collision avoidance (CA) system has become a necessity in every vehicle due to its ability to prevent collisions. Numerous techniques have been developed to improve the tracking system, thereby reducing the possibility of collision by increasing its accuracy relative to the reference signal. The path-tracking system is an important element in the CA system, whose function is to prevent deviation from the reference path. However, a conventional path-tracking system without an estimator is unable to track the reference signal in the presence of an external load disturbance. Moreover, the vehicle velocity, as well as variations in load disturbance, retard the vehicle's path tracking abilities while avoiding an obstacle. This deficiency can lead to fatal consequences during vehicle navigation due to understeering and oversteering. The primary purpose of this research is to design an integrated controller for a disturbance-rejection (IC-DR) path-tracking system to improve tracking performance by rejecting external disturbances while avoiding obstacle. The design process involved formulating a longitudinal force controller to track changes in vehicle acceleration during the CA scenario. Then, the lateral controller was formulated by following these orders: (1) Linearization of the model, (2) Optimal state estimator design by using a linear model, (3) Optimal state feedback regulator design. The CA scenario was simulated by using a nonlinear tire characteristic for vehicle model development in MATLAB Simulink. Next, both controllers were integrated with the vehicle system, and their performance was analyzed. The simulation results show that the path-tracking system prevents deviation from the reference trajectory in understeering and oversteering situations. The proposed path-tracking method can efficiently reduce external disturbances, and it is much simpler than other advanced controllers. The results show that by implementing IC-DR, the mean squared-error between the vehicle and reference trajectories is below 0.01 for all additional load disturbance percentages at different velocities.
way of determining health risk to public transport passengers is by measuring whole-body vibration (WBV) according to ISO 2631-1 standard. This Standard specifies the measurement and evaluation of RMS acceleration and Vibration Dose Value (VDV) in the range 0.5-80 Hz along three axes, using specific frequency-weighting curves. On longer routes over rough road surfaces, this measurement will help assess short-and long-term health effects. Existing bus seats are commonly bolted to the floor without vibration isolation, which may amplify vibration transmitted to passengers. This study aims to characterize and optimize the use of a passive rubber damper by integrating an EMT-FEA-PSO framework into an unmodified twin-seat passenger bus structure. Experimental Modal Testing (EMT) identified six dominant vibration modes in the 20-100 Hz range, which are validated against a Finite Element Analysis (FEA) model with an error within 10-15%. On-road measurements revealed that SEAT values consistently above 100% at speeds of 30-80 km/h, confirming vibration amplification by the unmodified seat. In laboratory tests, three rubber isolators were evaluated for effectiveness, and one damper reduced RMS acceleration by approximately 33%. Next, Particle Swarm Optimization (PSO) was applied to a 2-DOF seat-floor model to determine optimal damper stiffness, where the PSOsimulated FRF shows clear resonance peak attenuation. The proposed methodology offers a practical, low-cost design guidance tool for improving WBV isolation in bus seating and can be extended to other vehicle or machinery support structures.
Aerodynamic drag reduction and flow control remain major challenges in improving vehicle fuel efficiency, high-speed stability, and overall aerodynamic performance of ground vehicles. Flow separation and wake formation behind vehicle bodies significantly influence drag generation, especially for simplified vehicle geometries. Therefore, this study experimentally investigates the aerodynamic performance of three simplified ground vehicle models tested in a subsonic open-circuit wind tunnel. A Suzuki Vitara (Car 1) that is 6.67% smaller and a Lamborghini Aventador (Car 2) that is 5% smaller are two of the models. The third model (Car 3) has a new aerodynamic design that is based on the shape of a fastback. The Reynolds numbers (Re) of the cars that were tested were between 1.5 & times;10(5) and 6.0 & times;10(5), which is the same as wind speeds of 10 to 30 m/s. We employed a calibrated hot-wire anemometer and digital manometer to measure the local velocity, the distribution of the surface pressure coefficient, and the aerodynamic forces. Car 3 always had the lowest drag coefficient, with a minimum C-D of 0.242, which is 29% lower than Car 1 and 2% lower than Car 2. Adding a rear wing to Car 3 cut lift by up to 25% at high R-e, but only added a modest amount of drag (Delta(CD) = + 0.006). Based on numerous trials and device calibration, the maximum measurement errors for drag were +/- 1.4 % and for lift were +/- 1.3 %. The findings elucidate that the shape of the back and the use of aerodynamic devices can significantly impact the wake development, drag reduction, and stability enhancement.
which is defined as the ability of a structure to absorb impact energy through controlled, gradual deformation, is an important factor in the design of lightweight energy-absorbing structures. This study describes the crashworthiness behavior of multicell tubes with different internal cell geometries that have undergone quasi-static compression. Polylactic acid (PLA), nylon, and wood filaments were utilized in fused deposition modeling (FDM) to create thin-walled tubes with cross-shaped, equal-shaped, and strict inequality-shaped internal cell arrangements. The effects of material, internal geometry, build orientation, and loading orientation on the crashworthiness performance were analyzed experimentally. The cross-shaped design demonstrated the best crashworthiness performance among the examined configurations, especially for PLA specimens. The cross-shaped PLA tube produced the highest specific energy absorption (SEA) value of 10.75 J/g under axial compression, characterized by sequential folding deformation. Additionally, the specimens with a 90 degrees orientation showed the most stable progressive collapse behavior and the greatest energy-absorption capacity. In comparison to PLA specimens made at a 90 degrees build orientation, those made at a 45 degrees and 0 degrees build orientation absorbed 8.8% and 85.1% less energy, respectively. However, the wood specimens displayed the most severe brittle fracture, especially at lower build orientations. Under lateral compression, the cross-shaped arrangement offered the best compromise between structural weight and energy absorption (EA) capacity among the geometries examined. This result can be useful for the development of optimal energy-absorbing 3D printed thin-walled multicell structures, such as protective components and lightweight transportation structures.
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.
Al-Si-Mg alloys are widely used in automotive and aerospace applications due to their excellent castability and mechanical properties. Gravity die casting (GDC) is commonly employed to manufacture such components. While AISI H13 tool steel is typically preferred for GDC dies, mild steel dies may be used for low-volume production because of their lower cost and ease of fabrication. However, mild steel dies generally exhibit limited-service life due to lower hardness, reduced wear resistance, and poor thermal fatigue resistance. Therefore, optimisation of casting parameters is necessary to improve casting quality. This study evaluates the influence of die orientation and pouring temperature on the mechanical, physical, microhardness, and porosity characteristics of aluminium (Al) alloy 356 castings produced using an ASTM A36 mild steel gravity die. The casting process was conducted at different pouring temperatures using vertically oriented casting (VOC) and horizontally oriented casting (HOC) configurations. The resulting castings were evaluated through impact testing, microhardness measurements, and porosity analysis, including apparent porosity (AP) and bulk porosity (BP). Results show that VOC at a pouring temperature of 900 degrees C improved impact toughness by 59.3% (average 16 kJ/m2) compared with HOC, while reducing surface microhardness by 2.4% (82.7 HV). Additionally, VOC significantly reduced BP by 91.9% and AP by 69.5%. Compared with castings produced at 800 degrees C, VOC at 900 degrees C increased impact toughness by 34.3% and reduced BP by 75.15%. Overall, vertically oriented casting using mild steel dies significantly improves impact toughness and reduces porosity in 356 Al alloy castings.
inspection of additive manufacturing is time-consuming and error-prone, making it unsuitable for high-speed production. Real-time automated systems should ensure precision and consistency of defect detection. Hence, this paper presents the development and evaluation of an automated defect classification system for additive manufacturing (AM) products using You Only Look Once version 8 (YOLOv8) and LabVIEW. This study utilized a dataset of 1200 images of AM products from the Malaysia Automotive Robotics & IoT Institute (MARii). YOLOv8, a state-of-the-art object detection technique, was used to develop a defect classification model. Following the development of the classification model, it was implemented on the Karakuri machine by interfacing the hardware with National Instruments' myRIO and LabVIEW. An infra-red sensor triggers image capturing via a USB camera system, while the real-time classification system activates the servo-based sorting mechanisms. Data augmentation techniques were deliberately applied to improve robustness during model training. The system achieved an average accuracy of 93.39% and demonstrated satisfactory performance across all evaluation criteria: precision, recall, and F1-score; thereby confirming its effectiveness in classifying defect and non-defect products. In conclusion, the results validate the designed system as a practically feasible and efficient approach toward automating quality control in additive manufacturing, reducing dependence on manual inspection while improving consistency and operational efficiency.
foods have naturally high amounts of water content, they are prone to microorganism growth and chemical decomposition. This research aims to explore the drying process of Thai Jinda chilies pepper (TJCPs) in the form of hot-air convection drying by means of a direct-heating perforated air tray (1.5 & times;0.6 & times;1.0 m3). The effects of different temperatures (55, 60, 65, 70, and 75 degrees C) and Reynolds numbers (20000, 30000, 40000, and 50000) were studied. As the results indicated, the increase in drying temperature and Reynolds number significantly accelerated the drying process of the samples. The optimal drying conditions were achieved by performing tests at 75 degrees C and Re = 50000; this led to the minimum drying time of about 6-7 hours without deteriorating the physical properties of TJCPs. The effective moisture diffusivity increased between 0.374 & times;10-10 and 5.176 & times;10-10 m2/s while the activation energy decreased in correlation with the airflow intensity. This can be explained by the enhancement of heat and mass transfer due to an intense airflow circulation, thinning of the thermal boundary layer, and efficient convective transport inside the drying chamber. It is concluded that the perforated air tray method is an effective way to enhance thermo-fluid drying of TJCPs.
and efficiency are vital components in modern manufacturing systems, especially for rotating machinery that operates continuously under a variety of environments. Recently, there has been growing interest among researchers in developing and deploying a cross-machine fault-diagnosis system in real-world industrial settings. It allows engineers to perform fault diagnosis across multiple machines without having to build a new intelligent model each time. Unlike most studies that utilized complex transfer learning architectures, this paper proposes a simple and efficient cross-machine bearing fault diagnosis framework based on correlation alignment and deep extreme learning machine (DELM). It aligned time-domain statistical features from two datasets, Case Western Reserve University and the experimental dataset, to reduce the domain gap between them and improve generalization performance. The aligned features were classified using a DELM model. The experiment yielded reliable cross-machine generalization, with an average accuracy of 91.27%. These findings underscore the model's capability to offer a simple, effective, and low-computational-demand solution for cross-machine applications, making it suitable and practical for real industrial use.
Concentric magnetic gears (CMGs) offer significant advantages for electric vehicle drivetrains, including contactless torque transmission, high efficiency, and built-in overload protection. Despite these benefits, commercial viability is hampered by a heavy reliance on rare-earth permanent magnets (REPMs), raising serious concerns about costs, supply chain security, and sustainability. This review critically examines strategies to mitigate this reliance. Analysis of recent topological innovations shows that while torque density has improved, the fundamental dependence on REPMs remains unchanged. Furthermore, direct reduction strategies, including system integration, material substitution, topological optimization, passive conductors, and complete electrification, often entail significant performance trade-offs. Consequently, hybrid excitation is identified as a key paradigm shift. The core contribution of this review is the development of a clear taxonomy distinguishing “auxiliary electromagnetic integrations” for added functionality and “true hybrid excitation,” where windings act as a co-primary source of magnetic flux. The study concludes that true hybrid excitation is the most strategic yet underexplored research area, uniquely enabling features such as variable gear ratios, overload resilience, and the capability to replace REPM volume directly. Therefore, focused research on true hybrid-excited CMGs is presented as the essential path toward developing sustainable, high-performance, next-generation magnetic gearing systems.
automotive industry has always demanded innovation in renewable materials that are environmentally friendly. One of the natural materials that has the potential as a constituent material to make motor vehicle parts is blood clam shell waste (Anadara Granosa) which can be used as a raw material for making brake pads. This study has the purpose to analyze the effect of the addition of blood shell powder on the wear, toughness, and hardness value of epoxy resin matrix composite as a motorcycle brake pad. This study is a type of experimental research by comparing the experimental group with the control group (Honda Genuine Parts brand brake pads). The test results showed that the addition of 30% of blood shells powder got the most optimal results with a wear value of 1.27 & times;10-6 mm2/kg, impact value 2.41 & times;10-3 J/mm2, hardness of 19.98 kgf/mm2. The 30% variation has the closest results to the test value on the brake pads of the Honda Genuine Parts brand motorcycle. With this, it can be concluded that the more volume of blood shells powder, the more the strength of the composite increases. With these results, blood shell powder composite can be recommended as an alternative to brake pad friction material that is more environmentally friendly.
Diesel engines are inherently difficult to analyze due to the complex and nonlinear interactions among engine speed, throttle position, and load. This study investigates the key factors affecting the performance of a four-stroke diesel engine. It compares the predictive capabilities of multiple linear regression and Artificial Neural Networks (ANN) in modeling five critical performance indicators: power (kW), torque (Nm), Brake Thermal Efficiency (BTE, %), Brake Specific Fuel Consumption (BSFC, kg/kWh), and Air-to-Fuel (A/F) ratio. Experimental data were collected from a TD23 diesel engine tested on an engine dynamometer across a range of operating conditions, with engine speeds from 1100 to 1600 RPM, throttle positions from 10% to 40%, and varying loads, yielding a dataset of 24 observations. Regression models were developed using Minitab, while ANN models were implemented in MATLAB. Results show that engine speed and load exert the strongest influence on performance, whereas throttle position has a relatively minor effect. Although regression achieves slightly lower Root Mean Square Error (RMSE) for power (0.1406 kW vs. 0.3561 kW) and torque (1.0937 Nm vs. 1.4698 Nm), likely due to the small dataset favoring simpler linear fits, the ANN consistently demonstrates superior coefficient of determination (R2) values for nonlinear responses. It improves R2 by 44.38% for BSFC (0.9143 vs. 0.4705), 35.26% for BTE (0.8186 vs. 0.4660), 19.31% for torque (0.8136 vs. 0.6205), and 2.63% for A/F ratio (0.8589 vs. 0.8326). Notably, BSFC exhibits extremely small RMSE values due to its unit scale (kg/kWh) and low data variability, underscoring the importance of clear unit reporting. Overall, the ANN proves more effective at capturing the complex, nonlinear behaviour of diesel engine performance, particularly when sufficient data diversity is present, while regression remains competitive for near-linear outputs in data-limited scenarios.
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 approximate to 0.9999; RMSE approximate to 0.0071; MAE approximate to 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.
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.
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.
two-wheelers (PTWs) are highly vulnerable in road traffic accidents, with frontal collisions with cars frequently resulting in severe or fatal rider head injuries. However, while most research has focused on vehicle collisions and head injuries, studies on the influencing factors of rider head injuries in collisions where the vehicle front impacts the powered two-wheeler remain relatively limited. This study employs an orthogonal experimental design to conduct a simulation analysis of frontal collisions between cars and PTWs (motorcycles and electric two-wheelers). It investigates the impact of multiple factors, including vehicle speed, collision angle, and vehicle type, on rider head injuries. By reconstructing typical traffic accidents and combining multi-body dynamics models with human dummy models, the study quantifies variations in the Head Injury Criterion (HIC15) and the 3-millisecond acceleration peak (3 ms Clip). Results from the range analysis indicate that vehicle speed is the primary factor influencing HIC15 (Range value of 835.29), while collision angle most significantly affects the 3 ms Clip (Range value of 70.60). The research reveals significant differences in rider head injuries under various collision conditions and quantifies vehicle speed as the dominant factor for severe head injuries (HIC15). Consequently, it is recommended that accident mitigation strategies prioritize speed-control measures to reduce rider mortality effectively.
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
Formula SAE (FSAE) competition is an international student event that seeks to design a lightweight single-seater to maximize performance. In this context, the front steering knuckle represents a significant challenge, as its weight directly influences the vehicle's dynamic performance. However, previous studies have limitations regarding the combined use of topology optimization tools and the evaluation of new materials for this component. This research addresses this gap by topologically optimizing the front steering knuckle of an FSAE single-seater to reduce its weight while maintaining its structural strength. Initially, the stresses at the knuckle's support points were calculated, considering Formula SAE regulations and the vehicle's operational conditions (acceleration, braking, cornering, and overcoming obstacles). The knuckle was modeled in SolidWorks, and its structural parameters (stress, deformation, and factor of safety) were analyzed in ANSYS using lightweight and durable materials such as Aluminum 7075-T6 and Alumold, the latter being analyzed for the first time in this context. Finite element analysis was performed, and a suitable mesh was selected from six options. Subsequently, topology optimization was applied to remove unnecessary material using ANSYS and SolidWorks software, reducing the initial knuckle mass of 2.41 kg by 35% and 25%, respectively, while maintaining a minimum factor of safety of 1.5. This approach demonstrates the effectiveness of topology optimization in enhancing the structural design of automotive components.
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 & micro;m), whereas the lowest cutting temperature occurred at 5012 RPM with 0.15 mm per tooth (Tmax= 110.2 degrees 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 degrees 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.
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.
