Optimal Input Parameters Research Articles

Band offset optimization in MAGeI3 based perovskite solar cells

The study focuses on a complete modelling of perovskite solar cell (PSC) employing methyl ammonium germanium iodide (MAGeI3) as light absorbing material via optimizing device’s conduction band offset (CBO) and valence band offset (VBO), which helps in identifying the suitable transport materials that can be employed for extracting the best photovoltaic parameters for solar cell. The device architecture FTO/TiO2/MAGeI3/CuO/Pd has been considered for the study, which after the input parameter optimization provides a power conversion efficiency (PCE) of 12.33 %. An optimization of the CBO of the device configuration results in a PCE of 13.57 %, indicating that the chosen ETL, TiO2 is suitable for the device configuration. An alternate ETL, IGZO with the required electron affinity of 4.18 eV, when tested for the device shows a PCE of 12.39 %. The VBO optimization of the device configuration FTO/TiO2/MAGeI3/CuO/Pd suggests the inclusion of CZTS as the HTL and further input parameter optimization of the device provides PCE of 16.59 %. The VBO optimization of FTO/IGZO/MAGeI3/CuO/Pd suggests for the inclusion of CZTS as the HTL, and provides an excellent PCE of 14.57 %. Hence, the CBO and VBO optimized device configuration, FTO/IGZO/MAGeI3/CZTS/Pd is again optimized by changing the parameters of perovskite and transport layers and a PCE of 20.54 % is achieved. It is further analysed considering diverse back contact metal and optimum PCE of 20.57 % is attained, with Se as the back metal contact. An estimation of the QE of FTO/IGZO/MAGeI3/CZTS/Se configuration indicates efficient charge generation and collection in visible and NIR wavelength regimes.

Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances

The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.

Prediction of formation fracture pressure based on reinforcement learning and XGBoost

Abstract Clearly determining the magnitude of fracture pressure is a crucial indicator for fracturing design. Traditional methods for predicting fracture pressure suffer from challenges such as difficulties in obtaining required data, low prediction accuracy, and local limitations in application. In light of these issues, the article proposes a fracture pressure prediction model based on reinforcement learning and XGBoost utilizing geophysical well logging data. Based on the relevance analysis, optimal input parameters, including DEPTH, DEN, AC, GR, CRL, and RT, are selected from geophysical well logging data. We have developed a framework for a fracture pressure prediction model based on XGBoost, wherein hyperparameters are fine-tuned using an improved Q-learning algorithm. The optimized XGBoost model for fracture pressure prediction attains outstanding performance metrics, including an R 2 value of 0.992, a root mean square error of 0.006%, and a mean absolute error of 0.539%. In direct comparison with grid search, Bayesian optimization, and ant colony optimization, the improved Q-learning algorithm emerges as the most effective optimization approach. The predictions generated by the proposed method exhibit remarkable consistency with fracture pressure data measured on-site. This approach successfully addresses the shortcomings encountered with traditional fracture pressure prediction methods, such as inadequate accuracy, demanding data prerequisites, and constrained applicability.

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

On the prediction and optimization of the flow boiling heat transfer in mini and micro channel heat sinks

The most common methods for predicting flow boiling heat transfer in mini/micro channels-based heat sinks rely on semi-empirical correlations derived from experimental data. However, these correlations are often limited to specific testing conditions. This study proposes a novel approach using deep learning and genetic algorithms (GA) to predict and optimize refrigerants' flow boiling heat transfer coefficients (FBHTC) in mini/microchannels-based heat sinks. The dataset used in this study includes FBHTC observations from the literature for seven refrigerants (R1234yf, R1234ze, R134A, R513A, R410A, R22, and R32). The optimal input parameters identified include hydraulic diameters ranging from 1 to 7 mm, saturation temperature from 0 to 20 °C, flow qualities from 0.006 to 0.972, heat flux from 3 to 78.8 kW/m2, and mass fluxes between 100 and 1200 kg/m2s. Gradient-boost regression trees were employed to develop the deep learning and GA models for accurate estimation and optimization. Correlation analysis and feature engineering selected the most influential parameters to construct a precise and simple model. The results demonstrate that the models could estimate refrigerants' FBHTC with high accuracy, achieving an R2 of 0.988 and a mean squared error (MSE) of 0.05%. The GA-based method effectively optimized the FBHTC for each refrigerant by determining the appropriate input parameters, including the saturation temperature, heat and mass fluxes, quality, and hydraulic diameter. Additionally, a parametric analysis using explainable artificial intelligence was conducted to interpret the impact of each input parameter on the FBHTC.

Multi‐parameter design optimization of crash box for crashworthiness analysis

AbstractThis study focuses on the optimization process of crash box design. The design optimization process is resource‐intensive and requires multiple dynamic simulations. Numerous design parameters can be varied to satisfy the crashworthiness objectives. Therefore an intelligent and robust machine learning (ML) framework has been developed. This framework can be employed to assist in the optimization of various crashworthiness components with different crashworthiness objectives.Here, a reinforcement learning‐based (RL) machine learning framework is developed. It consists of a finite element method (FEM) surrogate and RL environment. The FEM surrogate is trained using data from FEM simulations as well as synthetic data generated by a Generative Adversarial Network (GAN). An inverse problem is solved for optimizing the geometrical parameters of the boundary value problem while the RL is receiving input from the FEM surrogate. RL has proven to be accurate in many fields due to its ability to explore and exploit the learned dynamics. Conventional algorithms require numerous iterations and tuned functions to achieve appropriate results and need to be initialized after a slight change in the problem. Due to an optimization of input parameters in an inverse problem, RL is chosen for the present investigation.For the simulation data needed, the crash box is designed with linear first‐order accurate four nodal shell elements. Also, bilinear elastoplastic material along with geometrical nonlinearity is used to simulate the dynamic deformation process.The RL agents learn to vary the crash box design parameters and search for the optimal parameters. The optimal parameters are based on the user‐defined crashworthiness objectives.

Analyzing the effect of different shielding atmospheres on fiber laser welding parameters for modified 9Cr-1Mo steel

The present research aims to investigate the influence of and the relationship between welding speed ( v), focal position ( FP), and laser power ( LP) as input parameters for fiber laser to weld P91 under pure argon and CO2 shielding atmospheres using response surface methodology and using the desirability approach; optimization of input parameters has been performed to maximize the depth of penetration ( DP) while minimizing the top bead width ( TW) and heat-affected zone width ( HW). The results indicate that laser power ( LP) has the most significant impact on depth of penetration ( DP), whereas welding speed ( v) affects top bead width ( TW) and heat-affected zone width ( HW). The optimal combination of parameters under argon shielding results in a significantly low heat input of 48 J/mm (0.048 kJ/mm) and 50 J/mm (0.050 kJ/mm) pure argon and CO2 shielding atmospheres, respectively. Despite the difference in heat input being only 4.08%, an 11.618% increase in DP is observed under the CO2 shielding atmosphere. Due to the high-speed nature of the welding process (argon: 82.65 mm/s and CO2: 79.283 mm/s) combined with the significant reduction in heat input, the heat-affected zone is limited to a mere width of 0.203 mm under an argon shielding atmosphere and 0.263 mm under the CO2 shielding atmosphere compared with that reported in the other welding processes without the dissolution of precipitates in the fusion boundary of heat-affected zone. Microstructural characterization of the fusion zone has revealed the presence of an untempered lath martensite structure with precipitate dissolution. The precipitate dissolution has resulted in grain coarsening of 47.76% in argon weld and 39.65% in CO2 weld compared with the base metal grain size of 6.07 µm.

Statistical modeling and optimization of clad geometry in laser cladding of Amdry 365 on Hastelloy X superalloy with response surface methodology

The aim of this study is to statistically investigate and optimize the laser cladding process of Hastelloy X superalloy using NiCoCrAlY powder (Amdry 365). In this study, response surface methodology was used to investigate the influence of laser input variables such as scanning velocity, laser power, and duty cycle on the clad geometry (including height, width, and angle) and dilution ratio. The results indicate that with increasing duty cycle and laser power, the clad width increases significantly, while the clad height increases slowly. However, increasing the scanning velocity leads to a decrease in the width and height of the clad. Furthermore, the clad angle and dilution ratio increase with increasing input parameters. The results of variance analysis shows that laser scanning velocity had the most significant impact on clad height, angle and dilution ratio, while laser power had the greatest influence on clad width. The laser power of 321.3 W, the scanning velocity of 6.3 mm/s and the duty cycle of 76 % proved to be the optimal input parameters. An experimental test was carried out to validate the optimal conditions based on the optimization responses. The validation results show that the developed models are able to describe the responses with an accuracy of less than 9 % error. The EDS analysis performed in the cladding zone of the optimum specimen showed the presence of the β phase in the dendritic region and the γ phase in the interdendritic region.

Enhancing tensile properties of polymer‐based triply periodic minimal surface metamaterial structures: Investigating the impact of post‐curing time and layer thickness via response surface methodology

AbstractThis research aims to explore the influence of post‐curing time and layer thickness on the tensile characteristics of various triply periodic minimal surface (TPMS) structures produced by mask stereolithography (MSLA). The study determined the best post‐curing duration, layer thickness, and TPMS lattice type to improve ultimate tensile strength (UTS) and absorbed energy. To experimentally evaluate the tensile characteristics, a dog bone‐shaped specimen was utilized. Three distinct TPMS structures, Gyroid (G), Neovius (N), and Diamond (D), were present in the test region. After investigating many process factors with response surface methodology (RSM), optimization methods are applied to find their best printing procedure. The work shows the novel use of RSM to optimize post‐curing and printing parameters on TPMS structure mechanical properties during manufacturing. According to the optimization results, the biggest factor affecting UTS is layer thickness, while the most significant factor increasing energy is curing time. The optimal operating parameters for MSLA printing based on the optimization results are a layer thickness of 0.05 mm, a post‐curing period of 40 min, and a lattice type of N. The optimum responses corresponding to the optimum parameters were determined as 7.16 MPa for UTS and 18.16 J for energy.Highlights Optimized the production process parameters of TPMS geometries. Compared TPMS structures for mechanical performance. Identified optimal input parameters to improve UTS and energy absorption. Conducted comprehensive experimental evaluations to validate the optimization.

Achieving High Precision and Productivity in Laser Machining of Ti6Al4V Alloy: A Comprehensive Study using a n-predictor polynomial regression model and PSO algorithm

Ti-6Al-4V, the Titanium alloy, has significant utilizations in aerospace, automotive, and marine sectors for its low density and high strength at elevated temperature. But its chemical activity and low thermal conductivity inhibits its machining by conventional method. Nd: YAG laser beam machining (LBM) finds extensive use in rapid and precise cutting of Ti6Al4V. This study has examined the influences of various LBM machining variables, including laser power, gas pressure and stand-off distance, in cutting 5mm thick Ti-6Al-4V plate. In assessing the effectiveness and performance of the LBM process, three response functions—surface roughness, angle of kerf, and material removal rate—have been designated. From the experimental data, different regression models have been established to estimate these response functions in terms of the machining parameters. Based on R2 score and RMSE, multi-dimensional polynomial regression is decided as the most suitable regression model. Subsequently, the Particle Swarm Optimization technique has been applied to identify the optimal machining parameters for reducing angle of kerf and surface roughness, while increasing material removal rate. Three individual single-objective functions underwent optimization, along with a multi-objective function. Furthermore, experimental verification was conducted for the optimal input parameters in the single-objective as well as the multi-objective optimization scenarios, resulting in an accuracy of 97% and 98%, respectively. Such a close agreement emphasizes the accuracy of the developed regression model as well as it signifies the reliability and efficacy of the optimization technique.

Tribological behavior of a Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy and prediction through response surface methodology based mathematical modeling

In the present work, a novel Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy (CCA) was developed using the vacuum arc melting technique. The as-cast CCA underwent a two-stage heat treatment process. In the first stage of heat treatment (HT 1), the alloy was heated to 900˚C. Subsequently, in the second stage of heat treatment (HT 2), the HT 1 samples were annealed at various temperatures, including 700˚C, 900˚C, and 1100˚C, for 20 h. The microstructure and mechanical response of the as-cast, HT 1 and HT 2 (annealed) samples were thoroughly investigated. The phase evolution, microstructure, and chemical composition were analyzed using XRD and SEM-EDS. The XRD analysis revealed major solid solution BCC phases (BCC 1 and BCC 2) with a small amount of Laves phase; however, the amount of Laves phase increased with increase in annealing temperature. The microhardness and elastic modulus of the CCA specimens were evaluated using instrumented micro-indentation. The CCA annealed at 1100˚C exhibits the highest microhardness and elastic modulus, with values of 5.94 ± 0.38 GPa and 124.40 ± 7.17 GPa, respectively. Response surface methodology (RSM) was used to develop a mathematical model aimed at predicting tribological characteristics, specifically the specific wear rate (SWR) and coefficient of friction (COF). RSM proposes a quadratic model to represent the mathematical relationship between input parameters for evaluating SWR and COF. The desirability function approach is employed to optimize input parameters to minimize both SWR and COF. The optimized values of SWR and COF are 6.87 × 10−4 mm3/N.m and 0.30 under a 27.52 N load, 2.86 Hz oscillation frequency, and 1100˚C annealing temperature. Surface topography analysis of the worn surface was evaluated using SEM and a profilometer to understand the wear mechanism and surface characteristics.

Machine Learning Enabled Design and Optimization for 3D‐Printing of High‐Fidelity Presurgical Organ Models

AbstractThe development of a general‐purpose machine learning algorithm capable of quickly identifying optimal 3D‐printing settings can save manufacturing time and cost, reduce labor intensity, and improve the quality of 3D‐printed objects. Existing methods have limitations which focus on overall performance or one specific aspect of 3D‐printing quality. Here, for addressing the limitations, a multi‐objective Bayesian Optimization (BO) approach which uses a general‐purpose algorithm to optimize the black‐box functions is demonstrated and identifies the optimal input parameters of direct ink writing for 3D‐printing different presurgical organ models with intricate geometry. The BO approach enhances the 3D‐printing efficiency to achieve the best possible printed object quality while simultaneously addressing the inherent trade‐offs from the process of pursuing ideal outcomes relevant to requirements from practitioners. The BO approach also enables us to effectively explore 3D‐printing inputs inclusive of layer height, nozzle travel speed, and dispensing pressure, as well as visualize the trade‐offs between each set of 3D‐printing inputs in terms of the output objectives which consist of time, porosity, and geometry precisions through the Pareto front.

Optimization and ranking of the input parameter settings of sustainable grinding using cashew nut shell liquid as cutting fluid

The current work advocates the use of Cashew Nut Shell Liquid/Oil (CNSL), an oil extract of the leftover cashew nut shells, as a novel environment-friendly cutting fluid in sustainable machining operations. The tribological characteristics of CNSL obtained on a pin-on-disc tribometer are found to be better compared to the traditionally used cutting fluid. Experiments are conducted on the surface grinder with EN8 material, considering input parameters, such as cutting fluid type, grinder speed and grade, work speed, and depth of cut, with Surface Roughness (Ra) and Grinding Temperature (Temp) being the responses. Input parameter optimization is performed using Taguchi’s statistical models. A total of 36 investigative and six validation experiments are conducted, and a prediction model is proposed. When Ra and Temp are optimized simultaneously, the prediction value of Ra is 0.071 μm, and the corresponding value of Temp is 31.6 °C for which the experimental values are 0.072 μm and 32 °C respectively. This work also applies the TODIM (TOmada de Decisao Interativa Multicriterio, in the Portuguese language), a multi-attribute decision-making method for ranking the input parameter settings. The study reveals that the performance of CNSL is better than that of a traditional cutting fluid, and the TODIM method can be successfully applied to rank the input parameter settings.

Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape

The performance of several heat transfer mechanisms, including solar energy sources, is being improved through optimization, which also effectively supports the most efficient feasible design. A numerical study has been done on a turbulator-enhanced flat plate solar collector (FPSC) while also introducing the utilization of a quad-lobed tube as a pioneering innovation in the system’s design. The incorporation of nanoparticles into the working fluid was implemented to enhance performance, with aluminum oxide particles selected for this purpose. Furthermore, assessing the second law and analyzing exergy generation was employed to discern irreversibility within the system. Optimization of both geometric and non-geometric elements has been accomplished through the integration of a multi-objective optimization (MOO) method with machine learning (ML). Random Forest (RF), LASSO Regression (LaR), and Support Vector Regression (SVR) are the machine learning models that were utilized in order to ascertain the correlations that exist between the system’s inputs and outputs. According to the findings, RF was found to be the most appropriate algorithm for capturing the impacts of parameter variations. This was demonstrated by the high coefficient of determination (R2) quantities that RF possessed, which were 0.95, 0.99, and 0.99 for friction factor (f), exergy loss (Xd), and second law effectiveness (ηII), respectively. However, the SVR and LaR algorithms showed significant deviations from the FPSC simulation results. The SVR algorithm achieved R2 values of 0.54 for f, 0.78 for Xd, and 0.84 for ηII, while the LaR algorithm achieved R2 values of 0.65 for f, 0.99 for Xd, and 0.98 for ηII. This research aims to find the optimal input parameters that minimize f and Xd while maximizing ηII. Therefore, to determine the Pareto optimal solutions the non-dominated sorting genetic algorithm (NSGA-II) has been utilized. The result was an illustration of the Pareto set, which stands for a collection of optimal solutions. Every solution in this collection achieved the desiredbalance, obtaining the best outcomes without compromising any objectives.

Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology

This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R2 between 0.99426 and 0.9998 was observed by ANN whereas R2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.

Quantitative analysis of ITER Poloidal Field joints through rigorous resistivity parameterization

The lap-type twin-box joints are integral components in International Thermonuclear Experimental Reactor (ITER) fusion magnets, with profound implications for magnet stability based on their electro-magnetic, thermal, and mechanical properties. Throughout the extensive R&D process, rigorous qualification tests are conducted to meet stringent standards. However, existing tests often prioritize global performance, which lack of strand-level details due to inherent limitations in test setups. Furthermore, as the referencing test facility of SULTAN falls short in replicating relevant ITER operating conditions, numerical methods that offer both accuracy and the requisite level of detail for comprehensive magnet and component analysis and development are necessary. This paper introduces the utilization of the JackPot-AC/DC code, developed at the University of Twente, as a fundamental tool for achieving strand-level precision in handling CICCs and joints, which encompasses copper and solder components. The primary focus of this study is to obtain precise input parameters, emphasizing their role in conducting a quantitative analysis using JackPot-AC/DC. The investigation centers on an ITER PF5 joint (PFJEU6), where contact resistances and AC losses were measured under parallel magnetic fields. Given the constraints in the measured results, an enhanced parameterization is performed to derive precise resistivity and solder-related parameters. Additionally, sensitivity analyses of individual parameters and cable compact configurations are thoughtfully evaluated. With the optimal input parameters acquired, systematic simulations of the joint exposed to transverse magnetic fields, mimicking SULTAN and ITER operating conditions, are processed and validated against experimental results. This research establishes a comprehensive foundation for the analysis of lap-type twin-box joints, including DC, AC, and stability properties. The outcomes will significantly contribute to advancing the understanding of the intricate behavior of these joints in the context of fusion magnet applications.

Hybrid ANFIS-ant colony optimization model for prediction of carbamazepine degradation using electro-Fenton process catalyzed by Fe@Fe2O3 nanowire from aqueous solution

Pharmaceutical compounds, such as carbamazepine, have become a concern in aquatic environments due to their wide usage, resistance to degradation, and inefficiency of wastewater treatment processes. Therefore, it is important to find methods to remove these compounds from aqueous solutions. This study focuses on using Fe@Fe2O3 nanowires combined with electro Fenton process in an electrochemical reactor to decompose carbamazepine (CBZ). Various parameters were considered in 81 experiments, such as pH, current density, concentrations of FeSO4·7H2O, carbamazepine, Fe@Fe2O3, and reaction time. The removal efficiency ranged from 30.4 % to 88.6 %. This study utilized a hybrid adaptive neuro fuzzy inference system (ANFIS) combined with the ant colony optimization algorithm (ACO). The ACO is integrated with the ANFIS models to determine the optimum values for the studied parameters. The implementation of optimization through ACO enhances the ANFIS models' ability. A total of 65 data points were used for training the model, and 16 data points were used to test it. The model's performance was assessed using the coefficient of determination (R2 = 0.9988), the root-mean-squared error (RMSE = 4.54E-03), and the average absolute relative deviation (AARD% = 0.7153). The optimal input parameters for CBZ removal were determined as follows: concentrations of CBZ, FeSO4·7H2O and Fe@Fe2O3 nanowire dose of 9.0 mg/L, 4.5 mg/L, and 1050.0 mg/L; contact time of 45.0 min; pH of 4.0; and current of 0.18 A, resulting in a removal efficiency of 91.2 %. Additionally, the extended Fourier amplitude sensitivity test (EFAST) sensitivity analysis was applied to assess the influence of individual input parameters or their interactions on the model's output.

Performance analysis of methanol fuelled SI engine with boosted intake pressure and LIVC: A thermodynamic simulation and optimization approach

Methanol (CH3OH) is a low-carbon alternative fuel that can be derived through renewable power-to-fuel conversion, offering lower emissions and a higher resistance to knock in SI engines. However, the sole methanol-powered SI engine delivers less brake power. Fuels with a higher calorific value have been blended to solve this problem. However, this may cause overburden with storage, handling, and correct blending (like when methane and hydrogen are blended). To resolve this, this research suggested a novel approach for increasing the power and efficiency of a SI engine using the LIVC (late inlet valve closing) miller cycle and boosted intake pressure under 14 geometrical compression ratio (GCR) with the addition of the start of spark timing (SOI) and equivalency ratio (λ) effects. A quasi-dimensional thermodynamic model (QTDM) was applied to simulate the engine performance with respect to operating variables of 0.6–1.0 λ, 35.50 ––650 aBDC (after Bottom Dead Centre) LIVC timing, 30.00 ––150 bTDC (before Top Dead Centre) SOI Timing and 0.8–1.5 bar intake pressure. After that, design of experiments (DoE) based response surface methodology (RSM) was applied to determine optimum operating setting to maximize power and efficiency and minimize emission and fuel consumption. The results from the RSM illustrate the optimum input parameters to be 0.793 equivalence ratio, 46.900 aBDC-LIVC, 20.750 bTDC-SOI timing, and 1.5 bar intake pressure. Correspondingly, response output performances were found to be 12.36 kW-indicated power (IP), 11.20 kW- brake power (BP), 43.32 % indicated thermal efficiency (ITE), 39.32 % brake thermal efficiency (BTE), 9150.6 kJ/kWh brake specific energy consumption (BSEC), and 0.3 V%- carbon mono oxide (CO), 1247.8 ppm-nitric oxide (NO) emission at exhaust valve opening (EVO). The ANOVA regression models resulted in 95 % accuracy in forecasting output response and composite desirability of optimization of 0.83. Overall, it was found that miller-based LIVC and boosted intake pressure considerably improve the performance of SI engines, and the results projected would be helpful for further study and industry application.

Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

Experimental Modeling, Statistical Analysis, and Optimization of the Laser-Cutting Process of Hardox 400 Steel.

Fiber laser cutting machines are widely used in industry for cutting various sheet metals. Hardox steel is widely used in the construction of machinery and equipment that are subjected to wear and impact due to its anti-wear properties and good impact resistance. In this experimental study, the effect of input parameters including laser output power (LOP), laser-cutting speed (LCS), and focal point position (FPP) of fiber laser on the surface roughness and kerf width of Hardox 400 steel sheets are studied. In addition, the optimization of input parameters to achieve the desired surface roughness and kerf width are investigated and analyzed using the response surface methodology (RSM). The experiments are performed using a 4 kW fiber laser-cutting machine and the output results including surface roughness and kerf width are measured using roughness meters and optical microscope. The results of the analysis of variance (ANOVA) for surface roughness and kerf width show that the FPP and LCS are the most significant process parameters affecting the surface roughness and kerf width. With a positive focal point, the surface roughness decreases while the kerf width increases. With increasing the laser-cutting speed, both the surface roughness and kerf width decrease.