Automotive Science and Engineering

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In this research, the effect of using three Nano fluids contains graphene oxide (GO), titanium oxide (TiO2) and aluminum oxide (Al2 O3) was analyzed on the heat transfer of the car radiator by experiment in physical conditions on the car engine. Distilled water and ethylene glycol (60:40) as the base fluid was companied with three nanoparticles contain graphene oxide, titanium oxide and aluminum oxide that each one separately with 0.1, 0.2 and 0.3 weight percent and flow rates of 10, 20, 32 and 40 liters per minute were used at normal engine temperature. After the temperature of the radiator cooling fluid reached 90 degrees Celsius and the fan was turned on for one minute, the results showed that increasing the weight percentage of nanoparticles to the base fluid increases the displacement heat transfer coefficient and most increase in the coefficient of heat transfer at 0.3 weight percent to an approximate value of 5.2% in aluminum oxide, 11.9% for titanium oxide and 28.7% for graphene oxide compared to the base fluid was received. With the increase in weight percentage, the pressure drop and Nusselt number increased.  The highest percentage increase in the radiator pressure drop for all three Nano fluids with 0.3 weight percentage and 2.2% for   aluminum oxide, 3.5% for Titanium oxide and 5.24% for graphene oxide were received.

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Harnessing nanomaterials and the piezo-phototronic effect, we engineered a high-performance ultraviolet (UV) photodetector (PD), unveiling a new frontier in optoelectronics. This novel device seamlessly integrates zinc oxide nanorods (ZnO NRs) onto a flexible polyethylene terephthalate- indium tin oxide (PET-ITO) substrate through a straightforward and efficient hydrothermal process. This unique nanostructure design outshines its competitors, producing significantly higher current under UV illumination despite a comparable detection area. The plot thickens with the intriguing "piezo-phototronic effect," where applying pressure under UV light amplifies the current and overall device efficiency. This groundbreaking discovery paves the way for cutting-edge optoelectronic applications, where nanomaterials and the piezo-phototronic effect join forces to redefine performance.
 

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Fueled by their potential for energy harvesting, ZnO nanorods (NRs) have sparked considerable enthusiasm in the development of piezoelectric nanogenerators in the last decade. This is attributed to their exceptional piezoelectric properties, semiconducting nature, cost-effectiveness, abundance, chemical stability in the presence of air, and, the availability of diverse and straightforward crystal growth technologies. This study explores and compares the piezoelectric properties of two promising nanostructured ZnO architectures: thin films deposited via radiofrequency (RF) magnetron sputtering and well-aligned nanorod arrays grown using a hydrothermal process. Both structures are fabricated on flexible polyethylene terephthalate (PET) with an indium tin oxide (ITO) electrode (PET-ITO substrate), presenting valuable options for flexible piezoelectric devices. By directly comparing these distinct morphologies, we provide insights into their respective advantages and limitations for energy harvesting and sensor applications. The investigation into the piezoelectric properties of ZnO NRs involved the construction of an actual piezoelectric nanogenerator. This device demonstrated a direct correlation between applied mechanical forces and the resultant voltage outputs. It was observed that when the same external force was applied to both devices, the ZnO NRs-based piezoelectric nanogenerator (PENG) exhibited a higher output voltage compared to the other device.

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A novel liquid cooling system for pouch-type lithium-ion batteries (LIBs) is proposed by focousing on uniform temperatue disturbution and effective heat dissipation. The system utilizes a michrochannel cold plate with an innovative coolant disturbution design. This study proposes a novel microchannel disturbution path design with each microchannel dimensioning 1 mm² and embeded in the battery's ciritical region to enhance the thermal contact among the LIB and the microchannels. This study aims to simulate and evaluate the performance of cooling system under varius Iranian environmental conditions (Tehran, Shiraz, Isfahan, and Bandar Abbas) and operational parametrs (channel pattern, flow rate) to achieve optimal battery temperature and reduce energy consumption.

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The transition from traditional internal combustion engine vehicles to electric vehicles is in progress. With their high energy density, low self-discharge rates, long cycle life, and absence of memory effects, lithium-ion batteries have become the primary power source for alternative vehicles. Throughout the battery's lifespan, its performance or health gradually deteriorates due to irreversible physical and chemical changes. Depending on the specific aging mechanisms, a battery may lose capacity or face increased internal resistance. Growing awareness of the importance of environmental protection and the potential implications associated with products and services has spurred interest in developing methods to better understand and address these impacts. Life cycle assessment is a method used to examine the environmental effects associated with all stages of product production. This study compares the operational conditions of an electric vehicle equipped with both new and old battery packs. The performance difference indicates that the vehicle with the aged battery has 17% less capacity, operates over 20% weaker in range, and its ohmic resistance increases by up to 150%. From a well-to-wheel perspective, using an electric vehicle with an old battery could result in a 2% increase in carbon dioxide emissions, reaching 56.638 g CO₂ equivalent per kilometer.

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Automotive radar systems operating in the 24 GHz band are widely used in Advanced Driver Assistance Systems (ADAS) due to their cost-effectiveness and robust performance across diverse environmental conditions. However, these systems face critical vulnerabilities from electromagnetic interference (EMI) and high-power microwave (HPM) threats, which can degrade detection accuracy. This study presents a novel plasma-based limiter employing a Gas Discharge Tube (GDT) within an optimized K-band waveguide (10.668 × 4.318 mm) filled with Rogers RO3035 dielectric (εr = 3.6). The design achieves exceptional metrics: 0.9 dB insertion loss and 21.5 dB return loss during normal operation, while providing over 30 dB isolation against HPM signals with a sub-100 ns response time. These characteristics position this solution as an industry-leading protection mechanism for next-generation automotive radars. 

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Target detection using cameras or automotive radar to identify traffic or prevent collisions is an important issue in Autonomous Vehicles (AV) research. Traditional Constant False Alarm Rate (CFAR) methods are commonly employed. Although these methods are suitable for lightweight hardware, improving the target detection process often leads to losing real-time performance. The method proposed in this paper improves detection accuracy. It reduces response time by modifying the position of guard cells in the first stage and employing harmonic averaging (inverse of the sum of the inverse of data) while eliminating data sorting in the second stage. Moreover, this approach exhibits better performance in the presence of interfering targets. Since the proposed method is more applicable to the Range-Doppler map, it has been named RD-CFAR. The proposed method also enhances target detection in Synthetic Aperture Radar (SAR) images. Simulation results demonstrate that the proposed algorithm improves detection probability by nearly 40% compared to conventional methods (like CA-CFAR), while maintaining comparable computational time.

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In metal casting, detecting defects like pores and cracks in X-ray images is crucial for product quality and safety. This study presents an advanced U-Net architecture for semantic segmentation of defects in the GDXray dataset, achieving superior accuracy. By formulating defect detection as an inverse problem reconstructing material density from X-ray projections the method integrates transfer learning, data augmentation, and Convolutional Block Attention Modules (CBAM) to address low contrast-to-noise ratios and limited data. Pretrained on synthetic Radon transform projections, the U-Net, enhanced with CBAM, sharpens focus on defect regions, improving boundary precision by 5%. Data augmentation, including rotations, flips, and noise injection, generates 5,000 synthetic images to overcome data scarcity. Experiments on 2,727 grayscale GDXray images demonstrate a mean Intersection over :union: (mIoU) of 0.85, a 15% improvement over baseline U-Net models, with 97.8% accuracy for pores and 94.5% for cracks. The inverse problem approach reduces false negatives by 12%, excelling in noisy conditions. Compared to methods like Mask R-CNN, this approach advances non-destructive evaluation (NDE) for casting applications, ensuring reliability and safety. Validated on laboratory X-ray data, the model offers a scalable solution for industrial defect detection. Future work will optimize computational efficiency and explore multi-modal data to enhance robustness.

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Generally, parts whose geometric form in the final configuration, or before additional operations such as machining, does not require special dimensional accuracy are produced using casting methods. Producing parts with this method results in significant deviations in dimensions and geometric forms from the main designed geometric model. Due to economic considerations, scrapping such parts will result in energy and material waste and high costs. In this study, dimensional deviations at the exhaust manifold outlet and deviations from the defined geometric tolerance limits of the part during the repair process are identified as geometric non-conformities and investigated using computer tools. Considering the harsh operating conditions under which the engine is under full load, the temperature distribution is determined using computational fluid dynamics, and the thermo-elastic stress distribution is calculated using the finite element method, accounting for the loads applied to the structure. The main part model and the non-conforming models with upper and lower limits in geometric dimensions have been investigated with respect to the deviation in thermo-elastic stress relative to the reference value in the part with the nominal size. The results showed that, given the amount of stress changes in the desired area of parts with deviations from the main design, these parts are also usable and have a lifetime almost the same as a part produced with the nominal size.

 

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Adhesively bonded joints, particularly those featuring Carbon Fiber Reinforced Polymer (CFRP) adherends, have become indispensable in aerospace and automotive industries due to their superior strength-to-weight ratios. However, the long-term structural integrity of these joints is severely challenged by hygrothermal environments (the synergistic effect of moisture and temperature) which induces degradation in both the polymer matrix and the adhesive interface. This review provides a systematic discourse on the fundamental principles of composites, nanotechnology, and the mechanisms of environmental aging. It critically analyzes various joint configurations and failure modes, such as cohesive and adhesive failures, under adverse conditions. A significant portion of this study is dedicated to the efficacy of incorporating zero-, one-, and two-dimensional nanoparticles to enhance the environmental resilience of epoxy adhesives. Furthermore, this review evaluates the recent advancements in Cohesive Zone Modeling (CZM) for predicting the residual strength and fracture energy of aged joints through environment-dependent traction-separation laws. This work identifies critical gaps in accelerated aging methodologies and highlights the necessity for high-fidelity predictive models to ensure the safety of hybrid structures in high-stakes engineering applications.
 

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This article introduces a scalable framework based on definition of probable future driving scenarios originated from multidisciplinary policies aimed at transport sustainability. The generation of realistic driving cycles based on recorded real microtrips is used for simulation of carbon emissions in future driving scenarios defined by target characteristic parameters. The suggested framework identifies possible contradicting multidisciplinary policies, highlighting the interdisciplinary requirement of policies for sustainable transportation in well-to-wheel life cycle. Battery electric vehicles were found to be unsustainable in regions with less than 30% share of renewable sources in electric grid, but substantial 25% reduction of carbon emissions is achieved with 50% share of renewables. The stability of clean power generation with different environmental conditions is highlighted.  Emerging low carbon fuels, sustainable urbanization and less intensive driving habits would keep internal combustion engines as a dominant powertrain of choice in future fleets of private vehicles.