Search Results (83)

This article investigates the impact of Arrhenius energy and the radius of a nanoparticle subject to an irregular heat source on tangent-hyperbolic nanofluid flow over a stretching sheet with nonlinear radiation. The convective boundary effect, higher-order slip, and micropolarity are all included for a water-based $Cu$ nanofluid. The present study investigates the significance of a nanoparticle’s radius under inclined MHD conditions. The thermally convective flow of the nanofluid is optimized for the heat-transfer rate using the response surface technique. The modeled governing equations are converted into a system of first-order ODEs using the proper similarity transformations, and the BVP5C algorithm—a finite-difference-based solver—is then used to solve these ODEs numerically. Microrotation, thermal boundary-layer thickness, and the skin-friction coefficient all decrease as the nanoparticle radius increases. The thermal layer thickens as the Biot number increases. As the higher-order slip parameter coefficient increases, the results indicate that the skin friction and local Nusselt number fall. Using tables, figures, contour plots, and surface plots, the effects of several influencing factors on the rates of heat and mass transfer, as well as on the skin-friction factor, are demonstrated. The study uses “Response Surface Methodology” (RSM) in conjunction with “Analysis of Variance” (ANOVA) to optimize the most important factors, which are probably the magnetic parameter and the nanoparticle radius that control the flow and heat-transfer properties. Additionally, with a Nusselt number $R^2$ value of $99.96$, indicating an excellent fit, the suggested model exhibits amazing precision. The reliability and efficiency of the estimated model are assessed using the residual versus fitted plot. Full article

The flow of a two-dimensional incompressible hybrid nanofluid over a stretching cylinder containing microorganisms with parallel effect of inclined magnetohydrodynamic was examined in the current study in relation to chemical reactions, heat source effect, nonlinear heat radiation, and multiple convective boundaries. The main objective of this research is the optimization of heat transfer with inclined MHD and variation in different physical parameters. The governing partial differential equations are transformed into a set of ordinary differential equations by applying the appropriate similarity transformations. The Runge–Kutta method is recognized for using shooting as a technique. Surface plots, graphs, and tables have been used to illustrate how various parameters affect the local Nusselt number, mass transfer, and heat transmission. It is demonstrated that when the chemical reaction parameter rises, the concentration and motile concentration profiles drop. The least responsive is the rate of heat transfer to changes in the inclined magnetic field and most associated with changes in the Biot number and radiation parameter shown in contour plot. The streamline graph illustrates the way fluid flow is affected simultaneously by the magnetic parameter M and an angled magnetic field. Local Nusselt number and local skin friction are improved by the curvature parameter and mixed convection parameter. The contours highlight the intricate interactions between restricted magnetic field, significant radiation, and substantial convective condition factors by displaying the best heat transfer. The three-dimensional surface, scattered graph, pie chart, and residual plotting demonstrate the statistical analysis of the heat transfer. The results support their use in sophisticated energy, healthcare, and industrial systems and enhance our theoretical knowledge of hybrid nanofluid dynamics. Full article

Due to its high content of lignocellulose, cotton stalk is difficult to degrade naturally and utilize effectively, so it is often regarded as waste. In this study, the effects of Pleurotus ostreatus XH005, Lactiplantibacillus plantarum LP-2, and cellulase enzyme on the cotton stalk substrate under aerobic solid-state fermentation (SSF) conditions were investigated, and the metabolites were analyzed to identify potential functional compounds in the cotton-stalk-fermented feed. Preliminary optimization results obtained through single-factor experiments were as follows: fermentation time 14 days, XH005 inoculum size 8.00% (v/m), material-to-water ratio 1:0.50 (v/m), LP-2 inoculum size 2.00% (v/m), and cellulase addition 0.60% (m/m). Based on these single-factor experimental results, XH005 inoculum size, LP-2 inoculum size, material-to-water ratio, and cellulase addition were selected as independent variables. Through response surface methodology (RSM) optimization experiments, 29 experimental groups were designed. Subsequently, based on Box–Behnken analysis of variance (ANOVA) of lignin and cellulose content, along with contour and response surface plots, the optimal aerobic solid-state fermentation parameters were determined as follows: fermentation time 14 days, XH005 inoculum: 7.00% (v/m), material-to-water ratio: 1:0.55 (v/m), LP-2 inoculum: 2.00% (v/m), and cellulase enzyme addition: 0.65% (m/m). Results showed that compared with the control group (CK), the optimized group exhibited a 27.65% increase in lignin degradation rate and a 47.14% increase in cellulose degradation rate. Crude protein (CP) content increased significantly, while crude fiber (CF), detergent fiber and mycotoxin contents decreased significantly. Non-targeted metabolic analysis indicated that adding cellulase and inoculating Pleurotus ostreatus XH005 and Lactiplantibacillus plantarum LP-2 in aerobic SSF of cotton straw feed produced functionally active substances such as kaempferol (C343), carvone (C709) and trilobatin (C604). Therefore, this study demonstrates that microbial-enzyme co-action SSF significantly enhances the nutritional composition of cotton stalk hydrolysate. Furthermore, this hydrolysate is suitable for the production of functional compounds, endowing the fermented feed with health-promoting properties and enhancing the utilization of cotton processing byproducts in the feed industry. Full article

The natural fiber-reinforced nanomaterial filler polymer matrix hybrid composite has superior applications in industrial and manufacturing fields due to its enhanced mechanical and machinability characteristics. However, in order to generate high-quality components, unconventional machining techniques, notably abrasive waterjet machining, have become more popular due to the inhomogeneity of composites, fiber pullout, greater surface roughness, and dimensional inaccuracy under traditional machining. Delamination typically refers to the separation that occurs along a plane parallel to the surface, such as the detachment of a coating from its underlying material or the separation between different layers within the coating itself. This paper investigates the AWJM characteristics of Hibiscus Rosa-Sinensis/Carbon nanotube/Epoxy (HRSCE)-based hybrid composite, focusing on delamination factors at entry, exit, and machining time. An L₂₇ orthogonal array was employed to optimize process parameters, revealing that DF-entry decreased with increasing CNT (wt.%), achieving its lowest values at 3 (wt.%) CNT and 2 mm stand-off distance due to enhanced composite toughness and precise jet focus. Conversely, DF-exit increased with higher CNT (wt.%), stand-off distance and traverse speed, attributed to the composite’s increased brittleness and reduced cutting efficiency. Machining time was predominantly influenced by CNT (wt.%) (92.4%), increasing with higher reinforcement levels due to enhanced material resistance. Response surface methodology models demonstrated high accuracy in predicting machining outcomes, with errors below 3%. Contour and surface plots identified optimal conditions for minimal delamination and machining time as 3 (wt.%) CNT, low stand-off distance (2 mm), and moderate traverse speed (200 mm/min). The SEM and optimal microscopy analysis confirmed that CNT reinforcement positively influenced fiber matrix interfacial integrity and reduced surface damage. Full article

A layerwise laminate FE model capable of predicting the dynamic response of delaminated composite beams with piezoelectric actuators and sensors encompassing local non-linear contact and sliding at the delamination interfaces was formulated. The kinematic assumptions of the layerwise model enabled the representation of opening and sliding of delamination interfaces as generalized strains, thereby allowing the introduction of interfacial contact and sliding effects through constitutive relations at the interface. This realistic FE model, assisted by representative experiments, was used to study the time response of delaminated active sensory composite beams with predefined delamination extents. The time response was measured and simulated for narrowband actuation signals at two distinct frequency levels using a surface-bonded piezoceramic actuator, while signal acquisition was performed with a piezopolymer sensor. Four different composite specimens, each containing a different delamination size, were used for this study. Experimental results were directly compared with model predictions to evaluate the performance of the proposed analytical approach. Damage signatures were identified in both the signal amplitude and the time of flight, and the sensitivity to delamination size was examined. Finally, the distributions of axial and interlaminar stresses at various time snapshots of the transient analysis are presented, along with contour plots across the structure’s thickness, which illustrate the delamination location and wave propagation patterns. Full article

Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The relationship between response values and influencing factors is mainly established using regression analysis techniques. These equations are then used to generate contour and surface response plots to provide researchers with further insights. The impact of regression techniques on response surface methodology (RSM) model building has not been studied in detail. This study uses complete regression techniques to analyze sixteen datasets from the literature on semiconductor manufacturing, steel materials, and nanomaterials. Whether each variable significantly affected the response value was assessed using backward elimination and a t-test. The complete regression techniques used in this study included considering the significant influencing variables of the model, testing for normality and constant variance, using predictive performance criteria, and examining influential data points. The results of this study revealed some problems with model building in RSM studies in the literature from three engineering fields, including the direct use of complete equations without statistical testing, deletion of variables with p-values above a preset value without further examination, existence of non-normality and non-constant variance conditions of the dataset without testing, and presence of some influential data points without examination. Researchers should strengthen training in regression techniques to enhance the RSM model-building process. Full article

Nylon reinforced with short carbon fibres exhibits superior mechanical properties. Its use as a feedstock for fused deposition modelling (FDM) can extend its applications to consumer goods and industrial products. To investigate the flexural and impact properties of the FDM-printed short carbon fibre-reinforced nylon, a central composite face-centred (CCF) design with four factors and three levels and the response surface method (RSM) were employed. The four primary process parameters are the extrusion and bed temperatures, printing speed, and layer thickness. The three investigated responses were the flexural strength, flexural modulus, and impact strength. Perturbation curves and contour plots were used to analyse the influences of the individual and two-way interactions of the response parameters, respectively. Second-order statistical models were constructed to predict and optimise the mechanical properties. The optimal comprehensive mechanical properties were determined using a desirability function combined with the entropy weighting method. The predicted results of best comprehensive mechanical properties are 169.881 MPa for the flexural strength, 9249.11 MPa for the flexural modulus, and 29.659 kJ∙m⁻² for the impact strength, achieved under the parameter combination of extrusion temperature of 318 °C, bed temperature of 90 °C, printing speed of 30 mm∙s⁻¹, and layer thickness of 0.1 mm. A small deviation between the predicted and experimental results indicated the high reliability of the proposed method. The optimal outcomes under the studied parameters showed higher robustness and integrity than previously reported results. Full article

Experimental design is important for researchers and those in other fields to find factors affecting an experimental response. The response surface methodology (RSM) is a special experimental design used to evaluate the significant factors influencing a process and confirm the optimum conditions for different factors. RSM models represent the relationship between the response and the influencing factors established with the regression analysis. Then these equations are used to produce the contour and response surface plots for observers to determine the optimization. The influence of regression techniques on model building has not been thoroughly studied. This study collected twenty-five datasets from the literature. The backward elimination procedure and t-test value of each variable were adopted to evaluate the significant effect on the response. Modern regression techniques were used. The results of this study present some problems of RSM studies in the previous literature, including using the complete equation without checking the statistical test, using the at-once variable deletion method to delete the variables whose p-values are higher than the preset value, the inconsistency between the proposed RSM equations and the contour and response surface plots, the misuse of the ANOVA table of the sequential model to keep all variables in the linear or square term without testing for each variable, the non-normal and non-constant variance conditions of datasets, and the finding of some influential data points. The suggestions for applying RSM for researchers are training in the modern regression technique, using the backward elimination technique for sequential variable selection, and increasing the sample numbers with three replicates for each run. Full article

This study evaluates the effects of steel slag powder (SSP), fly ash (FA), and steel slag sand (SSS) on mortar compressive strength. A response surface methodology (RSM) based on central composite design (CCD) was employed to model 7-day, 28-day, and 91-day strength development, considering three quantitative variables: SSP, FA, and SSS. Statistical results confirmed the reduced cubic models were significant and predictive (R² > 0.97), with non-significant lack of fit and adequate precision. Experimental results revealed that SSP and FA negatively affected early-age strength due to dilution effects and low initial reactivity, whereas SSS slightly improved it by enhancing particle packing. At later ages, SSP exhibited nonlinear effects, where moderate dosages enhanced strength, while excessive replacement led to strength reduction. SSS showed a continuously positive contribution across all ages, particularly at 91 days. Perturbation plots, contour maps, and gradient analyses indicated that SSS played a dominant role at later stages and that maintaining a proper balance among supplementary cementitious materials (SCMs) and aggregate replacements is crucial. The developed models and response surfaces provide practical guidance for designing slag-based mortars with improved mechanical properties and enhanced sustainability. Full article

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near distances. The user interface (UI) of the e-portal application creates an environment for customers to input their eye prescription data and geometric parameters to visualise the result of the designed freeform varifocal lens surface, which includes interactive 2D contour plots and 3D-rendered diagrams for both left and right eyes simultaneously. The e-portal provides a unified interactive platform where users can simultaneously access both the specialised Copilot demo web for lenses and the main Shiny/R version 4.4.0 programming app, ensuring seamless integration and an efficient process flow. Additionally, the data points of the 3D-designed surface are automatically saved. In order to check the performance of the designed varifocal lens before production, it is remodelled in the COMSOL Multiphysics 6.2 modelling and analysis environment. Ray tracing is built in the environment for the lens design assessment and is then integrated with the lens additive manufacturing (AM) using a Formlabs 3D printer (Digital Fabrication Center (DFC), London, UK). The results are then analysed to further validate the e-portal-driven personalised design and manufacturing approach. Full article

The present study investigates the effect of the passive jet control system on the performance of a vibration energy harvester system (VIVEHS). The shape of a bluff body plays a crucial role in determining the vortex shedding mechanism, while the passive jet control system influences the dynamic behavior of these vortices, either enhancing or suppressing the bluff body’s oscillatory performance. This study introduces key innovations, including the incorporation of perforations in the bluff body, variations in outlet angles, and different inlet and outlet configurations. In this regard, a two-dimensional numerical investigation has been carried out to understand and optimize the dynamic response from the bluff body and its effect on beam deflection. The validation of the numerical code has been carried out for a cylindrical shaped bluff body using ANSYS Fluent 23.2 numerical modelling software. Upon validation, the effects of a single inlet and a symmetrical dual outlet with different outlet angles are numerically analyzed under various flow conditions to assess their impact on the dynamic behavior of the system. The outlet angle varies between 30 degrees and 120 degrees with intervals of 30 degrees. The contours of vorticity and the bluff body dynamic characteristics were observed and plotted for various flow conditions ranging between 1 m/s and 8 m/s with intervals of 1 m/s. The results of this numerical study are crucial for designing passive jet control systems in practical energy harvesting applications. The optimization of outlet configurations and control strategies can significantly enhance both the efficiency and stability of energy harvesting systems. Full article

The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%. Full article

This study focuses on optical twin-core couplers, which facilitate light transmission between two closely aligned optical fibers. These couplers operate based on the principle of coupling, allowing signals in one core to interact with those in the other. The Kerr effect, which describes how a material’s refractive index changes in response to the intensity of light, induces the nonlinear behavior essential for generating solitons—self-sustaining wave packets that preserve their shape and speed. In our research, we employ fractional derivatives to investigate how fractional-order variations influence wave propagation and soliton dynamics. By utilizing the modified extended mapping method (MEMM), we derive solitary wave solutions for the equations governing the behavior of optical twin-core couplers under Kerr nonlinearity. This methodology produces novel fractional traveling wave solutions, including dark, bright, singular, and combined bright–dark solitons, as well as hyperbolic, Jacobi elliptic function (JEF), periodic, and singular periodic solutions. To enhance understanding, we present physical interpretations through contour plots and include both 2D and 3D graphical representations of the results. Full article

In the analytical quality by design (AQbD) framework, the design of experiments (DOE) plays a very important role, as it provides information about how experimental input variables influence critical method attributes. Based on the information obtained from the DOE, mathematical models are generated and used to build the method operable design region (MODR), which is a robust region of operability. Data treatment steps are usually carried out in software such as Fusion QbD, Minitab, or StaEase 360, among others. Although there are many studies in the literature that use the DOE, none of them address important aspects of data treatment for optimization and MODR generation and compare different software calculations. The purpose of this study is to contribute to a better understanding of data treatment aspects that are frequently misread or not fully understood, such as model selection, ANOVA results, and residual analysis. The discussion will be guided by the separation of curcuminoids, using ultra-high performance liquid chromatography and eight quality attributes as responses. This study highlights the importance of carefully selecting and evaluating models, as they significantly influence the generation of the MODR. Moreover, the findings emphasize that it is essential to incorporate uncertainties into the contour plots to accurately determine the MODR in compliance with the ICH Q14 guidelines and USP General Chapter <1220>. Full article

This study aimed to assess the predictive performance of first- and second-order regression models in optimizing bedaquiline (BQ) solid lipid nanoparticle (SLN) formulations. A three-step central composite design and graphical optimization process was employed. A design of experiments method was used to evaluate the impact of BQ, Tween 80 (T80), polyethylene glycol (PEG), and lecithin on the formulations’ response variables, including Z-average (PSD), polydispersibility index (PdI), and Zeta potential (ZP). Secondly, we quantified the relationship between experimental variables using the regression model coefficients. Lastly, we predicted the responses and verified the models’ adequacies to ensure accurate representation and effective optimization. The first-order polynomial showed poor model adequacy and required further refinement due to its lack of explanatory power and significant predictors. Conversely, the second-order models provided superior fitness, sensitivity to variability, complexity, and prediction consistency. The optimized formulation achieved a desirability value of 0.9998, indicating alignment with the desired criteria. Specifically, the levels of BQ (19.4 mg), T80 (25.2 mg), PEG (39.2 mg), and lecithin (200 mg) corresponded to PdI (0.41), PSD (250.99 nm), and ZP (−25.95 mV). Maintaining a BQ concentration between 10 and 20% and T80 between 15 and 18% is vital for maximizing ZP and minimizing PdI and PSD, ensuring stable SLN formulations. This study underscores the significance of precise model selection and statistical analysis in pharmaceutical formulation optimization for enhanced drug delivery systems. Full article