Parametric Study Research Articles

A meta-analysis of photosynthetic efficiency and stress mitigation by melatonin in enhancing wheat tolerance

BackgroundOur meta-analysis examines the effects of melatonin on wheat under varying abiotic stress conditions, focusing on photosynthetic parameters, chlorophyll fluorescence, leaf water status, and photosynthetic pigments. We initially collected 177 publications addressing the impact of melatonin on wheat. After meticulous screening, 31 published studies were selected, encompassing 170 observations on photosynthetic parameters, 73 on chlorophyll fluorescence, 65 on leaf water status, 240 on photosynthetic pigments.ResultsThe analysis revealed significant heterogeneity across studies (I² > 99.90%) for the aforementioned parameters and evidence of publication bias, emphasizing the complex interaction between melatonin application and plant physiological responses. Melatonin enhanced the overall response ratio (lnRR) for photosynthetic rates, stomatal conductance, transpiration rates, and fluorescence yields by 20.49, 22.39, 30.96, and 1.09%, respectively, compared to the control (no melatonin). The most notable effects were under controlled environmental conditions. Moreover, melatonin significantly improved leaf water content and reduced water potential, particularly under hydroponic conditions and varied abiotic stresses, highlighting its role in mitigating water stress. The analysis also revealed increases in chlorophyll pigments with soil drenching and foliar spray, and these were considered the effective application methods. Furthermore, melatonin influenced chlorophyll SPAD and intercellular CO2 concentrations, suggesting its capacity to optimize photosynthetic efficiency.ConclusionsThis synthesis of meta-analysis confirms that melatonin significantly enhances wheat’s resilience to abiotic stress by improving photosynthetic parameters, chlorophyll fluorescence, leaf water status, and photosynthetic pigments. Despite observed heterogeneity and publication bias, the consistent beneficial effects of melatonin, particularly under controlled conditions with specific application methods e.g. soil drenching and foliar spray, demonstrate its utility as a plant growth regulator for stress management. These findings encourage focused research and application strategies to maximize the benefits of melatonin in wheat farming, and thus contributing to sustainable agricultural practices.

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Parametric AC Loss Study on 7 MW Fully Superconducting Direct-Drive Wind Turbine Generators With Concentrated Stator Winding

Parametric AC Loss Study on 7 MW Fully Superconducting Direct-Drive Wind Turbine Generators With Concentrated Stator Winding

Developing machine learning models with metaheuristic algorithms for droplet size prediction in a microfluidic microchannel

Droplet-based microfluidics holds promise for advanced biomedical applications due to its enhanced accuracy, throughput, rapid reaction time, and minimal reagent consumption. However, achieving droplets with the desired radius often entails costly design iterations. In this paper, we propose a novel approach that combines machine learning algorithms with metaheuristic algorithms to leverage their respective strengths in speed and accuracy. Specifically, we demonstrate the effectiveness of the Rain Optimization Algorithm (ROA) in enhancing the accuracy of five machine learning models. Additionally, we introduce a hybrid model, optimized by ROA, for predicting droplet generation in a cross-junction microfluidic channel, achieving a coefficient of determination (R2) of 0.937, reflecting a 3.6 % increase in accuracy. We employ symbolic regression to validate the hybrid model, yielding an R2 value of 0.987, indicating strong agreement with real data. Furthermore, we utilize the SHAP method in Explainable AI to interpret the hybrid model's performance. Notably, the proposed hybrid model requires only four seconds to solve the prediction problem, significantly reducing computational time compared to the numerical model and experimental method. This approach facilitates the study of each input parameter's impact on droplet size prediction, offering otherwise challenging and costly insights to obtain experimentally.

Effect of the addition of graphite and graphene oxide in the mechanical properties and adsorption of CO2 in cementitious filaments

The study evaluated various aspects of cementitious filaments incorporating graphite and graphene oxide in different proportions (0.01 %, 0.03 %, and 0.06 %) and their effects on physical–chemical properties, mechanical strength, extrudability, constructability, and CO2 adsorption. The findings indicate that adding nanomaterials did not hinder filament extrudability, and all variations were successfully extruded. Regarding constructability, filaments with graphene oxide supported up to the third layer, while those with graphite supported up to the fourth layer. The flexion traction test showed that filaments with 0.01 % and 0.03 % graphite and 0.01 % graphene oxide exhibited superior performance. In axial compression tests, filaments with graphene oxide outperformed the reference mortar. Moreover, mortars with added graphite and graphene oxide displayed better CO2 absorption compared to standard mortar, with an optimized composition identified for this parameter. Specifically, the sample with 0.1 % graphene oxide increased CO2 adsorption by approximately 2.5 % by mass. The study demonstrates the feasibility of incorporating nanomaterials, namely graphite and graphene oxide, into cementitious filaments for additive manufacturing, resulting in enhanced mechanical properties, constructability, and CO2 adsorption. The overall optimized composition, determined via the desirability function, featured cement filament supplemented with 0.03 % graphite (sample 0.03G), achieving 70.55 % optimization in this study's parameters.

A Parametric Study of a SOA Based COEO for Phase Noise Performance and Reliable Operation

A Parametric Study of a SOA Based COEO for Phase Noise Performance and Reliable Operation

Magnetically driven winds from accretion disks in post-asymptotic giant branch binaries

Context. Jets are commonly detected in post-asymptotic giant branch (post-AGB) binaries and originate from an accretion process onto the companion of the post-AGB primary. These jets are revealed by high-resolution spectral time series. Aims. This paper is part of a series. In this work, we move away from our previous parametric modelling and include a self-similar wind model that allows the physical properties of post-AGB binaries to be characterised. This model describes magnetically driven jets from a thin accretion disk threaded by a large-scale, near equipartition vertical field. Methods. We expanded our methodology in order to simulate the high-resolution dynamic spectra coming from the obscuration of the primary by the jets launched by the companion. We present the framework to exploit the self-similar jet models for post-AGB binaries. We performed a parameter study to investigate the impact of different parameters (inclination, accretion rate, inner and outer launching radius) on the synthetic spectra. Results. We successfully included the physical jet models into our framework. The synthetic spectra have a very similar orbital phase coverage and absorption strengths as the observational data. The magnetohydrodynamic (MHD) jet models provide a good representation of the actual jet creation process in these evolved binaries. Challenges remain, however, as the needed high-accretion rate would induce accretion disks that are too hot in comparison to the data. Moreover, the rotational signature of the models is not detected in the observations. In future research, we will explore models with a higher disk ejection efficiency and even lower magnetisation in order to solve some of the remaining discrepancies between the observed and synthetic dynamic spectra.

Enhancing Structural Resilience: Exploring the Novel Sleeve Method for Steel T-Stub Connections

A new sleeve device has been introduced with the aim of enhancing the performance of steel structures under extreme loading conditions. Previous research has primarily focused on numerical simulations of this sleeve's application, emphasizing the need for further investigation to validate its effectiveness. This research paper presents a numerical analysis that explores how the sleeve device influences the deformation capacity and strength of a bolt up to the point of failure. The study involved the use of a T-stub connection with multiple sleeves, each featuring different geometric parameters. To assess the impact on strength and deformation capacity, a comparison was drawn between these findings and the behavior of a T-stub bolted connection without using the sleeve. In addition, Finite Element (FE) models were employed for additional parametric studies. The test outcomes demonstrated a substantial increase in the deformability of the bolted connection when utilizing the sleeve device, all the while preserving the strength and initial stiffness of the bolts. This proposed sleeve method in the study significantly enhances the connection's capacity by delaying bolt failure.

Waste management and salt recovery from solid residue of tannery common effluent treatment plant

ABSTRACT The considerable amount of solid residue generated by the common effluent treatment plant (CETP) of tanneries needs adequate treatment and reclamation before ending up in the environment as solid waste. A sustainable process aiming at tannery CETP waste minimisation and its reuse for sodium chloride salt recovery is presented. The integrated approach consists of dissolution, decolorisation, filtration, evaporation, and crystallization as unit operations. Lab-scale parametric studies have been undertaken to investigate the effects of various parameters at different process stages on product (sodium chloride) yield and quality. The developed process resulted in the recovery of 93% NaCl, having a purity of ∼98.5%. The optimised operating conditions were tested on a bench-scale system (15 kg batch scale of solid residue) deploying solar energy for evaporation-crystallization, and process feasibility was established. An economic analysis was performed, and the techno-economic feasibility was demonstrated to produce 15,733 tons per annum (TPA) of NaCl from 20,000 TPA of solid residue. The techno-economic analysis favours the treatment of tannery CETP waste with the method specified. The proposed scheme can tackle waste management and reuse the recovered product in the tanning process.

A comprehensive parametric study and multi-objective optimization of turbulent jet array impingement for uniform cooling of gas turbine blades with minimized compression power

In the present paper, a comprehensive parametric study and multi-objective optimizations on jet array impingement cooling are conducted for mid-chord sections of gas turbine blades to maximize the heat transfer uniformity on the target plate and minimize the air compression power consumption at different desired Nusselt numbers. The validated numerical method based on RANS equations is utilized to determine the effects of Reynolds number (2500≤Re≤35000), jet spacings (3≤Px,Py≤8), and the jet-to-target distance (0.75≤Pz≤3) on air compression power (Wc), average Nusselt number (Nu‾), and heat transfer uniformity index (UI). According to the parametric study, the increase of Re improves Nu‾ and UI, while intensively increasing Wc. The effect of jet-to-target distance is a function of jet spacings; with the increase of Pz at low jet spacings, Wc,Nu‾, and UI are reduced. Although, at large spacings, Nu‾ and UI increase with Pz, and Wc is independent of Pz. Additionally, the increase of Px decreases Wc, Nu‾, and UI. Increasing Py reduces Nu‾ and Wc. But at small Px and Pz, the UI is descending; while at large Px and Pz, the UI tends to ascend. Three high-accuracy surrogate models are developed using backpropagation artificial neural networks (ANN) for estimating Wc, Nu‾, and UI for input design variables. Sobol global sensitivity analysis is also performed based on the developed models for quantifying the influence of design variables and their interactions on objective functions. As expected, the results indicate that Wc and Nu‾ are the most sensitive to Re, and UI is mainly affected by Px, whereas Py is more affecting the uniformity index rather than compression power. Finally, to find out the best design and flow conditions, optimizations are conducted by the NSGA-II algorithm. The optimal Pareto frontier and final decided solutions by TOPSIS and LINMAP methods are then demonstrated for the desired Nusselt number (NuD) of 70. The TOPSIS method indicates Px=4.70, Py=3.25, Pz=1.40, and Re = 13800 as the best compromise for optimization. The analysis of Pareto solutions in the range of NuD from 35 to 130 suggests a variety of optimal flow and geometrical arrangements for a trade-off between objective functions; therefore, at most, 50% less compression power or 5% more uniformity is approachable corresponding to the design requirements.

Parametric Study of a Fixed-blade Runner in an Ultra-low-head Gate Turbine

Parametric Study of a Fixed-blade Runner in an Ultra-low-head Gate Turbine

Computational Pulsatile Flow and Efficiency Analysis of Biocompatible Microfluidic Artificial Lungs for Different Fiber Configurations.

Average-sized microfluidic artificial lungs consisting of rows and columns of fiber bundles with different column to row aspect ratios (AR) are numerically analyzed for flow characteristics, maximum gas transfer performance, minimum pressure drop, and proper wall shear stress (WSS) values in terms of biocompatibility. The flow is fully laminar and assumed to be incompressible and Newtonian. The transport analysis is performed using a combined convection-diffusion model, and the numerical simulations are carried out with the finite element method. The inlet volumetric flow is modeled as a sinusoidal wave function to simulate the cardiac cycle and its effect on the device performance. The model is first validated with experimental studies in steady-state condition and compared with existing correlations for transient conditions. Then, the validated model is used for a parametric study in both steady and pulsatile flow conditions. The results show that increasing the aspect ratio in fiber configuration leads to converging gas transfer, higher pressure drop, and higher WSS. While determining the optimum configuration, the acceptable shear stress levels play a decisive role to ensure biocompatibility. Also, it is observed that the steady analysis underestimates the gas transfer for higher aspect ratios.

Probabilistic pile reinforced slope stability analysis using load transfer factor considering anisotropy of soil cohesion

AbstractA probabilistic limit equilibrium framework combining empirical load transfer factor and anisotropy of soil cohesion is developed to conduct pile‐reinforced slope reliability analysis. The anisotropy of soil cohesion is determined conditioned on that the thrust force direction is parallel to the major principal direction and it is easily combined with load transfer factor, which are related with soil parameters, and pile parameters. The proposed method is illustrated against a homogeneous soil slope. The sensitivity studies of pile parameters on factor of safety (FS; calculated at respective means of soil parameters) and β demonstrated that the anisotropy of soil cohesion tends to pose significant effect on reliability index β than on FS. The effect of anisotropy of soil cohesion on FS is found to be slightly different under different pile locations, whereas its effect on β is observed to be least if piles are drilled at the middle part of slope and more significant effect is observed when piles are drilled at the lower and upper part of slope. The plots from the sensitivity studies provide an alternative tool for pile designs aiming at the target reliability index β. The proposed method contributes to the pile‐reinforced slope stability within limit equilibrium framework.

Impact of initial slide on downslope seafloor with a weak layer

Submarine landslides are a major offshore geohazard posing increasing threat to offshore and coastal developments. A submarine landslide may usually involve multistage failure triggered by the first failure. During submarine landslide evolution, the emergent sliding mass may act as an impact load on the downslope seafloor potentially leading to surficial erosion of sediments or deeper ploughing failure with the presence of a weak layer. The study presents a comprehensive analysis of seafloor instability and post-failure behaviors subjected to the first slide mass impact, using a large deformation finite element (LDFE) method. Different failure patterns are investigated and the corresponding critical impact loads for triggering the seafloor instability are analyzed. The parameters controlling the strain softening behaviors of soils and the undrained shear strength in the weak layer have significant effects on the critical condition for the seafloor failure. The dynamic inertia of the slide mass, however, has little effect on the results. An empirical equation for assessing the seafloor instability subjected to the first slide impact, with respect to the impact load from the initial slide and the gravity stress relative to the shear strength in the weak layer, is proposed based on a parametric study.

Effects of various parameters on the mechanical properties of composite lozenge‐shaped core sandwich panels subjected to flat‐wise compression test

AbstractThis paper studied the compressive behavior of a composite sandwich panel with a lozenge‐shaped core reinforced with graphene nanoparticles (GNPs) under different conditions. The fabrication process involves the utilization of Kevlar fibers and epoxy resin to construct the sandwich panels, followed by introducing polyurethane (PU) foam through vacuum‐assisted resin transfer molding (VARTM). Flat‐wise compression testing is conducted to assess the mechanical properties and behavior of the sandwich panels. The study investigates the effect of various parameters, including core angle, core height, weight percentage (wt%) of GNPs, and PU foam, on the compressive strength of the structure. The experimental results are validated through finite element simulation using ABAQUS software, and a satisfactory agreement is observed between the simulated and experimental data. The research findings indicate that the inclusion of 3 wt% of GNPs in the structure leads to a notable 25% enhancement in the compressive strength of the sandwich panel. However, when the GNP content was further increased to 0.4 wt%, a contrasting trend was observed and the strength of the structure decreased by about 12%. Furthermore, an increase in the core angle is associated with a substantial 46% improvement in the sandwich panel's strength. Conversely, an increase in the core height is found to cause an 18% reduction in the sandwich panel's strength. Also, the addition of PU foam results in a significant increase of approximately 42% and 58% in the strength and energy absorption of the sandwich panel, respectively. This comprehensive parametric study offers valuable insights to engineers and designers seeking to construct sandwich panels with optimal strength against compressive loads.Highlights Adding GNPs modifies the mechanical properties of the sandwich panel. Adding 0.4% of GNPs leads to the reduction of the structure's strength. Increasing the core angle increases the sandwich panel's strength. Increasing the core height decreases the sandwich panel's strength. PU foam significantly increases the sandwich panel's strength.

CFD Simulation of an Industrial Dust Cyclone Separator: A Comparison with Empirical Models: The Influence of the Inlet Velocity and the Particle Size on Performance Factors in Situation of High Concentration of Particles

The present work is dedicated to the study of multiphase turbulent and three-dimensional rotational flow in dust cyclones, a contribution to air pollution control. Cyclones are widely used devices for the separation of constituents from solid-gas mixtures in industry. In order to improve the filtration efficiency of cyclones, and to reduce the pressure drop, parametric numerical simulation studies using the Fluent code have been conducted to characterise the effects of the parameters affecting the operation of these devices through their performance indicators. In this work, the effect of inlet velocity and the particle size on the turbulent flow air in the cyclone is presented. Numerical simulation of the flow by Fluent code using three numerical models: the first based on the dissipation of kinetic energy by viscosity (RNG) K-epsilon and standard K-epsilon as well as the last based on the solution of Reynolds stress equations (RSM), combined with the multiphase mixing model, gave interesting results in terms of the pressure and flow field in the separator, the variation of inlet velocity, and the variation of particle size. Validation with experimental and empirical results showed the advantage of the Reynolds stress turbulence model (RSM) over the standard K-epsilon and RNG K-epsilon. The RSM model better captures physical phenomena in an intense vortex flow in the presence of walls. But it is characterised by a very long calculation time and requires large machine resources. An alternative to this model is RNG K-epsilon model, which offers a reasonable calculation time with acceptable results (maximum deviation of 5 %) for speed values below 10 m/s. In the absence of numerical resources, certain empirical models such as those of First (for the evaluation of pressure drop) and Iozia and Leith (for evaluation of efficiency) may well be useful for the dimensioning of the cyclone.

Analysis of Thermomechanical Stress of High-Temperature Ignition Surface Caused by Drop–Wall Interaction at Engine Conditions

Abstract Drop–wall interaction is a complex phenomenon encountered in diverse industrial applications. An important example is fuel droplets impinging on a high-temperature ignition plug in a direct-injection compression-ignition engine. The ignition plug, comprised of heat-resistant materials, will experience thermal shock due to abrupt temperature changes. The ensuing temperature fluctuation in the solid wall induces thermal stress, and if this stress surpasses the material's strength in that mode, failure can occur. Therefore, it is imperative to analyze the temperature dynamics on the high-temperature surface to enhance material durability. This study focuses on drop–wall interactions in the engine environment. Utilizing the Smoothed Particle Hydrodynamics (SPH) method, this research simulates fuel droplet impingement on an ignition plug with various materials to characterize heat transfer, thermal penetration, and temperature distributions in the heated wall. The investigation also delves into the behavior of ceramic material, specifically silicon nitride, assessing its thermomechanical stress and durability based on the stress–number of cycles (S-N) curve. Thermal stress is computed by considering temperature gradients and material properties, while mechanical stress is evaluated based on the bending momentum and momentum flux induced by the spray. A parametric study explores diverse materials such as tungsten carbide, iron, stainless steel, carbon steel, and aluminum. Results indicate that thermal stress outweighs bending and spray-induced stress. Moreover, the analysis reveals that silicon nitride exhibits the lowest thermal stress distribution and superior durability, potentially capable of operating for infinite cycles under engine-relevant conditions.

Revisiting the face stability of rock tunnels in the Hoek–Brown strength criterion with tension cutoff

AbstractIn this work, the three‐dimensional stability of deep tunnel faces is evaluated in rock masses characterized by the generalized Hoek–Brown (H–B) criterion from the perspective of the limit analysis theorem. Considering that underground engineering is gradually developing towards larger burial depths and larger sizes, and the tensile strength of rocks is usually overestimated, the concept of tension cutoff (T‐C) is introduced to substitute the parabolic form of the H–B envelope in the tensile region with a circular arc. The multitangent technique is used to piecewise approximate the H–B envelope, instead of the conventional linear substitution, accounting for the nonlinear dependence of the shear strength on the pressure. Meanwhile, the earthquake loading is considered by the classic pseudostatic method. The tunnel face stability is measured by a stability number, which is calculated based on a newly developed multicone failure mechanism. Parameter studies indicate that the T‐C has almost no impact on the critical support pressure, while has a significant effect on the critical stability number, especially in the presence of earthquakes. Nearly all stress points back‐calculated from the rupture angles fall within the T‐C region, except for the first stress point associated with the first segment. Interestingly, the influence of the geological strength index (GSI) on the face stability is inverse to that of mi due to the brittleness of rocks. Moreover, the critical stability number is not sensitive to mi, but sensitive with the introduction of earthquake loadings.

Investigation of experimental and numerical analysis of thermal load of WEDM on all metals through process parameters on wire electrode temperature

The thermal load on wire electrode in a wire electrical discharge machining (WEDM) is the most important factor in predicting wire electrode breakage. Thermal failure of wire electrode may be expected when the maximum temperature of the wire crosses a critical value. The maximum temperature of wire electrode is a function of process parameters of the machine and hence the failure depends upon what settings we choose to run WEDM process. In this investigation, simulation is done using finite-difference thermal model for continuous pulse without clustering of the spark. The model is used for determining distribution of the temperature throughout the length of wire electrode and analyzed the significance of various factors on wire breakage. The outcome of the present simulation model is validated by comparing it with the works of other researchers followed by its validation through experiments. A parametric study is also done to determine the effect of input process parameters and their interaction on the temperature of the wire. For finding the interaction effect, analysis of variance analysis is carried out followed by developing an empirical model. The predictive performance of our simulation model and developed regression models is assessed by comparing them with experimental value. The regression model is found to be closer, with an average percentage deviation of −2.55% on the experimental dataset, compared to 2.64% for the simulation model. During the validation test, the minimum and maximum deviations are 0.08% and 0.20% for simulation versus regression model predictions, −3.31% and −1.65% for experimental versus predicted results, and 1.82% and 3.40% for simulation versus experimental results, respectively. It is observed through this study that temperature of wire electrode mainly depends on the intensity of sparks per unit area which is a function of the input parameters of the WEDM.

Strength Optimization of Nanocomposite Cementitious Materials Using Nanoscale Modifications

Represent Volume Element (RVE) is broadly used by investigators to control the properties of nano-cementitious materials. This paper focuses on analyzing a set of RVE data and proposes parametric equations for determining the compressive and flexural strength characteristics (σc and σf). Primarily, a parametric study is performed with RVE analysis. The essential design parameters are used to fit rational equations. The RVE data are also applied to train artificial neural networks of σc and σf. RVE, neural networks, and rational equations are correlated. Regression equations are validated with the experimental study. SEM, TEM, XRD, and FTIR results are carried out in the microscopic and mechanical analysis for carbon nanofiber cement composites, which can lift their strength, constancy, integrity, and density and reinforce the composite microstructure. Lastly, the Pareto-optimal design results are presented with a multi-objective optimization problem.