Input Parameters Research Articles

Prediction of effect of fabrication parameters on the properties of B4C ceramic particle reinforced AA2024 matrix nanocomposites using neural networks

In the present study, a prediction model was created to predict the impact of B4C content, milling time and matrix size on the hardness and density of AA2024-B4C nanocomposites produced by means of flake powder metallurgy. The process involved synthesizing AA2024-B4C nanocomposite powders by mechanical milling followed by preparing nanocomposites by hot pressing. The matrix size of the AA2024 alloy powders, the milling time, and the B4C content were selected as the input parameters. On the other hand, the density and the hardness of the AA2024-B4C nanocomposites were designed as the output parameters. The results showed that the sintered density is significantly improved by decreasing matrix size. It was found that density of AA2024-3wt.%B4C nanocomposites is lower than AA2024-1wt.%B4C nanocomposites. However, the hardness of AA2024-3wt.%B4C nanocomposites is better than AA2024-1wt.%B4C nanocomposites. The AA2024-B4C nanocomposite prediction model could predict hardness and density with a mean absolute percentage error (MAPE) of 0.32% and 1.38%. The results show that the ANN model is a useful tool for the matrix size effect prediction, milling time and B4C content on the density and hardness of AA2024-B4C nanocomposites prepared by flake powder metallurgy.

Probabilistic Forecasting of Regional Solar Power Incorporating Weather Pattern Diversity

Power grid stability depends on the ability to forecast solar power generation at the regional level. Most previous research on probabilistic forecasting has focused on the use of machine learning to predict the output of individual solar power plants rather than regional solar power generation, and few studies have considered the effects of seasonal weather patterns at the regional level. In this study, climate and geographic data were collected from 83 weather stations between 2019 and 2021 for use in developing a probabilistic model by which to predict regional solar power generation. The results of weather pattern analysis based on unsupervised machine learning and ensemble voting were used to build a probabilistic quantile regression model for the short-term prediction of regional solar power generation capacity. The efficacy of the model was assessed using data from 48 solar PV power plants, which included four sub-datasets pertaining to four target regions. Highly accurate results were consistently obtained across all regions in both the winter and summer seasons. The proposed probabilistic model outperformed conventional deterministic model by 6.55% and conventional probabilistic model by 4.03% in terms of total normalized mean absolute error (NMAE). Prediction intervals generated by the proposed model could be used as input parameters for regional power dispatch decisions.

Risk, Sensitive Analysis, and Optimization of a New Multi-generation System Activated by Solar and Biomass Energies

In recent years, the use of clean energies has become much more important due to the increase of environmental pollutants and the reduction of fossil fuel resources. Another important parameter in the establishment of energy systems is the assessment of the risks caused by the construction of the system and the provision of operational solutions to manage and reduce these risks. In this research, a new multi-generation system that is activated using solar and biomass energies has been evaluated thermodynamically and thermo-risky. This system consists of a Brayton cycle, a steam Rankine cycle, a transcritical CO2 cycle, a proton membrane electrolyzer (PEME) unit, and an internal combustion engine (ICE). A notable characteristic of the proposed system is its capability to recover waste heat from the gas turbine cycle, which is subsequently utilized to drive both a steam Rankine cycle and a transcritical CO2 cycle. The sensitivity analysis has been performed to investigate the effects of the input parameters on the system performance and finally, an optimization for the effective parameters of the system has been conducted. The results of the calculations show that the proposed system has energy and exergy efficiencies of 22.05% and 15.93%, respectively. The risk analysis was conducted by considering the potential hazards such as jet fires, jet of combusted gases, and system overpressure. The analysis indicates that the system poses a low level of risk. The overall risk, along with the risk per unit of system exergy, has been estimated at 4.247 ×10-8 injuries/year and 3.73 ×10-11 injuries/year.kW, respectively. In optimal conditions, the energy and exergy efficiency of the system reaches 25.79% and 19.08%.

Modeling and performance investigation of novel inorganic Cs4CuSb2Cl12 nanocrystal perovskite solar cell using SCAPS-1D

For the next generation of photovoltaic devices, perovskite solar cells (PSCs) appear as a good choice because they are simple and convert energy to electricity very efficiently. Recent years have seen a great deal of interest in organic-inorganic lead halide perovskites due to their exceptional optoelectronic properties. Nonetheless, additional commercialization of the Pb element is still restricted by its inevitable toxicity. To address the instability and toxicity of the lead hybrid perovskite, it is imperative to produce entirely inorganic lead-free perovskite nanocrystals. Cs4CuSb2Cl12 offers promising qualities and strong stability against light, heat, and humidity to overcome these drawbacks. It possesses an efficient photo-generated carrier and good carrier mobility. In this study, a lead-free perovskite solar cell with the novel configuration of FTO/WS2/Cs4CuSb2Cl12/CuSbS2/Ni has been optimized using SCAPS-1D simulation software and investigates the potential of Cs4CuSb2Cl12 nanocrystal solar cell. The impact of different characteristics of charge transport materials on Cs4CuSb2Cl12 nanocrystal solar cells has been reported. Additionally, the study computationally optimizes the thickness of perovskite layer, doping concentration, defect density, and other input parameters to propose an effective PSC with power conversion efficiency of 23.10 %, Voc of 1.1675V, and FF of 83.33 %.

A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production

Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.

Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration

Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process.

Predictive modelling of strength characteristics of stabilized medium expansive soils

This research explores the use of Artificial Neural Network (ANN) modeling to predict the Unconfined Compression Strength (UCS) of stabilized medium expansive soil, aiming to optimize the parameters influencing soil stabilization. A database comprising 240 data points was compiled from laboratory tests involving seven input parameters: Stabilizers content (SCBA & WMD), Curing Period (CP), Liquid Limit (L.L), Plasticity Index (PI), Specific Gravity (Gs), and Free Swell (FS). ANN modeling, employing Levenberg-Marquardt algorithms, demonstrated successful UCS prediction. Increasing the number of neurons improved model accuracy, with optimum results achieved with 14 neurons. With 14 neurons for LMA, the R and R² value reaches 0.99, 0.98 Moreover, plots between experimental and predicted values shows strong correlation as majority of predicted UCS is closed to line of fit. Also error shows large count of data points closed to line of zero error. Sensitivity analysis highlighted SCBA, WMD, CP, L.L, and P.I as significant contributors to UCS prediction, emphasizing their importance in soil stabilization. The study underscores the effectiveness of ANN modeling in predicting soil strength and recommends 4% SCBA and 20% WMD for optimal stabilization. Overall, this research presents a comprehensive approach to predicting UCS in stabilized expansive soil, offering insights into parameter optimization and model enhancement for future geotechnical applications.

A Mathematical Analysis of Competitive Dynamics and Aggressive Treatment in the Evolution of Drug Resistance in Malaria Parasites

Experimental evidence supports the counterintuitive notion that rapid eradication of pathogens within a host, infected with both drug-sensitive and -resistant malaria parasites, can actually accelerate the evolution of drug-resistant pathogens. This study aims to analyze the competitive dynamics between these two strains through a mathematical model and evaluate the impact of aggressive treatment on the spread of drug resistance. We conducted equilibrium, uncertainty, and sensitivity analyses to assess the model, identifying and measuring the influence of key factors on the outcome variable (the population of drug-resistant parasites). Both equilibrium and local sensitivity analyses concurred that the density of drug-resistant parasites is notably affected by genetic instability, the production rate of red blood cells, the number of merozoites, and competition factors. Conversely, there is a negative relationship between genetic instability and one of the competition coefficients. Global sensitivity analysis offers a comprehensive examination of the impact of each input parameter on the temporal propagation of drug resistance, effectively accounting for the interplay among parameters. Both local and global sensitivity analyses underscore the continuous impact of drug treatment on the progression of drug resistance over time. This paper anticipates exploring the underlying mechanisms of drug resistance and providing theoretical support for developing more effective drug treatment strategies.

Improving the diagnostic performance of the neural network for COVID-19VN via weight assignment to pathological symptoms

In this article, we present how to create a database of Covid-19 diseases at National Hospital of Tropical Diseases (called the CovidVN database) and then develop a learning neural network based on this database to diagnose this disease. The CovidVN Database is built based on the processing of real diagnostic test results of Covid-19 patients with a large number of samples and in accordance with the structure of the Israeli Health System Covid-19 disease database (called COVIDIsr Database). Then two MLP Artificial Neural Networks corresponding to these two databases will be developed using the Deep Learning Toolbox of MATLAB software; the results of training these networks and their accuracy are compared with each other to assess the relative quality of the CovidVN database. Other, the paper presents the method of assigning weight corresponding to pathological symptoms for network input parameters. The results showed that the weighting of input attributes corresponding to the pathological symptoms is significant.

High-precision spectral inversions: Determining what is important for the accurate definition of incident radiation boundary conditions

Spectral inversions are used to analyse spectroscopic observations with the aim of deriving the physical properties of the observed plasma, such as the kinetic temperature, density, pressure, degree of ionisation, or macroscopic velocities. One of the key factors ensuring the high precision of the derived plasma properties is having accurately defined input parameters of the models on which spectral inversions rely. The illumination, which chromospheric and coronal structures receive from the solar surface (and corona), is one of the most crucial input parameters of these models. We do not perform spectral inversions in this work. Our aim is to study two important factors that contribute to the accurate definition of the incident radiation boundary conditions: the altitude above the solar surface and the dynamics of the illuminated plasma. This investigation takes into account a diverse range of solar structures from the high-rising eruptive prominences to low-lying spicules. To study the influence of the altitude and dynamics of the observed plasma on the incident radiation boundary conditions, we used geometrical principles valid for any spectral line. However, to demonstrate the strong impact of dynamics, we considered the specific case of narrow spectral lines of which are highly sensitive to the presence of velocities. We argue that the altitude of the illuminated plasma strongly influences the way we need to define the incident radiation boundary conditions to achieve the most accurate results. For low-lying structures, generally below 50,000 km, the incident radiation may need to be specified directly from the composition of the portion of the solar disc that illuminates them. For high-altitude structures, generally above 300,000 km, the fraction of the solar disc illuminating the analysed plasma is large enough to be realistically approximated by the composition of the entire disc. We also show that for the narrow spectral lines, such as the lines, the impact of dynamics on the incident radiation intensity and profile shapes starts from radial velocities of 30 km $. Such velocities are even exhibited by the fine structures of quiescent prominences and are easily exceeded in spicules or eruptive prominences. The two aspects of the incident radiation definition studied here are relevant for spectral inversions based on any kind of modelling approach. However, their impact on the precision of the results of spectral inversions is likely less significant than the impact of the choice of the complexity of the model geometry, for example.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

In order to obtain the optimal geometric structure for sand transport in desert roads, this study fully utilizes the natural sand transport capacity of the desert roads, aiming to reduce occurrences of road damage due to sand burial. The research integrates Computational Fluid Dynamics (CFD) with optimization analysis theory. Various typical geometric structures of desert highway roadbed were modeled using the Design Exploration module in CFD. Optimization analysis methods were employed to model and compute the sediment transport optimization design on both the roadbed and road surface. Then, the initial sample points are obtained by using the Design of Experiments (DOE), and the response surface is established by using the Kriging model to obtain the change trend of the input parameters and the objective function. Finally, the cross-section parameters of the sediment transport subgrade corresponding to different inflow conditions are obtained. The results show that the sediment transport performance of embankment, cutting and semi-filled uphill subgrade is negatively correlated with the height of subgrade. The relationship between slope gradient and sediment transport performance of subgrade depends on the height of subgrade and the type of subgrade section. For embankment and cutting, when the subgrade height is less than 0.5 m, the sediment transport performance of the subgrade is positively correlated with the subgrade slope; however, when the subgrade height exceeds 0.5 m, the sediment transport performance is negatively correlated with the subgrade slope. For the semi-filled uphill flow subgrade, the sediment transport performance of the subgrade is negatively correlated with the subgrade slope. Comprehensive analysis shows that semi-filled and excavated downhill subgrade has the best sand transport performance, embankment subgrade has the second highest sand transport capacity, then cutting subgrades, while semi-filled and excavated uphill subgrade has the worst sand transport performance. The research conclusions provide valuable scientific guidance for the design of sand control embankment structures tailored to local conditions for desert highways. This is of significant importance for enhancing the sediment transport capacity of desert highways and prolonging their service life.

ALMA Band 3 Source Counts: A Machine Learning Approach to Contamination Mitigation below 5 Sigma

We performed differential number counts down to 4.25 sigma using ALMA Band 3 calibrator images, which are known for their high dynamic range and susceptibility to various types of contamination. Estimating the fraction of contaminants is an intricate process due to correlated non-Gaussian noise, and it is often compounded by the presence of false positives generated during the cleaning phase. In addition, calibrator extensions further complicate the counting of background sources. In order to address these challenges, our strategy employs a machine learning-based approach utilizing the UMLAUT algorithm. UMLAUT assigns a value to each detection, and it considers how likely it is for there to be a genuine background source or a contaminant. With respect to this goal, we provide UMLAUT with eight observational input parameters, each automatically weighted using a gradient descent method. Our methodology significantly improves the precision of differential number counts, thus surpassing conventional techniques, including visual inspection. This study contributes to a better understanding of radio sources, particularly in the challenging sub-5 sigma regime, within the complex context of a high dynamic range of ALMA calibrator images.

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure.

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow

Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest—pressure and velocity along the central axes of the bifurcation—are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.

BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.

Numerical simulation of concrete strength based on microstructure and mineral composition analysis using micro-CT and XRD technology

The investigation of mechanical properties in porous materials, such as concrete, has been a significant area of interest in the fields of civil engineering and architecture. Traditional macroscopic mechanical testing methods are limited due to destructive experimental conditions, time-consuming procedures, and the inability to characterize internal structures. To overcome these limitations, researchers have utilized microscopic factors with micro-CT and simulation models. Different simulation methods demand mechanical parameters to effectively model the constituent materials; however, there exists variability in the amount and standard values of these input parameters, which leads to a decrease in the reliability of the obtained results. In order to tackle this issue, a novel approach is proposed, which integrates mineral component analysis using X-ray diffraction (XRD) and establishes coefficients to minimize the number of necessary input parameters. Simulation results demonstrate excellent agreement with experimental measurements, accurately reproducing the stages of elastic deformation, failure, and fracture in the stress-strain curve of concrete. Our findings reveal that changes in the concrete skeletal structure, microstructure, and mineral composition significantly influence compressive strength. Coarse aggregates and high-mechanical-performance minerals, in particular, have a substantial impact. Additionally, the mechanical properties show a significant linear relationship with the strength reduction coefficient of aggregates and concrete cores. In conclusion, the proposed method, combining CT scanning and XRD with FDEM, proves to be a reliable and practical approach for accurately assessing the mechanical properties of concrete. This methodology simplifies the intricate processes associated with the time-intensive parameter calibration, leading to a more efficient parameter allocation procedure. As a result, this method enhances concrete design, promotes a better understanding of parameter impacts, and enables accurate prediction of concrete behavior across diverse scenarios.

Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models

There is growing evidence from stellar kinematics and galactic chemical evolution suggesting that giant planets (M P ≥ 0.3M J ) are relatively young compared to the most commonly occurring population of small planets (M P < 0.3M J ). To further test the validity of these results, we analyzed the ages for a large number of 2336 exoplanet hosting stars determined using three different but well-established isochrone fitting models, namely, PARSEC, MIST, and Yonsei Yale. As input parameters, we used Gaia DR3 parallaxes, magnitudes, and photometric temperature, as well as spectroscopically determined more accurate temperatures and metallicities from the Sweet Catalog. Our analysis suggests that ∼50%–70% of stars with planets are younger than the Sun. We also find that, among the confirmed exoplanetary systems, stars hosting giant planets are even younger compared to small planet hosts. The median age of ∼2.61–3.48 Gyr estimated for the giant planet-hosting stars (depending on the model input parameters) suggests that the later chemical enrichment of the galaxy by the iron-peak elements, largely produced from Type Ia supernovae, may have paved the way for the formation of gas giants. Furthermore, within the giant planet population itself, stars hosting hot Jupiters (orbital period ≤10 days) are found to be younger compared to the stellar hosts of cool and warm Jupiters (orbital period >10 days), implying that hot Jupiters could be the youngest systems to emerge in the progression of planet formation.

Mass-Based Hybrid Nanofluid Model for Thermal Radiation Analysis of MHD Flow over a Wedge Embedded in Porous Medium

This study addresses the intricate interplay of magnetohydrodynamics (MHD), thermal radiation, and porous media effects, which are crucial in numerous engineering applications, including aerospace, energy systems, and environmental processes. The development of a mass-based hybrid nanofluid model signifies a novel approach, potentially yielding more accurate predictions and insights into the thermal behavior of fluids in diverse scenarios. Thus, the current research explores the heat transfer characteristics of a unique nanofluid known as TiO2 (titania)-CuO (copper oxide)/H2O (water) hybrid nanofluid. This nanofluid flows past a static or moving wedge considering the impact of thermal radiation and magnetic field in the appearance of porous medium. To calculate the effective thermophysical attributions of the hybrid (TiO2-CuO) nanofluid, a mass-based strategy is employed. This approach involves analyzing the masses of both the first and second nanoparticles, along with the mass of the base fluid, as essential input parameters. The proposed mathematical model is modified to a dimensionless form by applying similarity transformations. The numerical solution is obtained by utilizing the bvp4c built-in function within the MATLAB environment. Graphs illustrate the influence of various parameters on temperature and velocity trends, including the magnetic field parameter and heat absorption/generation parameter as well as the thermal radiation parameter. It is noted that along with the enhancement in the values of parameters related to porous medium or magnetic field, the velocity of the hybrid nanofluid improves. This occurs when the moving wedge parameter’s value is below 1. Conversely, when the moving wedge parameter’s value exceeds 1, the velocity of the hybrid nanofluid decreases. The shape factor is more effective in the temperature profile for developed inputs of heat absorption/generation parameter. A juxtaposition of enhancement in heat transfer rate due to nanofluid (TiO2/H2O) and hybrid nanofluid (TiO2-CuO/H2O) is likewise presented. The main outcome indicates that the hybrid nanofluid exhibits superior thermal conductivity relative to the conventional nanofluid.

Taguchi-Grey relational analysis driven multi-objective optimization of weld bead geometry and hardness in laser welded Ti6Al4V Alloy

AbstractThis study used Taguchi-based Grey relational analysis to optimize the bead geometry (bead width and length) and the hardness of laser welded 3 mm thick Ti6Al4V alloy sheets joined using the butt configuration. Firstly, the L9 (32) orthogonal array, with varying laser power and welding speed, was employed to optimize the input parameters (bead length, width, and hardness). The Taguchi optimization showed that the welding speed and laser power have a significant effect on the hardness of the welds and that laser power has a significant impact on the bead length. Meanwhile, the welding parameters had little effect on the bead width. These results were further affirmed with the Analysis of Variance (ANOVA). The Grey relational multi-objective optimization shows the optimized parameters for all responses to be 3 kW laser power and 2.2 m/min welding speed. It is important to note that the combination of Taguchi and Grey relational analysis and ANOVA is useful in optimizing welding parameters to achieve suitable weld properties.