assisted approaches.Lastly,an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

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.

Molecular Mechanism and Molecular Design of Lubricating Oil Antioxidants

To overcome the limitations of traditional experimental“trial and error”methods in lubricant additive design,a new molecular design method based on molecular structure parameters is established here.The molecular mechanism of the antioxidant reaction of hindered phenol,diphenylamine,and alkyl sulfide are studied via molecular simulations.Calculation results show that the strong electron-donating ability and high hydrogen-donating activity of the antioxidant molecule and the low hydrogen-abstracting activity of free radicals formed after dehydrogenation are the internal molecular causes of the shielding of phenol and diphenylamine from scavenging peroxy free radicals,and the strong electron-donating ability is the internal molecular cause of the high activity of thioether in decomposing alkyl hydrogen peroxide.Based on this antioxidant molecular mechanism,a molecular design rule of antioxidant is proposed,namely“high EHOMO,large Q(S),low bond dissociation energy BDE(O—H)and BDE(N—H)”.Two new antioxidants,PAS-I and PAS-II,are designed and prepared by chemical bonding of hindered phenol,diphenylamine,and sulfur atoms.Experimental results show that these antioxidants both have excellent antioxidant effects in lubricating oil,and that PAS-II is the superior antioxidant,consistent with theoretical predictions.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.

双十字路口交通灯控制Stateflow和App Designer仿真

为培养学生能够独立从事自动化相关领域的工程设计、应用研究,解决自动化领域复杂工程实施过程中遇到的关键技术问题,以双十字路口交通灯为研究对象,为计算机辅助建模与仿真类课程设计了一个综合性教学案例。教学案例主要内容为双十字路口Simulink/Stateflow建模与基于Matlab App Designer的交通灯控制可视化仿真。通过此教学案例的实施,明显提高了学生对Matlab的综合应用和工程设计能力,并为后续课程“单片机原理及应用”和“电气控制和PLC”作铺垫。

Rationally designing electrolyte additives for highly improving cyclability of LiNi_(0.5)Mn_(1.5)O_(4)/Graphite cells

High voltage is necessary for high energy lithium-ion batteries but difficult to achieve because of the highly deteriorated cyclability of the batteries.A novel strategy is developed to extend cyclability of a high voltage lithium-ion battery,LiNi_(0.5)Mn_(1.5)O_(4)/Graphite(LNMO/Graphite)cell,which emphasizes a rational design of an electrolyte additive that can effectively construct protective interphases on anode and cathode and highly eliminate the effect of hydrogen fluoride(HF).5-Trifluoromethylpyridine-trime thyl lithium borate(LTFMP-TMB),is synthesized,featuring with multi-functionalities.Its anion TFMPTMB-tends to be enriched on cathode and can be preferentially oxidized yielding TMB and radical TFMP-.Both TMB and radical TFMP can combine HF and thus eliminate the detrimental effect of HF on cathode,while the TMB dragged on cathode thus takes a preferential oxidation and constructs a protective cathode interphase.On the other hand,LTFMP-TMB is preferentially reduced on anode and constructs a protective anode interphase.Consequently,a small amount of LTFMP-TMB(0.2%)in 1.0 M LiPF6in EC/DEC/EMC(3/2/5,wt%)results in a highly improved cyclability of LNMO/Graphite cell,with the capacity retention enhanced from 52%to 80%after 150 cycles at 0.5 C between 3.5 and 4.8 V.The as-developed strategy provides a model of designing electrolyte additives for improving cyclability of high voltage batteries.

OptoGPT: A foundation model for inverse design in optical multilayer thin film structures

Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.

OpenRoads Designer软件在云华水库下游灌区总干渠设计中的应用

利用OpenRoads Designer软件,以云南省云龙县云华水库下游灌区工程为例,提出一套适用于线路工程的正向设计方法。实例证明:可精准统计结构工程量及土方开挖量,提高工作效率,降低手动计算的潜在出错率,为类似工程提供参考。

以色彩为媒介的Design Thinking在艺术设计中的创新实践

色彩作为Design Thinking中的重要媒介,在艺术设计创新实践中具有重要潜力。本文探讨了以色彩为媒介的Design Thinking在艺术设计创新实践中的应用。色彩不仅在Design Thinking中占据重要地位,而且对人类的情绪和行为产生影响,同时在设计中起着至关重要的作用。将Design Thinking引入艺术实践领域,旨在培养艺术创新思维和问题解决能力,强调创新实践和跨学科合作。职业院校在艺术设计创新实践中面临着诸多挑战,但也存在机遇。通过案例分析,本文展示了如何实施基于色彩的Design Thinking艺术设计实践以及这种创新对艺术创新能力的积极影响。

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。

Product family modeling technology for customized cosmetic packaging design based on basic-element theory

Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable in the domain of cosmetic packaging design.Objective:To explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization.Methods:To accomplish this objective,the basic-element theory has been introduced and applied to the design and development system of the product family.Results:By examining the mapping relationships between the demand domain,functional domain,technology domain,and structure domain,four interrelated models have been developed,including the demand model,functional model,technology model,and structure model.Together,these models form the mechanism and methodology of product family modeling,specifically for cosmetic packaging design.Through an analysis of a case study on men’s cosmetic packaging design,the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design,and the design efficiency has been enhanced.Conclusion:The product family modeling technology employs a formalized element as a module configuration design language,permeating throughout the entire development cycle of cosmetic packaging design,thus facilitating a structured and modularized configuration design process for the product family system.The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design,while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

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。