Phase Change Materials (PCMs) have excellent energy density and reliable temperature control which enables them to store thermal energy with a prominent efficiency. But, challenges like low thermal stability, phase separation, leakage during phase transition, and low thermal conductivity limit their broader use in thermal energy storage (TES) systems. The development of PCM composites, enabled by integrating PCMs with nanomaterials, mitigates the problem of low thermal conductivity; however, a low thermal stability issue persists. To tackle this issue, the strong tendency of nanoparticles to form robust intermolecular bonds with PCMs is addressed by combining nanoparticles with PCMs. Consequently, review provides a comprehensive analysis of TES and the classification of PCMs, highlighting specific enhancements in thermal and mechanical properties achieved through the use of hybrid and other nanomaterials. These enhancements, including increased thermal conductivity, mechanical robustness, and shape stability, lead to more efficient heat transfer and improved thermal performance in TES systems, thereby addressing thermal stability issues that degrade after multiple thermal cycles. Additionally, the review examines the preparation methods of PCM composites by dispersing various hybrid nanomaterials and bio-derived carbonaceous substances. Authors observed that maximum augmentation in thermal conductivity of a hybrid PCMs is 900 %. Moreover, emerging applications based on the thermos-mechanical properties of the PCMs are comprehensively discussed. At last, the limitations of hybrid particle-dispersed PCM composites are evaluated, along with current challenges and future research prospects.
Wavy fin heat sinks offer significant potential for thermal management due to their enhanced surface area compared to conventional straight-fin designs. However, the effect of surface area on thermal resistance is not straightforward since thermal resistance depends on the inverse product of surface area and the convective heat transfer coefficient. This study investigates the relationship between surface area, convective heat transfer coefficient, and thermal resistance for wavy fin heat sinks under natural convection. Experimental tests were conducted on a l parallel-fin heat sink (HS1) and a wavy-fin heat sink (HS2) using a polyimide heater supplying 4 W of input power. Thermal resistance is determined from steady-state temperature measurements using three thermocouples located at the heat sink base. Despite having an approximately 10% greater surface area, HS2 exhibited a thermal resistance of 14.85 K/W, about 4.7% higher than HS1 (14.19 K/W). This highlights that increased area alone does not guarantee improved performance. This is attributed to a lower convective heat transfer coefficient (8.38 vs. 9.71 Wm⁻²K⁻¹) caused by restricted airflow within the wavy fin channels. Three-dimensional steady state computational fluid dynamics simulations on four cross-cut variants of HS2 are performed to investigate the effect of geometric modifications on the convective heat transfer performance. The HS2B showed a reducing thermal resistance by up to 3% (5.57 K/W) while slightly decreasing surface area compared to HS2(16.21 K/W). These results demonstrate that effective heat sink design under natural convection requires the balancing surface area and convective heat transfer coefficient.
One of the most essential processes in manufacturing is continuous operations without any downtime. The Energy Balanced Hybrid Flow Shop Scheduling Problem (EMBHFSP) is an interesting study as it provides huge impact on machine effectiveness while balancing between minimizing completion time and energy consumption. Limited study has focused on hybrid flow shops (HFS) with a concentration on energy-machine balanced production. This paper aims to develop a computational model and evaluate the exploration effectiveness of Differential Evolution in optimizing the EMBHFSP. The most popular as well as latest optimization algorithms including Simulated Annealing, Grey Wolf Optimization, Henry Gas Solubility Optimization, Harmony Search, Imperialist Competitive Algorithm, Multi-verse Optimizer, and Thermal Exchange Optimization were evaluated against Differential Evolution utilizing the EMBHFSP model across 20 Optimization repetitions. The experimental results indicate that the Differential evolution algorithm surpassed others by 45% in mean fitness value and exhibited a 67.5% enhancement in standard deviation across all benchmark problems. In addition, a case study was performed in a manufacturing facility to validate the applicability of the model. Three different scheduling solutions, optimizing makespan, energy balance and machine utilization balance are generated using differential evolution. The results show the ability of the model to solve trade-offs between efficiency of production and sustainability in real industrial environments.
Detonation combustion modes offer potential benefit over deflagration-based combustion due to rapid heat release and pressure-gain characteristics. A small-scale rotating detonation engine (RDE) is a compact detonation-based device capable of continuous and high-frequency operation. Small-scale RDEs are promising for propulsion related and laboratory-scale applications, especially for compact operation under low flow-rate conditions. In the present study, wave propagation behaviour in a small-scale RDE running on methane–oxygen was investigated experimentally using high-speed imaging. The combustor annulus had an inner diameter and outer diameter of 38 mm and 46 mm, respectively. The operating conditions were varied over equivalence ratios (φ = 0.8–1.2) and total mass flow rates (ṁtotal = 3.8, 4.0, and 4.2 g/s). The high-speed image sequences showed alternating single-wave and dual-wave propagation modes within the RDE annulus under the tested conditions. The wave propagation velocity and wave propagation frequency were determined from the recorded image sequences. The average wave propagation velocity was found to be significantly lower than the ideal Chapman–Jouguet value, indicating non-ideal wave behaviour under the present low flow rate operating conditions. Overall, the results provide useful insight into the propagation characteristics of a small-scale RDE operating with methane–oxygen mixtures at very low total mass flow rates.
Surface Rolling (USR) is a surface deformation strengthening technology. By introducing cavitation effects into USR, the extreme high-pressure shock waves generated by the collapse of microscale bubbles can further enhance the surface strengthening effect. However, the synergistic mechanism governing near-wall double-bubble collapse in cavitation assisted USR remains unclear, limiting further optimization of the cavitation strengthening effect. This study investigates the dynamic behavior and synergistic mechanism of near-wall spherical double-bubble collapse in cavitation assisted USR through a combined numerical and experimental approach. A coupled dynamic model for spherical double bubbles and a shock wave pressure propagation model for the rolling region were developed and validated against cavitation assisted USR experiments. The results indicate that when double-bubble collapses, its synergistic effect increases the peak impact pressure by 23.6% compared to single-bubble. Analysis of the parameters reveals that the wall impact pressure is related to initial bubble radius, ultrasonic amplitude, dimensionless distance, and inter-bubble distance. This study has clarified the synergistic mechanism of the near-wall double-bubble collapse, providing theoretical support for controllably utilizing the cavitation effects in USR.
The article exquisitely delves to study the transmission of shear waves (SH) in a tri-layered geological media which is fundamental to nondestructive testing and SAW devices, including a piezoelectric layer sandwiched between a dry sandy half-space and a fiber-reinforced half-space under smooth-contact conditions. The analyticall study investigates the transference of SH waves through such a configuration, aiming to derive the complex dispersion relation and asses the inpact of sandy parameters, fiber reinforcement anisotropy, piezoelectric coupling and thickness of layer on phase velocity and mode confinement. Using the Biot's theory for dry sandy media, constitutive relations for fiber-reinforced elastic materials and linear piezoelectricity under quasi-static approximation, the governing equations are formulated and solved via potential functions under smooth contact boundary conditions, stress and displacement continuity, vanishing stress at the upper interface and open-circuit electrical conditions producing the secular equation, which is then evaluated numerically. Results demonstrate that the phase velocity remains heavily sensitive to the reinforcement anisotropy and the frictional coefficient of the dry sandy medium; the piezoelectric layer introduces electromechanical coupling that generates distinct dispersion branches and band gaps at high wavenumbers, while smooth contact effectively confines energy within the layered structure. The model offers immediate applications in smart seismic wave barriers, improved inversion of shear-wave splitting in desert regions underlain by reinforced strata, and piezoelectric surface acoustic wave sensors for structural health monitoring. By bridging classical geomechanics and modern smart materials, this model may serve as a rigorous benchmark for future experimental and computational studies.
Suboptimal thermal efficiency of gas turbine operation is caused by a significant portion of the input energy lost as waste heat. This study investigates the integration of advanced waste heat recovery systems to mitigate these losses, enhance overall plant performance and increase operating revenue. A comprehensive thermodynamic model was developed based on the first and second laws of thermodynamics to simulate a gas turbine integrated with a combined cycle gas turbine, electric turbocompounding and a regenerative cycle. The feasibility of on-site waste-to-hydrogen production from the electrolysis process of the recovered energy was also evaluated. The model was analysed across a range of compressor pressure ratios and combustion chamber temperature rises. For the baseline gas turbine, the maximum exergetic efficiency was 11% at PRC of 12 and a combustion chamber temperature rises of 1000 K, with efficiency declining at higher compressor pressure ratios. The integrated system with waste heat recovery significantly increased the exergetic efficiency up to 26% at a higher compressor pressure ratios of approximately 12. A preliminary exergoeconomic analysis indicated that the integrated system could reduce electricity fuel costs by up to 50% relative to the baseline plant and generate up to $4,000 per hour in additional revenue stream through on-site production of cost-competitive hydrogen. The results indicate that the strategic integration of multi-stage energy recovery systems can more than double the exergetic efficiency of gas turbine power plants, thereby maximising useful work output and enabling a sustainable thermal system with hydrogen co-production.
316L and P91 steels, commonly used for high-temperature applications in power plants, experience low-cycle fatigue. In this work, the cyclic stress-strain behaviour of 316L and P91 materials was analyzed. Experimental data from the material's hysteresis loops and cyclic stress response were used to derive material parameters for numerical simulation of the cyclic behaviour. The displacement-controlled model with strain amplitudes of 0.4% and 0.6% and a constant strain rate of 0.001 s-1 was used in the simulation. The simulation results show that 316L stainless steel exhibits cyclic hardening, with the maximum stress increasing from 311.3 MPa in the first cycle to 354.7 MPa at the half-cycle, corresponding to approximately 14% cyclic hardening. In contrast, P91 steel exhibits cyclic softening of about 8.3%, as the stress decreases from 497.9 MPa in the first cycle to 456.7 MPa at the half cycle. The findings also demonstrated that tension and compression loadings with larger strain amplitudes produced higher maximum stress. It also demonstrates that higher strain amplitudes result in a greater decrease in stress near the half cycle because hardening/softening is more pronounced at higher strain amplitudes.
This study aims to develop a hybrid dual-layer smart coating system incorporating multiple corrosion inhibitors to enhance the durability and corrosion resistance of carbon steel substrates. The proposed system consists of a hydrophobic zinc oxide–stearic acid (ZnO-STA) top layer and a self-healing epoxy bottom layer containing benzotriazole-loaded halloysite nanotubes (BTA-HNT) and boiled linseed oil microcapsules (BLO-MC). Five coating systems were fabricated, including pure epoxy, conventional dual-layer coating, and hybrid dual-layer coatings with ZnO-to-epoxy ratios of 2:1, 4:1, and 6:1. Structural and chemical characteristics were verified using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy. At the same time, corrosion performance was evaluated using Electrochemical Impedance Spectroscopy and scratch tests over 2 weeks of immersion in 3.5 wt.% NaCl solution. The results indicate that the hybrid coating with a ZnO: epoxy ratio of 4:1 exhibited the best corrosion resistance, maintaining impedance values between 4.45 Ω and 4.16 Ω at low frequency and showing the lowest corrosion area of 4.4% after 2 weeks of exposure. However, the hybrid coatings demonstrated reduced adhesion strength, approximately 77.30% lower than the conventional dual-layer coating. Overall, the integration of ZnO-STA, BTA-HNT, and BLO-MC in a hybrid dual-layer system enhances corrosion protection through synergistic hydrophobic barrier and self-healing mechanisms. However, further optimisation is required to improve coating adhesion for long-term application.
Inconel 718 is widely employed in the aerospace and energy sectors due to its exceptional mechanical and thermal stability, yet it remains one of the most challenging materials to machine. This study develops and validates a comprehensive three-dimensional finite element model using ANSYS to simulate orthogonal cutting of Inconel 718. The developed model incorporates Johnson-Cook constitutive and damage laws to represent strain hardening, rate sensitivity, thermal softening, and failure behaviour under high-speed cutting conditions. A full factorial design combined with response surface methodology was employed to analyze how variations in cutting speed, rake angle, nose radius, clearance angle, and depth of cut affect key machining responses, including cutting force, von Mises stress, cutting temperature, and energy consumption. Simulation outputs were rigorously validated against available experimental data, achieving close agreement. Parametric and ANOVA analyses revealed that rake angle and cutting speed significantly affect chip segmentation and thermal gradients. Empirical regression models demonstrated high predictive accuracy and were used for multi-objective optimization, yielding an optimal parameter set that minimized cutting force, energy, and thermal load. The findings provide a validated, computationally efficient simulation framework with direct relevance to machining process optimization and tool performance prediction in superalloy applications.
Natural fiber composites are widely studied as an alternative in engineering, especially for tribological applications such as brake pads in the automotive industry. This study analyzes the effect of Casuarina equisetifolia fruit powder on wear, impact strength, and hardness of epoxy matrix brake pads. The research used a true experimental design with a posttest-only control group. The results showed that the 30% CEFP composite was almost equivalent to the control group, with a specific wear value of 8.33×10-4 mm3/kg · m, having a difference of 28.93% from commercial products, an average impact strength value of 2.08×10⁻³ J/mm², a difference of 20.19%, and an average hardness value of 13.4 HV, a difference of 36.04%, which shows potential for low-load automotive applications.
Stir casting is a widely used metallurgical technique for producing aluminum matrix composites. Many studies in this area have focused on fabricating these composites using fixed stir casting parameters, often overlooking the importance of an optimization approach. These parameters significantly influence the microstructure and overall performance of the composites. This investigation aims to refine stir-casting parameters to produce Al 6061 composites reinforced with B4C microparticles, thereby improving their performance. The sample was prepared using the two-step stir-casting technique with a 2 wt% B4C particle composition. The Taguchi method was utilized to optimize three critical parameters in stir casting, such as melting temperature (700-800 ºC), stirring speed (100-300 rpm), and stirring time (10-30 minutes), which were systematically adjusted. A systematic analysis using an L9 orthogonal array was conducted to determine how varying levels of process parameters affected hardness properties. The optimization of two-stage stirs casting parameters using the Taguchi method identified stirring speed as the most dominant factor, with an optimal combination of 700 ºC melting temperature, 200 rpm stirring speed, and 20 min stirring time producing the highest Brinell hardness in Al 6061–2 wt.% B₄C composites. Analysis of variance results indicated that all three stir casting parameters significantly influenced the property responses, with stirring speed being the most dominant factor in achieving the highest Brinell hardness (HB) in the composite material.
Electrically non-conductive advanced engineering materials, such as glass, ceramics, quartz, and composites, pose significant challenges for conventional machining methods. Electrochemical discharge machining (ECDM) emerges as a notable hybrid non-conventional technique specifically tailored for brittle, hard-to-machine, nonconducting materials. This technique achieves a delicate balance between thermal energy and chemical interactions. Variations of ECDM have been developed to enable the fabrication of complex miniature profiles. However, prevalent issues such as unstable electrolyte conditions and inadequate flushing within the machining zone compromise the process accuracy and repeatability, thereby undermining the industrial viability, sustainability, and stability of the ECDM process. To address these limitations, researchers have developed hybrid ECDM variants. Despite this advancement, challenges persist, particularly in maintaining high surface integrity and robust process stability. This review aims to provide a comprehensive overview of recent developments in the mechanism underlying ECDM and its variants. It examines the influence of process parameters and triplex hybridisation on the performance matrices. Additionally, the review outlines potential research directions across various aspects of ECDM, highlighting areas for further exploration.
Crack propagation in metallic structures is a critical issue in industries such as aerospace, oil and gas, and automotive, where conventional repair methods such as welding or part replacement are often costly and time-consuming. This study investigates the effectiveness of Glass Fibre Reinforced Polymer (GFRP) composite patches for repairing cracked aluminium sheets. Centre- and edge-notched specimens with crack lengths of 5, 10, and 15 mm were fabricated and repaired using hand lay-up GFRP patches of varying thicknesses (two and four layers). Tensile tests were performed according to ASTM E8 to evaluate the mechanical performance of repaired specimens compared with unrepaired samples. The results demonstrated that composite patches significantly improved the load-carrying capacity of cracked specimens, with thicker patches providing higher strength recovery. Specifically, specimens with four-layer GFRP patches produced the highest maximum stresses of 152.44 MPa and 184.67 MPa for edge and centre cracks of 5 mm, respectively, compared with substantially lower strengths in unrepaired samples. An increase in patch thickness led to greater tensile-strength recovery; however, this improvement must be weighed against weight considerations, particularly in weight-sensitive applications such as aerospace structures.
Natural ventilation (NV) is an effective method to enhance ventilation in enclosed areas or buildings without incurring any cost to fit in mechanical ventilation systems. In Malaysia, double- and triple-storey terraced houses account for a large share of residential properties. Hence, understanding the impact of NV on these types of houses is essential for achieving optimal air circulation and ensuring a healthy, comfortable living environment. The purpose of this study is to determine the optimal door-opening configuration for indoor ventilation in a typical Malaysian double-storey terraced house, under cross ventilation (CV) and single-sided ventilation (SSV), and to examine the influence of adjacent buildings. Computational fluid dynamics using OpenFOAM is used to evaluate ventilation on the house's ground floor under two prevailing wind directions: towards the front façade (forward wind) and towards the rear façade (backward wind). A validation study of a generic building block was conducted and compared against published experimental data. Our findings indicate that excluding upstream units leads to overestimations of approximately 100% to 150%, depending on indoor location. Additionally, CV configurations were significantly more effective than SSV configurations in terms of ventilation rate and area-weighted velocity. Under CV mode, wind enters the house through the opening at the back rather than the opening at the front, which faces the prevailing wind.
This study integrates advanced statistical techniques with Linear Elastic Fracture Mechanics (LEFM) to assess geometric parameter contributions to crack behaviour in a stepped bar. The component was subjected to static flexural loading (600 kN) using ANSYS Workbench, where Stress Intensity Factor (SIF) and crack extension were determined via Separating Morphing and Adaptive Re-meshing Technology (SMART). Twenty-seven Central Composite Design (CCD) experiments evaluated the influence of minor and major radii, height, fillet radius, and bore depth. Regression and ANOVA analyses identified height as the most significant factor affecting crack extension. Response Surface Methodology (RSM) contour plots demonstrated that higher height, combined with low fillet radius and bore depth, resulted in SIF values exceeding 2200 MPa√mm. The developed models explained up to 99.07% of the variability. These findings provide guidelines for geometry optimisation to enhance the fracture resistance of stepped components in critical load-bearing applications.
This study investigates the aerodynamic performance and structural deformations of finite rectangular planform wings with a spoiler with a NACA 6409 airfoil section, fabricated from 3D-printed plastic materials. Unlike conventional aluminum or carbon fiber spoilers, plastic components with a high aspect ratio exhibit more pronounced aeroelastic effects, making their characterization important for practical applications. The main objective was to evaluate aerodynamic loads and resulting deformations through both experimental wind tunnel testing and numerical simulations. Wings with dimensions of 100 × 400 mm were fabricated using additive manufacturing techniques (MJF and SLS) with Nylon PA12, Resin CUV9400, and Nylon PA12GB materials. Tests were conducted at uniform wind speeds up to 30 m/s and varying angles of attack. Results show that the maximum Y displacement occurred at 10° and 30 m/s, with values of –51.03 mm for Nylon PA12, –26.52 mm for Resin CUV9400, and –21.73 mm for Nylon PA12GB. The maximum von Mises stress reached 10.7 MPa, 10.9 MPa, and 11 MPa for the three materials, respectively. Numerical simulations showed close agreement with experiments, with deviations below 6% for deflection and 4.37% for torsion. In conclusion, Nylon PA12GB demonstrated superior performance due to its lower deformation, confirming its suitability for cost-effective and lightweight aerodynamic applications.
Gas bubbles formed on electrodes during electrochemical processes increase overpotential and ohmic voltage drop, leading to higher energy consumption in water electrolysis. This study investigates the effect of surface roughness on hydrogen bubble dynamics and hydrogen evolution reaction (HER) performance using flat and electrochemically etched SS-316L working electrodes. Electrochemical experiments were conducted at room temperature in a three-compartment acrylic cell using a potentiostat. The SS-316L electrodes served as the working electrode (WE), with platinum and Ag/AgCl wires functioning as the counter and reference electrodes in 0.5 M KOH electrolyte. The etched electrode was prepared through electrochemical etching in a freshly prepared dilute Aqua Regia solution, followed by ultrasonic cleaning and drying. Bubble evolution was recorded using a high-speed visualization system. Results reveal that the etched electrode exhibits smaller bubble detachment radii (average 162 μm vs. 248 μm), shorter growth time (5.8 s vs. 9.2 s), and lower voltage fluctuations than the flat electrode, indicating improved bubble release dynamics. Enhanced surface roughness promotes higher HER activity, with the etched electrode showing a reduced onset potential of 0.43 V (0.11 V lower than flat WE), decreased overpotential from 331.4 to 247.8 mV at 10 mA cm-2, and a lower charge transfer resistance (67.2 Ω vs. 234.3 Ω). The double-layer capacitance increases from 0.38 to 0.91 mF cm-2, confirming a larger electrochemically active surface area. The etched electrode also demonstrates superior stability, maintaining consistent polarization behaviour after 500 CV cycles at 100 mV s-1.
Developing efficient and reliable battery thermal management systems (BTMS) has emerged as a key focus in EV research. The oscillating heat pipe (OHP) is a relatively new technology in BTMS applications. This study proposes a novel hybrid BTMS based on an inverted T-shaped OHP to enhance the heat transfer performance through the coupling of liquid cooling with OHP cooling. Equivalent thermal resistance experiments were used to comprehensively analyze the effect of graphene nanofluid coolant on the hybrid BTMS. The results indicate that increasing the concentration of graphene nanofluids further improves the cooling performance of the hybrid BTMS. At 280W with a concentration of 0.2 wt%, the equivalent BTMS thermal resistance (RBTMS) and maximum temperature (Tmax) are reduced by 20.2% and 32.9%, respectively. However, increasing nanofluid concentration weakens the forced convection effect of the working fluid between the evaporation and condensation sections of the OHP, thereby decreasing the OHP's heat transfer performance within the BTMS. This study deepens the comprehension of the performance enhancement effects of nanofluid coolant in the hybrid system, providing practical guidance for OHP-based cooling system development.
The optimization of sheet metal forming processes is a main goal in the mechanical industry, particularly in the widely used drawing technique. However, the lack of material databases concerning metal ductility presents significant challenges. To address this issue, this study develops machine learning (ML) methods to optimize the sheet metal forming process. The Erichsen cupping tests are employed to evaluate the formability and damage characteristics of A36 sheet parts, aiming for successful drawing outcomes. These tests consider three key parameters: punch diameter, friction between tools and sheet metal, and sheet thickness. Experimental findings show that punch diameter greatly affects the Erichsen index (IE). Microstructural analysis reveals a notable impact of sheet thickness on the maximum punch force (Fmax), which is further confirmed by X-ray diffraction analysis. A finite element (FE) model based on the Johnson–Cook material law is developed to simulate the deep drawing tests. Numerical predictions show good agreement with experiments, with an average error of less than 4% for IE and 5% for Fmax. By comparing numerical and experimental results, the isotropic model demonstrates satisfying and consistent performance. Using both experimental and numerical datasets, ML models are trained to predict IE and Fmax. Among the tested algorithms (LR, RF, DT, SVR, and XGB), XGBoost (XGB) provides the most accurate predictions, with R² values of 99.60% for IE and 97.46% for Fmax. The results indicate that XGB offers a robust and efficient approach for optimizing sheet metal forming processes through accurate prediction of formability and damage indicators.
