In recent decades, researchers have begun to investigate innovative sustainable construction materials for the development of greener and more environmentally friendly infrastructures. The main purpose of this article is to investigate the possibility of employing date palm tree waste as a natural fiber alternative for conventional steel and polypropylene fibers (PPFs) in concrete. Date palm fibers are a common agricultural waste in Middle Eastern nations, particularly Saudi Arabia. As a result, this research examined the engineering properties of high-strength concrete using date palm fibers, as well as the performance of traditional steel and PPF concrete. The concrete samples were made using 0.0%, 0.20%, 0.60%, and 1.0% by volume of date palm, steel, and polypropylene fibers. Ten concrete mixtures were made in total. Compressive strength, flexural strength, splitting tensile strength, density, ultrasonic pulse velocity (UPV), water absorption capability, and water permeability tests were performed on the fibrous-reinforced high-strength concrete. With a 1% proportion of date palm, steel, and polypropylene fibers, the splitting tensile strength improved by 17%, 43%, and 16%, respectively. By adding 1% fiber, flexural strength was increased by 60% to 85%, 67% to 165%, and 61% to 79%. In addition, date palm fibers outperformed steel and PPFs in terms of density, UPV, and water permeability. As a result, date palm fibers might potentially be employed in the present construction sector to improve the serviceability of structural elements.

In the current work, the experimentations have been accomplished to assess the impact of diffusing nanosilica particles in epoxy matrix on the mechanical and thermal performance of the luffa fibre reinforced epoxy composite material. The matrix and fibre composition are fixed as 80 : 20 throughout the study, and the nanosilica is disbanded in diversified volume fractions of 0%, 0.5%, 1.0%, 1.5%, 2.0%, and 2.5%, respectively, while preparing luffa/epoxy/nanosilica (LES) composite samples. The mechanical characteristics, such as tensile, flexural, and impact behaviour of the composite, and the thermal properties, namely, thermal stability and thermal conductivity, are examined for the LES samples. The experiments are accomplished as per the ASTM standards. The results revealed that the assimilation of nanosilica particles in epoxy has improved the mechanical and thermal characteristics of the composite significantly. The tensile, flexural, and impact strength of the composite have been amended by 157.58%, 66.9%, and 16.5% with 1.5% addition of nanosilica in epoxy. Similarly, thermal conductivity of the composite is improved by 47.53% with the dispersion of 2.5% nanosilica in epoxy matrix. In addition, the thermal stability of the LES composite samples is substantially improved while disbanding nanosilica in epoxy matrix. However, the better results are reported for the LES composites containing 1.5% nanosilica content, compared to the next higher volume fractions of nanosilica content in epoxy matrix.

This study includes an artificial neural network (ANN) analysis of irreversibility in Johnson–Segalman nanofluid flow through a peristaltic channel under the influence of motile microorganisms, viscous dissipation, and slip effects. The nonlinear partial differential equations are transformed into ordinary differential equations by applying the lubrication approximation and Debye–Hückel transformations with the help of suitable dimensionless variables. The resultant dimensionless ordinary differential equations are solved analytically using the homotopy perturbation method (HPM) by linearizing and assuming a series solution. The linear subproblems from HPM are solved successively to find the symbolic series solution in MATLAB by utilizing the dsolve command. The symbolic solutions for velocity, temperature, concentration, and bioconvection are plotted against different physical parameters to visualize their behavior and profiles. Moreover, data for velocity, thermal, concentration, and bioconvection profiles are extracted to train the ANN model. The ANN model is trained in Python using TensorFlow version 2.17.0., and it consists of one input layer, two hidden layers (each with 64 neurons), and one output layer. The ReLU activation function is used in the hidden layers, and the Adam optimizer is employed in our model. Performance metrics such as mean square error (MSE), regression (R2), error histogram, gradient, and relative error, and absolute error are computed to monitor the performance of the ANN model. Results show that the ANN demonstrates promising accuracy in predicting and learning the velocity, thermal, concentration, and momentum profiles. The findings indicate that the magnetic field and Prandtl number significantly influence the thermal profile, while the velocity profile is affected by the Darcy parameter. This work has potential applications in biomedical engineering, particularly in the design of microfluidic devices for targeted drug delivery, and also holds relevance for environmental engineering.

The current work investigates the performance of cylindrical‐shaped sintered wick heat pipe at different orientations, numerically. The results are compared and validated with the experimental findings. The study is extended by using a nanofluid (comprising nano‐Curo in deionized water) as a working fluid and the thermal performance of heat pipe with deionized (DI) water has been compared with that of heat pipe with nanofluid (containing various concentrations of CuO nanoparticles in DI water). During the investigation, the nanofluid with 1.0 weight fraction of CuO nanopaticles found to be optimum, which has produced the better results. The numerical analysis has been carried out to study the temperature difference, fluid velocity, and pressure drop of the sintered wick heat pipe using the commercial CFD software, Ansys Fluent R14.5. The computational results are observed to be much closer to the experimental data, and the vapor velocity at the heat pipe’s core has been determined to be 64.54% higher than the liquid flow over the wick structure. Interestingly, the heat pipe pressure drop has been reduced by adding CuO nanoparticles to the working fluid. Finally, the heat pipe loaded with a 1.0% concentration of nano‐CuO in nanofluid has exhibited a notable reduction in pressure drop of 35.33%.