Radar absorbing materials (RAM) are critical for stealth performance of unmanned aerial vehicles (UAVs), but their design typically requires expensive parametric sweeps over frequency, incidence angle, and material thickness. This work presents a physics-informed neural network (PINN) surrogate model for rapid prediction of reflection loss (RL) of a metal-backed carbon-fiber-reinforced polymer (CFRP) absorber over the 2–18 GHz band and 0–80° transverse-electric (TE) incidence range. The model uses random Fourier feature embedding to mitigate spectral bias, combined with physics-based monotonicity and angular boundary regularization. Trained on a transfer matrix method (TMM) reference dataset of 6,400 samples in 42 s on a single CPU, the surrogate achieves a root-mean-square error of 0.22 dB and mean absolute error of 0.12 dB against the analytical baseline. Multi-band thickness optimization identifies the quarter-wave optima at S-band (3.9 mm, RL = -4.64 dB ), X-band (1.9 mm, RL = -8.93 dB ), and Ku-band (1.5 mm, RL = -11.31 dB ). Practical structural integration of the CFRP / Ti-6Al-4 V ELI bilayer onto medium-altitude long-endurance UAV airframes is assessed quantitatively, demonstrating mass overheads of 1.3–8.5
Bubble columns are systems of simple construction that are capable of providing adequate conditions for heat and mass transfer, being commonly used for biochemical processes such as microalgae cultivation or wastewater treatment. Their multiphase nature introduces a higher level of mathematical complexity during reactor design and optimization. The present work proposes a Computational Fluid Dynamics (CFD) procedure to predict the behavior of a bubble column reactor, using a large eddy simulation (LES) based model combined with a finite difference method (FDM) approach. The results obtained were compared with experimental and numerical data available in the literature for the air volume fractions distributions over different regions of the column and for the time-averaged gas holdup. A grid independence analysis resulted in a uniform mesh with low discretization errors. Different models for the estimation of the drag coefficient were considered, and the resulting profiles were compared with the experimental data. When compared to RANS-based models, the present FDM-LES model resulted in a significantly better agreement with experimental data, particularly for gas fraction profiles and average gas holdup in both the lower and upper regions of the column. This highlights the advantages of the LES approach.
This study explores the influence of swirl-inducing piston modifications and key engine operating parameters on the performance characteristics of a diesel engine. Experiments were carried out using a single-cylinder direct injection (DI) diesel engine with a fixed compression ratio of 17.5. The investigation covered a range of injection pressures from 210 to 270 bar in 30-bar increments and injection timings from 19° before top dead center (bTDC) to 27°bTDC in 4° intervals and three custom-designed piston heads incorporating three, six, and nine swirl grooves were employed. To enhance in-cylinder air motion, three custom-designed piston heads incorporating three, six, and nine swirl grooves were employed. A Design of Experiments (DOE) methodology based on Response Surface Modeling (RSM) was applied to evaluate the statistical relationships among the input variables and engine responses. To complement and validate the RSM-based findings, several supervised machine learning algorithms namely Linear Regression, AdaBoost, Huber Regression, and XGBoost were implemented to predict performance and emission metrics. Among these, XGBoost exhibited superior predictive capability, yielding a low Mean Squared Error (MSE) of 0.288, a Root Mean Squared Error (RMSE) of 0.537, and a Mean Absolute Error (MAE) of 0.433. Notably, the configuration with five grooves and an injection pressure of 245.22 bar resulted in the most efficient combustion and the lowest hydrocarbon (HC) emissions. Additionally, a desirability-based multi-objective optimization approach was employed to identify the optimal combination of parameters. The integrated use of experimental testing and predictive modeling offers a reliable framework for engine performance optimization and provides valuable insights for enhancing combustion efficiency in diesel engines.
Rotating flow systems serve as fundamental platforms for studying enhanced heat and mass transfer in advanced engineering processes. This study primarily aims to investigate the flow, thermal, and bioconvective behaviors of a trihybrid Williamson nanofluid via a rotating disk in the presence of motile microorganisms. The trihybrid nanofluid comprised copper, iron oxide, and aluminum oxide nanoparticles dispersed in a base fluid of water. A mathematical model was developed under steady, incompressible flow conditions, incorporating thermal radiation, heat source/sink effects, Brownian motion, thermophoresis, viscous dissipation, and Darcy-Forchheimer porous media. The governing nonlinear equations were transformed and solved numerically using the MATLAB BVP4C algorithm to ensure accuracy and numerical stability. Additionally, the Response Surface Methodology (RSM) based on a Central Composite Design yielded a high coefficient of determination (99.96
The present study is concerned with simulation strategies for analysis and prediction of uncertainties in the response of structures consisting of materials with randomly disordered microstructures. Different numerical methods are discussed to analyze the propagation of uncertainties across the scales. Starting from the microstructure, methods and measures for the randomized generation of equivalent computational microstructural models are introduced. Approaches for their homogenization are compiled, including a discussion on the necessity of probabilistic assessment as well as the requirements for the further analysis on the structural or at least next higher scale of structural hierarchy. The analysis on the structural scale is based on random field representations of the effective material properties accounting for all intercorrelations between the individual properties as well as their spatial correlation. To formalize the approach, a semantic interoperable data model is defined. The approaches are illustrated on examples including random microheterogeneous materials such as structural foams and discontinuously fibre reinforced plastics, the failure probability of filament wound pressure vessels containing distributed manufacturing induced defects, the fatigue of polycrystalline steels as well as the fracture initiation in nodular cast iron with distributed spherical graphite inclusions.
This paper presents a study of the physical and mineralogical properties of archaeological sandstone blocks extracted from the burial environment at the Dendera Temple site, Egypt. These blocks have been affected by several deterioration factors since their extraction and are still suffering from damage factors at the moment, including cracking, granular disintegration, surface flaking, loss of material, salt efflorescence, black spots in stone surface, and hard black crust. To determine the causes and mechanisms of deterioration, sandstone and associated mortar samples were analyzed using several analytical techniques, including X-ray diffraction (XRD), polarized light microscopy (PLM), and scanning electron microscopy with X-ray energy dispersion analysis (SEM–EDS), and physical property tests. The analyses revealed that the sandstone is primarily composed of quartz with minor amounts of carbonate minerals, clay minerals, iron oxides, and soluble salts such as gypsum, bassanite, and halite. Physical property tests revealed a medium apparent density, with relatively high porosity and high-water absorption. The results indicate that the deterioration of the sandstone blocks is primarily linked to chemical weathering processes resulting from the crystallization of salts within the pores under the influence of burial environment conditions. These processes generate internal stresses that cause cracking, loss of cohesion, and weakening of the stone structure.
Classical continuum theories neglect surface-dominated effects that govern the mechanical response of nanostructures at high surface-to-volume ratios (SVR). Using molecular dynamics simulations, this work examines the size-dependent free transverse vibration of solid and hollow silicon nanobeams and shows that hollow geometries exhibit a non-monotonic dependence of natural frequency on cavity size, in contrast to the monotonic surface-induced softening displayed by solid nanobeams. For small cavities, geometric stiffening from the redistribution of material away from the neutral axis outweighs surface softening, increasing the frequency despite a rising SVR; as the cavity enlarges and the walls thin, the negative surface elastic constants of silicon dominate, producing a pronounced frequency drop. This competition is shown to be quantitatively consistent with an extension of the Miller–Shenoy surface-elasticity framework to hollow cross-sections, and is distilled into a minimal two-term dimensionless scaling law that balances a geometric stiffening term against a surface-softening term governed by the ratio of an intrinsic material length scale to the beam's outer dimension. The scaling law predicts the cavity ratio at which the stiffening-to-softening transition occurs, and the molecular dynamics results show this transition at a smaller cavity ratio than the continuum prediction, pointing to an additional, cross-section-dependent atomistic contribution to surface softening not captured by the linear continuum model. These results clarify the competing mechanisms governing the vibrational response of hollow nanobeams and provide a predictive, extensible framework connecting molecular-scale surface elasticity to continuum-level nanoscale design.
The Artificial Neural Networks provide a visually intuitive, computationally efficient framework for approximating arbitrarily nonlinear physical systems, circumventing the high computational costs commonly associated with all conventional numerical solvers. In this study, we investigated the flow kinematics of a Newtonian fluid flowing over/around a porous sphere, which is embedded with a Micropolar Casson fluid through the inner permeable region. Using a stream-function formulation, we reframe governing nonlinear flow equations and obtain analytic solutions. The rationale behind this approach is that those exact solutions then serve as the baseline dataset required to train and validate the neural network. The ANN architecture that is proposed in the work is subsequently utilized to provide mappings of stream-function responses for a range of physical parameters controlling the flow regime. In particular, we investigate the perturbation of local stream-function distribution arising from variations in the couple stress parameter, Darcy number, Casson fluid parameter, and azimuthal angle. For a quantitative measure of how well the ANN predicted it, we compute a variety of conventional error metrics measured in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), Pearson Correlation Coefficient (R), normalized root mean square error (NRMSE) and maximum absolute errors (MaxAE) as well as symmetric MAE before carrying out more specific evaluations through statistical comparisons using SMAPE. For a more concrete footing of these results, relative performance with respect to a variety of traditional machine learning tools is compared. The comparative baseline includes Multiple linear, Ridge, Lasso, Poly (Polynomial), Decision Tree, Random Forest, Support Vector, and Gaussian Process regressions. Results show that, among all the models considered, ANN achieves consistently minimal residual errors and the strongest correlation with analytical baselines, thereby significantly validating its predictive accuracy and generalization ability. Thus, this neural network architecture serves as an extremely powerful surrogate model for complex fluid-flow dynamics in porous media.
This study presents a comprehensive numerical investigation of bioconvective transport in a lid-driven vertical cavity filled with an Ag–Cu/water hybrid nanofluid containing oxytactic microorganisms under the influence of spatially localized magnetic fields. Unlike conventional magnetohydrodynamic analyses that assume uniform magnetic forcing, the present work incorporates discrete horizontal and vertical magnetic strips, producing non-uniform Lorentz forces that significantly alter vortex dynamics and transport mechanisms. The governing equations, formulated using the stream–vorticity approach within a single-phase nanofluid framework, are solved via an efficient Alternating Direction Implicit (ADI) scheme. The coupled effects of Reynolds number (Re), Richardson number (Ri), magnetic parameter (Mn), bioconvection Rayleigh number (Rb), and nanoparticle volume fractions are systematically examined. The results reveal that the magnetic parameter significantly suppresses heat transfer, reducing the Nusselt number by about 74
Cement kiln dust (CKD), a by-product, is typically disposed of in landfills, but landfilling poses risks of leachate formation and the transport of aggressive substances to groundwater and soils. In this study, the properties of cement paste and concrete, with or without a Mineral admixture (MA), were evaluated, with CKD added at 0, 5, 10, and 20
Disk-cam mechanisms are widely used in mechanical systems where synchronized motion transmission is essential. However, the concurrent optimization of cam profile and its manufacturing parameters remains underexplored, leading to suboptimal production costs and compromised profile quality in industrial practice. This research presents a novel integrated methodology for minimizing the total production cost of disk-cam mechanisms by concurrently optimizing the cam profile shape and manufacturing parameters. The proposed approach combines the Taguchi method for experimental design with Grey Relational Analysis (GRA) for multi-objective decision-making. A total of sixteen experimental cases were generated using an L16 orthogonal array to investigate the influence of four control parameters: motion type, number of B-spline control points, tool type, and mathematical optimization method. Each case was solved using a MATLAB–ANSYS integrated framework that incorporates both shape and cost considerations into a nested optimization structure. The results demonstrate that carbide tooling and the Interior Point Method (IPM) yield the highest production cost reductions, while the number of B-spline control points most significantly influences cam area and structural stress. Production cost reductions of up to 67.9
This research enhances the understanding of contaminant migration in bentonite-amended soil liners, aiding in the creation of more efficient pollution control strategies. The study focuses on deriving breakthrough curves for lead and iron ions using locally sourced soil samples mixed with 10
This study investigates the behaviour of a rotating Newtonian electrically conducting fluid subjected to an unsteady magnetohydrodynamic (MHD) free-stream flow past an infinite vertical plate in the presence of chemical reaction, heat absorption, Hall current, and ion-slip effects. The flow is modelled using the Brinkman framework, while the governing dimensionless equations are formulated with the Caputo–Fabrizio fractional time derivative to capture memory and anomalous diffusion effects more effectively than classical models. Exact solutions for the velocity, temperature, and concentration fields are obtained using the Laplace transform technique. The influence of the governing physical parameters on the flow characteristics is analyzed through graphical illustrations and tabulated results. The findings reveal that increasing the Hall and ion-slip parameters enhances the resultant velocity near the plate but reduces it farther from the surface, whereas the fractional parameter decreases the velocity throughout the flow region. Rotation significantly increases skin friction, and buoyancy forces further strengthen it with time. Heat absorption reduces the fluid temperature, while chemical reactions decrease concentration levels across the flow domain. The obtained results show excellent agreement with previously published studies, validating the accuracy of the present model. Owing to its ability to describe complex transport phenomena with memory effects, the proposed model has potential applications in plasma physics, nuclear fusion reactors, astrophysical and space plasma flows, liquid–metal cooling systems, chemical and catalytic reactors in porous media, and emerging microfluidic and nanofluid technologies.
This study investigates the sustainable use of recycled plastic waste aggregates (PWA)—specifically polyethylene terephthalate (PET) and polyvinyl chloride (PVC) as partial replacements for natural aggregates in high-strength concrete (HSC) with a target compressive strength of about 65 MPa. Four types of PWA, including mixed irregular-shaped plastic waste particles, PVC, heat-treated pellets (PEL), and PET, were added into concrete mixes at replacement levels of 5
This study presents a comprehensive experimental and decision-analytic framework for optimizing the drilling performance of Aluminum Alloy 5083, a material extensively used in marine and aerospace structures. Precision drilling of AA5083 remains challenging due to burr formation, built-up edge (BUE), and geometric deviations. To systematically address these issues, a comprehensive full-factorial Design of Experiments (3 × 4 × 4) structure was employed, necessitating 48 unique experimental runs to evaluate the influence of varying the primary input factors—drill diameter (6, 8, and 10 mm), spindle speed (3000, 3500, 4000, and 4500 rpm), and feed rate (150, 200, 250, and 300 mm/min)—on drilling performance. The captured output responses comprise top burr thickness, bottom burr thickness, circularity error, cylindricity error, and chip thickness. Chip morphology was characterized via Scanning Electron Microscopy (SEM) to elucidate the deformation mechanisms influencing hole quality. Analysis of Variance (ANOVA) revealed that drill diameter exerted the most significant influence on top burr formation (26.06
In this study, an analysis of the impacts of MHD, Joule heating, and activation energy on Blasius-Sakiadis flow with hybrid nanofluid (Ag–Cu/EG) is carried out. The transformation of nonlinear differential equations is done through the process of similarity transformation of nonlinear governing equations. Furthermore, for the consideration of uncertainty that occurs due to the variation in nanoparticle volume fraction, triangular fuzzy number of fuzzy logic is adopted. Further, ANN models are designed using Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms. From this analysis, it is observed that an increase in magnetic parameter reduces the velocity due to the presence of Lorentz force but increases the temperature due to the impact of Joule heating effect. The accuracy of ANN predictions is very high as indicated by R values approximating 0.999 and MSE values between 10^-6 and 10^-17 . The Bayesian Regularization algorithm yields superior results in terms of generalization ability. The Bayesian approach gives the most consistent results. The artificial neural network-based fuzzy model offers a systematic procedure in dealing with hybrid nanofluids with uncertainties and is applicable to other nonlinear flow processes as well.
The present study elucidates the influence of transpiration in a uniform shear flow over a stretching/shrinking surface of a Powell–Eyring fluid in the presence of thermal radiation. Owing to the strong nonlinearity of the governing partial differential equations, exact analytical solutions are not feasible. Consequently, Lie group analysis is employed to derive appropriate similarity transformations, reducing the system to a set of ordinary differential equations. These transformed equations are solved numerically using MATLAB’s ‘bvp4c’ solver. The results reveal the existence of dual solution branches for both stretching and shrinking surfaces over a wide range of material and transpiration parameters. Although both solutions satisfy the mathematical requirements, they exhibit distinct velocity and temperature characteristics. A linear temporal stability analysis is conducted to identify the physically realizable solution, demonstrating that the first solution branch is stable and hence physically meaningful. Furthermore, multiple linear regression analysis is performed to establish predictive relationships between key physical quantities and the governing parameters. The regression models exhibit excellent predictive performance, with R^2 > 0.999 in all cases, indicating strong statistical correlations. It is observed that the fluid velocity and temperature decrease in the first solution branch with increasing suction parameter (S). In contrast, increasing the Powell–Eyring parameter (ϵ ) enhances the fluid velocity while reducing the fluid temperature in the first solution branch. The effects of the controlling parameters on the physical quantities, velocity and thermal fields are discussed in detail through graphical and tabular presentations.
Conventional direct combustion and gasifier stoves operate a packed bed combustion technology, which is not favorable for combustion of fine solid biomass such as charcoal fines because of the compact nature of these fuels, which blocks airflow, leading to incomplete combustion. Conversely, fluidized bed combustion technology would effectively combust fine fuels; however, this is largely a monopoly of the industrial sector for processes such as heating, drying, separation, power generation, among others, and has never been adopted in cookstove designs despite its high-quality combustion and heat transfer potential. This study, therefore, presents the design, modeling, and simulation of a fluidized bed cookstove specifically developed for the direct combustion of charcoal dust, without the need for briquetting. The cookstove employed a bubbling fluidized bed combustion mechanism to enhance fuel–air mixing, hence promoting complete combustion and improving heat transfer. The stove design was a result of mathematical modeling using empirical formulae from previous studies. Computational Fluid Dynamics (CFD) was employed to model and simulate both the hydrodynamic behaviour and combustion processes within the reactor, thereby predicting the model performance. The cookstove featured a funnel-shaped combustion chamber with a dense phase region (Ø0.106 m × 0.119 m), a lean phase region (Ø0.212 m × 0.064 m), and a total reactor height of 0.182 m. CFD results indicated dense phase particle concentration with no entrainment and a maximum combustion temperature of 726.8℃.
