Hot-deformation kinetics analysis and extrusion parameter optimization of a dilute rare-earth free magnesium alloy

The fundamental research on thermo-mechanical conditions provides an experimental basis for high-performance Mg-Al-Ca-Mn alloys.However, there is a lack of systematical investigation for this series alloys on the hot-deformation kinetics and extrusion parameter optimization. Here, the flow behavior, constitutive model, dynamic recrystallization(DRX) kinetic model and processing map of a dilute rare-earth free Mg-1.3Al-0.4Ca-0.4Mn(AXM100, wt.%) alloy were studied under different hot-compressive conditions. In addition, the extrusion parameter optimization of this alloy was performed based on the hot-processing map. The results showed that the conventional Arrhenius-type strain-related constitutive model only worked well for the flow curves at high temperatures and low strain rates. In comparison, using the machine learning assisted model(support vector regression, SVR) could effectively improve the accuracy between the predicted and experimental values. The DRX kinetic model was established, and a typical necklace-shaped structure preferentially occurred at the original grain boundaries and the second phases. The DRX nucleation weakened the texture intensity, and the further growth caused the more scattered basal texture. The hot-processing maps at different strains were also measured and the optimal hot-processing range could be confirmed at the deformation temperatures of 600~723 K and the strain rates of 0.018~0.563 s^(-1). Based on the optimum hot-processing range, a suitable extrusion parameter was considered as 603 K and 0.1 mm/s and the as-extruded alloy in this parameter exhibited a good strength-ductility synergy(yield strength of ~ 232.1 MPa, ultimate strength of ~ 278.2 MPa and elongation-to-failure of ~ 20.1%). Finally, the strengthening-plasticizing mechanisms and the relationships between the DRXed grain size, yield strength and extrusion parameters were analyzed.

Fracture propagation,proppant transport and parameter optimization of multi-well pad fracturing treatment

This paper establishes a 3D multi-well pad fracturing numerical model coupled with fracture propagation and proppant migration based on the displacement discontinuity method and Eulerian-Eulerian frameworks,and the fracture propagation and proppant distribution during multi-well fracturing are investigated by taking the actual multi-well pad parameters as an example.Fracture initiation and propagation during multi-well pad fracturing are jointly affected by a variety of stress interference mechanisms such as inter-cluster,inter-stage,and inter-well,and the fracture extension is unbalanced among clusters,asymmetric on both wings,and dipping at heels.Due to the significant influence of fracture morphology and width on the migration capacity of proppant in the fracture,proppant is mainly placed in the area near the wellbore with large fracture width,while a high-concentration sandwash may easily occur in the area with narrow fracture width as a result of quick bridging.On the whole,the proppant placement range is limited.Increasing the well-spacing can reduce the stress interference of adjacent wells and promote the uniform distribution of fractures and proppant on both wings.The maximum stimulated reservoir volume or multi-fracture uniform propagation can be achieved by optimizing the well spacing.Although reducing the perforation-cluster spacing also can improve the stimulated reservoir area,a too low cluster spacing is not conducive to effectively increasing the propped fracture area.Since increasing the stage time lag is beneficial to reduce inter-stage stress interference,zipper fracturing produces more uniform fracture propagation and proppant distribution.

Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System

The voltage source converter based high voltage direct current(VSC-HVDC)system is based on voltage source converter,and its control system is more complex.Also affected by the fast control of power electronics,oscillation phenomenon in wide frequency domain may occur.To address the problem of small signal stability of the VSCHVDC system,a converter control strategy is designed to improve its small signal stability,and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters.Based on the modeling of the VSC-HVDC system,the general architecture of the inner and outer loop control of the VSCHVDC converter is established;and the damping controllers for DC control and AC control are designed in the phase-locked loop and the inner and outer loop control parts respectively;the state-space statemodel of the control system is established to analyze its performance.And the electromagnetic transient simulation model is built on the PSCAD/EMTDC simulation platform to verify the accuracy of the small signal model.The influence of the parameters of each control part on the stability of the system is summarized.The main control parts affecting stability are optimized for the phenomenon of oscillation due to changes in operation mode occurring on the AC side due to faults and other reasons,which effectively eliminates system oscillation and improves system small signal stability,providing a certain reference for engineering design.

Parameter Optimization of Intercalated Meltblown Nonwovens Based on NSGA-II

The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated to establish a good mechanism for product performance regulation. In this study, we first used Wilcoxon test and Pearson correlation analysis to investigate the effect of intercalation rate on structural variables and product performance. Then, regression models were constructed to predict the values of each structural variable under different combinations of process parameters. Finally, we constructed a multi-objective constrained optimization problem based on the stepwise regression model and the product variable conditions. The problem was solved using the NSGA-II algorithm. The optimal was achieved when the acceptance distance was 2.892 cm and the hot air speed was 2000 r/min.

Predictive Modeling and Parameter Optimization of Cutting Forces During Orbital Drilling

To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.

Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle

This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.

An application of the A-4DEnVar to coupled parameter optimization

In variational methods,coupled parameter optimization(CPO) often needs a long minimization time window(MTW) to fully incorporate observational information,but the optimal MTW somehow depends on the model nonlinearity.The analytical four-dimensional ensemble-variational(A-4DEnVar) considers model nonlinearity well and avoids adjoint model.It can theoretically be applied to CPO.To verify the feasibility and the ability of the A-4DEnVar in CPO,“twin” experiments based on A-4DEnVar CPO are conducted for the first time with the comparison of four-dimensional variational(4D-Var).Two algorithms use the same background error covariance matrix and optimization algorithm to control variates.The experiments are based on a simple coupled oceanatmosphere model,in which the atmospheric part is the highly nonlinear Lorenz-63 model,and the oceanic part is a slab ocean model.The results show that both A-4DEnVar and 4D-Var can effectively reduce the error of state variables through CPO.Besides,two methods produce almost the same results in most cases when the MTW is less than 560 time steps.The results are similar when the MTW is larger than 560 time steps and less than 880 time steps.The largest MTW of 4 D-Var and A-4DEnVar are 1 200 time steps.Moreover,A-4DEnVar is not sensitive to ensemble size when the MTW is less than 720 time steps.A-4DEnVar obtains satisfactory results in the case of highly nonlinear model and long MTW,suggesting that it has the potential to be widely applied to realistic CPO.

Parameter Optimization Method for Gaussian Mixture Model with Data Evolution

To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model(GMM)is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.

Non-undercutting condition and parameter optimization for cycloid ball planetary transmission

The transmission performance of cycloid ball planetary transmission(CBPT) is affected by cycloid tooth undercutting directly, and the design of CBPT can be optimized by the non-undercutting condition. Firstly, the theoretical equation of cycloid tooth is given, and the curvature radius of cycloid tooth profile is derived. Secondly, according to the relationship between the curvature radius and the distribution circle of balls, the non-undercutting condition of cycloid tooth profile is established, and the non-undercutting critical condition is deduced. Finally, the validity of the non-undercutting critical condition is verified by simulation. The result shows that the non-undercutting critical condition can be used to optimize the design of CBPT.

Parameter optimization and experiment of the negative pressure precision seed-metering device for wheat

In order to ensure the most reasonable distribution of wheat seeds in the field to improve seeding quality and uniformity,a set of negative pressure precision seed-metering device was designed,which shares a hollow shaft.Every seed-metering device can sow two rows of wheat.By the STAR-CCM+,the analysis of nephogram,vectogram and streamline graph showed that more ideal structural parameters of the seed-metering device are 0.5 mm width of the slit sucking seed(WSS),150-200 mm diameter of the seed-metering disc(DSD),2.0 mm axial depth of air chamber in the seed-metering disc(ADS),and arc-shaped cross-section shape of the ring groove sucking seed(CSGS).Single-factor test on the JPS-12 test-bed analyzed the influence of the CSGS,WSS,DSD,and ADS on the qualified index(Iq),multiple index(Imul),miss index(Imiss)and coefficient of variation of qualified seed spacing(CV).Through the orthogonal on the JPS-12 test-bed,it is found that the influence of vacuum negative pressure and seed-metering device shaft speed is significant on the Iq,Imiss and Imul.Based on these,the structural parameters of the seed-metering device were optimized.The DSD is 180 mm,the WSS is 0.7 mm,the ADS is 2.5 mm,and the CSGS is arc-shaped.The optimization seed-metering device was tested on the JPS-12 test-bed.The Iq is 86.66%,the Imiss is 5.09%,the Imul is 8.25%,and the CV is 24.50%.These testing results fully coincide with the standard JB/T 10293-2013 Specifications of single seed drill(precision drill).The seed-metering device meets fully the requirements for wheat precision seeding.

Experiment and parameter optimization of an automatic row following system for the traction beet combine harvester

To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation,this study used a self-developed sugar beet combine harvester and field simulation experiment platform,based on the single-factor bench test of the automatic row following system in the early stage,taking hydraulic flow A,spring preload B,and forward speed C which have significant influence on performance indices as test factors,and taking the missed excavation rate,breakage rate and reaction time as performance indices,the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out.The results of the orthogonal experiments were analyzed using range analysis and variance analysis.The results showed that there were differences in the influence degree,factor priority order and first-order interaction,and the optimal parameter combination on each performance index.A weighted comprehensive scoring method was used to optimize and analyze each index.The optimal parameter combination of the overall operating performance of the automatic row following system was A 2B 2C 1,that is,the hydraulic flow was 25 L/min,the forward speed was 0.8 m/s,and the spring preload was 198 N.Under this combination,the response time was 0.496 s,the missed excavation rate was 2.35%,the breakage rate was 3.65%,and the operation quality was relatively good,which can meet the harvest requirements.The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns.The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large"S"ridges were 2.23%and 2.69%,respectively,and the maximum values were 2.39%and 2.98%,respectively;the average damage rates were 3.38%and 4.14%,and the maximum values were 3.58%and 4.48%,which meet the industry standards of sugar beet harvester operation quality.The overall adaptability of the automatic row following system is good.This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters.

Performance improvement of licklider transmission protocol in complex deep space networks based on parameter optimization

A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.

A systematic design of multifunctional lattice structures with energy absorption and phononic bandgap by topology and parameter optimization

Lattice structure can realize excellent multifunctional charac-teristics because of its huge design space,and the cellular configuration directly affects the lattice structural performance and lightweight.A novel energy-absorbing multifunctional lat-tice structure with phononic bandgap is presented by topol-ogy and parameter optimization in this paper.First,the two-dimensional(2D)cellular configuration is lightweight designed by using independent continuous mapping(ICM)topology optimization method.The 2D cell is reconstructed by geo-metric parameters and rotated into a three-dimensional(3D)cell by using chiral shape to achieve bandgap.Subsequently,the surrogated model with energy absorption as the object and first-order natural frequency as the constraint is estab-lished to optimize a parametric 3D cell based on the Response Surface Methodology(RSM).Finally,the lattice struc-tures are assembled with dodecagonal staggered arrange-ments to avoid the deformation interference among the adjacent cells.In addition,the lattice structural energy absorp-tion and bandgap characteristics are analyzed and discussed.Compared to Kelvin lattice structure,the optimal lattice struc-ture shows significant improvement in energy absorption effi-ciency.Besides,the proposed design also performs well in damping characteristics of the high-frequency and wide-bandgap.The lattice structural optimization design framework has great meaning to achieve the equipment structural light-weight and multi-function in the aerospace field.

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.

Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated Systems for Damping Ultra-low Frequency Oscillation

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

Parameter Optimization and Precision Enhancement of Dual-Coil Eddy Current Sensor

To enhance the measurement precision of eddy current sensor in particular environments such asextreme temperature changes and limited available space in aerospace, we optimized the structural parameters ofthe traditional dual-coil eddy current sensor probe by electromagnetic field analysis and finite element simulationmodeling, and further presented the criteria for determining the optimal coil distance of the dual-coil probe. Thesimulation results are verified by setting up an experimental platform. For the extreme temperature environment,the displacement measurement error caused by the full range temperature variation of the dual-coil sensor underthe optimal distance is less than 21.0% of that of the single-coil sensor. On this basis, we analyzed and verified thethermal stability of the structurally optimized dual-coil eddy current sensor. After temperature compensation,the displacement measurement accuracy can reach 14.9 times more accurate than that of the single-coil sensor.The method proposed in this paper can provide a design reference for the structural optimization of the axialdual-coil eddy current sensor probe.

Parametric Optimization of Wheel Spoke Structure for Drag Reduction of an Ahmed Body

The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25%of the total drag on modern vehicles.In this study,the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated.Subsequently,the 35°Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction.The Fluent software is employed for this investigation,with an inlet velocity of 40 m/s.The accuracy of the numerical study is validated by comparing it with experimental results obtained from the classical Ahmed model.To gain a clearer understanding of the effects of the wheel spoke parameters on the aerodynamics of both the wheel and Ahmedmodel,and five design variables are proposed:the fillet angleα,the inside arc radius R1,the outside radius R2,and the same length of the chord L1 and L2.These variables characterize the wheel spoke structure.The Optimal Latin Hypercube designmethod is utilized to conduct the experimental design.Based on the simulation results of various wheel spoke designs,the Kriging model and the adaptive simulated annealing algorithm is selected to optimize the design parameters.The objective is to achieve the best combination for maximum drag reduction.It is indicated that the optimized spoke structure resulted in amaximum drag reduction of 5.7%and 4.7%for the drag coefficient of the isolated wheel and Ahmed body,respectively.The drag reduction is primarily attributed to changes in the flow state around the wheel,which suppressed separation bubbles.Additionally,it influenced the boundary layer thickness around the car body and reduced the turbulent kinetic energy in the wake flow.These effects collectively contributed to the observed drag reduction.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Pulsed gas–liquid discharge plasma catalytic degradation of bisphenol A over graphene/Cd S:process parameters optimization and O_(3)activation mechanism analysis

In the present work,pulsed gas–liquid hybrid discharge plasma coupled with graphene/Cd S catalyst was evaluated to eliminate bisphenol A(BPA)in wastewater.The optimization of a series of process parameters was performed in terms of BPA degradation performance.The experimental results demonstrated that nearly 90%of BPA(20 mg l^(-1))in the synthetic wastewater(p H=7.5,σ=10μS m^(-1))was degraded by the plasma catalytic system over 0.2 g l^(-1)graphene/Cd S at 19k V with a 4 l min^(-1)air flow rate and 10 mm electrode gap within 60 min.The BPA removal rate increased with increasing the discharge voltage and decreasing the initial BPA concentration or solution conductivity.Nevertheless,either too high or too low an air flow rate,electrode gap,catalyst dosage or initial solution p H would lead to a decrease in BPA degradation.Moreover,optical emission spectroscopy was used to gain information on short-lived reactive species formed from the pulsed gas–liquid hybrid discharge plasma system.The results indicated the existence of several highly oxidative free radicals such as·O and·OH.Finally,the activation pathway of O_(3)on the catalyst surface was analyzed by density functional theory.

Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.