International Journal of Automotive and Mechanical Engineering
IJAME aims to disseminate original research in Automotive and Mechanical Engineering and presents the latest development and achievements in scientific research to stimulate and promote academic exchange between scientists, engineers, and researchers. Original, innovative and novel contributions providing insight into analytical, computational modelling, and experimental research are encouraged. IJAME is indexed in Scopus, Web of Science (ESCI), Asean Citation Index (ACI), Index Copernicus, Ulrichsweb, MyCite, Google Scholar, ProQuest and Chemical Abstracts Service.
MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS ON GRINDING OF DUCTILE CAST IRON USING MINIMUM QUANTITY LUBRICATION
A promising alternative to conventional fluid coolant application is minimum quantity lubrication (MQL). Despite much research, there have been few investigations about the influence of MQL parameters on the process results, such as oil flow rate, workpiece speed and depth of cut. The objective of this project is to develop a mathematical model of the material removal rate and surface roughness on grinding of ductile cast iron using minimum quantity lubrication. The experiment was carried out according to the design of experiment principle, prepared based on central composite design. The experimental data was utilized to develop the mathematical model for first- and second-order models. The second order gives acceptable performance of the grinding. The result shows that the highest value of the grinding ratio is with single-pass MQL, and the lowest value is with multiple-pass conventional coolants. The model fit was adequate and acceptable for sustainable grinding using a 0.15% volume concentration of ethylene glycol. This paper quantifies the impact of water-based ethylene glycol on the surface quality achieved. It is concluded that the surface quality is most influenced by the depth of cut and table speed. It is recommended that future research is also conducted using another parameter such as the speed of the grinding wheel or the distance from the wheel– workpiece contact zone. Besides that, further research can be conducted using different nozzle angles and different types of grinding wheel to see how these affect the surface of the material.
Copyright (c) 2015 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Risk-aware buckling design of functionally graded porous beams via machine-learning surrogates and reliability analysis
Functionally graded porous beams offer high stiffness-to-weight ratios, but their buckling strength is sensitive to induced porosity variability. Designers, therefore, need tools that are both fast and explicitly risk-aware. This study develops and validates an interpretable methodology that combines higher-order shear deformation theory, machine learning surrogates, and structural reliability analysis to support buckling design of functionally graded porous beams. Deterministic buckling responses are first generated using a higher-order shear deformation theory for two boundary conditions (simply supported and clamped-clamped), two slenderness ratios (L/h=10 and 40), geometric controls (taper and width), porosity indices 0<α<0.3, and two porosity patterns (Regular and Uneven). A gradient-boosted tree surrogate is then trained on the log-transformed dimensionless critical buckling load using five-fold cross-validation. Manufacturing variability in porosity is propagated through the surrogate via Monte Carlo simulation, and a companion First-order Reliability Method (FORM) in the porosity dimension provides efficient estimates of failure probability. Risk is summarized through domain-wise mean buckling capacity and Conditional Value-at-Risk (CVaR) at the 5% level (CVaR5%), from which risk-return frontiers and “knee” designs distinguish risk-neutral from risk-averse choices. The surrogate generalizes strongly (out-of-fold log coefficient of determination, R2log=0.94; mean absolute error, MAE=2.7; mean absolute percentage error, MAPE=9.5%), while FORM tracks Monte Carlo with near-perfect concordance (average R2=0.9997; MAE(Pf)=0.0005; MAPE=0.33%), with small, interpretable deviations confined to slender, simply supported beams with uneven porosity. Across all regions, clamped-clamped, regular, and thick beams systematically dominate both mean and lower tail performance. Knee designs typically incur only 1-2% loss in mean capacity while retaining 95-99% of the best attainable CvaR, providing a reliable basis for compact, risk-aware buckling design guidance.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
A hybrid entropy-VIKOR-Taguchi approach for sustainable optimization of diesel engine performance and emissions
The urgent demand for sustainable transport fuels highlights biodiesel-hydrogen dual-fueling as a promising pathway to improve engine efficiency while reducing carbon-based emissions. Nevertheless, optimizing combustion parameters to balance performance with stringent emission constraints remains a major challenge. This study introduces a hybrid Entropy-VIKOR-Taguchi framework to optimize the performance, combustion, and emission characteristics of a compression-ignition engine operating on a cottonseed biodiesel (B20)- hydrogen blend. The methodology integrates entropy-based weighting for objective parameter prioritization, VIKOR for compromise decision-making, and Taguchi design for efficient experimentation, thereby minimizing trial costs and enhancing reproducibility. Experiments evaluated three key parameters, namely injection pressure (200-240 bar), compression ratio (16.5-18.5), and hydrogen flow rate (10-20 L/min). Results showed that hydrogen enrichment improved combustion, with peak pressure reaching 85 bar and a maximum heat release rate of 86 J/deg. Engine efficiency increased to 33.52% BTE and a minimum BSFC of 0.213 kg/kWh, while emissions decreased (HC = 65 ppm, CO = 0.016%). However, NOx rose to 1380 ppm at high hydrogen flow, underscoring the trade-off between efficiency and emissions. The optimal condition, IP = 240 bar, CR = 17.5, and H2 = 10 L/min, yielded the lowest VIKOR index (Q = 0.0588), ensuring balanced sustainability. This framework offers a transferable optimization pathway for dual-fuel engines, supporting decarbonization targets under SDG7 and SDG13.
Tool Wear Identification and Monitoring of Hard Alloy End Mills Using an Improved WOA and ConvLSTM
The wear state of carbide end mills directly affects the processing efficiency and quality. Traditional methods (such as Support Vector Machine (SVM)) have problems such as insufficient feature extraction and poor robustness when dealing with high-dimensional nonlinear data. To this end, an SVM classification model optimized by the Whale Optimization Algorithm (WOA) is proposed. The study combines the Convolutional Long Short-Term Memory (ConvLSTM) network and the Attention Mechanism (AM) to construct the wear prediction model (ConvLSTM-AM). Experiments show that the average classification accuracy of SVM-WOA for the four types of wear exceeds 97%, and the classification time is only 1.15 seconds. The prediction accuracy of ConvLSTM-AM in the severe wear stage reaches 98.64%, and the prediction error is significantly lower than that of the comparison models. This method provides an efficient solution for real-time monitoring and intelligent maintenance of tool wear.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0/
Generalized bearing fault diagnosis using comparative time-frequency scalogram features and machine learning with advanced feature ranking
Bearings are critical components of rotating machinery, ensuring smooth operation by supporting shafts and loads. Even minor defects can severely degrade performance, causing unplanned downtime and economic loss. Accurate and timely fault diagnosis is therefore essential for condition-based maintenance. Conventional time- and frequency-domain methods often fail to capture the non-stationary and transient nature of bearing vibration signals. This work proposes a time–frequency diagnostic framework using Continuous Wavelet Transform (CWT)–based scalograms and machine learning. Coiflet and Morlet wavelets are selected using the Maximum Relative Wavelet Energy (MRWE) criterion, yielding peak MRWE values of 0.1347 for the CWRU dataset and 0.0584 for the Machinery Fault Simulator (MFS) dataset, ensuring optimal fault localization. Scalograms are converted into RGB images, from which 21 texture features are extracted and ranked using the Fisher score. The method is validated on two independent datasets: the benchmark Case Western Reserve University (CWRU) data and an experimental MFS dataset using SKF 6004 bearings with induced inner-race, outer-race, and ball defects. Logistic Regression, SVM, KNN, and Bagged Tree classifiers are evaluated using tenfold cross-validation. On the CWRU dataset, KNN and Bagged Tree achieve accuracies of 98.3% with eight ranked features, while the Bagged Tree reaches 100% accuracy using all 21 features.
Machine learning-based prediction of the coefficient of performance for low global warming potential refrigerants in a vapor compression system
This study addresses the energy significance of the coefficient of performance (COP) in vapor compression systems and the practical need to forecast COP quickly and reliably. Because COP directly reflects the amount of cooling delivered per unit of input power, accurate prediction supports energy savings, refrigerant selection, and early-stage design decisions, especially for low global warming potential (GWP) refrigerants. Authors develop data-driven models to estimate COP without full thermodynamic calculations. A synthetic dataset of 2,000 samples is generated in the Engineering Equation Solver (EES) for four refrigerants (R1234yf, R134a, R290, R600a) by using five inputs: refrigerant type, evaporation temperature, condensing temperature, subcooling, and superheat. Five supervised learning algorithms are trained and compared: linear regression, polynomial regression, random forests, decision trees, and support vector machines. The study evaluates model performance using the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) based on an 80/20 train/test split. Results show Polynomial Regression (degree 3) delivers the highest accuracy (R² ≈ 0.9999; RMSE ≈ 0.0071; MAE ≈ 0.0053), with Random Forest as the next strongest baseline. The findings suggest that lightweight, well-tuned regressors can provide fast, precise COP predictions, reduce analysis time, and guide system design and parameter optimization. The approach offers an accessible tool for engineers seeking efficient, low-carbon refrigeration solutions.
A comparative study of power and vibration signals for different tool pin sizes of friction stir welding
Process signals play an important role in revealing weld formation, reflecting the interaction between mechanical mixing and process parameters. This study focuses on how different tool pin lengths and material thicknesses influence the signals generated during friction stir welding of AA6061. Three pin lengths that were 2 mm, 4 mm, and 6 mm were tested on plates with thicknesses of 4 mm, 6 mm, and 8 mm (made by stacking two 4 mm plates). Spindle speed of 1200 RPM and a traverse speed of 50 mm/min were constant. Shorter pins cannot fully penetrate thicker plates in a single pass; therefore, welding was performed in dual passes, top and bottom, to complete the joint. During welding, power consumption and vibration signals were recorded. Data collected from the bottom side were used for detailed analysis, as the top pass helped hold materials before full penetration. The signals were analyzed using the Fast Fourier Transform, and standard deviation and defect accuracy were calculated to evaluate the weld condition. The findings show that the 2 mm pin achieved the highest defect accuracy of 90.63% based on power signals, while the 6 mm pin achieved the highest accuracy of 85.10% based on vibration signals. Larger vibration standard deviations from the 6 mm pin indicate energetic material stirring. Hardness results suggest that the 4 mm pin provides the most favorable material consolidation. These outcomes highlight that pin length and plate thickness significantly affect process signals, making them crucial variables for real-time weld quality monitoring and optimization.
Unsteady Drag Analysis of Platoon Vehicles using Unsteady Reynolds-Averaged Navier-Stokes and Improved Delayed Detached Eddy Simulation
The aerodynamic interaction of vehicles in platoon formation generates complex unsteady flow phenomena that strongly influence drag performance. This study investigates the unsteady aerodynamic drag and associated flow structures of two simplified Ahmed body models (30° slant angle) traveling in platoon using transient Computational Fluid Dynamics (CFD). Unsteady Reynolds-Averaged Navier–Stokes (URANS) and Improved Delayed Detached Eddy Simulation (IDDES) are employed to analyze the effect of longitudinal spacing ratios (d/L) ranging from 0.1 to 2.0. Following a mesh independence study, a grid with 13.3 million cells is adopted to resolve the flow field and quantify drag unsteadiness for both the lead and trailing vehicles across spacing configurations. Validation is performed by comparing the predicted drag coefficients with experimental measurements. Results show that the lead vehicle experiences a substantial drag reduction, with the coefficient decreasing to 0.16 at close spacing, before rising to 0.40 (URANS) and 0.37 (IDDES) at d/L=2. In contrast, the trailing vehicle exhibits only minor drag variation over d/L = 0.5–2, ranging from 0.35 to 0.37. Additional analyses include pressure coefficient distributions, instantaneous velocity fields, and vortex structures identified through Q-criterion visualizations. Comparisons indicate that IDDES captures finer flow structures and better reflects the unsteadiness of drag than URANS. Overall, the results suggest that the lead vehicle benefits most from platooning, achieving up to 60% drag reduction at small spacings, whereas the trailing vehicle gains comparatively little.
Impact of Throat Diameter, Length, and Divergent Angle on Bubble Formation and Flow Characteristics in Venturi Microbubble Generators
The design of a Venturi microbubble generator (VMBG) is crucial in influencing the size and distribution of produced microbubbles, which are essential in several industrial, medicinal, and environmental applications. This study examines the impact of three critical geometric parameters: throat diameter, throat length, and divergent angle, on the formation of bubbles and the characteristics of flow. A combination of experimental visualization using Particle Image Velocimetry and computational fluid dynamics simulations using the Euler–Lagrange approach with the realizable k-epsilon turbulence model was employed. Nine VMBG configurations were tested, with throat diameters of 8.5 mm, 12 mm, and 20 mm, throat lengths of 10 mm, 15 mm, and 30 mm, and divergent angles of 30°, 45°, and 60°. The findings showed that decreasing the throat diameter from 12 mm to 8.5 mm produced smaller and denser bubbles, resulting in an increase in flow velocity by 68% and vorticity by 30%. Results showed that smaller throat diameters (8.5 mm) increased flow velocities, turbulence, and vorticity, while longer throat lengths (30 mm) provided smoother fluid transitions. Larger divergent angles caused a significant drop in velocity, increased turbulence, and produced smaller bubble sizes. However, they also promoted bubble expansion and denser formation in the divergent section. Both throat diameter and throat length also played a crucial role in undergoing complete bubble formation stages. Experimental results, which were very similar to the computer results, verified the four stages of bubble formation: nucleation, expansion, coalescence, and breakdown. The study provides valuable insights for enhancing VMBG designs, enabling the creation of microbubbles with features suitable for specific applications.
Multi-factorial analysis and optimization of delamination damage parameters in the drilling of woven ramie/epoxy resin composite
The growing demand for lightweight and eco-friendly materials has accelerated the use of natural fiber composites in structural applications. However, drilling-induced delamination remains a significant challenge in machining. This study investigates the influence of drilling parameters on the delamination behavior of woven ramie/epoxy resin composites using a TPR 1100 drilling machine. TPR is the model designation of the pillar-drilling machine used in the experiments. The composite was fabricated from S-type 12/3 woven ramie fiber reinforced with epoxy resin and a polyaminoamide hardener (60:40 ratio). Delamination was quantified using 2400 DPI macrophotography and analyzed with Image-Pro Plus v4.5 software. Four process parameters, namely number of layers (3, 4, and 5), drill bit diameter (6, 8, and 10 mm), spindle speed (88, 455, and 1500 rpm), and feed rate (0.05, 0.09, and 0.15 mm/rev) were optimized using the Taguchi method, with analysis of variance used to assess parameter significance. The results showed that drill bit diameter was the dominant factor influencing delamination (entry: 70.1%; exit: 44.8%), followed by the number of layers and spindle speed, while feed rate had the least effect. The optimal parameter combination (3 layers, 6 mm drill, 88 rpm spindle speed, and 0.09 mm/rev feed) reduced delamination by 7.87% at the entry side and 10.42% at the exit side. These findings provide practical guidelines for minimizing structural degradation during machining, thereby extending the mechanical reliability of ramie/epoxy composites. The outcomes are directly applicable to the automotive, construction, and sustainable product manufacturing sectors, advancing eco-friendly machining standards and providing significant benefits for future industrial applications.
Crystalline Nanocellulose as a Potential Alternative to Metallic Nanofuel Additives: An Experimental Investigation
Crystalline nanocellulose (CNC), derived from various biomass sources, is gaining attention as a sustainable nanomaterial for energy applications due to its high surface area-to-volume ratio, lightweight nature, biodegradability, and associated environmental benefits. This experimental research investigated the potential of CNC as an alternative to traditional metallic nanofuel additives, presenting preliminary results that have not been previously reported. Commercially available CNC was blended into standard diesel fuel at concentrations of 100 ppm, 250 ppm, and 500 ppm, and engine performance and emission tests were conducted. CNC demonstrated potential as a nanofuel additive in diesel, offering benefits like improved combustion, enhanced engine performance, and reduced NOx emissions and exhaust smoke opacity. However, higher concentrations were less effective in lowering nitrogen oxides (NOx) levels, and carbon dioxide (CO2) emissions increased due to more complete combustion. The highest engine performance was achieved with a CNC concentration of 500 ppm, resulting in an average reduction in brake-specific fuel consumption (BSFC) by 3.95% and an increase in brake thermal efficiency (BTE) by 2.73% compared to diesel fuel. The fuel cost analysis indicated that CNC is economically viable at a 100-ppm concentration; however, higher concentrations resulted in a substantial increase in fuel costs, despite reducing fuel consumption. The initial results are promising and suggest CNC could be a suitable alternative to traditional metallic nanofuel additives.
Material-adaptive kurtosis thresholding for real-time multi-parameter condition monitoring in CNC milling
Tool wear remains a major contributor to dimensional defects and unplanned downtime in Computer Numerical Control (CNC) machining. Most existing monitoring strategies employ fixed vibration thresholds that cannot accommodate the distinct dynamic responses of different workpiece materials. Thresholds calibrated for hard materials such as cast iron often fail to detect early wear, whereas the same settings applied to softer polymers lead to excessive false alarms. This limitation highlights the need for material-dependent condition assessment rather than universal thresholding. This study proposes a material-adaptive monitoring framework based on kurtosis thresholds that automatically adjust when the machined material changes. Experimental validation was conducted on three materials representing a wide hardness range: cast iron (220 to 260), aluminum (95 to 100), and polyvinyl chloride (PVC) (80 to 85). A full factorial design comprising 81 milling trials of 27 per material was performed using spindle speeds of 1,500 to 3,500 rev/min, feed rates of 125 to 250 mm/min, and axial depths of cut between 0.2 and 0.7 mm. Vibration signals were acquired using accelerometers mounted on both the spindle and workpiece, and material-specific kurtosis thresholds were derived by correlating statistical features with measured tool wear. The current method achieved classification accuracies of 95.2% for cast iron, 97.8% for aluminum, and 96.7% for PVC, representing improvements of 6-11% over conventional fixed-threshold approaches. The monitoring system was further implemented on an Internet of Things platform to enable real-time remote diagnostics and automated alerts. Pilot deployment indicated a 25-30% reduction in maintenance costs compared with the existing practice. These results demonstrate that material-adaptive thresholding substantially improves the reliability and practicality of vibration-based tool condition monitoring in CNC milling environments.
Effect of machining conditions for enhanced surface integrity and cutting temperature in trimming hybrid fibre-reinforced polymer composites
This study aims to evaluate how cutting parameters influence surface quality and cutting temperature during the trimming of Hybrid Fibre Reinforced Polymer (HFRP) used in aerospace components. Although HFRP is increasingly adopted in aircraft structures, it remains difficult to machine because trimming can trigger delamination, matrix degradation, and non-uniform heat generation across the laminate. To address this, a Taguchi L9 Orthogonal Array was applied to systematically examine the effects of spindle speed and feed per tooth on two key outcomes: surface roughness (Ra) and maximum cutting temperature (Tmax). Trimming experiments were performed on a Roland MDX540 CNC router, and surface integrity was assessed using optical microscopy, thermal imaging, and analysis of variance. Two parameter settings emerged as optimal, depending on the targeted response. The lowest surface roughness was achieved at 7518 RPM with 0.10 mm per tooth (Ra = 2.44 µm), whereas the lowest cutting temperature occurred at 5012 RPM with 0.15 mm per tooth (Tmax = 110.2 °C). Since surface integrity is the primary quality requirement for aerospace trimming, the condition of 7518 RPM / 0.10 mm per tooth was selected as the most practical optimum. This setting provides a noticeably improved surface finish while keeping the cutting temperature at a moderate level (approximately 115 °C), which remains safely below the threshold for polymer matrix degradation. Analysis of variance results further indicate that feed per tooth is the dominant factor governing surface roughness, while spindle speed has the strongest influence on cutting temperature. Overall, the findings support a dual-objective optimisation approach that balances mechanical surface integrity and thermal control, providing practical parameter guidance for consistent, high-quality trimming of HFRP aerospace parts.
A 14 V battery-supercapacitor hybrid power supply for vehicle low-voltage systems: Reducing battery stress and enhancing dynamic response
In response to the inherent limitations of single-battery low-voltage supply and the escalating demand for electrical equipment power in vehicles, the feasibility of replacing single-battery with battery/supercapacitor (SC) hybrid power supply (HPS) has been proposed and verified. This HPS directly connects a lithium-ion battery in parallel with the SC and combines a bidirectional DC-DC power converter to boost the voltage to the required level. It can drive the vehicle at low speeds and power electrical equipment. A MATLAB/Simulink model is constructed and simulated using the Federal Test Procedure-75 driving cycles. In addition, experiments are conducted to verify the HPS's boost characteristics. Furthermore, a case study is conducted to compare the proposed HPS with 14 V and 48 V single-battery systems under the China Light-duty Vehicle Test Cycle-Passenger Car operating condition. The results indicate that during frequent load fluctuations, the SC quickly responds to dynamic power and high-power demands, thereby significantly reducing battery stress. The proposed HPS demonstrates key advantages by enhancing dynamic response capability, reducing battery stress to extend lifespan, and improving energy efficiency. This study provides a new solution for the practical application of low-voltage power supply in vehicles.
Cooling Slope-Cast LM25 Aluminum Fabrication for Additive Friction Stir Deposition: A Microstructural and Mechanical Study
Additive Friction Stir Deposition (AFSD) is an innovative solid-state additive manufacturing (AM) technique that has offered a promising solution for joining and fabricating high-performance aluminum alloys over the past decade. However, the use of heat-treated feedstock produced by cooling slope casting (CSC) remains largely unexplored. This initial study investigates the feasibility of using CSC LM25 feedstock in AFSD to achieve better microstructure and hardness, while preventing early feedstock breakage. Microstructural examination indicated a shift from coarse dendritic to globular grains, with an average grain size of 1.828 µm (0.351-60.835 µm), a reduction of approximately 96.3% compared to the as-cast material. While some samples (notably C5 CSC_HT and D2 PMC_HT) achieved smooth, defect-free deposition, continuous deposits under higher rotational speed (800 rpm) and feed rate (3 mm/min), the hardness of deposited parts reduced to 63–72 HV compared to 89HV in the base material, reflecting a ~20–29% decline due to thermal softening, over-aging of precipitates, and Si coarsening during AFSD thermal cycles. Although CSC helped to refine the grain structure and enhance deposition flow, this alone was not enough to improve the mechanical behavior of the deposited parts. Additional processing steps, such as thixoforming or heat treatment after deposition, may be needed to achieve better results. This study offers early observations on how cast feedstock behaves in AFSD and identifies important challenges and future directions for improving the solid-state processing of aluminum alloys, especially for lightweight parts in aerospace and automotive applications.
Optimization of AA7075–SiC Composite Machining by WEDM Using Biosilica Additives
The study herein discusses the wire-cut electrical discharge machining (WEDM) with the addition of biosilica from maize cobs for the precision cutting of AA7075 aluminum and silicon carbide (SiC) metal matrix composites. The new, harmless addition of biosilica as a dielectric agent not only supports the sustainability of the process but also enhances surface quality and cutting speed. A Taguchi L9 design was used to conduct an experiment investigating the effects of peak current, gap voltage, and pulse-on time on material removal rate (MRR) and surface roughness (Ra). The optimal machining parameters were determined using Grey Relational Analysis and an artificial neural network (ANN). These model developments aimed to predict performance outcomes. The findings indicated that the dielectric fluid with biosilica increased MRR by 25% and simultaneously decreased Ra by 15% when compared with the typical dielectric. SEM and AFM analysis confirmed surface uniformity improvement and reduced microcrack formation. The artificial neural network model, trained using the backpropagation method on the experimental data, produced predictions for material removal rate and average roughness with an R² of 0.96, indicating that the model is highly reliable. In conclusion, the present research not only reveals a non-conventional machining process but also provides an eco-friendly approach to optimizing wire EDM of metal matrix composites with nano-reinforcement.
Numerical modelling of the thermomechanical behaviour of a polymer seal for an automotive cabin
Automotive door seals play a critical role in ensuring cabin tightness, acoustic comfort and thermal insulation, yet their performance is strongly influenced by temperature-dependent material behaviour. The objective of this study is to develop and validate a comprehensive nonlinear coupled thermo-mechanical finite element model capable of predicting the stiffness, deformation and heat-transfer characteristics of a T4-type elastomer seal under realistic operating conditions. A temperature-dependent Mooney–Rivlin hyperelastic formulation was implemented, with material coefficients calibrated using an inverse-fitting procedure based on manufacturer specification data. The model incorporates transient heat conduction, contact interactions and the effect of trapped air within the seal geometry. Quantitative results demonstrate that increasing temperature from −8°C to 34°C reduces peak contact stresses by approximately 38%, increases total displacement by more than 50%, and enhances heat flux from 10 W/m² to 57 W/m² due to improved thermal conformity. The findings highlight the significant impact of coupled thermo-mechanical effects on seal performance and provide a robust numerical framework for virtual prototyping and optimisation of sealing systems in modern vehicles, including EVs with stringent thermal management requirements.
CFD Analysis of Patient-Specific and Idealized Models for Predicting Stenosis Locations and Hemodynamic Disturbances in Peripheral Arterial Disease
Peripheral arterial disease (PAD), a condition caused by atherosclerosis, poses significant cardiovascular risks by disrupting blood flow. Computational fluid dynamics offers insights into vascular remodeling mechanisms, leveraging patient-specific anatomical data from computed tomography angiography (CTA) to enhance the accuracy of blood flow analysis. This study aims to evaluate the capability of an idealized arterial model in simulating hemodynamic parameters and blood flow patterns by comparing it with patient-specific geometries. Additionally, the study investigates the impact of stenosis at different locations within the femoral artery: upstream, downstream, and at the Profunda on flow disturbances and downstream regions. Blood flow was modeled as a Newtonian fluid, assuming a constant viscosity independent of shear rate, which is a reasonable approximation for femoral arteries where shear rates are typically high. Both an idealized PAD geometry and a patient-specific model derived from CTA data were employed for simulations. Results showed elevated blood velocities at bifurcations, notably at the superficial femoral artery (SFA) and profunda femoral artery, with peak velocities exceeding 1.90 m/s. Regions of low wall shear stress (WSS) were identified at key branching points and along arteries such as the popliteal and tibial arteries. The idealized model effectively replicated patient-specific flow patterns. Upstream stenosis caused severe flow disturbances, with velocities up to 3.9 m/s and Reynolds numbers (Re) of 1272 in the mainstream region, disrupting flow recovery. Downstream stenosis caused severe disturbances, with Re of 1835 beyond the bifurcations, whereas profunda stenosis had minimal effect, maintaining Re below 1000. This study emphasizes the importance of patient-specific anatomical factors in predicting stenosis and highlights the utility of idealized models for generating hemodynamic profiles. These findings enhance the pre-treatment planning and management methods for PAD patients.
Air-cooled thermal management of electric vehicle batteries: Insights from CFD simulations on enclosure geometry configurations
In the rapidly advancing field of electric vehicles (EVs), optimizing battery thermal management is crucial for enhancing performance, safety, and lifespan. EV batteries are prone to overheating, which can significantly degrade their efficiency and longevity, posing safety risks. This study employs Computational Fluid Dynamics (CFD) simulations to evaluate six air-cooled battery thermal management system (BTMS) designs, including the high-performing BTMS-H and BTMS-J configurations. The analysis focuses on the effects of inlet air velocity and battery pack enclosure geometry on temperature distribution, airflow patterns, and pressure gradients. The methodology involved steady-state, incompressible simulations using ANSYS Fluent, with mesh construction, grid-independence testing, and validation against experimental data, yielding relative errors between 6.5% and 11.9%. Results indicate that higher inlet velocities significantly improve heat dissipation and temperature uniformity. BTMS-H and BTMS-J achieved the highest heat transfer coefficients (190.020 W/m²·K and 187.266 W/m²·K, respectively). In BTMS-H, increasing inlet velocity from 2 m/s to 4 m/s reduced the average temperature from 310.38 K to 307.75 K. Enlarging inlet/outlet sizes further decreased temperatures, demonstrating the importance of optimizing duct dimensions for improved airflow. These findings provide practical guidance for designing compact, energy-efficient, and thermally safe BTMS in EVs, thereby supporting the development of more reliable and high-performance electric vehicle battery systems.
Wheel–Road Separation Phenomenon in the Frequency Response of a Half–Car Model
The paper presents the vibration dynamics of a half–car system in the frequency domain, taking into account the tyre–road separation. It focuses on how the car changes between two states: when the tyres are in contact with the road and when they lose contact. The vibration equations were developed to incorporate this change using a special condition known as the "separation condition." This condition was applied to both left and right wheels using indicator functions, which allows for observing the actual frequency response, including the vertical and roll dynamics. The study also examined how different road surfaces and car designs impact the car's motion when tyre separation occurs. The simulation results show that this new model is 16% more accurate than older models when the mass centre is far from the middle. Overall, the research offers valuable insights for designing more effective suspension systems and refining control strategies to prevent rollovers and maintain vehicle stability.
Optimization of neem biodiesel blends and hydrogen addition to improve compression ignition engine performance
This research investigates the effects of Neem biodiesel and hydrogen-enriched air on the emissions and performance of a common-rail direct-injection diesel engine operating under variable load conditions of 25%, 50%, 75%, and 100%. The aim is to improve engine efficiency and promote sustainable energy solutions. Several Neem biodiesel blends (B10-B30) were initially evaluated, and B15 was selected for comprehensive analysis due to its optimal performance. Hydrogen as a gaseous fuel was subsequently inducted into the inlet air at rates of 3.34 to 9.27 liters per minute to assess its influence on engine behavior. Key parameters, including Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), and emissions of carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx), were analyzed. The B15 blend exhibited a BSFC of 0.27 kg/kWh and a BTE of 31.38% at full load. With hydrogen supplementation at 5.19 liters per minute, BTE increased to 33.31%, and BSFC decreased to 0.25 kg/kWh. NOx and CO emissions were reduced to 488 ppm and 0.04%, respectively, while HC emissions remained unchanged. Hydrogen’s high flame speed and broad flammability range contributed to emission reductions; however, higher hydrogen levels led to higher NOx emissions, necessitating ongoing monitoring to comply with regulations. The artificial neural network model, trained on experimental data, was very good at predicting performance and emissions, suggesting it could be used for real-time combustion diagnostics and fuel optimization. In summary, adopting dual-fuel systems utilizing hydrogen and Neem biodiesel offers significant potential to reduce the environmental impact of diesel engines.
Investigation of the charging and discharging cycle in a thermal energy storage system
Phase change materials (PCMs) are the most suitable for storing thermal energy, as they store latent heat with a high storage energy density per unit volume. PCMs are a proven scheme of thermal management in the context of cooling electronic devices. This paper focuses on enhancing the efficiency as well as reducing the time during charging and discharging of the PCMs. Various aluminum fin structures in contact with PCM are analyzed using the finite volume method. Lauric acid-PCM is employed for the analysis in applications at low and mid-temperature ranges. The analysis was carried out with a 40 mm x 40 mm 2D vessel, a heat flux of 180 W/m2 from the top surface, and a 3 mm-thick aluminum plate; the other sides are insulated with glass wool. Three cases are considered to contrast efficiency: vessels with no fins, vessels with 3 mm-diameter straight aluminum fins, and vessels with 1 mm-diameter periodic-structured aluminum fins (mesh fins) with a 10 mm cell base size. The three cases are analyzed in Ansys Fluent for charging time; the straight and periodic structured fins are also analyzed for discharging time. It was inferred from the results that vessels with straight fins had a 58% decrease in charging time as compared to vessels with no fins. Vessels with periodic structured fins had a 82% decrease in charging time as compared to vessels with no fins. Also, the periodic structured tubes required 61.5% less time to discharge than the straight tube structure. Hence, Periodic Structured fins and tubes could overcome the problem of a long time taken for charging and discharging PCM.
Bridging development and operations: A model to improve new product innovation practices and manufacturing readiness
In today’s ever-changing competitive markets, organizations need to stay competitive to maintain their market position, which sets the expectation of flexibility towards new technology adoption and the introduction of products that are coupled with innovation and consistency. The focus is not just on a timely market launch. Factors such as product development cycle time, meeting quality expectations, manufacturing readiness, and a solid execution plan to achieve product cost targets determine the success of new product releases. Though other factors may define New Product Introduction (NPI) success, this research is primarily focused on bridging development and operations within an organization to analyze and improve innovation practices during new product development and identify the key factors for successful NPI. From the literature, four factors were identified: collaborating with operations early in development phases, establishing an NPI team, using digital methods, and an organizational support environment. Various hypotheses were developed based on the literature and tested through a questionnaire survey. The collected data were analyzed. Findings and recommendations are shared to enhance the success rate of new product development and introduction, as well as product technical documentation and training efficiency.
Impact of Surface Roughness on Interference Resistance in Delft 372 Catamarans
Surface roughness from marine biofouling significantly compromises catamaran performance, yet current models fail to predict its speed-dependent interference effects, a critical gap in sustainable vessel design. This study employs validated Reynolds-averaged Navier-Stokes-based computational fluid dynamics with the k-ω Shear Stress Transport turbulence model to quantify hydrodynamic impacts across Froude numbers ranging from 0.1 to 0.8. Four different biofouling conditions were systematically analyzed, with rigorous experimental validation against towing-tank data. Key results demonstrate that heavy fouling increased total resistance by 35% at Fr 0.8 versus hydrodynamically smooth surfaces, yet paradoxically generated favorable interference, IF = -0.08, through turbulence-mediated wave suppression. Conversely, hydraulically smooth surfaces exhibited peak detrimental interference, IF = 0.15, at Fr 0.5 due to constructive wave reinforcement. Concurrently, the heavy slime surface reduced inter-hull wave amplitude by 14.5% at Fr 0.5, while expanding the wetted area by 1.24%. The findings address the biofouling paradox by demonstrating that surface roughness facilitates a net reduction in resistance at Fr ≥ 0.65, challenging traditional beliefs regarding the uniformly negative impact of fouling. This study presents the inaugural predictive framework for strategic fouling management, emphasizing bow and midship interference zones, thereby addressing a significant gap in hydrodynamic prediction within realistic operational contexts.
Effect of Windbreakers on the Aerodynamic Performance of a High-Speed Train Using CFD Analysis
High-speed trains are designed to reduce air resistance, lower noise levels, and ensure stable, smooth travel. Windbreakers, which serve to reduce side forces caused by crosswinds, are essential for maintaining the stability and safety of high-speed trains, especially at high speeds. This study investigates the effects of windbreaker height and porosity on the aerodynamics of a high-speed train under 30° and 60° crosswind conditions. The windbreakers tested have heights of 65 mm, 135 mm, and 190 mm, with porosities varying in hole sizes of 12.5 mm, 25 mm, and 50 mm, resulting in nine windbreaker designs. Simulations were conducted at 80 m/s to measure drag, side, and lift force coefficients, and to analyze flow structure and pressure contours. The results show that the best aerodynamic performance occurs under 30° crosswind conditions, with the optimal windbreaker design being the tallest height and smallest porosity. Specifically, the drag coefficient (Cd) reached 0.0101, and the side and lift force coefficients (Cs and Cl) were 0.3383 and 0.3290, respectively. The study concludes that optimizing windbreaker height and porosity significantly improves aerodynamic performance, with medium porosity offering the best overall results. However, windbreaker height must be carefully considered under varying crosswind conditions to achieve optimal performance.
Effect of Inclination Angle on the Micro Dimple Machined Using Inclined Milling Method
In micro-dimple fabrication using the inclined milling method, center marks commonly form at the dimple base, resulting in inconsistent dimple depth and a rough surface texture. These defects can lead to uneven lubricant distribution, increased localized friction, and premature surface damage in tribological applications. This study investigates the influence of inclination angle (θ) on milling center mark formation and overall dimple quality during inclined milling. Experimental work was performed using inclination angles ranging from 0° to 45°, and the machined dimples were analyzed using a Laser Confocal Microscope. The results show that at θ = 45°, the milling center mark on the dimple base was eliminated, and the average area surface roughness (Ra) improved by 70% compared to the perpendicular dimple (θ = 0°). These quantitative improvements highlight the significance of tool inclination in minimizing surface defects and enhancing lubrication pathways. The study demonstrates that optimizing the inclination angle not only mitigates center mark formation but also enhances dimple uniformity and surface finish, addressing critical challenges in micro-texture quality and lubrication efficiency for precision engineering applications.
Effects of oil palm frond fiber reinforcement on morphology, mechanical, and acoustic properties of rigid polyurethane foam
The quest for innovative, eco-friendly materials has spurred the exploration of agricultural biomass waste as a potential resource for polymer composites. The current study examines the impact of untreated oil palm frond (OPF) fiber on the multifunctional and sustainable performance of rigid polyurethane (PU) foam in the oil palm industry, in alignment with Sustainable Development Goals 12 and 13. Using a one-shot polymerization method, OPF foams were fabricated with fiber contents of 0, 5, 10, 20, and 30 (wt%). The foams were tested for density, morphology, compressive strength, and sound absorption performance in accordance with ASTM D1621 and ISO 10534-2. Experimental densities were compared with theoretical values to confirm that the foam production process was consistent. Scanning electron microscopy analysis revealed that OPF contents above 20 wt% disrupted the cellular structure, resulting in non-uniform cell sizes and increased cell wall collapse. These morphological defects contributed to a reduction in compressive strength from 0.1901 N/mm² for neat PU foam to 0.0697 N/mm² at 30 wt% OPF. In contrast, the 30 wt% OPF foam showed greater porosity and a higher proportion of open cells, resulting in superior sound absorption performance in the 1000–2500 Hz frequency range. Overall, the findings reveal a clear trade-off between mechanical strength and acoustic efficiency. PU foams with high OPF content possess eco-sustainable benefits and enhanced sound-absorbing qualities. Despite the reduction in compressive strength, high OPF-loaded PU foams show potential for lightweight, non-structural applications, such as automotive parts and interior building materials.
Defect Detection in Disc Brake Pads Using an Improved AlexNet Convolutional Neural Network
In the last few years, with the rapid advancement of the automotive industry, disc brake pads have been increasingly widely used in automotive braking systems. However, due to long-term friction and high temperature effects, they are prone to defects such as cracks, wear, and dents. To raise the accuracy and efficiency of disc brake pad defect detection, the study first optimized image preprocessing and, based on the AlexNet network, introduced a multi-scale convolution module, batch normalization, and global average pooling for structural improvement. Finally, a new type of disc brake pad defect detection model was proposed. The experiment findings denoted that the highest defect detection accuracy of the model was 97.12%, and the mean detection time was 10.53 milliseconds. Compared to other advanced methods, this new model had the lowest missed detection rate of only 1.78%, the lowest resource consumption rate of 36.45%, and the lowest model complexity of 53.23%. This denotes that the new model can efficiently and accurately detect various kinds of defects in disc brake pads, which is suitable for practical industrial inspection scenarios and provides a reliable technical support for the field of disc brake pad defect detection.
Transient thermohydrodynamic of turbocharger thrust bearings with circumferential V-grooves
The primary objective of this paper is to numerically analyze the effect of circumferential V-grooves on the thermohydrodynamic performance of turbocharger thrust bearings, with a focus on lubricant temperature distribution and variation. To investigate the relationship between shaft speed and lubricant temperature, a thermohydrodynamic lubrication model was employed to obtain numerical results. The numerical methodology utilizes the finite difference method coupled with the Newton-Raphson scheme to solve the nonlinear modified Reynolds and energy equations concurrently. To ensure accuracy, steady-state numerical predictions of pressure and temperature distributions were validated against existing research. Subsequently, the numerical findings, focusing on oil pressure, friction, and oil temperature under varying shaft speeds, are thoroughly discussed. The numerical findings indicate that the implementation of grooved thrust bearings with a 5 mm clearance reduces the average lubricant temperature in the contact area by 14% at maximum operating speeds, while maintaining friction levels comparable to those of ungrooved counterparts. However, an excessive increase in groove width and depth adversely affects the bearing's load-carrying capacity and results in higher peak temperatures at the bearing plate edge, especially at widths and depths greater than 30 mm.
A Collision Risk Control Model for Mechanical Engineering Vehicles Based on Image Road Condition Monitoring Method
Mechanical engineering vehicles play an important role in various construction projects, but they face various collision risks under different road conditions. To improve the safety of engineering vehicles in operation, a collision risk control model has been proposed. During the process, a generative adversarial network is used as the basis for designing image road condition monitoring methods. DeepLabv3+ is used for generator design, and ResNet101 is used as the encoder backbone network. Subsequently, the risk of vehicle collision is calculated using the artificial potential field method, and the repulsive field adjustment factor is introduced to adjust the repulsive force potential field function. The experiments confirmed that the research method remained below 294 k in parameter testing when the pixel size reached 50 M in two datasets. When testing the obstacle recognition accuracy, the research method achieved a recognition accuracy of 97.8% when obstacles accounted for 10% of the detection area during the day. When conducting obstacle avoidance success rate testing, the obstacle avoidance success rate of the research method remained above 94.7% when the proportion of obstacles in the detection area was 10%. These results confirmed that the research method had good operational performance and collision risk control effect, which could effectively reduce the collision risk of engineering vehicles. The main contribution of this study lies in the proposal of a collision risk control model for mechanical engineering vehicles based on image road condition monitoring and the optimization of the repulsive force potential field function, which solves the problem of local optima.