Second-order Regression Model Research Articles

Fuzzy logic-driven genetic algorithm strategies for ultrasonic welding of heterogeneous metal sheets

There are a lot of problems with the conventional fusion welding process, so ultrasonic welding has been used for about 20 years and has helped a lot of manufacturing industries, including aviation, medicine, and microelectronics. Ultrasonic welding takes less than one second, making it suitable for mass production. Poor weld quality and joint strength are common issues that industries encounter as a result of this process. Actually, the success and quality of the welding are determined by its control parameters. This research examines the impacts of weld time, vibrational amplitude, and weld pressure on the welding of 0.6 mm thick sheets of two different metals, specifically copper and aluminum (AA2024). Responses, including tensile shear stress, weld area, and T-peel stress, are acquired through experiments that follow a full factorial design including four replicas. The highest recorded tensile shear stress was 4.34 MPa, the maximum weld area measured was 63.6 mm2, and the peak T-peel stress reached 1.22 MPa. A second-order non-linear regression model was constructed using all of these data points, which related the responses to the predictors. Due to the importance of quality in the production sector, the process parameters were determined by the combination of genetic algorithm (GA) and fuzzy logic (FL) approaches. The impact of the weld zone temperature on various quality characteristics has been investigated through experiments. It has been noted from the confirmatory test that FL produces superior output outcomes compared to the genetic algorithm, with FL achieving a fuzzy multi-performance index of 0.94 compared to 0.61 for GA. By conducting microstructural analysis, weld quality levels, including “under-weld,” “good weld,” and “over-weld,” were established.

Investigating the adsorption potential of char derived from waste latex for methylene blue removal

This study presents the adsorption of methylene blue (MB) dye using latex char derived from pyrolysis of latex gloves. The adsorption process was investigated systematically using Response Surface Methodology (RSM) with a Central Composite Design (CCD). The effects of four key variables, namely pH, time, temperature, and adsorbent dosage, were studied using a factorial design enriched with center points and axial points. Experimental data were analyzed using a second-order polynomial regression model to construct a response surface model, which elucidated the relationship between the variables and MB removal efficiency. The study found that the char obtained at 800 °C exhibited the highest adsorption capacity due to its increased carbonization, expanded surface area, and diverse pore structure. Analysis of Variance (ANOVA) confirmed the significance of the quadratic model, with remarkable agreement between predicted and experimental outcomes. Diagnostic plots validated the model's reliability, while 3D contour graphs illustrated the combined effects of variables on MB removal efficiency. Optimization using DoE software identified optimal conditions resulting in a 99% removal efficiency, which closely matched experimental results. Additionally, adsorption isotherms revealed that the Freundlich model best described the adsorption behavior, indicating heterogeneous surface adsorption with multilayer adsorption. This comprehensive study provides valuable insights into the adsorption process of MB dye using latex char, with implications for wastewater treatment and environmental remediation.

Effect of operating conditions and surface roughness on grease lubrication efficiency

The aim of this paper is to investigate the effect of operating parameters (load and rotational speed) and arithmetic mean deviation (Ra) roughness value on temperature rise and tribological behavior of greased point contact under heavy load. The 3-factor, 4-level orthogonal friction experiments are designed and carried out. The moving point heat source integration method is applied to calculate the transient temperature rise of interfaces. The results show that the case with load 1900 N, rotational speed 260 rpm and Ra 0.4 μm has the highest friction coefficient and temperature rise. The grease fails and layered spalling and oxidative wear occur on the wear scar. Four second-order regression models are established to predict friction coefficient and the maximum temperature rise under different load, rotational speed and surface roughness. And the determination coefficients R2 of them are higher than 97 %. The models are helpful to determine if the working conditions fall within effective lubrication range and avoid severe damage.

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

The examination of the machining of 7075-T6 aluminum alloy polymer mold cavities using Taguchi optimization and analysis of variance is presented in this paper. This study identified the best CNC milling cutting parameters and used a mathematical model to quantify the surface roughness of the machined cavities. The findings showed that while using a flat endmill, the spindle speed multiplied by feed rate contributed 28.01% to surface roughness, and when using a ball endmill, the squared depth of cut contributed 41.27%. Using both flat and ball endmills, the depth of the cut contributed 98.53% to the material removal rate. A refined second-order linear regression model was employed to forecast the endmill-machined surface roughness. The Warp Surf Portable tester measured values that were outside the error range of approximately 0.257% and 2.8%, respectively, for the expected values. Surface roughness has a 99.97% correlation coefficient in the regression model, indicating a very significant link. Additionally, the study improved the cutting parameters for a ball endmill, which were 3005 Rpm, 726.7 mm/min, and 0.43 mm, and for a flat endmill, these were spindle speed (2500 Rpm), feed rate (650 mm/min), and axial cut depth (0.5 mm). The outcomes demonstrated how well the techniques enhanced mold cavity machining and cost estimation using Ra and MRR data. Consequently, these results can be applied to future academic studies and industrial applications.

Corner cutting accuracy for thin-walled CFRPC parts using HS-WEDM

Carbon Fiber-Reinforced polymer (CFRP) composite parts with thin-walled corners are in great demand in aircraft, cars, and precision instruments. Nonetheless, the fabrication of these parts is difficult due to their low stiffness. High-speed WEDM is an advanced technique for cutting thin CFRP components as it is a non-contact method for removing materials. Nonetheless, testing results demonstrate an unavoidable deformation in the thin-walled corners of the CFRP composite. The objective of this study is to improve the accuracy of corners in thin-walled CFRP composite parts. The research utilized a Taguchi L16 orthogonal array to investigate the influence of various process parameters, including pulse-on duration (Pon), pulse-off duration (Poff), and input current (I), as well as the parameter CFRP plate thickness (T), on corner inaccuracy. The CFRP thickness varied between 0.5, 1.0, 1.5, and 2.0 mm, and the corner angles examined were 30°, 60°, 90°, and 120°. Additionally, a second-order polynomial regression model was used to determine the correlation between the process parameters and corner inaccuracy at various corner angles. Also, a multi-response optimization technique using a composite desirability coupled with a generalized reduced gradient were used to find the optimal process combination across various CFRP thicknesses. According to the research findings, the most relevant process parameters impacting corner accuracy at different angles were the pulse-on duration and input current. To achieve accurate corners with different angles, the optimal process parameters were identified: Pon (40µs), Poff (15µs), and I (4A) for CFRP thicknesses 0.5 and 1.0mm, and Pon (45μs), Poff (30μs), and I (2A) for thicknesses 1.5 and 2.0mm.

Impact of the Structural Parameters on the Performance of a Regenerative-Type Hydrogen Recirculation Blower for Vehicular Proton Exchange Membrane Fuel Cells

The compact structure and stable performance of regenerative blowers at small flow rates render them attractive for the development of hydrogen recirculation devices for fuel cells. However, its optimization of structural parameters has not been yet reported in the literature. Along these lines, in this work, a mechanistic study was carried out in terms of examining the role of the flow channel structure on the performance of a regenerative-type hydrogen recirculation blower for the fabrication of automotive fuel cells. A three-dimensional computational fluid dynamics (CFDs) model of the regenerative blower was established, and the accuracy of the proposed model was verified through experimental data. The impact of structural parameter interactions on the performance of the regenerative blower was investigated using CFD technology, response surface methodology (RSM), and genetic algorithm (GA). First, the range of the structural parameters was selected according to the actual operation, and the influence of a single geometric factor on the efficiency was thoroughly investigated using CFD simulation. Then, a second-order regression model was successfully established using RSM. The response surface model was solved using GA to obtain the optimized geometric parameters and the reliability of the GA optimization was verified by performing CFD simulations. From our analysis, it was demonstrated that the interaction of the blade angle and impeller inner diameter has a significant impact on efficiency. The entropy generation analysis showed also that the internal flow loss of the optimized regenerative blower was significantly reduced, and the design point efficiency reached 51.7%, which was significantly improved. Our work provides a novel solution for the design of a recirculation blower and offers a reference for the optimization of regenerative-type hydrogen blowers.

Isometric fiber distribution in the roller drafting zone

Roller drafting, a key mechanical operation for attenuating slivers to a desired thickness, holds critical significance in the spinning processes of the textile industry. Within the drafting stage of spinning, the motion state of fibers is influenced by their intrinsic characteristics, the force applied, and the distribution of both fast-moving and slow-moving fibers. This research focused on man-made fibers, which are isometric, and studied their distributions in the roller drafting zone. In addition, the cut-off weighting method was incorporated to measure the fiber distributions in the drafting zone, the impact of drafting parameters on fiber distribution was discussed, and the associated fiber distribution curves were generated. Findings reveal that fiber distributions are highly affected by factors such as the fiber length, roller-setting, and draft ratio. Conversely, the input sliver linear density and doubling number exhibited negligible influence on the fiber distribution. The total fiber distribution curve appeared as a dichotomy, including a horizontal line and an oblique line. This study also presented a second-order regression model and a deduction model to predict the distribution of isometric fibers in the roller drafting zone.

Fretting wear characteristics of Ti6Al4V/TiC nano composites using response surface method

This study explores the impact of titanium carbide (TiC) nanoparticles on the microstructure and fretting wear performance of Ti64-GV alloys. The Ti64 alloys, featuring varying TiC concentrations, were fabricated through spark plasma sintering (SPS) process. Fretting experiments followed a face-centered central composite design methodology, employing a second-order quadratic regression model to analyze wear volume loss and coefficient of friction (CoF). The findings revealed a notable reduction in wear volume loss and friction coefficient with increasing TiC content. The presence of TiC significantly enhanced the wear resistance of Nano-composites due to heightened hardness and oxidation wear resistance. Employing hybrid desirability-based response surface methodology (RSM) led to substantial improvements in wear characteristics (37% in CoF and 46% in wear volume loss). Microstructural analysis affirmed enhanced surface quality and textures using the RSM hybrid approach for predicting optimal process parameters.

Investigation of machining rate and surface roughness in wire EDM of Al6063/WC/ZrO2 composite using response surface methodology

The aim of the current work is to explore the machinability behavior of Al6063/WC/ZrO2 metal matrix composites (MMC) produced through a stir casting process through wire electrical discharge machining (WEDM) process. In order to examine the effects of process parameters such as voltage (V), pulse on time (Ton) and pulse off time (Toff) on material removal rate (MRR) and surface roughness (SR), the experiments were conducted by adapting Response Surface Methodology (RSM) in conjunction with the central composite design (CCD). A second-order regression model has been developed to predict the response parameters and an analysis of variance (ANOVA) was performed to validate the significance of the models. Using the desirability function approach, the parameters were set for the highest MRR and the minimum SR value. The prediction was within a tolerable average error range. A total of 19 sets of tests were developed to obtain six possible solutions. The most suitable solution among these six has been found by a confirmatory experiment. The results indicate that voltage and its interactions have significantly affected both the responses than Ton and Toff. Overall, it can be said that the study of the Al6063/WC/ZrO2 MMCs using WEDM process parameters demonstrated improved casting and machining qualities.

Optimization of the catalytic production of methyl stearate by applying response surface Box-Behnken design: an intensified green option for high-cetane biofuel manufacture.

To enhance the efficiency of processes by decreasing the reaction severity and energy consumption, and reducing the equipment size, facilities' space and operation cost, process intensification is an increasingly used option in the chemical industry. Within this framework and in agreement with some of the green chemistry principles (design for energy efficiency and use of renewable feedstocks), this work deals with the implementation of high-shear mixing (HSM) to intensify the homogeneous esterification of stearic acid (SA) with methanol to methyl stearate, a high-cetane number alkyl ester suitable to be added into biofuel streams. The response surface Box-Behnken design (BBD) is applied to quantify the main effects and two-way interactions of four key input reaction factors: methanol : SA ratio (7-16 mol mol-1), catalyst mass (0.25-4.0 wt%), temperature (40-60 °C), time (1-12 min), and to approximate the optimal conditions on the intensified SA esterification. The statistical BBD results indicates that the four linear effects, two of the four possible quadratic effects (catalyst mass and temperature) and only one (catalyst mass-time) of the six existing two-way interactions are statistically relevant at the 95% confidence level. Catalyst mass is the most influencing factor in the reaction, followed by methanol : SA ratio, temperature, and time. The proposed second-order regression model predicts that the intensified esterification requires only 12 min to practically convert all SA (99% ± 6.8%) running the reaction at 12.4 methanol : SA ratio, 4 wt% catalyst mass, 60 °C and 500 rpm, a value experimentally validated (93.2% ± 0.7%). Under these conditions and with the assistance of HSM, the typical reaction length of conventional heterogeneous and homogeneous-phase esterification processes decreases from 5 to 117 and 35 to 90 times, respectively.

Modeling and analysis of process parameters in EDM of Ni35Ti35Zr15Cu10Sn5 high-temperature high entropy shape memory alloy by RSM Approach

The Electrical Discharge Machining (EDM) technique demonstrates proficiency in fabricating precise and intricate geometries, especially in challenging-to-machine materials like high-entropy shape memory alloys. Analyzing and optimizing machining parameters are crucial for their direct impact on mechanical properties and overall product efficiency. The main responses chosen to evaluate the processes are material removal rate (MRR), electrode wear rate (EWR), and surface roughness (Ra). At the same time, the associated machining conditions were discharge current (Ip), pulse-on time (Ton), and pulse-off time (Toff). EDM is a multi-response process; therefore, the method of Response Surface Methodology (RSM) is utilized to assess the influence of machining parameters on Ni35Ti35Zr15Cu10Sn5 (at%) high-temperature high entropy shape memory alloy (HT-HE-SMA) using a copper electrode. Based on a center composite design (CCD), experiments were analyzed using Minitab19 software. To identify the most influential parameters, a thorough analysis of variance (ANOVA) at various significance levels (5%) was performed, checking the sufficiency of all fitted second-order regression models. Discharge current, pulse-on time, and pulse-off time were identified as significant factors that affect output (MRR, EWR, and Ra). The model adequacy of the current experimental investigation is perfect, with determination coefficients (R2) of 97.82% for MRR, 95.36% for EWR, and 99.53% for Ra.

The effect of physicochemical properties on paracetamol photodegradation in cuboid bubble column

Paracetamol is one of the most anthropogenic micropollutants, and their removal from the environment often requires a specialized method of remediation. In this study, a photocatalyst technique aided with air bubbles was used to degrade the pharmaceutical pollutant paracetamol (PCT) from the water via the COD test and HPLC analysis under different operating conditions. The experiments were carried out in a semi-batch rectangular bubble column with dimensions of 1500 mm height, 30 mm depth, and 200 mm width under UV light. Titanium oxide (TiO2) was used as a source of catalyst. The effect of operating conditions of pH (3-10), air flow rate (0-2) L/min, salinity of solution represented by NaCl concentration (0-1000) mg/L, and 240 min irradiation time on the paracetamol removal were studied. The Box–Behnken design was adopted to study the individual effects of pH (A), air flow rate (B), and salinity (C) and their interactive effects. From the experimental and regression data, a second-order polynomial regression model is predicted, and the variance analysis of the regressions shows that the linear terms (A and B), and all quadratic terms (A, B, and C) have significant effects on the removal percentage of COD. According to numerical optimization, the greatest %COD removal is 76.7 in the process conditions of 5.3 pH, 1L/min, and 269 mg/L of NaCl. The experimental results show that the maximum %COD removal was 78% at pH=7, 1L/min, and 0mg/L of NaCl. HPLC analysis shows 91.2% of paracetamol degradation.

Optimisation of PAHs biodegradation by Klebsiella pneumonia and Pseudomonas aeruginosa through response surface methodology

ABSTRACT Response Surface Methodology (RSM) with Box–Behnken Design (BBD) is used to optimise the Phenanthrene (PHE) degradation process by Klebsiella pneumoniae (K bacteria) and Pseudomonas aeruginosa (P bacteria). Wherein substrate concentration, temperature, and pH at three levels are used as independent variables, and degradation rate of PHE as dependent variables (response). The statistical analysis, via ANOVA, shows coefficient of determination R2 as 0.9848 with significant P value 0.0001 fitting in second-order quadratic regression model for PAHs removal by Klebsiella pneumonia, and R 2 as 0.9847 with significant P value 0.0001 by P bacteria. According to the model analysis, temperature (P < 0.0006) is the most influential factor for PHE degradation efficiency by K bacteria, while pH (P < 0.0001) is the most influential factor for PHE degradation by P bacteria. The predicted optimum parameters for K bacteria, namely, temperature, substrate concentration, and pH are found to be 34.00℃, 50.80 mg/L, and 7.50, respectively, and those for P bacteria are 33.30℃, 52.70 mg/L, and 7.20, respectively. At these optimum conditions, the observed PHE removal rates by two bacteria are found to be 83.36% ± 2.1% and 81.23% ± 1.6% in validation experiments, respectively. Thus RSM can optimise the biodegradation conditions of both bacteria for PHE.

Integrated Assessment of MIG Welding Parameters on Carbon Steel using RSM Optimisation

This study examined how parameter optimisation in MIG welding affects material properties, weld quality, flexural strength, and weld bead geometry using an experimental design. Structural testing reveals that weld pool imperfections, especially in welded joints, often lead to metal failure. Incorrect selection of welding parameters, material weldability, and metal forming contribute to defects and necessitate post-treatment. The study aims to minimize weld defects and maximize material strength in welded parts using a quadratic second-order regression model to predict optimal process parameters. The chosen JIS G3131 hot-rolled carbon steel material is commonly used in automotive parts, is cost-effective, and can be easily welded. The welding process is MIG buttweld according to AWS 1.1 standards. The tests match the RSM design's 3-by-3 orthogonal array. The process parameters are welding current, arc voltage, and welding speed. The responses are bead height, bead width, penetration, and flexural strength. NDT X-ray radiography detects flaws in weld pools. The welded connection's durability is determined with a three-point bending test. Experiments are conducted to find the best input parameters with 95% confidence. This study also uses an experimental design based on an orthogonal array and an analysis of variance (ANOVA). Contribution to factors and regression analysis. The predicted flexural strength was 903.1 MPa, with a 0.66% error compared to the experimental value of 897 MPa. Predicted values for weld penetration, bead width, and bead height are 3.01, 9.45, and 2.04. In the RT inspection test, the test sample with the optimum parameters had no defects in the welded joint. The optimal parameter values for the regression analysis of the RSM combination are 115, 20, and 18 for current, speed, and voltage. Confirmation tests for DT and NDT were performed to validate the optimal parameter settings. This research shows that optimizing process parameters for welded products improves weld geometry and flexural strength. This method is widely used and valuable in metal fabrication.

Optimization of the design of an oscillating water column for the Pacific Ocean conditions using the response surface methodology

Wave energy possesses a significant potential as a sustainable and renewable source of power due to its abundant presence in the Earth’s oceans. This research focused on the assessment of the hydrodynamic efficiency (ɛ) of a resonant chamber within an OWC (oscillating water column) system. Four geometric design parameters of the resonant chamber (h1/h, b2/h, d/b2 and α) were selected to optimize ɛ, being h the depth of the water; h1 and α correspond to the vertical length and angle of inclination of the front wall, respectively; b2 is the width of the resonant chamber, and d represents the diameter of the air outlet hole. To analyze the system, a combined approach utilizing CFD with the VoF (volume of fluid) method and the response surface methodology (RSM) was adopted. The RSM employed a FCCD (face-centered central composite design) to optimize the factors, while the VoF method enabled to simulate in a detailed way the fluid behavior. The ANSYS-Fluent software was employed to perform a transient simulation. A second-order regression model was constructed and validated based on the simulation results. The quadratic regression model exhibited a high Radj2 value (82.81%) and had a p-value associated ¡0.05 (4.66 × 10-8). Therefore, the model was highly significant in accurately representing the ɛ of the OWC resonant chamber. The highest ɛ value (67.01%) was obtained for h1/h, b2/h , d/b2 and α equal to 0.7786, 0.4455, 0.0863, and 89.38°, respectively. The numerical results were validated with the Stokes second-order wave theory and a good correlation was observed with the numerical findings. This study demonstrates that the designed and optimized OWC can be effectively utilized in the Colombian Pacific Ocean to generate electrical energy, promoting economic development in the area.

Numerical optimization and experimental validation of the runner of a gravitational water vortex hydraulic turbine with a spiral inlet channel and a conical basin

This paper describes the use of the response surface methodology for the numerical optimization of the runner of a gravitational water vortex hydraulic turbine with a conical basin and a spiral inlet channel. The effect of the relative position of the runner (p), the number of blades (n), and the relation between the mean diameter of the blades (Db), and the diameter of the basin (D), (Db/D) on the efficiency of the turbine was evaluated, and the optimum runner was determined. A second-order regression model representing the efficiency of the turbine was developed. From this model, it was found that the highest efficiency of 65.18% was reached when all the independent variables were set to their higher values, i.e., Db/D, n, and p equal to 0.45, 6, and 0.6, respectively. Experiments were performed using the optimal runner (Db/D=0.45, n=6) and four values for p (0.4, 0.45, 0.50, and 0.55). It was not possible to test the optimal condition with p=0.6, due to the final quality of the acrylic model, the runner collided with the walls of the cone. The experimental efficiencies for the p values were 30.13, 38.35, 53.19, and 60.77%, respectively. According to the response model, these efficiencies correspond to 28.84, 40.46, 50.80, and 59.99%, respectively.

Discrete element contact model and parameter calibration for clayey soil particles in the Southwest hill and mountain region

Distinct physical properties of red clay soil in hilly and mountainous regions of southwest China, including high adhesiveness and density, challenge the operation of agricultural machinery. A scarcity of accurate discrete element simulation parameters for this soil type restricts computational modeling. The study was focused on red clay soil with a moisture content of 12.50% ± 1% and a measured repose angle of 35.54°. The soil's inherent physical properties were identified through experimental assessments. Soil contact mechanical parameters were obtained from the GEMM database, and optimal contact parameter ranges were determined using Steepest Ascent Experiments, with the simulated soil particle repose angle serving as the response value. A second-order regression model was developed using a quadratic regression rotation orthogonal combination test. By taking the actual repose angle as the optimization criterion, parameters were optimized. The optimal contact mechanical parameters in EDEM simulations were identified as: JKR surface energy at 8.981 J/m2, recovery coefficient at 0.474, dynamic friction coefficient at 0.196, and static friction coefficient at 0.45. The model yielded a repose angle of 36.21°, closely corresponding with the observed value, with a relative error of 1.80%. The parameters calibrated in this study offer a valuable reference for future soil-tool interaction studies and tillage implement optimization in these regions.

Performance Analysis and Optimization of Regenerative Gas Turbine Power Plant using RSM

In the present study, a thermodynamic analysis of thermal performance is carried out in a regenerative GT power plant. The optimization procedure of design parameters is realized by the response surface methodology (RSM). The thermodynamic simulations were carried out using the EES code for numerous variables such as compression ratio (2≤rp≤12), inlet temperature (273≤T1≤313K), turbine inlet temperature (1200≤T3≤1600K), and regenerator effectiveness (45≤ε≤85%). Analysis of variance (ANOVA) was carried out to identify the process parameters that influence thermal efficiency (ηth) and specific fuel consumption (SFC). Then, a second-order regression model was developed to correlate the process parameters with ηth and SFC. Consequently, numerical and graphical optimizations were performed to achieve multi-objective optimization for the desired criteria. According to the desirability function approach, it can be seen that the optimum objective functions are ηth=50.61% and SFC=0.117 kg/kWh, corresponding to process parameters T1=273.26K, T3=1597.64K, rp=6.95 and ε=84.89%. Lastly, verification simulations were conducted to validate the importance of the generated statistical models.