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.

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

Metallographic structure,mechanical properties,and process parameter optimization of 5A06 joints formed by ultrasonic-assisted refill friction stir spot welding

Novel hybrid refill friction stir spot welding （RFSSW） assisted with ultrasonic oscillation was introduced to 5A06 aluminum alloy joints. The metallographic structure and mechanical properties of 5A06 aluminum alloy RFSSW joints formed without ultrasonic assistance and with lateral and longitudinal ultrasonic assistance were compared, and the ultrasonic-assisted RFSSW process parameters were opti- mized. The results show that compared with lateral ultrasonic oscillation, longitudinal ultrasonic oscillation strengthens the horizontal bond- ing ligament in the joint and has a stronger effect on the joint＇s shear strength. By contrast, lateral ultrasonic oscillation strengthens the ver- tical bonding ligament and is more effective in increasing the joint＇s tensile strength. The maximum shear strength of ultrasonic-assisted RFSSW 5A06 aluminum alloy joints is as high as 8761 N, and the maximum tensile strength is 3679 N when the joints are formed at a tool rotating speed of 2000 r/rain, a welding time of 3.5 s, a penetration depth of 0.2 mm, and an axial pressure of 11 kN.

A New Approach for Parameter Optimization in Land Surface Model

In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER

The dynamic working process of 52SFZ-140-207B type of hydraulic bumper isanalyzed. The modeling method using architecture-based neural networks is introduced. Using thismodeling method, the dynamic model of the hydraulic bumper is established; Based on this model thestructural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result showsthat the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamicperformance of the hydraulic bumper is improved through parameter optimization.

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.

Parameter optimization for plasma-sprayed Al2O3p/Al composite coatings on AZ31 magnesium alloy

Al2O3p/Al composite coatings were prepared on the surface of AZ31 magnesium alloy by plasma spraying technology with mixed powders of Al and Al2O3. An orthogonal test containing six factors and five levels was carried out to acquire the optimum technical parameters. Mierostruetures and properties of the composite coatings were studied. The results show that the coatings consist of Al2O3 particulates distributed uniformly and Al matrix, and the interface between the particulate and matrix is continuous, compact and clean. With increasing the mass fraction of Al2O3 in the mixed powders, the volume fraction of Al2O3 in the coatings iacreases. The Al2O3p/Al composite coating with 14% Al2O3 volume fraction has more compact microstrueture and more satisfactory properties.

Process parameter optimization for local post-weld heat treatment.of turbine rotor based on uniform design

During the process of local post-weld heat treatment （ PWHT） , the temperature difference inside the weld overlay is a very influential factor on relieving residual stress. In this paper, a commercial program （ABAQUS） was used to simulate the temperature field in turbine rotor, and the influence of heat treatment parameters on temperature was investigated by finite element method. Weight analysis shows that the holding temperature and heating rate are the main factors affecting on temperature, especially the holding temperature. Besides, two regression equations that reflect the relationship between temperature and heat treatment parameters were fitted with uniform design method. Both the correlation coefficients of the regression equations are up to O. 999 2, and the maximum residual error is only O. 7. The agreement between simulation results and regression results is shown to be excellent. At the end, local heat treatment parameters were optimized using the simplex method.

Body Structure Design, Simulation and Parameter Optimization of a Crawler Type Mobile Flat Bedplate Frame Composite Car

The report mainly studied the crawler frame motion platform to reduce weight and increase the intensity. Report described the structural design process which using CAD/CAE technology for solid modeling, simulation analysis, parameter optimization. And it also explained the outstanding advantages of CAD/CAE technology in mechanical design as well as simulation analysis.

Structural Parameter Optimization of Multilayer Conductors in HTS Cable

In this paper, the design optimization of the structural parameters of multilayer conductors in high temperature superconducting （HTS） cable is reviewed. Various optimization methods, such as the particle swarm optimization （PSO）, the genetic algorithm （GA）, and a robust optimization method based on design for six sigma （DFSS）, have been applied to realize uniform current distribution among the multilayer HTS conductors. The continuous and discrete variables, such as the winding angle, radius, and winding direction of each layer, are chosen as the design parameters. Under the constraints of the mechanical properties and critical current, PSO is proven to be a more powerful tool than GA for structural parameter optimization, and DFSS can not only achieve a uniform current distribution, but also improve significantly the reliability and robustness of the HTS cable quality.

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.

Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

Many factors influencing range of extended range guided munition （ERGM） are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm （GA） is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.

The Structural Design, Simulation Analysis and Parameter Optimization of the Cheetah Robot's Lumbar Vertebrae

The quality of skeleton system for the cheetah robot goes hand in hand with its bionic result of its shape, structure and functions. In view of the skeleton system constitution and structural characteristic of the cheetah, the team applied structure design, stimulation analysis and parameter optimization to developing the cheetah robot. In addition, after the invention of cheetah robot＇s anterior lumbar vertebra based on its functional attribute and connectivity attribute, the Solidworks Simulation was utilized to analyze the design, according to which improvement on the lumbar vertebra was made. Plus, the advantages of the CAD and CAE made the high efficiency of design work and high quality of the cheetah robot possible.

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.