Journal of Mechanical Engineering and Sciences
JMES publishes original and review articles that advance the understanding of both the fundamentals of engineering science and its applications. Original contributions providing insight into the use of analytical and computational modelling, structural mechanics, behaviour and applications of advanced materials, impact mechanics, strain localization and other effects of nonlinearity, fluid mechanics, robotics, tribology, thermodynamics, and materials processing are encouraged. JMES is indexed in Emerging Sources Citation Index (ESCI), DOAJ, Ulrichsweb, MyCite, Google Scholar, and EBSCOHost.
The effect of adding Casuarina equisetifolia fruit powder on the wear, impact strength, and hardness of motorcycle disc brake pad composites
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.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Dual-layer smart coating system with a hybrid corrosion inhibitor for improved durability of carbon steel pipeline
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.
Hybrid approaches for sheet metal formability prediction: A synergy of experimental, numerical and machine learning tools
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.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Cyclic hardening and softening of high-strength steels
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.
Crack repair of aluminium components using glass fibre reinforced polymer composite patches: Effect of patch thickness on tensile behaviour
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.
Numerical-simulation hybrid analysis of orthogonal cutting of Inconel 718
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.
Mechanical properties and wear resistance of SiC-reinforced aluminium matrix composite with nickel addition
This study aims to analyze the effect of nickel addition on SiC-reinforced aluminum matrix composites in terms of mechanical properties (hardness and tensile strength), wear rate and microstructure produced through the casting process. The aluminum matrix used in this study is Al6061 and Al2024 with a concentration of 15 wt% SiC (silicon carbide) and nickel additions of 1, 3 and 5 wt%. The casting process uses a metal mold, mold temperature of 673.15 K, a stir-casting speed of 350 rpm and a pouring temperature of 923.15 K. The results of this study indicate that the addition of nickel increases hardness, tensile strength and wear resistance. Al2024/SiC with nickel addition exhibits higher hardness and wear resistance than Al6061/SiC under the same nickel addition. The highest hardness value in Al2024/SiC with 5% nickel is 113.78 HV with a wear rate of 0.05 mg/s. However, Al6061/SiC with nickel addition demonstrates better tensile strength and strain, with the highest tensile strength in Al6061/SiC + 5% Ni recorded at 282 MPa. The results of the microstructure examination shows that nickel addition can refine grains, particularly in Al6061, which contributes to the improvement in mechanical properties. SEM/EDS analysis confirms that Ni is evenly distributed and there is no agglomeration in either Al6061 or Al2024.
Electrochemical discharge machining for micro fabrication of insulating brittle material: State of the art and future directions
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.
Computational fluid dynamics analysis of wind-driven ventilation in a double-story terraced house sheltered by adjacent buildings
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.
Insights into hydrogen bubble dynamics and electrochemical performance on flat and electrochemically etched stainless steel electrodes using high-speed imaging
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.
Optimized manufacturing and temperature-dependent structural and property analysis of multi-phase functionally graded materials
The functionally graded materials (FGMs) have been realised to be potential candidates when it comes to high-pressure projects and applications where thermal and mechanical stability is to be ensured in extreme environments. In the research, the drawback of the widely used stainless steel SS316L facing high-stress conditions in the thermal environment will be overcome by the innovation of two new FGMs composed of SS316L and Inconel 625, Ti6Al4V, and Inconel 718. The aim was to conduct the fabrication and testing of a multi-phase FGM with the help of advanced techniques of manufacturing namely additive manufacturing and powder metallurgy, with the strict control of layer thickness of 0.2 mm and contents of its materials (60% SS316L, 20% Inconel 625 or Ti6Al4V, and 20% Inconel 718). Tensile testing, yield testing, fatigue and creep behaviour were performed at temperatures of −20°C and +60°C. The findings indicated that the FGM containing SS316L, Inconel 625, and Inconel 718 proved to be superior to SS316L at every point where its tensile strength is 992 MPa and its yield strength is 602 MPa, also at a temperature of +60 C versus 460 MPa and 186 MPa tensile and yield strengths in SS316L. The advanced fatigue performance and creep resistance were also indicated because of the better qualities of the alloys Inconel. Such results are indicative of gradient composition and layer formation in augmenting thermal and mechanical capabilities. The research ends up with a conclusion that these FGMs can be considered as excellent prospects in terms of the aerospace and power generation industries where strength and thermal endurance are of essence to the next generation of the industry.
Optimization of hybrid flow shop scheduling in a machine shop: Achieving energy efficiency and minimizing machine idleness with multi-objective Tiki Taka optimization
Hybrid Flow Shop Scheduling (HFS) has garnered significant interest in terms of problem formulation and solution approaches. This work introduces an optimization approach for a case study on a hybrid flow shop scheduling problem. The objective is to minimize the makespan, energy consumption, and idle machines in manufacturing shop. The HFS consists of many concurrent production lines, each containing several machines, operating in one or more stages. A case study was conducted using fourteen jobs across three stages, which utilized lathe, milling, and deburring machines. The EE-HFS was optimized using Multi-Objective Tiki Taka Optimization (MOTTA). The study considered machine idle time as a key factor influencing energy efficiency, incorporating it into the scheduling evaluation. The optimization result was compared to established algorithms, such as the Non-dominated Sorting Genetic Algorithm-II, the Multi Objectives Evolutionary Algorithm Based on Decomposition, the Multi Objectives Particle Swarm Optimization, and the recent algorithm Multi Objectives Grey Wolf Optimizer. The metrics used for comparison include Error Ratio (ER), Pareto Percentage (%), Spacing, Maximum Spread, computational speed, Hyper Volume, Inverted Generational Distance (IGD), and Generational Distance (GD). The results indicate that MOTTA exhibits superior performance with 78.42% best overall and 100% better in the convergence and domination of the case study solution (ER, ND, GD, and IGD). Overall, the findings have important implications for Hybrid flow shop scheduling in terms of the energy utilization model, reducing idle machine time, and the promising potential of MOTTA for application in other combinatorial scheduling challenges. This case study provides substantial advantages to the organization by effectively decreasing its daily energy consumption, equipment usage, and enhancing resource management.
Optimization of setting parameters for aluminium matrix composite reinforced with boron carbide using two-stage stir casting techniques
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.
Fabrication of aneurysm blood vessel silicone phantom using 3D printed semi-reusable mould
Aneurysms are vascular diseases with a low survival rate. Studying blood flow characteristics is essential to understand the disease, which would require experimentation on an anatomical phantom structure for simulating and measuring blood flow under controlled conditions. Conventional phantom fabrication often relies on single-use moulds, which are economically ineffective and time-consuming especially when replication is needed. In this study, we developed a method for fabricating an aneurysmal vascular phantom using a 3D-printed semi-reusable mould. The novelty lies in the reusable outer mould cope that was 3D-printed using polylactic acid (PLA), while the inner core was 3D-printed using water-soluble polyvinyl alcohol (PVA). A silicone-based elastomer, Sylgard 184 was used to create the vascular phantom because it approximates the mechanical properties of actual blood vessels. The silicone was injected onto the mould and the PVA core was dissolved in hot water to create a hollow structure. The resulting phantom is elastic and translucent, suitable for macro-scale flow visualization. It is also fabricated at a lower cost than those reported in previous studies. The semi-reusable mould concept enhances scalability by being low-cost and enables repeatable phantom production, offering an efficient way for research, training, and medical device testing.
The impact of baffle configuration on the performance of a shell-and-tube heat exchanger using the Bell-Delaware approach
Shell-and-tube heat exchangers (STHXs) are frequently used across industries. Their performance is determined by the thermal and hydraulic characteristics, which depend on inlet conditions, flow conditions, and the geometry of the shell and tubes bundle. A computational model was generated to solve the governing equations, utilising Microsoft Excel 2016. The program predicts the performance of the STHX for shell-side heat transfer coefficient (hs), pressure loss (ΔPs) and the performance ratio (hs /ΔPs) by employing the Bell-Delaware method. This method showed the best reliability against experimental results. The aim of the study is to analyse the influence of variations in baffle number, baffle cuts, tube layouts, six fluid characteristics, and mass flow rate. The findings of the investigation indicate that the heat transfer coefficient and pressure loss increase, while the value of (hs /ΔPs) decreases as the baffle number increases for all the tube layouts. The hs and ΔPs decrease with increasing baffle cuts for all layouts. A baffle count of six exhibits the highest value of (hs /ΔPs) for all the fluids and layouts. The rotated square tube arrangement yields higher hs and moderate ΔPs, which in turn yields the highest (hs /ΔPs) ratio compared to triangular and square layouts.
Smart coating with pH-responsive micro-capsules containing corrosion inhibitors for anti-corrosion of AZ31 magnesium alloys
The widespread use of AZ31 magnesium (Mg) alloys in automotive, aerospace, and biomedical applications is often hindered by their high susceptibility to corrosion. In this study, a novel pH-responsive smart coating was developed, integrating multilayer microcapsules containing benzotriazole (BTA) as a corrosion inhibitor and linseed oil (LO) as a self-healing agent to enhance the corrosion resistance of Mg alloys. The BTA in the shell provides immediate corrosion protection by forming a passive barrier layer under acidic or alkaline conditions. At the same time, the LO in the core offers sustained self-healing by sealing defects over time. Dual-functional poly(urea-formaldehyde) (PUF) microcapsules were synthesized via in-situ polymerization, incorporating BTA within a chitosan-alginate multilayer shell and LO in the core. The controlled release behaviour of BTA was analysed in acidic (pH 4) and alkaline (pH 10) conditions, revealing an enhanced release profile in acidic environments, which facilitates targeted corrosion inhibition. The structural and chemical properties of the microcapsules were characterized using FTIR, UV-Vis spectroscopy, SEM-EDX, and optical microscopy. The corrosion protection performance of the smart coating was evaluated on AZ31 Mg alloy substrates using electrochemical impedance spectroscopy. The results confirmed that the smart coating significantly improved the corrosion resistance of the AZ31 Mg alloy, with film resistance (Rfilm) values of 7.211 × 10⁴ Ω·cm² at pH 4 and 7.964 × 10⁴ Ω·cm² at pH 10. These values are higher than those of the epoxy-coated and uncoated AZ31 Mg alloy, indicating the formation of strong passive layers that impede electrolyte penetration. The increased Rfilm shows the effectiveness of coating in enhancing corrosion inhibition and self-healing capabilities under both acidic and alkaline conditions. Overall, this study demonstrates that utilizing biodegradable, pH-responsive multilayer microcapsules is a promising and environmentally friendly approach to protecting magnesium alloys from corrosion. These results suggest that this smart coating could be a practical and environmentally friendly solution for protecting lightweight metals, particularly in vehicles and similar applications.
Simultaneous humidity and temperature measurement sensor based on coated multiplexed fibre bragg gratings
Fibre Bragg Grating (FBG) sensors are preferred over traditional electrical sensors due to their high sensitivity, electromagnetic immunity, and multiplexing capability. However, the simultaneous measurement of humidity and temperature remains challenging due to cross-sensitivity issues. This study presents the development of a simple and efficient coated multiplexed FBG sensor to address this challenge. The sensor was fabricated using two separate single-mode fibre FBGs, each coated with polydimethylsiloxane (PDMS) and polyvinyl alcohol (PVA), respectively. Experimental results demonstrated that PDMS exhibited excellent temperature sensitivity, while PVA showed high moisture sensitivity. The measured sensitivities were 0.0989 pm/% RH (equivalent to 9.89 pm.φ-1) and 28.775 pm/°C (equivalent to 28.775 pm/°K) for PDMS-coated FBG, and 12.593 pm/% RH and 14.515 pm/°C for PVA-coated FBG, indicating that PDMS had minimal response to humidity, whereas PVA exhibited a degree of temperature sensitivity. To mitigate cross-sensitivity, a sensitivity matrix was employed, enabling accurate simultaneous measurement of humidity and temperature. The experimental validation confirmed that the sensor achieved a percentage error below 10%, demonstrating high accuracy and reliability. Given the simplicity of fabrication and calibration, the proposed coated multiplexed sensor exhibits strong potential for practical applications in environmental monitoring and industrial sensing.
Investigation of the structural deformations of various plastic spoilers with NACA 6409 cross section at subcritical Reynolds Numbers under different angles of attack
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.
Computational analysis of hemodynamic parameters in coronary arteries: Effects of stenosis and bifurcation angle
Cardiovascular diseases (CVDs) are still becoming the number one cause of death around the world. Coronary artery disease (CAD) is one of the most prevalent types. It is exceedingly crucial to monitor hemodynamic parameters early and precisely in order to determine progresses to become more severe, particularly in regions where arteries bifurcated. This study employs computational fluid dynamics (CFD) simulations to investigate how stenosis and bifurcation angle influence blood flow in the coronary arteries. Seven idealized 3D models of coronary arteries have been developed to show various degrees of narrowing in the arteries. The Carreau model was used to model blood as a non-Newtonian fluid, and the Navier-Stokes equations were used to perform simulations across the cardiac cycle. Key hemodynamic variables like velocity, pressure, and wall shear stress (WSS) were extracted. Results demonstrate that areas with 85% stenosis had maximum velocity of 3.52 m/s, wall shear stress of 78 Pa, and pressures of up to 230 mmHg at time t = 0.63 s. These are high-risk areas for disease progression. These outcomes assist in understanding how the shape of arteries affects abnormal flow dynamics, which could help medical professionals find and treat CAD early on.
Dynamic and sensitivity analysis of V-shaped cross section piezoelectric beam as mass sensor for high-order vibration modes
Resonators represent a new generation of sensors with superior performance and high sensitivity, making them well-suited for mass sensing applications. While V-shaped cross-section beams have demonstrated enhanced particle absorption and improved performance over conventional rectangular beams in the first bending mode, their behavior in higher-order vibration modes including lateral bending, torsion, and in-plane bending remains unexplored. This study presents a dynamic model of the vibratory motion of V-shaped beams after particle adsorption, employing both modal analysis and finite element methods. A sensitivity analysis based on Sobol’s method is conducted to evaluate the influence of beam geometry on resonance frequency shifts post-adsorption and to quantify the extent of this effect. Simulation results reveal that V-shaped cross-section beams exhibit superior performance compared to rectangular beams not only in the fundamental bending mode but also in higher-order vibration modes, including lateral bending, torsion, and in-plane bending. These findings highlight the potential of V-shaped resonators for advanced mass sensing applications requiring multi-mode vibrational sensitivity.
Development of an intelligent jet engine controller using a model-based deep deterministic policy gradient technique
The rapid advancement of artificial intelligence has the potential to significantly enhance the aerospace industry, particularly through the development of intelligent engine control systems. This study seeks to tackle the challenges of controlling complex, nonlinear aero-engines by applying Deep Reinforcement Learning techniques. Specifically, the Deep Deterministic Policy Gradient (DDPG) algorithm within an actor-critic framework to design an adaptive controller for the nonlinear thermodynamic model of the J85 jet engine are employed. The proposed method is evaluated against traditional PI controllers under various operating conditions, including different altitudes, Mach numbers, and humidity levels. Simulation results reveal that the DDPG-based controller outperforms PI control by achieving faster response times, 1.75 seconds (7.18%) faster during acceleration and 0.55 seconds (1.24%) during deceleration in standard conditions, and 1.09 seconds (4.79%) and 3.44 seconds (7.13%), respectively, under altered conditions. Moreover, the DDPG controller reduces turbine inlet temperature by up to 44.97% in the first case and 38.21% in the second case, and decreases surge margin by 54.83% and 56.18%, respectively, ensuring safer operation within limits. These findings demonstrate the DDPG algorithm's potential for substantial engine control performance and safety improvements. The study underscores the transformative potential of AI-driven control systems in aerospace applications, paving the way for more intelligent and adaptable engine management solutions.
Statistical analysis of the influence of geometric parameters on static crack growth
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.
Effect of vibration analysis towards dynamic properties in dissimilar joining materials: A review
This paper presents a comprehensive overview of vibration analysis in structures comprising dissimilar materials, highlighting the challenges and opportunities associated with various joining techniques and material selection. By exploring the impact of different joining methods on vibration performance, including factors such as structural design and dynamic characteristics, it synthesizes diverse methodologies employed in research on vibration analysis for jointed structures. Additionally, it explores the factors influencing the vibration performance of dynamic structures, focusing on structural joining techniques, material properties, and analytical methods. Through a systematic review, the study categorizes dissimilar joints into mechanical, thermal, and chemical joining processes, offering insights into their impact on vibration behavior and microstructural dynamics. By synthesizing research findings and methodologies, the paper underscores the significance of integrating experimental and numerical analyses to optimize the vibrational performance and reliability of joint structures, especially when dealing with dissimilar materials. This collaborative approach not only enhances predictive accuracy but also offers a deeper understanding of the intricate dynamics of jointed systems, fostering advancements in structural engineering and materials science. The paper also discusses the application of Finite Element Model Updating (FEMU) methods in vibration analysis to reduce uncertainties in model assumptions and enhance accuracy. FEMU involves iteratively refining numerical models to align with actual structural behavior, crucial for design, construction, and engineering applications. Various FEMU methods, including sensitivity-based approaches, iterative optimization, Bayesian techniques, and computational intelligence algorithms, offer effective means to update finite element models and minimize discrepancies between predicted and observed structural behavior. Despite computational challenges, these methods provide valuable tools for optimizing design and operational parameters in vibration analysis, advancing structural reliability and precision.
Optimization of exoskeleton design for post-stroke ankle rehabilitation based on kinematic and structural model evaluation
Ankle rehabilitation is an important indicator of walking ability recovery because it is used as a marker of early recovery of mobility function in post-stroke patients. Robot-assisted ankle rehabilitation has been proven to be more optimal for restoring range of motion, balance, and gait proprioception in patients. This study aims to optimize the design of an ankle rehabilitation exoskeleton through structural simulation, biomechanical alignment, and efficiency based on several alternative actuator designs. Alternative exoskeleton designs are focused on the rehabilitation of dorsiflexion-plantar flexion and inversion-eversion movements. The analysis method for assessing the best exoskeleton design alternatives uses an engineering design methodology approach based on static and dynamic test parameters, namely kinematics and FEA. The results of the design engineering implementation show that the exoskeleton design with Concept B is more efficient based on several mechanical test parameters compared to Concept A. Simulation results show that Design B alternative is superior in all test parameters with a value of (4.22 versus 3.68) in the safety factor, a lower peak stress of (30.43 MPa versus 39.15 MPa), and produces energy efficiency with lower torque requirements. The mechanical stability of Concept B is characterized by using a more efficient actuator design with superior safety improvements for users. Based on the parameters and characteristics of the simulation test using design engineering, Design B is more feasible to be developed as a robotic mechanical system for the needs of post-stroke patient ankle rehabilitation.
Mesh independence study for an onshore OWC using Richardson's extrapolation: Numerical and analytical analysis
Wave energy converters can be a promising option for wave energy, which is among them, aims to be a crucial device for new studies and essential for energy transformation due to its geographical versatility and adaptation to different wave conditions, still being the subject of research and development in the field of renewable energy. The spatial study to evaluate the behavior of Oscillating Water Column (OWC) has been a major problem towards the simulated analysis of these devices due to their high computational cost. OWC is a device that harnesses the oscillatory motion of seawater inside a partially submerged chamber, this movement compresses and decompresses the air column above, driving a turbine connected to a generator and converting wave energy into usable electricity. This study aimed to evaluate the spatial and temporal mesh independence of an onshore OWC using Computational Fluid Dynamics (CFD) and Richardson’s Extrapolation (RE). The CFD analysis was performed using ANSYS-Fluent software, and an RE study was conducted to improve the accuracy of the results by extrapolated solutions for different efficiency values obtained from the domain discretization levels. Indeed, 10 treatments were carried out to study the spatial (M0, M1, M2, M3 and M4) and temporal (M2T1, M2T2, M2T1to2, M2T3 and M2T4). mesh independence. The results demonstrate, in addition to the inclusion of the analytical second-order Stokes equation, that RE was instrumental in testing the incoming wave front, observing the behavior of the OWC and reducing its computational cost. M2 and M2T1to2, were the treatments chosen for the spatial and temporal independence analysis, respectively. The extrapolated values correspond to about 28.0227% (M2) and 33.5412% (M2T1to2). These findings support the use of RE as a reliable tool for mesh validation in CFD simulations, optimizing computational efficiency while ensuring robust results.
Development of laser cleaning state classification model through the acquired acoustic signal using the empirical mode decomposition and one dimensional convolutional neural network
Laser cleaning is an efficient, non-invasive method that utilizes high-energy laser beams to eliminate contaminants. However, variations in laser process parameters can lead to challenges such as inconsistent cleaning depth, thermal damage, and uneven surface treatment, ultimately compromising the quality of the cleaned surface. To address these issues, developing a predictive model for cleaning states is crucial to enhance online monitoring systems, enabling earlier detection of potential problems. This manuscript outlined the development of a classification model intended for predicting the states of laser cleaning by employing the EMD-1DCNN methodology. The primary objective of integrating Empirical Mode Decomposition (EMD) is to enhance the precision and reliability of the model generated from a one-dimensional Convolutional Neural Network (1D-CNN). The laser cleaning experiments were executed at velocities of 100 mm/s and 300 mm/s on corroded boron steel substrates. Acoustic signals within the frequency spectrum of 20 Hz to 10,000 Hz were systematically recorded during the entirety of the cleaning procedure.These signals were categorized into three phases, which were corrosion removal stage, low roughness formation, and engraving stage, which indicates the surface damage. The results show that the time-domain signal recorded a random non-linear pattern during the corrosion removal stage. The frequency was active at 6300 Hz for all laser cleaning conditions, but the peak amplitude decreased as the grooves started to form on the cleaned area. Instead, the peak at 1800 Hz was increased. However, the implementation of EMD revealed a significant trend that could separate corrosion removal and groove formation stage at another bandwidth, which was 20 Hz to 500 Hz. Moreover, the EMD-1D-CNN classification model achieved an average accuracy of 95.75% with a deviation of 1.99%, demonstrating enhanced performance compared to a model developed without EMD. This research highlights the importance of the classification model in predicting cleaning process stages, facilitating real-time monitoring and ensuring cleaning quality. The preprocessing methods employed not only enhanced model accuracy but also improved consistency, potentially reducing computational demands while fostering a stable model.
Performance enhancement of centrifugal pumps through axial clearance reduction: An experimental investigation
It is a well-known fact that the disk friction phenomena due to axial clearance has a significant impact on centrifugal pumps performance. Enhancing the efficiency of centrifugal pumps is crucial, as these pumps are widely used in industries and contribute significantly to energy consumption, offering substantial potential for energy savings and cost reduction. In the present work, the disk friction due to the axial clearance between the impeller and the back cover plate was examined experimentally for three commercial centrifugal pumps with specific speeds, = 19, 34 and 54 rpm at various flow rates. Hydraulic performance parameters were obtained for two different cases: one with original axial clearance and other with minimum axial clearance at rated/design (1450 rpm) and off-design (1000 rpm) conditions. In both conditions, pumps with minimum clearance provides the higher overall efficiency compare to the original clearance. At design conditions, = 34 rpm, the result shows higher performance improvement compared to = 54 rpm with efficiency rise of 5.20%. Although, the highest efficiency of 84.48% was obtained for = 54 rpm at maximum flow rate. Similarly, significant rise in parameters were observed for = 34 followed by = 19 and = 54 rpm at off-design conditions. Change in experimental disk friction head loss due to clearance reduction were compared to provide a clearer image about performance enhancement.
The emerging hybrid renewable energy systems in the global domain: A bibliometric approach to the study
Hybrid renewable energy systems (HRES), particularly those integrating solar photovoltaic (PV) and wind technologies, have emerged as vital solutions for enhancing energy security and sustainability. This study aims to examine the evolution, thematic focus, and global collaboration trends in HRES research by applying a comprehensive bibliometric analysis. Using Scopus data spanning 2009 to 24 May 2024, the study evaluates 162 carefully selected documents out of 1,023 initial records. Analytical tools such as Lotka’s Law, co-occurrence networks, keyword frequency analysis, and cluster mapping are employed to explore publication patterns, author productivity, and dominant research themes. The field demonstrates a notable annual growth rate of 21.69% and an international co-authorship rate of 35.14%, indicating expanding global engagement. A significant portion of contributions comes from single-publication authors (93%), suggesting a wide range of interdisciplinary interests. Iran and India lead global research output, followed by Malaysia, Turkey, and China. Key themes identified include system optimisation, economic analysis, energy storage, and environmental sustainability. Influential authors’ contributions were identified to showcase the foundational development of the field. Cluster analysis reveals prominent areas, including rural electrification, techno-economic evaluation, and power converter control, underscoring the diversity of applications. The results affirm HRES as a rapidly advancing, multidisciplinary domain. Future research should prioritise region-specific system design, affordable storage technologies, grid integration, life cycle assessment, and stronger international collaboration. These focus areas are essential to accelerate the deployment of cost-effective and environmentally resilient hybrid systems across varied geographical contexts.
Effect of divergent angle and water flowrate on mean bubble size in venturi-type generator: An experimental and computational approach
Venturi nozzles, valued for their simple design and flexibility, are widely used to generate microbubbles (MB) via hydrodynamic cavitation, particularly in water treatment and agriculture. MB efficiency depends on bubble size, which is influenced by the Venturi geometry and flow parameters, such as divergent angle and water flow rate. However, the relationship between these factors and bubble size remains underexplored, especially through combined experimental and simulation approaches. This study addresses that gap by combining experimental visualization and computational fluid dynamics to assess the impact of divergent angle and water flow rate on MB generation. Three Venturi nozzles with divergent angles of 8°, 10°, and 12°, and flow rates of 13 L/min, 33 L/min, and 66 L/min were tested. Bubble size was measured using high-speed imaging and analyzed in MATLAB. Simulations were performed using ANSYS FLUENT with a coupled Eulerian–Lagrangian model and a Population Balance Model, enabling the detailed prediction of bubble size. Experimental and simulation results showed a relative error of less than 5%, confirming the reliability of simulations. Increasing the divergent angle and flow rate decreased the MB distribution, yielding smaller bubbles. The Venturi 3 (12° divergent angle) produced the smallest bubbles, measured experimentally at 519 µm and in simulations at 505 µm, at a flow rate of 66 L/min. The strong agreement between experimental and simulation results, with a maximum error of 4%, was supported by velocity profile analysis, which revealed the highest velocity (14.09 m/s) in Venturi 3, resulting in the formation of the smallest bubbles. These findings not only validate the effectiveness of the simulations in predicting bubble size but also offer valuable insights for optimising venturi designs.
Heat transfer characteristics of a battery liquid-cooling system based on inverted T-shaped oscillating heat pipe with nanofluids
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.