Thermodynamic,Economic,and Environmental Analyses and Multi-Objective Optimization of Dual-Pressure Organic Rankine Cycle System with Dual-Stage Ejector

A novel dual-pressure organic Rankine cycle system(DPORC)with a dual-stage ejector(DE-DPORC)is proposed.The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the highpressure expander to pressurize a large quantity of exhaust gas to performwork for the low-pressure expander.This innovative approach addresses condensing pressure limitations,reduces power consumption during pressurization,minimizes heat loss,and enhances the utilization efficiency of waste heat steam.A thermodynamic model is developed with net output work,thermal efficiency,and exergy efficiency(W_(net,ηt,ηex))as evaluation criteria,an economicmodel is established with levelized energy cost(LEC)as evaluation index,anenvironmentalmodel is created with annual equivalent carbon dioxide emission reduction(AER)as evaluation parameter.A comprehensive analysis is conducted on the impact of heat source temperature(T_(S,in)),evaporation temperature(T_(2)),entrainment ratio(E_(r1),E_(r2)),and working fluid pressure(P_(5),P_(6))on system performance.It compares the comprehensive performance of the DE-DPORC system with that of the DPORC system at TS,in of 433.15 K and T2 of 378.15 K.Furthermore,multi-objective optimization using the dragonfly algorithm is performed to determine optimal working conditions for the DE-DPORC system through the TOPSIS method.The findings indicate that the DEDPORC system exhibits a 5.34%increase inWnet andηex,a 58.06%increase inηt,a 5.61%increase in AER,and a reduction of 47.67%and 13.51%in the heat dissipation of the condenser andLEC,compared to theDPORCsystem,highlighting the advantages of this enhanced system.The optimal operating conditions are TS,in=426.74 K,T_(2)=389.37 K,E_(r1)=1.33,E_(r2)=3.17,P_(5)=0.39 MPa,P_(6)=1.32 MPa,which offer valuable technical support for engineering applications;however,they are approaching the peak thermodynamic and environmental performance while falling short of the highest economic performance.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm

A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are：（i） maximizing drilling depth,（ii） minimizing drilling time and （iii） minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are：（i） equivalent circulation mud density,（ii） drill bit rotation,（iii） weight on bit and （iv） Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth.

Multi-objective Optimization of Geothermal Extraction from the Enhanced Geothermal System in Qiabuqia Geothermal Field, Gonghe Basin

A geothermal demonstration exploitation area will be established in the Enhanced Geothermal System of the Qiabuqia field, Gonghe Basin, Qinghai–Xizang Plateau in China. Selection of operational parameters for geothermal field extraction is thus of great significance to realize the best production performance. A novel integrated method of finite element and multi-objective optimization has been employed to obtain the optimal scheme for thermal extraction from the Gonghe Basin. A thermal-hydraulic-mechanical coupling model(THM) is established to analyze the thermal performance. From this it has been found that there exists a contraction among different heat extraction indexes. Parametric study indicates that injection mass rate(Q_(in)) is the most sensitive parameter to the heat extraction, followed by well spacing(WS) and injection temperature(T_(in)). The least sensitive parameter is production pressure(p_(out)). The optimal combination of operational parameters acquired is such that(T_(in), p_(out), Q_(in), WS) equals(72.72°C, 30.56 MPa, 18.32 kg/s, 327.82 m). Results indicate that the maximum electrical power is 1.41 MW for the optimal case over 20 years. The thermal break has been relieved and the pressure difference reduced by 8 MPa compared with the base case. The optimal case would extract 50% more energy than that of a previous case and the outcome will provide a remarkable reference for the construction of Gonghe project.

Energy Optimization of the Fin/Rudder Roll Stabilization System Based on the Multi-objective Genetic Algorithm （MOGA）

Energy optimization is one of the key problems for ship roll reduction systems in the last decade. According to the nonlinear characteristics of ship motion, the four degrees of freedom nonlinear model of Fin/Rudder roll stabilization can be established. This paper analyzes energy consumption caused by overcoming the resistance and the yaw, which is added to the fin/rudder roll stabilization system as new performance index. In order to achieve the purpose of the roll reduction, ship course keeping and energy optimization, the self-tuning PID controller based on the multi-objective genetic algorithm （MOGA） method is used to optimize performance index. In addition, random weight coefficient is adopted to build a multi-objective genetic algorithm optimization model. The objective function is improved so that the objective function can be normalized to a constant level. Simulation results showed that the control method based on MOGA, compared with the traditional control method, not only improves the efficiency of roll stabilization and yaw control precision, but also optimizes the energy of the system. The proposed methodology can get a better performance at different sea states.

Optimization of technical measures for improving high-temperature performance of asphalt-rubber mixture

Asphalt-rubber pavements often become dam-aged in high-temperature regions and appear rutted or wavy, and experience slippage. To improve the high-temperature performance of the asphalt-rubber mixture, technical measurements, such as, the optimal adjustment of gradation, technique of composite modification, and control of compaction were investigated. An optimal adjustment of aggregate gradation based on stone matrix asphalt improves the high-temperature stability of the asphaltrubber mixture significantly. Through composite modifi- cation, the effect of asphalt-rubber modification was enhanced, and the dynamic stability and relative defor- mation indices of the asphalt-rubber mixture were improved significantly. Furthermore, compaction parame- ters had a significant influence on the high-temperature stability of the asphalt-rubber mixture. The rolling times for compacting the asphalt-rubber mixture should be controlled to within 18-20 round-trips at a molding temperature at 180℃; if the rolling time is a 12 round-trip, the compaction temperature of the asphalt-rubber mixture should be controlled between 180 and 190℃.

Integrated Performance Optimization of Satellite Communications Constellation

In order to attain better communications performance rather than just expand coverage and save system cost,criteria related to the communications quality and capacity are extracted and revised to build an integrated performance metric system which aims to effectively guide the satellite communications constellation design.These performance metrics together with the system cost serve as the multiple objectives whilst the coverage requirement is regarded as the basic constraint in the optimization of the constellation configuration design applying a revised NSGA-II algorithm.The Pareto hyper-volumes lead to the best configuration schemes which achieve better integrated system performance compared with the conventional design results based merely on coverage and cost.

Satellite Constellation Design with Multi-Objective Genetic Algorithm for Regional Terrestrial Satellite Network

Constellations design for regional terrestrial-satellite network can strengthen the coverage for incomplete terrestrial cellular network. In this paper, a regional satellite constellation design scheme with multiple feature points and multiple optimization indicators is proposed by comprehensively considering multi-objective optimization and genetic algorithm, and "the Belt and Road" model is presented in the way of dividing over 70 nations into three regular target areas. Following this, we formulate the optimization model and devise a multi-objective genetic algorithm suited for the regional area with the coverage rate under simulating, computing and determining. Meanwhile, the total number of satellites in the constellation is reduced by calculating the ratio of actual coverage of a single-orbit constellation and the area of targets. Moreover, the constellations' performances of the proposed scheme are investigated with the connection of C++ and Satellite Tool Kit(STK). Simulation results show that the designed satellite constellations can achieve a good coverage of the target areas.

Multi-objective optimization of thermochromic glazing based on daylight and energy performance evaluation

Many efforts have been detected to investigate thermochromic(TC)glazing for improving building energy saving,while only a few approaches for daylight performance analysis.In this study,the performance of TC glazing is investigated based on multi-objective optimization for minimizing energy demand while maximizing daylight availability.The effects of five parameters including transition temperature,solar transmittance in clear state,solar transmittance modulation ability,luminous transmittance in clear state,and luminous modulation ability on the building energy consumption and useful daylighting illuminance(UDI_(300-3000))are examined.Linear Programming Technique for Multi-dimensional Analysis of Preference(LINMAP)is used for the decision-making of Pareto frontier.This research aims to explore the ideal thermochromic glazing by considering the daylight and energy performance of a typical office room,taking the weather condition of Xiamen,China as an example.Although it is impossible to achieve both optimal values of energy consumption and UDI_(300-3000)simultaneously,the proposed multi-objective optimization method could still provide low energy consumption with sufficient daylight.The non-dominated sorting of Pareto optimal solution(POS)demonstrated that the optimum building energy consumption and UDI_(300-3000)for single glazed windows are 46.64 kWh/m^(2)and 70.92%,respectively,while the values for double glazed windows are 44.40 kWh/m^(2)and 71.88%,respectively.The selected hypothetical TC windows can improve the building energy and daylighting performance simultaneously when compared with traditional clear single and double glazed windows.The presented framework provides a multi-objective optimization method to determine the most suitable TC glazing technologies for designers and architects during the design and retrofit procedure.

Multi-objective evolutionary optimization and thermodynamics performance assessment of a novel time-dependent solar Li-Br absorption refrigeration cycle

This research paper aims to perform dynamics analysis,3E assessment including energy,exergy,exergoeconomic,and the multiobjective evolutionary optimization on a novel solar Li-Br absorption refrigeration cycle.The research is time-dependent,owing to solar radiation variability during different timelines.Theoretically,all the necessary thermodynamic,energy,and exergy equations are applied initially.This is followed by the thermoeconomic analysis,which takes place after defining the designing variables during the thermoeconomic optimization process and is presented together with the economic relations of the system and its thermoeconomic characteristics.Furthermore,the sensitivity analysis is undertaken,the source of system inefficiency is determined,the multi-objective evolutionary optimization of the whole system is carried out,and the optimal values are compared with the primary stage.Engineering Equation Solver(EES)software has been used to accomplish comprehensive analyses.As part of the validation process,the results of the research are compared with those published previously and are found to be relatively consistent.

Influence of Design Reference on Tracking Performance of Feedback Control

In this paper, we present an investigation on the tracking performances of feedback control as a function of reference signals. We use multi-objective optimal designs of feedback controls as a fair basis for comparing different control designs, and examine step, ramp, and periodic signals at various frequencies. Through comparing the tracking performances of controls designed with different reference signals,we find that the controls designed with ramp signals perform better in tracking step and ramp references than those designed with step signals. To track periodic signals, we find that the controls designed with periodic signals at the same frequency generally provide the best performance, and those designed with step and ramp signals perform comparably.

Numerical Simulation and Optimization of Perforated Tube Trolley in Circular Cooler

Three symmetrically perforated tubes were arranged in the circular cooler trolley as auxiliary cooling inlet to improve the cooling performance of the sintered body during the production process. Fluent 15.0 has been used to simulate the process;the study shows that the perforated tube structure trolley has changed the temperature field within the sintering area, thereby improving the sintering area of the cooling effect and uniformity, also greatly reducing the cooling time. Compared with the traditional trolley, the best structure of the porous tube trolley has reduced 41% cooling time and increased 50% waste heat recovery.

An Experimental Study on the Performance of Storage Pulverizing System after Renovation of Importing Hot Air

A thermal power plant of Sinopec has 9 boilers, which generally have problems of high exhaust gas temperature and high flying ash carbon content. In order to improve the adaptability of coals, the stability of coal powder ignition, the burn-off rate of pulverized coals and the boiler efficiency, a series of renovation projects about importing hot air into mill exhauster are proposed. For the sake of verifying the renovation effects, an efficiency performance test is conducted on the renovated #5 boiler. The test result shows that the boiler heat efficiency has improved by 0.4% and it operates more safely and reliably after the renovation. At last, this paper recommends an optimized operation mode.

Analysis and Optimization of Inside-Cushion Structure in High-Speed Hydraulic Cylinders

An inside-cushion structure with sidestep and taper-shaped plungers is studied to address the problems of high impact and vibration in high-speed hydraulic cylinders.First,the three stages of cushion processes are discussed according to the varying flow area as the piston moves.Then,to establish a precise mathematical model,the states of the flow field are estimated in terms of the Reynolds number.Accordingly,the simulation model parameterized against measured data is developed and verified by experiment.Last,the average velocity,peak cushion pressure,and terminal velocity are defined to evaluate cushion performance.According to these optimized objectives,the non-linear programming by quadratic Lagrange(NLPQL)algorithm is applied to optimize the structure parameters.The optimization results indicate that the peak cushion pressure is reduced by 28%and terminal velocity is reduced by 21%without reduction of average velocity.

Optimization Design and Analysis of a Hybrid Permanent Magnet Flux-Switching Motor with Compound Rotor Configuration

This paper proposes a type of flux-switching permanent magnet(FSPM)motor,where the design concept of the hybrid permanent magnets(HPM)and the compound rotor are incorporated into the motor design.In such design,the proposed motor can not only realize the significant reduction of NdFeB volume,but also artfully convert external magnetic flux leakage into the air-gap field to achieve competitive torque density and desirable PM usage efficiency.For extensive investigation,two topologies of the HPM are designed and analyzed for the proposed motor,which consist of the parallel-magnetic-hybrid(PMH)mode and serial-magnetic-hybrid(SMH)mode.To fully exploit the potential advantages of the proposed motor,a multi-objective optimization design is conducted,where the response surface method(RSM)and sequential non-linear programming(SNP)method are purposely utilized.After optimization,the electromagnetic performances of the motor with PMH mode and SMH mode are evaluated and compared by using finite element method(FEM),which include the back-EMF,cogging torque,output torque,and so on.Furthermore,the partial demagnetization of the ferrite PM is also investigated in the paper.Finally,the theoretical analysis and simulation study verify the effectiveness of the proposed motor and corresponding optimization design.

Multi-objective robust controller synthesis for discrete-time systems with convex polytopic uncertain domain

Multi-objective robust state-feedback controller synthesis problems for linear discrete-time uncertain systems are addressed. Based on parameter-dependent Lyapunov functions, the Gl2 and GH2 norm expressed in terms of LMI (Linear Matrix Inequality) characterizations are further generalized to cope with the robust analysis for convex polytopic uncertain system. Robust state-feedback controller synthesis conditions are also derived for this class of uncertain systems. Using the above results, multi-objective state-feedback controller synthesis procedures which involve the LMI optimization technique are developed and less conservative than the existing one. An illustrative example verified the validity of the approach.

Conceptual design and heat transfer performance of a flat-tile water-cooled divertor target

The divertor target components for the Chinese fusion engineering test reactor(CFETR)and the future experimental advanced superconducting tokamak(EAST)need to remove a heat flux of up to20 MW m-2.In view of such a high heat flux removal requirement,this study proposes a conceptual design for a flat-tile divertor target based on explosive welding and brazing technology.Rectangular water-cooled channels with a special thermal transfer structure(TTS)are designed in the heat sink to improve the flat-tile divertor target’s heat transfer performance(HTP).The parametric design and optimization methods are applied to study the influence of the TTS variation parameters,including height(H),width(W*),thickness(T),and spacing(L),on the HTP.The research results show that the flat-tile divertor target’s HTP is sensitive to the TTS parameter changes,and the sensitivity is T>L>W*>H.The HTP first increases and then decreases with the increase of T,L,and W*and gradually increases with the increase of H.The optimal design parameters are as follows:H=5.5 mm,W*=25.8 mm,T=2.2 mm,and L=9.7 mm.The HTP of the optimized flat-tile divertor target at different flow speeds and tungsten tile thicknesses is studied using the numerical simulation method.A flat-tile divertor mock-up is developed according to the optimized parameters.In addition,high heat flux(HHF)tests are performed on an electron beam facility to further investigate the mock-up HTP.The numerical simulation calculation results show that the optimized flat-tile divertor target has great potential for handling the steady-state heat load of 20 MW m-2under the tungsten tile thickness<5 mm and the flow speed7 m s^(-1).The heat transfer efficiency of the flat-tile divertor target with rectangular cooling channels improves by13%and30%compared to that of the flat-tile divertor target with circular cooling channels and the ITER-like monoblock,respectively.The HHF tests indicate that the flat-tile divertor mock-up can successfully withstand 1000 cycles of20 MW m-2of heat load without visible deformation,damage,and HTP degradation.The surface temperature of the flat-tile divertor mock-up at the 1000th cycle is only930℃.The flat-tile divertor target’s HTP is greatly improved by the parametric design and optimization method,and is better than the ITER-like monoblock and the flat-tile mock-up for the WEST divertor.This conceptual design is currently being applied to the engineering design of the CFETR and EAST flat-tile divertors.

基于多目标演化优化的SVM对抗仿真测试算法

机器学习通常从数据中挖掘潜在的模式与规则,容易受到数据的影响而产生诸如过拟合、欠拟合等现象,进而影响学习模型的泛化与鲁棒性能。从对抗仿真测试的角度考察SVM可能存在的脆弱不稳定性,采用的对抗仿真策略是通过选择性地污染训练样本标签,模拟攻击SVM分类器使其性能退化,以测试其对训练样本的依赖性。为探究SVM分类器在不同样本组合攻击下的性能损失上限,设计了最小攻击代价-最大攻击成效这一对矛盾目标,构建了SVM仿真测试的多目标优化模型。该模型本质上是一种典型的多目标组合优化问题,可采用适当的多目标演化算法求解目标间的一组非支配解集,揭示分类器在不同样本组合攻击下的分类性能表现。在人工及真实数据集上的仿真对比实验结果表明:所提方法能够一次性生成不同攻击水平下的最优攻击样本组合,取得最大的分类性能损失,更能全面测试SVM分类器性能的稳定性。