Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Nickel-Nitrogen-Carbon(Ni-N-C)Electrocatalysts Toward CO_(2)electroreduction to CO:Advances,Optimizations,Challenges,and Prosoects

Electrocatalytic reduction of CO_(2)into high energy-density fuels and value-added chemicals under mild conditions can promote the sustainable cycle of carbon and decrease current energy and environmental problems.Constructing electrocatalyst with high activity,selectivity,stability,and low cost is really matter to realize industrial application of electrocatalytic CO_(2)reduction(ECR).Metal-nitrogen-carbon(M-N-C),especially Ni-N-C,display excellent performance,such as nearly 100%CO selectivity,high current density,outstanding tolerance,etc.,which is considered to possess broad application prospects.Based on the current research status,starting from the mechanism of ECR and the existence form of Ni active species,the latest research progress of Ni-N-C electrocatalysts in CO_(2)electroreduction is systematically summarized.An overview is emphatically interpreted on the regulatory strategies for activity optimization over Ni-N-C,including N coordination modulation,vacancy defects construction,morphology design,surface modification,heteroatom activation,and bimetallic cooperation.Finally,some urgent problems and future prospects on designing Ni-N-C catalysts for ECR are discussed.This review aims to provide the guidance for the design and development of Ni-N-C catalysts with practical application.

Interface optimization and defects suppression via Na F introduction enable efficient flexible Sb_(2)Se_(3) thin-film solar cells

Sb_(2)Se_(3) with unique one-dimensional(1D) crystal structure exhibits exceptional deformation tolerance,demonstrating great application potential in flexible devices.However,the power conversion efficiency(PCE) of flexible Sb_(2)Se_(3) photovoltaic devices is temporarily limited by the complicated intrinsic defects and the undesirable contact interfaces.Herein,a high-quality Sb_(2)Se_(3) absorber layer with large crystal grains and benign [hkl] growth orientation can be first prepared on a Mo foil substrate.Then NaF intermediate layer is introduced between Mo and Sb_(2)Se_(3),which can further optimize the growth of Sb_(2)Se_(3)thin film.Moreover,positive Na ion diffusion enables it to dramatically lower barrier height at the back contact interface and passivate harmful defects at both bulk and heterojunction.As a result,the champion substrate structured Mo-foil/Mo/NaF/Sb_(2)Se_(3)/CdS/ITO/Ag flexible thin-film solar cell delivers an obviously higher efficiency of 8.03% and a record open-circuit voltage(V_(OC)) of 0.492 V.This flexible Sb_(2)Se_(3) device also exhibits excellent stability and flexibility to stand large bending radius and multiple bending times,as well as superior weak light photo-response with derived efficiency of 12.60%.This work presents an effective strategy to enhance the flexible Sb_(2)Se_(3) device performance and expand its potential photovoltaic applications.

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

Fiber Bundle Topology Optimization for Surface Flows

This paper presents a topology optimization approach for the surface flows on variable design domains.Via this approach,the matching between the pattern of a surface flow and the 2-manifold used to define the pattern can be optimized,where the 2-manifold is implicitly defined on another fixed 2-manifold named as the base manifold.The fiber bundle topology optimization approach is developed based on the description of the topological structure of the surface flow by using the differential geometry concept of the fiber bundle.The material distribution method is used to achieve the evolution of the pattern of the surface flow.The evolution of the implicit 2-manifold is realized via a homeomorphous map.The design variable of the pattern of the surface flow and that of the implicit 2-manifold are regularized by two sequentially implemented surface-PDE filters.The two surface-PDE filters are coupled,because they are defined on the implicit 2-manifold and base manifold,respectively.The surface Navier-Stokes equations,defined on the implicit 2-manifold,are used to describe the surface flow.The fiber bundle topology optimization problem is analyzed using the continuous adjoint method implemented on the first-order Sobolev space.Several numerical examples have been provided to demonstrate this approach,where the combination of the viscous dissipation and pressure drop is used as the design objective.

Parameters Optimization and Performance Evaluation of the Tuned Inerter Damper for the Seismic Protection of Adjacent Building Structures

In order to improve the seismic performance of adjacent buildings,two types of tuned inerter damper(TID)damping systems for adjacent buildings are proposed,which are composed of springs,inerter devices and dampers in serial or in parallel.The dynamic equations of TID adjacent building damping systems were derived,and the H2 norm criterion was used to optimize and adjust them,so that the system had the optimum damping performance under white noise random excitation.Taking TID frequency ratio and damping ratio as optimization parameters,the optimum analytical solutions of the displacement frequency response of the undamped structure under white noise excitation were obtained.The results showed that compared with the classic TMD,TID could obtain a better damping effect in the adjacent buildings.Comparing the TIDs composed of serial or parallel,it was found that the parallel TIDs had more significant advantages in controlling the peak displacement frequency response,while the H2 norm of the displacement frequency response of the damping system under the coupling of serial TID was smaller.Taking the adjacent building composed of two ten-story frame structures as an example,the displacement and energy collection time history analysis of the adjacent building coupled with the optimum design parameter TIDs were carried out.It was found that TID had a better damping effect in the full-time range compared with the classic TMD.This paper also studied the potential power of TID in adjacent buildings,which can be converted into available power resources during earthquakes.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.

Investigation of influence factors on CO_(2) flowback characteristics and optimization of flowback parameters during CO_(2) dry fracturing in tight gas reservoirs

CO_(2) dry fracturing is a promising alternative method to water fracturing in tight gas reservoirs,especially in water-scarce areas such as the Loess Plateau.The CO_(2) flowback efficiency is a critical factor that affects the final gas production effect.However,there have been few studies focusing on the flowback characteristics after CO_(2) dry fracturing.In this study,an extensive core-to-field scale study was conducted to investigate CO_(2) flowback characteristics and CH_(4) production behavior.Firstly,to investigate the impact of core properties and production conditions on CO_(2) flowback,a series of laboratory experiments at the core scale were conducted.Then,the key factors affecting the flowback were analyzed using the grey correlation method based on field data.Finally,taking the construction parameters of Well S60 as an example,a dual-permeability model was used to characterize the different seepage fields in the matrix and fracture for tight gas reservoirs.The production parameters after CO_(2) dry fracturing were then optimized.Experimental results demonstrate that CO_(2) dry fracturing is more effective than slickwater fracturing,with a 9.2%increase in CH_(4) recovery.The increase in core permeability plays a positive role in improving CH_(4) production and CO_(2) flowback.The soaking process is mainly affected by CO_(2) diffusion,and the soaking time should be controlled within 12 h.Increasing the flowback pressure gradient results in a significant increase in both CH_(4) recovery and CO_(2) flowback efficiency.While,an increase in CO_(2) injection is not conducive to CH_(4) production and CO_(2) flowback.Based on the experimental and field data,the important factors affecting flowback and production were comprehensively and effectively discussed.The results show that permeability is the most important factor,followed by porosity and effective thickness.Considering flowback efficiency and the influence of proppant reflux,the injection volume should be the minimum volume that meets the requirements for generating fractures.The soaking time should be short which is 1 day in this study,and the optimal bottom hole flowback pressure should be set at 10 MPa.This study aims to improve the understanding of CO_(2) dry fracturing in tight gas reservoirs and provide valuable insights for optimizing the process parameters.

基于PGA-2-OPT算法的焊接机器人路径规划

针对白车身焊接过程中焊点数量大、焊接顺序规划凭借经验的问题,提出了将焊接顺序和路径的规划问题转换为TSP问题的方案。传统遗传算法对种群多样化要求较高,后期易陷入局部最优问题;基本单亲遗传算法存在路径交叉问题。文章先对焊接任务采用单亲遗传算法得到相对最优解,然后采用2-OPT领域搜索策略,消除路径存在的交叉问题。文章以宝马某车型右前门板焊接任务为例,对焊接轨迹进行优化。改进后的车门焊接轨迹在同等进化代数的情况下,比传统遗传算法缩短了46%,比原有的单亲遗传算法缩短了29%。实验表明,通过PGA-2-OPT算法可以实现车门板焊接路径的优化,并能很好地消除路径交叉问题。

跨临界CO_(2)循环系统控制优化策略的研究进展

控制策略作为跨临界CO_(2)循环系统的重要组成部分,是保证系统高效节能运行的关键。介绍了系统最优排气压力经验计算和泊金汉π定理的反馈控制、基于梯度追踪和极值寻优的实时在线控制以及基于神经网络的预测控制等,详细分析了系统控制策略的发展历程和未来发展趋势,并总结如下:离线控制建立简单、成本低,但易受到环境因素和系统部件变化的影响而导致控制性能降低;实时在线控制策略可以实时追踪系统最大能源效率对应的排气压力,但由于寻优过程费时较长,导致控制系统的收敛时间过长;模型预测控制系统可以实现实时优化和快速收敛,有着良好的发展前景。结合新能源汽车、建筑供暖、轨道交通、商超冷藏、军工等实际场景对跨临界CO_(2)循环系统控制策略的应用特点和未来发展趋势进行分析,进一步说明了提高控制策略的适用性是未来研究的重要方向,并分析将广义预测控制、强化学习等具有自适应属性的方法应用于跨临界CO_(2)循环系统控制策略的可行性,同时探讨了开发适用于大规模循环系统和储能系统控制策略在我国“双碳”背景下的重要意义。

2-氨基苯甲酰胺衍生物的合成与应用

以2-氨基苯甲腈衍生物(2a~2f)为原料,在氢氧化钾碱性条件下发生水解反应合成了2-氨基苯甲酰胺衍生物(1a~1f),对合成条件进行了优化,通过1 HNMR对产物结构进行了表征。结果表明,在氢氧化钾用量n(KOH)∶n(2a)为4∶1、回流反应14 h的最佳条件下,衍生物1a的收率达到84%;以多聚磷酸为催化剂,衍生物1a与苯甲醛在微波辅助下可以合成7-甲基-2-苯基-2,3-二氢喹唑啉-4(1 H)-酮(3)。

荨麻多糖提取及其对HepG_(2)细胞的降糖作用研究

该文以荨麻为原材料,通过单因素试验和响应面试验优化半仿生提取荨麻多糖工艺。采用α-葡萄糖苷酶和α-淀粉酶抑制实验,测定荨麻多糖的降糖作用。通过构建胰岛素抵抗HepG_(2)细胞(insulin resistant HepG_(2),IR-HepG_(2))模型,测定甘油三酯(triglyceride,TG)、己糖激酶(hexokinase,HK)、丙酮酸激酶(pyruvate kinase,PK)、糖原含量来探讨荨麻多糖的降糖作用。结果表明,当pH_(1)3、pH_(2)8、料液比1∶16(g∶mL)、提取温度65℃、提取时间77 min时,荨麻多糖的得率为7.04%。α-葡萄糖苷酶和α-淀粉酶抑制实验表明,荨麻多糖可通过抑制α-葡萄糖苷酶和α-淀粉酶来表现降糖作用。细胞实验表明,荨麻多糖可通过影响IR-HepG_(2)细胞的葡萄糖消耗来减轻胰岛素抵抗;通过提高HK、PK含量,促进糖原合成,降低TG含量来调节糖脂代谢,从而发挥降糖作用。综上所述,荨麻多糖具有较强降糖活性,为将其应用于糖尿病患者辅助功能食品提供理论依据。

高速S-CO_(2)向心透平几何参数优化及变工况特性分析

透平是热力循环中实现热功转换的核心设备,优化透平几何参数以改善气动性能,是提升系统热效率的关键。该文以面向某船舶主机烟气余热利用的超临界二氧化碳布雷顿循环透平设计参数为基础,给出向心透平主要几何参数,采用耦合CO_(2)真实物性参数的CFX仿真方法,分析几何参数动、静叶包角以及叶轮出口几何角对透平性能的影响规律并进行参数优化,进一步研究非设计工况下的透平运行特性。结果表明:增大静叶包角会使动叶前缘出现扰动涡流并减少余速损失,增大叶轮出口几何角,动叶包角对透平效率的影响规律不变,但效率峰值点会沿动叶包角减小的方向移动;动、静叶包角和叶轮出口几何角分别为49°、21.5°、25°时透平设计点总-静等熵效率高达87.99%,相比优化前,提高0.54个百分点;透平偏离设计点±20%工况下等熵效率大于80%。结果可为超临界二氧化碳高速向心透平设计提供一定参考。

密码子优化和信号肽对猪圆环病毒ORF2基因在毕赤酵母表达中的影响

为了在毕赤酵母中高效表达PCV2ORF2基因,对PCV2ORF2基因编码的187个氨基酸密码子进行毕赤酵母偏爱性优化,利用两步PCR全化学合成优化后的ORF2基因。将优化前后的PCV2ORF2基因片段插入毕赤酵母表达载体pPICZα-C,构建重组质粒pPICZα-C-ORF2和p PICZα-C-ORF2op。将线性化的重组质粒电转化毕赤酵母GS115,并筛选Zeocin抗性重组子。同时,也研究了信号肽对PCV2ORF2表达效率的影响。SDS-PAGE和Western-blot分析结果表明,只有经密码子优化且去除ORF2基因自身信号肽的重组子能够分泌表达PCV2 ORF2蛋白,目的蛋白分子质量约为30 ku,表达的重组ORF2蛋白可被PCV2阳性血清识别,具有生物学活性。

长岭断陷龙凤山气田致密凝析气藏CO_(2)吞吐效果评价

长岭断陷龙凤山气田致密凝析气藏产量递减快,采出程度低,为了减弱近井地带反凝析污染现象和凝析油采出困难的开发难题,通过数值模拟方法和注CO_(2)吞吐实验,分析评价CO_(2)吞吐效果,探讨提高气井产量和气藏采收率的方法。结果表明:与注干气吞吐相比,致密凝析气藏注CO_(2)吞吐更能较为有效地解除反凝析污染;气井反凝析分为反凝析初期(对生产基本无影响)、反凝析加重(对油生产有影响)、反凝析严重(对油气生产影响大)、极端反凝析(几乎不出油)共4个阶段;不同反凝析特征阶段的气井CO_(2)吞吐机理和目的不同,CO_(2)吞吐参数应当相应优化。现场实施表明,注CO_(2)吞吐的5口井在吞吐前处于不同的反凝析阶段,吞吐后取得了快速增加地层能量、解堵近井地带反凝析污染、提高注入能力、增加凝析油和天然气产量等效果。研究成果可为CO_(2)吞吐技术在致密凝析气藏的规模应用提供了经验。

多种构型超临界CO_(2)循环热力学解构分析与参数优化

新型超临界CO_(2)(S-CO_(2))循环可通过流程改良提高效率,构型复杂多样。为了直观阐明各种流程改良措施对循环效率提升的作用机制,本文将预压缩、后压缩、再压缩、间冷、再热等五种构型的S-CO_(2)循环解构为若干热功转换过程,建立各解构过程与循环效率之间的关联方程,进而开展流程参数优化。研究结果表明,预压缩、后压缩和再压缩方案均是通过增加压缩耗功,减少吸热量实现循环效率提升,其中再压缩方案效果最优,再压缩流量优化后循环效率提高5.1%;采用部分间冷方案,可有效降低压缩功耗,同时避免高品位热量贬值,间冷压力优化后循环效率提高2.2%;再热方案在不改变压缩耗功的前提下,增加透平出功,再热压力优化后循环效率提高1.9%;最后,循环联用再压缩、间冷和再热三种节能措施,可使效率提高9.3%。

基于响应曲面法优化氮掺杂活性炭的制备和CO_(2)吸附性能研究

氮掺杂活性炭制备过程中影响其性能的因素极其复杂,单一因素的定性分析无法满足对制备条件的准确调控,而多因素耦合量化分析可以弥补不足。以煤焦油沥青为原料,采用KOH活化法,与尿素共热解制备氮掺杂活性炭,通过响应曲面法(RSM)进行建模,以量化分析多因素耦合作用对活性炭产率和不同温度下(25℃,50℃和75℃)对CO_(2)吸附性能的影响,同时优化制备工艺参数,并且在此实验设计基础上利用线性拟合方法预测不同温度下CO_(2)吸附量的相关性。结果表明:对活性炭产率的影响由大到小的单因素依次为掺氮比(尿素与煤沥青的质量比)、活化温度、碱碳比(KOH与煤沥青的质量比)、活化时间,活化温度和活化时间的耦合作用影响最显著,碱碳比是影响活性炭CO_(2)吸附量最为关键的因素,但随吸附温度升高其他因素影响显著性增强;不同温度下CO_(2)吸附量可以进行相互预测,且相邻温度下CO_(2)吸附量之间的预测效果更好;得到的最佳制备条件为活化温度650℃、活化时间1.25 h、碱碳比2.5、掺氮比0.3,相应的活性炭产率可达59.316%,在25℃,50℃和75℃下CO_(2)吸附量分别为3.474 mmol/g,2.355 mmol/g,1.358 mmol/g。

AgInS_(2)量子点调控及其在WLED应用上的研究进展

AgInS_(2)量子点是一种有趣的材料,其近红外带隙范围从1.87到1.98 eV,作为直接带隙半导体,它能通过调节Ag/In比例来调节带隙,它还具有较宽的PL峰和较大的Stokes位移,同时低毒环保的特性也使它成为有望替代含Cd、Hg和Pb等重金属元素的二元量子点的理想材料,在光电器件领域尤其在发光二极管中有着广泛的应用前景。本文详细阐述了AgInS_(2)量子点晶体结构和发光机制,总结了其合成方法的特点并综述了通过壳体工程和掺杂的方法对AgInS_(2)量子点进行调控的策略,最后介绍了该类量子点在发光二极管方面的应用进展。

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.