Optimal Parameter Combination Research Articles

Research on Green Building Design Optimization Based on Building Information Modeling and Improved Genetic Algorithm

The energy consumption of the construction industry has been increasing year by year, posing a huge challenge to China’s dual carbon goals of peaking carbon emissions and achieving carbon neutrality. The Chinese construction industry has huge potential for energy conservation and emission reduction, and the government has therefore put forward requirements for constructing green buildings and formulated strict evaluation standards. The carbon emissions of the construction industry involve various stages of the entire life cycle and are closely related to the green building design standards that meet the requirements. This article sets multiple objective functions based on the two dimensions of the carbon emissions of the entire life cycle of buildings and green building evaluation and uses the NSGA-II algorithm in genetic algorithms to optimize ten indicators selected from the two objectives. Based on this, building information modeling (BIM) modeling was carried out for an office building project in Southwest China, and energy consumption analysis and evaluation were conducted based on the project’s multidisciplinary model. The dialectical relationship between the carbon emissions of the entire life cycle of buildings and the green building evaluation values was discovered, and the optimized parameter combination scheme corresponding to the Pareto solution set was obtained, providing a reference for using improved genetic algorithms and BIM technology to optimize green building design.

Design and Experiment of Air-Suction Maize Seed-Metering Device with Auxiliary Guide

Due to the irrational design of the seed discharge plate and the vacuum chamber, the high-speed seed filling effect of the air-suction maize precision seed-metering device is poor. Therefore, an air-suction maize seed-metering device with an auxiliary guide is designed to realize high-speed precision seed discharging. An auxiliary guide filling theory is put forward, and the design of the seed plate type hole charging structure is formulated. Fluent 2022 software is used to analyze nine kinds of vacuum chamber structures; the optimal vacuum chamber structure parameters were determined by polar analysis. In order to investigate the changes of negative pressure and flow speed under the dynamic flow field, a slip grid was used to analyze the dynamic flow field with three different operating speeds and negative pressures. It found that the size of negative pressure did not affect the flow field distribution, and the pressure and flow speed gradually decreased as the distance from the inlet was farther away; meanwhile, the negative pressure distribution and air speed distribution were almost unchanged when the holes at different rotational speeds were at the same position. Finally, bench tests were carried out, and three indexes, namely, the qualified index, the multiple index and the missing index, were selected, with operating speed and negative pressure as factors, two-factor five-level orthogonal test was carried out, and the optimal parameter combinations at 6.0, 7.5, 9.0, 10.5, and 12 km/h forward velocity were derived and verified by regression equations. The results showed that the designed seed-metering device was repeated five times when the pressure of the vacuum chamber was −3.5 kPa and the rotational speed of the seed-metering device was 23 r/min, the average grain spacing qualified index was 95.8%, the missing index was 1.6%, the multiple index was 2.6%, and the indexes met the requirements of precision sowing. It is of great significance for our country’s seeder to develop in the direction of high-speed and precision.

Application of machine learning in 3D bioprinting of cultivated meat

Cultivated meat production, an innovative and sustainable alternative to conventional animal farming, has gained significant attention in recent years. As the demand for ethical and environmentally friendly protein sources continues to rise, the need for efficient and scalable production strategies becomes critical. Notably, the integration of advanced technology, such as machine learning (ML), can enhance the efficiency of the cultivated meat production process. The goal of this review paper is to highlight the advantages and limitations of various ML approaches and provide a balanced discussion on the integration of ML techniques for three-dimensional (3D)-bioprinted cultivated meat. This review paper explores the application of ML techniques in various facets of 3D-bioprinted cultivated meat and highlights the potential for ML to optimize various aspects of the process, from predicting printability and optimizing printing parameters to characterizing meat flavor and monitoring meat quality. ML plays a pivotal role in optimizing the material formulation to improve ink printability and identifying an optimal combination of printing parameters to achieve high printing resolution and accuracy. Furthermore, ML can aid in modeling sensory attributes, ensuring that the cultivated meat replicates the desired meat flavor. Finally, ML can be applied for meat quality control as it facilitates the automated detection of harmful pathogens, ensuring the safety and consistency of 3D-bioprinted cultivated meat.

Calibration of Simulation Parameters for Fresh Tea Leaves Based on the Discrete Element Method

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves.

Multi-response optimisation of EDM process machined by P/M electrodes made of micron and nano level particles

ABSTRACT In electric discharge machining (EDM), the use of customised powder metallurgy (P/M) electrodes is of tremendous importance. The particle size of the P/M electrode, compaction pressure, and elemental composition of tool electrodes significantly influences the machining efficiency of the EDM. However, particle size variations were hardly studied, importantly when the particle size is at ‘Nano level’. In the present investigation, machine tool and electrode parameters are considered for evaluating the machining efficiency by measuring the material removal rate (MRR) and tool wear rate (TWR). The workpiece and tool electrodes are D2 alloy steel and WC/Co P/M electrodes, respectively. Taguchi’s L18 orthogonal array was used in the design of experiments. The parameters Particle size (PS), Peak current (IP), Compaction Pressure (CP) and wt% of Co (%Co) have all been found to have a substantial impact on improving MRR and TWR simultaneously. At the optimal combination of parameters, MRR was improved from 109.1 to 151.9 mg/min, and TWR was lowered from 7.1 to 5.9 mg/min with the proposed method. Predicted results are compared with confirmation experiments to validate the findings. The probable reasons that contributed to the enhancement of performance measures are discussed.

Design and Experiment of Active Spiral Pushing Straw Row-Sorting Device

Aiming to solve the problems of excessive straw residue and large soil loss in the seeding belt of the straw row-sorting operation when the full volume of straw is crushed and returned to the field in the northeastern region of China, an active spiral pushing straw row-sorting device (ASPSRD) was designed in this paper. The straw on the surface of the land is collected and stirred by the high-speed rotating spiral-notched blades in the device and pushed to the non-seeding area for the purpose of cleaning the seeding belt. The parameters of key components were determined through theoretical analysis. The orthogonal combination test method of four factors and three levels was adopted. The EDEM discrete element simulation experiment was carried out by selecting the straw mulching quantity (SMQ), the rotating speed of the active rotating shaft (RSARS), the forward speed of the tractor (FST), and the notch width of the spiral notch blade (NWSNB) as the test factors, and the straw-cleaning rate (SCR) and soil loss rate (SLR) as the test indexes. Through range analysis, the parameters were optimized, and the optimal operation parameters of the straw row-sorting device were determined as follows: SMQ was 1.8 kg/m2, RSARS was 120 rpm, FST was 4 m/s, and NWSNB was 9 mm. The operational performance of the device was verified by field experiments. The results of the test showed that after the device was operated with the optimal combination of parameters, the SCR of the 20 cm wide seeding belt was 95.32%, and the SLR was 7.12%, which met the agronomic and technical requirements of corn no-tillage sowing operation in Northeast China. This study can provide a reference for the design of a straw row-sorting device of a no-tillage machine.

Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression.

Laboratory determination of trihalomethanes (THMs) is a very time-consuming task. Therefore, establishing a THMs model using easily obtainable water quality parameters would be very helpful. This study explored the modeling methods of the random forest regression (RFR) model, support vector regression (SVR) model, and Log-linear regression model to predict the concentration of total-trihalomethanes (T-THMs), bromodichloromethane (BDCM), and dibromochloromethane (DBCM), using nine water quality parameters as input variables. The models were developed and tested using a dataset of 175 samples collected from a water treatment plant. The results showed that the RFR model, with the optimal parameter combination, outperformed the Log-linear regression model in predicting the concentration of T-THMs (N25 = 82-88%, rp = 0.70-0.80), while the SVR model performed slightly better than the RFR model in predicting the concentration of BDCM (N25 = 85-98%, rp = 0.70-0.97). The RFR model exhibited superior performance compared to the other two models in predicting the concentration of T-THMs and DBCM. The study concludes that the RFR model is superior overall to the SVR model and Log-linear regression models and could be used to monitor THMs concentration in water supply systems.

Multi-objective optimization of dew point indirect evaporative coolers for data centers

This study introduces a novel counter-flow dew point indirect evaporative cooler (DPIEC) tailored for data center cooling. A numerical model for DPIEC was developed in COMSOL and validated experimentally, followed by the formulation of regression models using response surface methodology. Eight critical design parameters (five operational and three geometrical) were linked to three performance evaluation indexes (outlet primary air temperature T1,out, coefficient of performance COP, and cooling capacity per unit volume Qv). The magnitude of parameter influence on the evaluation indexes was assessed. Employing the three regression models as objective functions, a genetic algorithm, implemented in MATLAB, was adopted to perform multi-objective optimization for DPIEC under four climatic conditions, yielding an array of optimal parameter combinations. The results underscored the robust predictive accuracy of the established regression models, with R2 and Adeq Precision exceeding 0.96 and 53.7, respectively. The ideal point method achieved design solutions of the DPIEC with T1,out below 21.6 °C, COP above 61.3, and Qv over 15.5 kW/m3. Compared to the original design, the optimized design featured reductions of over 13.84 %, elevations of over 23.50 %, and enhancements of over 104.10 % in these three indexes, respectively. In addition, the confirmation study of the Pareto front proved the reliability of the obtained DPIEC design reference.

Optimal design of locally improved structure for enhancing mass transfer in PEMFC cathode flow field

At high current density, the mass transfer in proton exchange membrane fuel cells (PEMFC) is significantly influenced by the flow field structure. In this paper, a bilateral track flow field (BTFF) is proposed to improve the transverse and longitudinal mass transfer as well as the water removal capacity of PEMFC. The geometry of the BTFF is optimized by orthogonal tests to obtain the optimal combination of geometric parameters and levels. Then using numerical modeling, the impact of the optimized BTFF (BT6) on the efficiency of the PEMFC was examined. The results show that BT6 significantly improves the utilization of the under-rib region by shortening the reactive gas diffusion path length to obtain a more uniform oxygen distribution, and the average oxygen concentration in the midline of the under-rib of the BT6 is increased by 102.60%, furthermore, the current density is increased by 16.32% compared with that of the parallel flow field (case0). In addition, by reducing the cross-sectional area of the gas flow channel, the BT6 has a higher gas flow velocity, which accelerates the removal of cathode liquid water, and the average liquid water saturation in the midline of the under-rib of the BT6 is reduced by 15.26% compared with case0. Finally, by analyzing the net power density of PEMFC, it can be found that the net power density of BT6 is increased by 15.68% compared with case0, which has good practical application value.

Optimized measurement of frozen soil thermal properties using a heat-pulse sensor

The measurement of thermal properties in partially frozen soil is crucial for global climate models. As the primary method for soil thermal property measurement, the Dual-Probe Heat-Pulse (DPHP) sensor experiences significant errors when used in partially frozen soils. The application of heat pulses leads to ice melting, and the existing DPHP theory, which does not consider phase changes, fails to accurately determine thermal conductivity (k) and heat capacity (C) in the temperature range of − 5 °C to 0 °C. There is a lack of DPHP theory specifically designed for use in partially frozen soils. The existing DPHP theory employs the Infinite Linear Source (ILS) approximation, which does not account for the melting process and the moving ice-liquid interface in transient heat conduction theory. Currently, there is no effective and reliable method to assess DPHP errors associated with measuring the thermal properties of partially frozen soil. To accurately determine k and C in frozen soil, it is essential to perform an error analysis regarding ice melting caused by a DPHP heat pulse input. In this study, (1) we considered the latent heat of ice melting and a moving ice-liquid interface in finite element simulations and compared the results with an analytical solution presented by Paterson (1952); (2) we performed simulations of DPHP measurements based on the temperature-dependent ice content, liquid water content, and melting latent heat of three frozen soils used by Watanabe and Wake (2009); (3) based on COMSOL simulation results for the three frozen soils, we determined the optimal heating parameter combinations (heating duration, t0, and accumulated heating energy, ΔQ) to minimize DPHP measurement errors in frozen soil. The results showed that (1) Simulations that included the melting phase changes were in agreement with the analytical solution by Paterson (1952). (2) Including the ice-liquid moving interface significantly altered the spatial distribution characteristics of temperature, which were not captured by the ILS model in terms of the temperature distribution near the interface. (3) Below a temperature of − 5.5 °C, the simulations that included phase changes were consistent with measured results; (4) For partially frozen soils with initial temperatures ranging from − 0.5 °C to 0 °C, the relative errors in thermal conductivity fitted by the ILS model exceeded 100 %. To mitigate the influence of ice melting, we recommend using 200 < ΔQ < 400 J m−1 for sand, loam, and silt loam soils with 8 s < t0 < 60 s. For a loam soil with t0 = 60 s, ΔQ = 800 J m−1 should be used. For a silt loam soil with t0 = 60 s, we recommend using ΔQ = 600 J m−1. If the ILS model is chosen to calculate soil thermal properties, we recommend using 200 <ΔQ < 600 J m−1 and 30 s < t0 < 60 s for sandy and silt loam soils. For a loam soil, we recommend using 400 < ΔQ < 800 J m−1 and 30 s < t0 < 60 s.

Calibration and Testing of Parameters for the Discrete Element Simulation of Soil Particles in Paddy Fields

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model’s parameters for paddy field soils was further validated through a comparative analysis of the simulation test’s cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p &lt; 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p &lt; 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m2, a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model’s contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields.

Optimizing micro-EDM with carbon-coated electrodes: A multi-criteria approach

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining ([Formula: see text]EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in [Formula: see text]EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by [Formula: see text]EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the [Formula: see text]EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160[Formula: see text]V, a capacitance of 10,000[Formula: see text]pF and a tool rotation of 600[Formula: see text]RPM, the research contributes valuable insights into the intricate realm of [Formula: see text]EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation.

Design and test of tangential and longitudinal-axial threshing and separating unit for wheat

To achieve low loss and efficient wheat harvest in the Huanghuaihai region of China, this paper designed a tangential and longitudinal-axial threshing and separating unit (TLTSU). Key components such as the tangential threshing rotor (TTR), longitudinal-axial threshing rotor (LTR), and tangential concave were designed. Dynamic balance simulation of the threshing rotors was done to obtain the reaction forces at supporting ends of the threshing rotors and the amount of unbalance, and the amount of unbalance of the LTR was corrected to meet the standard requirement. Response surface optimization experiments of wheat threshing were conducted using throughput, TTR speed, and LTR speed as influencing factors and entrainment loss rate and impurity rate as test indicators. The regression models for the threshing device's entrainment loss rate and impurity rates were established. Through multi-objective parameter optimization analysis, the optimal threshing parameter combination for TLTSU was determined to be throughput of 8 kg s−1, TTR speed of 557 r·min−1, and LTR speed of 900 r·min−1. A validation experiment was conducted on this parameter combination, and the results showed that the entrainment loss rate was 0.27 % and the impurity rate was 28.23 %. The relative errors between the evaluation indicators and the predicted values of their regression models were 3.85 % and 1.4 %, respectively, which were less than 5 %, which proved the correctness of the regression model and provided a reference for the design of threshing and separating unit of wheat combine harvester.

Modeling of creep-fatigue features of hydrogen storage bed and its parameter optimizing based on finite element method and orthogonal experimental design with artificial neural networks

Regarding the creep-fatigue behavior of hydrogen storage bed (HSB) structures under high-temperature loading and internal pressure loading under repeated hydrogen absorption and desorption process, the heat transfer characteristics and creep-fatigue life during a complete service process in HSB were calculated using Ansys Workbench and Ncode software, and the reliability of the life results calculated by finite element method were validated using ASME code. Based on orthogonal experimental design (OED) theory, an orthogonal experimental table with 4 factors and 5 levels was compiled for the four process parameters: diameter of cooling tube, thickness of cylinder wall, spacing between inner and outer layers, and heat transfer coefficient of condensate. Moreover, an optimization method based on OED coupled with BP ANNs was proposed to find the best process parameters. And the importance of each parameter was analyzed based on range analysis and analysis of variance (ANOVA) to divide the process parameters into rough optimized parameters and precise optimized parameters in this method. In order to identify the optimal process parameter combination of HSB, the maximum life and corresponding process parameter combinations were identified based on OED and the combined method, respectively. The maximum life identified by the combined method proposed in this work is increased by 10.23 % than that identified by OED, which shows the superiority of this combined method.

Parallel structure of crayfish optimization with arithmetic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery applications

Intelligent techniques play a vital role in predicting the friction force during the wear of Ti-6Al-4V alloy under different lubricating conditions. The effective assessment of friction forces and lubricating conditions allows for the replacement of the material before catastrophic failure. However, it remains challenging to utilise friction forces under different lubrication conditions to predict the wear through intelligent techniques. In this work, an advanced technique based on artificial intelligence has been proposed to address this issue. Intially parallel structure of crayfish optimization and arithmetic optimization algorithm (PSCOAAOA) is developed to duly address the issues of slow convergence, stucking in local optima and quality of the solution. The PSCOAAOA is further implemented for finding the optimal parameters (regularization parameter and kernel function) of the Support Vector Machine (SVM). The quantitative and qualitative analysis of PSCOAAOA is carried out on CEC2014 benchmark functions to validate its efficacy and robustness. The friction force generated during wear testing under different lubricating conditions is bifurcated into training and test data. Out of which, training data trains the SVM at an optimal combination of parameters. The overall accuracy of the built SVM model is found to be 95.85% with a computation time of 26.85 s.

A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning

Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.

Optimal design of the response surface for the root cutting of a hydroponic lettuce harvesting tool

A new type of continuous harvester is designed to address the problems of high labor cost and low efficiency in the process of harvesting lettuce in greenhouse hydroponics. The designed harvester is mainly composed of three parts, namely, reciprocating cutter, driving mechanism, and belt conveyor. The response surface method is used to establish the mathematical model between the force acting on the lettuce roots and the reciprocating speed of the cutter, the inclination angle of the cutter, and the conveying speed of lettuce. The reliability of the mathematical model is verified, and the optimal parameter combination of the force on the lettuce root is obtained. Considering the actual harvesting method, the destruction rate, damage rate, and success rate of lettuce are used as the design indexes in the experiment. Results showed that the force on the lettuce stem is the smallest under the optimal parameter combination of the reciprocating motion speed of the cutting blade (100 mm/s), the inclination angle of the cutting blade (2.31°), and the conveying speed of the lettuce (64.49 mm/s). When the minimum force is 1.91 N, the destruction rate of lettuce harvest is 1.85%, the damage rate of lettuce is 3.71%, and the success rate of lettuce harvest is 94.44%. This study offers a potential solution for the automatic harvesting of hydroponic lettuce in a greenhouse.

Ultrasonic spot welding of open-cell Cu foam and Al plate: A study on the quality of joints

As a type of novel material, open-cell copper (Cu) foam has been widely applied in electrode production of batteries, due to its functional and structural properties. The use of ultrasonic welding technique to join Cu foam and aluminium (Al) plate has been proven to be feasible, but quality evaluation of the joints remains a blank to be filled in. In this study, orthogonal test design is used to investigate the effects of welding pressure, welding amplitude, welding energy and the interaction among the three parameters on the shear-tensile strength of ultrasonically welded joints of Cu foam/Al plate, and it is influenced by the three parameters in the following order: welding pressure > welding energy > welding amplitude. Based on the results of the orthogonal test, a Cu foam/Al plate ultrasonic welding joint evaluation system is established, based on which, qualitative and quantitative analysis on the quality of the joints classified the joints into three grades. The optimal parameter combination of the welding joint of Cu foam/Al plate is obtained as follows: 0.5 MPa, 65 %, 130 J, under which the porous structure of the joint is complete while its tensile strength is equal to or even better than the base material.

Influence of operating parameters on hydrogen DISI engine at injection pressure-drop by experimental investigation and Taguchi method

Hydrogen, as a low-density, zero-carbon energy source, often faces injection pressure drop when used as an alternative fuel for multi-parameter regulation in direct injection spark ignition (DISI) engines. In this paper, the effects of operating parameters including torque, excess air coefficient (λ), ignition timing, and injection timing on combustion, emissions, and energy distribution of a hydrogen DISI engine were first investigated at injection pressures of 100 bar and 30 bar, respectively, followed by Taguchi methodology and analysis of variance (ANOVA) to determine the optimal parameter combinations and their contribution to typical indicators of engine performance. The results showed that injection pressure-drop deteriorated the coefficient of variation (COVIMEP), nitrogen oxides (NOx) emissions, and brake specific fuel consumption (BSFC) in the set of cases. And the deteriorating BSFC is mainly due to the increase in heat loss power (PHL) or the exhaust power (PEx). Taguchi method and ANOVA showed that injection pressure drop changed COVIMEP and BSFC regarding optimal ignition timing selection and PEx regarding optimal injection timing selection and it also increased the torque contribution to COVIMEP by 26.54 %. PHL and PEx should be simultaneously considered because their choice of optimal operating parameters is a “Trade-off” relationship in most cases.

Ultrasound signal processing based on joint GWO-VMD wavelet threshold functions

To overcome the problem of noise interference in the ultrasonic echo signal received by the probe in ultrasonic nondestructive testing, a method of ultrasonic signal processing using the jointed wavelet threshold function of GWO-VMD (Grey Wolf Optimisation, Variational Mode Decomposition) is proposed. The method first uses the GWO algorithm to optimise the VMD parameters to find the optimal parameter combination penalty factor α and decomposition modulus number K, and then uses the correlation coefficient method to differentiate between the desired signal and noise components after decomposition to obtain the intrinsic mode function (IMF) components. Secondly, the noise components are processed by combining the improved wavelet threshold function algorithm, and lastly, each modal component is restored to produce a denoised signal. Experimental results show that the method improves the signal-to-noise ratio (SNR) by 2.55%, reduces the root mean square error (RMSE) by 3.8%, preserves more useful information, and is more effective in denoising.