Phase-Based Optical Flow Method with Optimized Parameter Settings for Enhancing Displacement Measurement Adaptability

To enhance the applicability and measurement accuracy of phase-based optical flow method using complex steerable pyramids in structural displacement measurement engineering applications, an improved method of optimizing parameter settings is proposed. The optimized parameters include the best measurement points of the Region of Interest (ROI) and the levels of pyramid filters. Additionally, to address the issue of updating reference frames in practical applications due to the difficulty in estimating the maximum effective measurement value, a mechanism for dynamically updating reference frames is introduced. Experimental results demonstrate that compared to representative image gradient-based displacement measurement methods, the proposed method exhibits higher measurement accuracy in engineering applications. This provides reliable data support for structural damage identification research based on vibration signals and is expected to broaden the engineering application prospects for structural health monitoring.

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.

Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil cultivation.There comes a demand for computer-related soil classification methods to support agriculturalists.This study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)technique.The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches.In the presented GBODL-ASC technique,three major processes are involved.At the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature vectors.For soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)model.The design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,respectively.To demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five classes.The simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

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.

Optimal Structural Parameters for a Plastic Centrifugal Pump Inducer

The aim of the study is to determine the optimal structural parameters for a plastic centrifugal pump inducer within the framework of an orthogonal experimental method.For this purpose,a numerical study of the related flow field is performed using CFX.The shaft power and the head of the pump are taken as the evaluation indicators.Accordingly,the examined variables are the thickness(S),the blade cascade degree(t),the blade rim angle(β1),the blade hub angle(β2)and the hub length(L).The impact of each structural parameter on each evaluation index is examined and special attention is paid to the following combinations:S2 mm,t 2,β1235°,β2360°and L 140 mm(corresponding to a maximum head of 98.15 m);S 5 mm,t 1.6,β1252°,β2350°and L 140 mm(corresponding to a minimum shaft power of 63.06 KW).Moreover,using least squares and fish swarm algorithms,the pump shaft power and head are further optimized,yielding the following optimal combination:S 5 mm,t 1.9,β1252°,β2360°and L 145 mm(corresponding to the maximum head of 91.90 m,and a minimum shaft power of 64.83 KW).

Review of Design of Process Parameters for Squeeze Casting

Squeeze casting(SC)is an advanced net manufacturing process with many advantages for which the quality and properties of the manufactured parts depend strongly on the process parameters.Unfortunately,a universal efficient method for the determination of optimal process parameters is still unavailable.In view of the shortcomings and development needs of the current research methods for the setting of SC process parameters,by consulting and analyzing the recent research literature on SC process parameters and using the CiteSpace literature analysis software,manual reading and statistical analysis,the current state and characteristics of the research methods used for the determination of SC process parameters are summarized.The literature data show that the number of pub-lications in the literature related to the design of SC process parameters generally trends upward albeit with signifi-cant fluctuations.Analysis of the research focus shows that both“mechanical properties”and“microstructure”are the two main subjects in the studies of SC process parameters.With regard to materials,aluminum alloys have been extensively studied.Five methods have been used to obtain SC process parameters:Physical experiments,numeri-cal simulation,modeling optimization,formula calculation,and the use of empirical values.Physical experiments are the main research methods.The main methods for designing SC process parameters are divided into three categories:Fully experimental methods,optimization methods that involve modeling based on experimental data,and theoreti-cal calculation methods that involve establishing an analytical formula.The research characteristics and shortcomings of each method were analyzed.Numerical simulations and model-based optimization have become the new required methods.Considering the development needs and data-driven trends of the SC process,suggestions for the develop-ment of SC process parameter research have been proposed.

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.

Simulation Research on the Effect of Spreading Process Parameters on the Quality of Lunar Regolith Powder Bed in Additive Manufacturing

Lunar surface additive manufacturing with lunar regolith is a key step in in-situ resource utilization.The powder spreading process is the key process,which has a major impact on the quality of the powder bed and the precision of molded parts.In this study,the discrete element method(DEM)was adopted to simulate the powder spreading process with a roller.The three powder bed quality indicators,including the molding layer offset,voidage fraction,and surface roughness,were established.Besides,the influence of the three process parameters,which are roller’s translational speed,rotational speed,and powder spreading layer thickness on the powder bed quality indicators was also analyzed.The results show that with the reduction of the powder spreading layer thickness and the increase of the rotational speed,the offset increased significantly;when the translational speed increased,the offset first increased and then decreased,which resulted in an extreme value;with the increase of the layer thickness and the decrease of the translational speed,the values for voidage fraction and surface roughness significantly reduced.The powder bed quality indicators were adopted as the optimization objective,and the multi-objective parameter optimization was carried out.The predicted optimal powder spreading parameters and powder bed quality indicators were then obtained.Moreover,the optimal values were then verified.This study can provide informative guidance for in-situ manufacturing at the moon in future deep space exploration missions.

Tuning microstructures of TC4 ELI to improve explosion resistance

A reasonable heat treatment process for TC4 ELI titanium alloy is crucial to tune microstructures to improve its explosion resistance.However,there is limited investigation on tuning microstructures of TC4 ELI to improve explosion resistance.Moreover,the current challenge is quantifying microstructural changes'effects on explosion resistance and incorporating microstructural changes into finite element models.This work aims to tune microstructures to improve explosion resistance and elucidate their anti-explosion mechanism,and find a suitable method to incorporate microstructural changes into finite element models.In this work,we systematically study the deformation and failure characteristics of TC4 ELI plates with varying microstructures using an air explosion test and LS-DYNA finite element modeling.The Johnson-Cook(JC)constitutive parameters are used to quantify the effects of microstructural changes on explosion resistance and incorporate microstructural changes into finite element models.Because of the heat treatment,one plate has equiaxed microstructure and the other has bimodal microstructure.The convex of the plate after the explosion has a quadratic relationship with the charge mass,and the simulation results demonstrate high reliability,with the error less than 17.5%.Therefore,it is feasible to obtain corresponding JC constitutive parameters based on the differences in microstructures and mechanical properties and characterize the effects of microstructural changes on explosion resistance.The bimodal target exhibits excellent deformation resistance.The response of bimodal microstructure to the shock wave may be more intense under explosive loading.The well-coordinated structure of the bimodal target enhances its resistance to deformation.

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.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Parameters Optimization of a Novel 5-DOF Gasbag Polishing Machine Tool

The research on the parameters optimization for gasbag polishing machine tools, mainly aims at a better kinematics performance and a design scheme. Serial structural arm is mostly employed in gasbag polishing machine tools at present, but it is disadvantaged by its complexity, big inertia, and so on. In the multi-objective parameters optimization, it is very difficult to select good parameters to achieve excellent performance of the mechanism. In this paper, a statistics parameters optimization method based on index atlases is presented for a novel 5-DOF gasbag polishing machine tool. In the position analyses, the structure and workspace for a novel 5-DOF gasbag polishing machine tool is developed, where the gasbag polishing machine tool is advantaged by its simple structure, lower inertia and bigger workspace. In the kinematics analyses, several kinematics performance evaluation indices of the machine tool are proposed and discussed, and the global kinematics performance evaluation atlases are given. In the parameters optimization process, considering the assembly technique, a design scheme of the 5-DOF gasbag polishing machine tool is given to own better kinematics performance based on the proposed statistics parameters optimization method, and the global linear isotropic performance index is 0.5, the global rotational isotropic performance index is 0.5, the global linear velocity transmission performance index is 1.012 3 m/s in the case of unit input matrix, the global rotational velocity transmission performance index is 0.102 7 rad/s in the case of unit input matrix, and the workspace volume is 1. The proposed research provides the basis for applications of the novel 5-DOF gasbag polishing machine tool, which can be applied to the modern industrial fields requiring machines with lower inertia, better kinematics transmission performance and better technological efficiency.

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

Optimization of investment casting process parameters to reduce warpage of turbine blade platform in DD6 alloy

The large warping deformation at platform of turbine blade directly affects the forming precision. In the present research, equivalent warping deformation was firstly presented to describe the extent of deformation at platform. To optimize the process parameters during investment casting to minimize the warping deformation of the platform, based on simulation with Pro CAST, the single factor method, orthogonal test, neural network and genetic algorithm were subsequently used to analyze the influence of pouring temperature, shell mold preheating temperature, furnace temperature and withdrawal velocity on dimensional accuracy of the platform of superalloyDD6 turbine blade. The accuracy of investment casting simulation was verified by measurement of platform at blade casting. The simulation results with the optimal process parameters illustrate that the equivalent warping deformation was dramatically reduced by 21.8% from 0.232295 mm to 0.181698 mm.

Optimal Microwave Radiation Field Parameters for Mercury Ion Microwave Frequency Standards

We propose a method to determine the optimal power of the microwave resonance transition that simultaneously improves the signal-to-noise ratio and reduces line width based on saturation broadening theory and experiment. Saturation broadening spectra of the ground state hyperfine transition of trapped 199Hg＋ ions are measured and analyzed. The value of the optimal microwave power is obtained by using the proposed method and is verified. Rabi oscillations decay spectra of trapped 199Hg＋ ions are observed and the optimal microwave irradiation time for the maximum transition signal intensity is determined. This work will help to improve the short-term frequency stability of the mercury ion microwave frequency standard.

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous decade.Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries.However,some diseases that are blocking the improvement in paddy production are considered as an ominous threat.Convolution Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.Nevertheless,the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge.This approach is time-consuming,and high computational resources are mandatory.In this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification.Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.

Parameters Optimization and Energy-Saving of Highway Tunnel Backlighting with LED

In order to acquire the most energy-saving luminairedistribution-parameters(LDPs)of highway tunnel interior zone backlighting,the parameters optimization model(POM)of backlighting for tunnel interior zone was established.Yanlieshan tunnel of Jiujing highway was taken as an example for the optimization.The optimal LDPs of the backlighting system of the tunnel interior zone were obtained by the POM,a comparison between the optimization results and those of Yanlieshan tunnel’s actual lighting system was performed,which showed that the optimized backlighting system with LED lamps installed according to the optimized LDPs could save energy remarkablely even under full capacity lighting condition.Illuminance and illuminance uniformity of the tunnel road surface still met the lighting demands even the LED lamp’s luminance decreased by 30%.A backlighting simulation experiment with the optimized backlighting LDPs for Yanlieshan tunnel was accomplished in the software Dialux.The simulation results basically agreed with the optimization calculated results from the POM which proved the correctness of the backlighting POM.

Parameters Optimizing of Ammonia-Recovery System in the Modified Equipment of Flax Fiber

A detailed analysis of operational process and principle of ammonia-recovery system in the modified equipment of flax fiber,which will be applied to parameters optimizing of the ammoniarecovery system as a foundational principle,is presented. According to the principle,an ammonia compressor,whose working conditions are based on key operational parameters of the whole ammoniarecovery system, is the mainly energy-consumption part of ammonia-recovery system in the modified equipment of flax fiber. A generally mathematical model based on work efficiency of an ammonia compressor is founded,which is available to rate effective work and energy consumption of the ammonia compressor. The optimum operation-efficiency of the ammonia compressor is chosen as the goal to analyze and calculate the key operational parameters of the ammonia-recovery system. In the above analyzing and calculating,a mathematical model on ammonia flowing from the reactor to the register 1 is developed,in order to provide further understanding of the principle of an ammonia-recovery system. At the meantime,the ammonia flow regime in the pipeline and the process of ammonia inflation and deflation from the reactor to the register 1 are taken separately into account in the model. An iterative method is for obtaining parametric solutions of the mathematical model on ammonia flowing from the reactor to the register 1 and the key operational parameters of the ammoniarecovery system. A parametric analysis is put forward to complete showing the ammonia velocity or the state of the reactor and the register 1. The key optimized parameters will be achieved in term of the minimum efficiency after comparing the work efficiencies of an ammonia compressor at different working conditions.