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.

Microstructure and Mechanical Properties of AZ31 Alloys Processed by Residual Heat Rolling

To produce high strength and ductility Mg alloys with high productivity and low energy consumption,the residual heat rolling (RHR) process was initially proposed.The microstucture and mechanical properties of AZ31 processed by RHR were investigated by optical micrscopy (OM),electron backscatter diffraction (EBSD),and electron universal testing machine.The yield strength (YS),ultimate tensile strength (UTS),and elongation to failure of RHRed AZ31 sheet were 194 MPa,311 MPa,and 22%,respectively.The RHRed AZ31 alloys after annealing have very fine and homogeneous grains.The symmetrical rolled (SR) and RHRed AZ31 exhibit typical {0002} basal textures.The RHRed AZ31 has double-peak basal texture distribution.The basal poles of RHRed AZ31 split from normal direction (ND) to rolling direction (RD).There are few hard orientation distributions on the basal slip and more soft orientation distributions on the prismatic slip in the RHRed AZ31 sheets than those in the SRed AZ31sheets.

Influence of Texture on Hall-Petch Slope in Hot-extruded AZ31 Magnesium Alloys

To study the influence of texture on Hall-Petch slope,the Hall-Petch relationships of two hot-extruded AZ31 alloys(A117 and A189)were determined by the measurement of yield strength at room temperature.To determine the real texture difference,the texture changes were facilitated by the variation of extrusion ratio and demonstrated by the actual grain orientation distributions.The A117 alloys exhibited higher Hall-Petch slope in comparison of A187 in the condition of tension and compression,because A117 alloys had lower volume fraction of grains in the range of basal pole 75°-90°.The Hall-Petch slope differences for both alloys were analyzed and discussed by boundary obstacle against deformation propagation in grains with various orientation.Due to the higher volume fraction of grains in the range of basal pole 75°-90°in A189,the effect of hindering dislocation slip or twinning propagation by grains boundary was weaker than of A117.

Thermoelectric signature of Majorana zero modes in a T-typed double-quantum-dot structure

The thermoelectric effect of the system is theoretically investigated,by coupling Majorana zero mode to the T-typed double-quantum-dot-structure in different ways.It is found that when a single Majorana zero mode is coupled to one of the quantum dots(QDs),the thermoelectric efficiency is suppressed due to the leakage of Majorana zero modes into the QDs.When the Majorana zero mode is coupled to QD2,the suppression of the thermoelectric efficiency is more serious than that of QD1.Furthermore,when two Majorana zero modes are introduced simultaneously,suppression of the thermoelectric effect still takes place.We believe that such results can be candidates for the detection of Majorana bound states and help us understand the role of Majorana zero mode in thermoelectricity.

Suppression of leakage effect of Majorana bound states in the T-shaped quantum-dot structure

We theoretically study the transport properties in the T-shaped double-quantum-dot structure,by considering the dot in the main channel to be coupled to the Majorana bound state(MBS)at one end of the topological superconducting nanowire.It is found that the side-coupled dot governs the effect of the MBS on the transport behavior.When its level is consistent with the energy zero point,the MBS contributes little to the conductance spectrum.Otherwise,the linear conductance exhibits notable changes according to the inter-MBS coupling manners.In the absence of inter-MBS coupling,the linear conductance value keeps equal to e^(2)/2 h when the level of the side-coupled dot departs from the energy zero point.However,the linear conductance is always analogous to the MBS-absent case once the inter-MBS coupling comes into play.These findings provide new information about the leakage effect of MBSs in quantum-dot structures.

Effects of Orthogonal Heat Treatment on Microstructure and Mechanical Properties of GN9 Ferritic/Martensitic Steel

Microstructure and mechanical properties of GN9 Ferritic/Martensitic steel for sodium-cooled fast reactors have been investigated through orthogonal design and analysis.Scanning electron microscopy(SEM),transmission electron microscopy(TEM),differential scanning calorimeter(DSC),tensile and impact tests were used to evaluate the heat treatment parameters on yield strength,elongation and ductile-to-brittle transition temperature(DBTT).The results indicate that the microstructures of GN9 steel after orthogonal heat treatments consist of tempered martensite,M23C6,MX carbides and MX carbonitrides.The average prior austenite grains increase and the lath width decreases with the austenitizing temperature increasing from 1000°C to 1080°C.Tempering temperature is the most important factor that influences the dislocation evolution,yield strength and elongation compared with austenitizing tempera-ture and cooling methods.Austenitizing temperature,tempering temperature and cooling methods show interactive effects on DBTT.Carbide morphology and distribution,which is influenced by austenitizing and tempering tempera-tures,is the critical microstructural factor that influences the Charpy impact energy and DBTT.Based on the orthogo-nal design and microstructural analysis,the optimal heat treatment of GN9 steel is austenitizing at 1000°C for 0.5 h followed by air cooling and tempering at 760°C for 1.5 h.

Synthesis of 2-Amino-4,6-dimethoxypyrimidine(ADM)

Using malononitrile as the raw material, a novel synthesis route of 2-amino-4,6-dimethoxypyrimidine(ADM) was put forward, in order to solve the instability in first step reaction of industrial synthesis. The influence of reactant ratio, temperature and time on product quality and yield was explored. The results showed that ADM was successfully prepared through acetyl chloride route, the yield was higher than that of industrial production process, and the process stability was good, with the product purity over 99%. Therefore, the new synthesis route is suitable for industrial production.

Synthesis and Crystal Structure of an Inorganic-organic Hybrid Compound Containing a New Polymolybdate Cluster {Mo(12)}

A new low-nuclear polymolybdate cluster [Cu_4~Ⅱ（opr）_2（Mo_（12）O_（40））]_n·2n H_2 O was successfully isolated by the introduction of flexible multidentate 1,1A-（1,3-propanediyl）bis[2-（2-pyridyl）benzimidazole ligand（opr）,（NH_4）_6Mo_7O_（24）·4H_2O,and copper（Ⅱ） ions under hydrothermal conditions. Single-crystal X-ray analysis reveals that the complex crystallizes in the P2_1/n space group with a = 13.8437（5）,b =19.3953（6）,c =15.7681（6） A,β =112.698（4）o,V = 3905.9（2） A^3,Z = 4,D_c = 2.502 g/cm^3,μ = 3.020 mm^-1,F（000） = 2824,the final R = 0.0352,wR = 0.0523 and S = 0.783 for 7111 observed reflections with I 〉 2σ（I）. The structure of the compound contains a new type of low-nuclear（Mo_（12）O_（40））^8-polymolybdate cluster. These dodecamolybdate anions are linked together through sharing edges to form a 1D molybdenum oxide chain. Its photocatalytic performance for methylene blue（MB） was also studied under UV irradiation.

Multi-objective reliability optimization design of high-speed heavy-duty gears based on APCK-SORA model

For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status.Firstly,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status.The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

Study on Flow Field Optimization of Flow Distribution Disk of Double-unloaded Groove Piston Pump

By referring to all kinds of research data and summarizing the analysis and research of various kinds of distribution disks,a new type of distribution disc with double V-shaped unloading slots is designed again.Then,the new designed disk is loaded into the piston pump by CFD software to simulate.Finally,through simulation,it is concluded that the width of the triangle slot of the distribution plate is about 10°,the slope angle is about 12°,and the diameter of the unloading hole is about 0.8mm.When the diameter of the perforated hole is about 1.5mm,the flow curve of the unloading hole is the smoother.Pressure shock and noise are also minimized at this time.Draw By improving the damping tank,the flow noise and cavitation change are smaller than before.

Simulation Analysis of Extrusion Process of Equilateral L-shaped Aluminum Profiles Based on Deform-3D

The extrusion deformation process of L-shaped aluminum profiles was numerically simulated using the finite element program Deform-3D.The simulation findings revealed that the deformation of the profiles was mostly caused by unequal material flow velocity,which resulted in the profiles bending.Determine the impact of extrusion parameters on the bending deformation of the profile after studying various parameters that may affect the material flow mode(hole position,extrusion speed).

Research and Analysis of Shear Performance of Inclined Blade Shears Based on ANSYS

Nowadays,enterprise customers have higher and higher requirements for steel plate quality,and the performance research of shearing machine has been widely paid attention to.In this paper,the finite element analysis software ANSYS is used to analyze and verify the key parts of the downcut inclined blade shears.Through continuous analysis and improvement of the model,the weak points areoptimized to improve the shear performance andproduction efficiency of the shears.

Design and Optimization of New Smoke Exhaust Pipe for Mining Trucks

In order to improve the maintenance efficiency,extend the use time,ensure that the exhaust emission meets the standard,forthe 830E truck heating bucket exhaust pipe design defects,the current single smoke exhaust system is transformed into atime period,convertible smoke exhaust system.After the transformation,it can not only realize the side row to prevent direct corrosion of the box bucket in summer,but also realize the heating of the box bucket at low temperature in winter to prevent snow and ice and frozen blocks from sticking to the box bucket and the materials transported.After the transformation can save a lot of manpower,material resources,financial resources,improve the service life.

Design of Damping Damping Design of New Boring Head Drive Structure

This paper takes the boring head of 20 roll mill as the research object,optimizes the vibration inside the boring head through a differential damping structure,and then conducts the 3 d model inside the boring head through SolidWorks,and checks the interference of the boring head model.Finally,Ansys workbench finite element analysis software is used to analyze and verify the vibration damping characteristics of the differential damping structure.

Design of High Precision CNC Vertical Lathe Turning Tool Holder

A large aspect ratio vibration reducing tool holder based on passive damping vibration reduction technology is designed to solve the vibration problem that occurs in deep hole machining of vertical lathes.A dynamic model of passive damping vibration reduction tool holder was established,and the optimal damping ratio,optimal frequency ratio,and maximum relative amplitude were derived.Modal analysis of the passive damping vibrationreduction tool holder was conducted using software.The results showed that the maximum response amplitude of the passive damping vibration reduction tool holder decreased significantly compared to the original one.

Fault monitoring and diagnosis of motorized spindle in five-axis Machining Center based on CNN-SVM-PSO

A spindle fault diagnosis method based on CNN-SVM optimized by particle swarm algorithm(PSO)is proposed to address the problems of high failure rate of electric spindles of high precision CNC machine tools,while manual fault diagnosis is a tedious task and low efficiency.The model uses a convolutional neural network(CNN)model as a deep feature miner and a support vector machine(SVM)as a fault state classifier.Taking the electric spindle of a five-axis machining centre as the experimental research object,the model classifies and predicts four labelled states:normal state of the electric spindle,loose state of the rotating shaft and coupling,eccentric state of the motor air gap and damaged state of the bearing and rolling body,while introducing a particle swarm algorithm(PSO)is introduced to optimize the hyperparameters in the model to improve the prediction effect.The results show that the proposed hybrid PSO-CNN-SVM model is able to monitor and diagnose the electric spindle failure of a 5-axis machining centre with an accuracy of 99.33%.In comparison with the BP model,SVM model,CNN model and CNN-SVM model,the accuracy of the model increased by 10%,6%,4%and 2%respectively,which shows that the fault diagnosis model proposed in the paper can monitor the operation status of the electric spindle more effectively and diagnose the type of electric spindle fault,so as to improve the maintenance strategy.

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold,a CNN-LSTM tool wear prediction model based on particle swarm algorithm(PSO)optimization with multi-channel feature fusion is proposed.Firstly,the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction;secondly,the time-domain,frequency-domain and time-frequency-domain features of each channel signal are extracted,and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features;finally,the data set is input to the CNN-LSTM-PSO model for training and testing.The results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848,a root mean square error RMSE value of 0.7281,and a coefficient of determination R2 value of 0.9964;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,its tool wear prediction accuracy improved by 7.56%,2.60%,2.98%,and 1.63%,respectively.

Hypereutectic Al-Si Matrix Composites Prepared by In Situ Fe_(2)O_(3)/Al System

Al_(2)O_(3) particles reinforced hypereutectic Al-Si composites were prepared by in situ Fe_(2)O_(3)/Al reaction system.The thermodynamic analysis and microstructure evolution were investigated by differential scanning calorimetry,optical microscope,scanning electronic microscopy and transmission electron microscope.Results show that the reaction between Fe_(2)O_(3) and Al is spontaneous which can be separated into two steps at different temperatures.The in situ Al_(2)O_(3) particles in nano size distribute on the Al matrix accompanied with long needle-shapedβFe-rich intermetallic phase.With different content of Mn addition,βphase can be modified toα-Al15(Mn,Fe)3Si2 andδ-Al4(Fe,Mn)Si2.Both tensile strength and elongation results at room temperature and 300℃reveal that the optimal Fe-rich intermetallic phase is finer Chinese-script and polyhedralαphase with a Mn/Fe mass ratio 0.5 for the composites.Both in situ Al_(2)O_(3) particles andα-Fe phases contribute to the properties improvement of the composites。