Automotive Engineering Research Articles

A Critical Review on the Comparative Assessment of Rare-Earth and Non-Rare-Earth Alloying in Magnesium Alloys

Magnesium (Mg) alloys have emerged as highly sought-after alloys in aerospace, automotive, and biomedical engineering industries due to their low density and excellent mechanical properties. The addition of alloying elements plays a critical role in improving the performance of these Mg alloys, with rare-earth (RE) elements being especially helpful in improving mechanical properties, specifically strength and ductility. However, due to the higher cost and limited availability of RE elements, researchers are trying to explore non-rare-earth (non-RE) alternatives, such as aluminum, calcium, zinc, etc. These non-RE elements offer various advantages including cost effectiveness and enhanced manufacturability, but they may not always match the performance improvements of RE elements. This review critically examines and compares the effects of RE and non-RE alloying elements on the microstructural evolution, corrosion resistance, and strengthening implications of Mg alloys. Furthermore, it explores the recent advancements in alloy development and evaluates the trade-offs between RE and non-RE alloying elements, offering key insights into the optimal approaches for enhancing the performance of Mg alloys across various applications.

Machine Learning-Based Grading of Engine Health for High-Performance Vehicles

This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. Data are collected from sensors monitoring revolutions per minute, boost, rail pressure, timing, and temperature. The data are processed for supervised learning and analyzed using visualizations such as a heatmap and t-SNE plots. Models are trained, innovatively tuned through hyperparameter optimization, and tested for their ability to grade new data logs. The results highlight K-Neighbors, Extra Trees, and Extreme Gradient Boosting as exceptional regressors for this task. The automated grading of engine health and performance enhances objectivity and efficiency in the tuning process and potentially serves as a basis for a digital twin. The developed methodology is discussed in the context of health evaluation for any sensor-based system, with practical applications extending across various domains and industries.

Memory-like NK cell differentiation, inhibitory NKG2A blockade, and improved recognition via antibody or CAR engineering combine to enhance NK cell attack against multiple myeloma

Memory-like NK cell differentiation, inhibitory NKG2A blockade, and improved recognition via antibody or CAR engineering combine to enhance NK cell attack against multiple myeloma

EHL Traction Coefficient Is Not a Property of the Liquid Alone

Abstract The slow progress in prediction of elastohydrodynamic film thickness and friction may be attributed to the mistaken notion that a traction curve is equivalent to a rheological flow curve. A similar concept is being standardized by Society of Automotive Engineers (SAE) for the traction coefficient of aerospace oils. Four standards teach that traction coefficient is an inherent physical property of an oil and that full film traction does not depend upon the roller material and that there can be traction measurements which do not depend on the test rig. These traction coefficients are to be employed in models for thermal management analyses without the need for measuring primary properties. Through example calculations, using primary properties of a turbine oil, it is shown here that elastohydrodynamic lubrication (EHL) friction is not a property of the liquid alone, and that it depends on at least two contact characteristics as well; namely, the aspect ratio and scale. Within the tribology literature, there is much evidence of the dependence of EHL friction on other contact parameters as well that are not characteristic of the fluid.

Fuelling Electric Vehicles Growth: Factors that Matter in India’s Electric Vehicles Growth Story

The recent impetus to the electric vehicle (EV) policies at the national and sub-national levels with the medium to long-term objective of reducing greenhouse gas emissions from vehicle traffic motivates an assessment of the key drivers of EVs in India. The sale of EVs especially two-wheelers is rising due to the push by government policies, reduction in upfront costs and increased awareness about EVs. However, the sale of electric cars has not picked up significantly and is uneven across states. The study finds a strong correlation between the use of electric cars in states and the availability of public charging stations. The empirical analysis finds that the availability of public charging infrastructure and the price differentials between electric cars and comparable internal combustion engine cars are significantly associated with the demand for cars across states. A focused and liberal policy assumes importance to stimulate the demand for EVs and to harness its manifold environmental and financial gains. JEL Classifications: Q42, Q48, Q49

Tensile and corrosion properties of copper and alumina hybrid reinforcement on aluminum matrix composite

Aluminum composites are widely used in aviation, space, automotive, and marine applications due to their excellent properties, including high strength and strong resistance to corrosion. This study investigates the microstructure, mechanical properties, and corrosion behavior of aluminum matrix composites (AMCs) reinforced with alumina (Al₂O₃) and copper (Cu) particulates, aimed at enhancing the material’s structural performance for potential engineering applications. Composite samples were prepared via double stir casting, utilizing varying ratios of Al₂O₃ and Cu. Microstructural analysis revealed uniform dispersion of Al₂O₃ in the aluminum matrix, promoting grain refinement and improved load-bearing capability, while Cu particles contributed to enhanced ductility by forming Al-Cu intermetallic phases. The addition of Al₂O₃ notably improved the composites’ hardness and tensile strength, whereas Cu increased ductility but led to reduced corrosion resistance due to galvanic effects between Al and Cu phases, especially in saline environments. Corrosion testing indicated that Cu-reinforced samples displayed a comparatively higher corrosion rate. These findings underscore the promise of Al₂O₃-reinforced AMCs for applications requiring improved mechanical strength and durability, while Cu reinforcement could be tailored for applications needing enhanced ductility. This research provides insight into designing AMCs with specific property trade-offs and advancing material options for the automotive and marine engineering industries.

Optimizing Lightweight Material Selection in Automotive Engineering: A Hybrid Methodology Incorporating Ashby’s Method and VIKOR Analysis

The automotive industry is responsible for about 20% of greenhouse gas emissions in Europe, and it is under notable pressure to meet the reduction targets set by the European Union for the next decades. In this context, lightweighting is a very effective design strategy for which materials selection plays a key role. One of the main challenges of lightweighting is selecting materials with enhanced structural properties but a reduced weight in comparison with traditional solutions. The spectrum of available materials is very large, and the choice needs to be carefully evaluated based on multiple factors, such as mechanical behavior, raw materials cost, the availability of manufacturing processes, and environmental impact. This article presents an innovative methodology for materials selection in the lightweight automotive field based on the Ashby approach for mechanical performance coefficients as an initial filtering criterion. Following this preliminary screening, this study adopts the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) MCDA (Multi-Criteria Decision Analysis) technique to rank feasible design solutions based on case study boundary conditions. The evaluation criterion of different design options encompasses crucial factors, such as mechanical properties, cost considerations, and environmental impact measures. The method is finally validated by the application of a redesign case study, a motor bracket of an electric commercial car.

Impact of nozzle pressure ratio and convergent-divergent length on the exit Mach number of supersonic jet

PurposeThe purpose of the study is to find the effect of convergent and divergent section length on the exit flow characteristics. Converging-diverging (CD) nozzle design can be difficult because of the necessity for precise geometry and an understanding of compressible fluid flow dynamics. To obtain the ideal supersonic speeds, it is challenging to make sure that the flow chokes at the throat, where the Mach number approaches one and then expands appropriately in the diverging region. The design needs to take into consideration things like the relationship between the area and Mach number, the impact of various pressure ratios and the flow’s isentropic interactions.Design/methodology/approachAn ideal thrust production is achieved through the effective acceleration of exhaust gases through proper nozzle design. This paper numerically investigates impact of convergent, divergent length and nozzle pressure ratio on the exit Mach Number of CD nozzle supersonic jet. Exit Mach Number 1.6 convergent-divergent nozzle was used. In total, five cases were taken as the length of the both the convergent-divergent sections were modified with 50% of increment and decrement in its base length. At four different NPR, the analysis was carried out in over-expanded, correctly expanded and under-expanded conditions. The NPR used were 2, 3.2, 4 and 5.FindingsFrom the results, it is found that the convergent length linearly affects the exit Mach number, while the divergent length variation is not in order. Both the decreased and increased divergent length reduce the supersonic jet exit Mach number. The subsonic region is not majorly affected by the length. There is no rapid change in the flow properties whether the length is reduced or increased. Maximum of 2% to 3% variation is only noticed. On the contrary, a small change in supersonic region or divergent section makes major modification in the flow.Originality/valueTo achieve the desired Mach number, not only the area of the nozzle but also the length affects it. In terms of divergent angle and area ratio, only most of the studies on nozzle have been focused. This study aims to find the impact of convergent length and divergent length on the exit Mach number. This could be used in a wide range of applications, including laser cutting, thermal spraying, gas turbines for power generation, rocket and jet engines, supersonic wind tunnels and turbo chargers in automotive engineering, because of their capacity to accelerate fluids to supersonic speeds.

A multi-level feature fusion artificial neural network for classification of acoustic emission signals.

In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing anindependent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals. These features are concatenated to form a new feature called LMGC, which serves as input data for the four branches of FUSION-ANN. The network performs AE signal recognition and classification through forward propagation in each branch, utilizing multi-level feature fusion. We evaluate FUSION-ANN's performance on the ORION-AE benchmark dataset, which contains AE signals from various loading conditions simulating loosening phenomena in aeronautics, automotive, and civil engineering structures. Our results demonstrate an impressive average accuracy of 98% in AE signal classification. Additionally, FUSION-ANN boasts high training efficiency, robustness, and accuracy, making it suitable for reliable AE signal analysis. However, given the current limitations, we aim to conduct more comprehensive investigations in the future. Our plan includes further testing of the network's performance across various categories of AE signals to assess its generality. Additionally, we will select richer and more efficient feature sets to characterize these signals.

Examine the Heat Transfer Characteristics in Car Radiator Utilizing the Water/Anti-Freezing and Al2O3/Cuo/Tio2 Based Nanofluid as Coolant

Automobile radiators have been using traditional heat transfer fluids like water and motor oil for a long time. However, there is an increasing demand for improved heat transfer fluids in order to greatly increase the system's thermal performance. Traditional fluids often suffer from low thermal conductivities, and the flat tube's limited surface area hinders the enhancement of heat transfer. Improving heat transmission between the radiator and coolant is primarily intended to increase the cooling capability of car engines, guaranteeing peak performance and averting malfunctions. This study investigates two methods to achieve this goal. The first method focuses on modifying the radiator’s flat tube design by altering the fin configuration. One design incorporates 34 continuous louvered fins, while the other uses 46 continuous louvered fins with a U-shaped configuration. In order to improve heat transmission, the second technique adds solid particles that are nanoscale in size to the base fluid. Three nanoparticles—Al₂O₃, CuO, and TiO₂—are used at concentrations of 0.05%, 0.15%, and 0.3%. By combining these modified designs with various nanoparticle concentrations, a total of 10 cases were analyzed. Throughout the investigation, the flat tube's coolant's intake velocity remained constant. ANSYS Fluent 23.2 was used to assess the heat transfer properties, taking into account variables including velocity distribution, temperature distribution, pressure drop, and heat transfer rate. The coolant containing 0.3% TiO₂ nanoparticles had the greatest heat transfer capability, surpassing all other examples examined. Its outlet temperature was 360.53 K, and its heat transfer rate was 76.73 W.Keyword: Diabetes detection, machine learning, Support Vector Machine, Gradient Boosting, voting ensemble, early diagnosis.

PHASE CHANGE MATERIALS (PCMs) FOR BUILDINGS AND AUTOMOTIVE APPLICATIONS: A REVIEW STUDY

Phase change materials (PCMs) play a pivotal role in various sectors, particularly in automotive engineering, electric vehicles, and building construction. In the automotive sector, phase change materials are crucial for thermal management systems, aiding in temperature regulation of components such as batteries and engines. In electric vehicles, phase change materials are instrumental in enhancing battery performance and lifespan by effectively managing thermal loads during charging and discharging cycles, thus ensuring optimal operating conditions. These materials offer significant energy efficiency benefits by absorbing and releasing large amounts of latent heat during phase transitions, which helps in maintaining stable temperatures and reducing the load on heating and cooling systems. Additionally, PCMs contribute to sustainable building practices by enhancing thermal regulation, thereby lowering energy consumption and associated costs. This study explores the diverse applications and properties of phase change materials for improving thermal management and energy efficiency in vehicles, residences, and buildings. This research provides a comprehensive review of innovative solutions, including PCM-based heat pumps, PCM-integrated cementitious composites, and hybrid active-passive battery thermal management systems.

Flow of SWCNT and MWCNT based hybrid nanofluids in a semi-circular enclosure with corrugated wall

Heat transfer mechanisms participate to fulfill the necessities of modern technologies. The phenomenon of heat transport has implementations in multiple fields including metal working, heating systems, thermal management in spacecraft, solar energy, and automobile engines. In the current novel work, heat transfer mechanism in a semi-circular enclosure with corrugated circular wall is studied numerically. A hybrid nanofluid consisting of water as the base fluid and solid particles of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) is considered. Prevailing mathematical equations are explained by finite element method with the help of COMSOL Multiphysics. The study examines various volume fractions of solid particles over a broad range. The SWCNT volume fraction is adjusted from 0.02% to 0.1%, while the MWCNT volume fraction ranged from 0.01% to 0.04%. Velocity, temperature, and pressure contours are imagined. Local and average Nusselt numbers are studied for each of the cases. The results indicate that heat transfer in the semi-circular enclosure improves with higher volume fractions of solid particles. As the volume fraction of solid particles increases, the average Nusselt number over the heated surface decreases.

Beware of cats in car engines during cold weather!

Beware of cats in car engines during cold weather!

Investigating the mediating role of environmental efficiency in the impact of data privacy practices on enhancing reverse logistics: Evidence from the automotive engineering sector

Investigating the mediating role of environmental efficiency in the impact of data privacy practices on enhancing reverse logistics: Evidence from the automotive engineering sector

Research on multi-stage topology optimization method based on latent diffusion model

Topology optimization aims to identify the optimal material distribution to enhance structural performance. Traditional methods such as Solid Isotropic Material with Penalization (SIMP) involve extensive finite element iterative calculations, limiting their ability to address complex or large-scale problems. This paper introduces a novel topology optimization method based on latent diffusion models (TOLDM). It is a multi-stage topology optimization approach that incorporates latent diffusion models with the SIMP method. TOLDM reduces the dimensionality and memory usage of data processing by shifting the diffusion process from the image space to a lower-dimensional latent space, decreasing the reliance on high-performance computing resources and accelerating the training and inference processes. Moreover, by incorporating a cross-attention module, the proposed method introduces physical conditions of various modalities into the diffusion process, enhancing the feasibility and manufacturability of the generated topological structures. This integrated optimization strategy not only improves the diversity and efficiency of structural designs but also achieves an optimized balance between resource consumption and performance. We have also compared TOLDM with other methods based on deep generative models, demonstrating its superiority. Furthermore, by incorporating transfer learning, TOLDM was successfully applied to the structural optimization of automobile engine hoods, enabling the generation of efficient and manufacturable topological structures. This indicates that TOLDM has high applicability in resource-constrained engineering fields, effectively advancing the practical implementation of topology optimization technology and expanding its technical boundaries.

OPTIMIZATION OF USED ENGINE OIL AS ADMIXTURE IN CONCRETE USING RESPONSE SURFACE METHODOLOGY

Maintenance of industrial machinery and automobile engines produces a large amount of Used Engine Oil (UEO) as waste. This waste oil is harmful to the environment, pollutes the land and kills the aquatic habitats. Used engine oil poured into household drains or directly onto the field will pollute fresh water by entering rivers and groundwater. Efforts are shifted to conversion of Used Engine Oil into useful material for concrete production as a replacement for SP430 superplasticizer. This research deals with optimizing Used Engine Oil in M20 grade concrete using response surface methodology. The oxide composition of UEO revealed 37% of SO3, 15.5% of CaO, 17.7% OF ZnO, 9% of P2O5. The research revealed that the workability of the concrete mixes were between medium and high. The control mix slump value was 50mm but when UEO and superplasticizer were used, the slump obtained were 54mm and 52mm respectively. Concrete samples were produced and strength properties were tested at 7, 14 and 28 days of curing by water immersion. The test result showed that water absorption decreased with increase in UEO. Compressive strength increased with increase in dosage and curing days. Also, there was gradual increase in flexural and splitting tensile tests with the increase in UEO. Model equations and optimal values of UEO concrete were generated by numerical optimization. The optimal value was 11.4ml of UEO at 28 days which gave compressive strength result of 26.7 N/mm2 , flexural strength 8.3N/mm2 , tensile strength 18.9 N/mm2 and water absorption of 0.7%. Therefore, 11.4ml of UEO is recommended for use as superplasticizer in M20 grade concrete.

Digital Twin Development for the Automotive Industry

This study explores Digital Twin Development for the Automotive Industry which aims to bridge the divide between automotive and digital engineering. Results, which were analyzed using demographic and regression analyses, indicate that there are significant correlations between control accuracy and protection level, but not with data integrity and privacy, and real-time monitoring effectiveness.

Автомобиль радиаторларын тазалау әдістерін талдау

An analysis of car engine cooling systems and methods for cleaning car radiators is provided, an ultrasonic method for cleaning radiators by cavitation in tubes filled with water is proposed, and radiator designs are described. A physical picture of the process based on the cavitation of water in radiator tubes is given. An experimental setup for ultrasonic cleaning of radiators is described. The differences between hydrodynamic and acoustic cavitation are given. The results of the experiment confirming the effectiveness of the proposed method are given.

Model-based compensation and uncertainty analysis of support bearing losses in a chain drive dynamometer

Abstract Efficiency testing of chain drives has received renewed attention in recent years from sectors including elite cycling, e-mobility and automotive engineering. This is because of a desire to maximise performance, reduce energy consumption and minimise wear rate at a component level. In particular, chain and lubricant manufacturers have developed test rigs that replicate transmission drives in order to measure the efficiency and wear behaviour of the chain and sprocket. This paper explores the impact that compensating parasitic losses from test rigs has on the measurement uncertainty. The analytical process of propagating measurement uncertainty for a dynamometer rig is presented, based on the use of modelled bearing friction losses in the rig and quoted datasheet values for the measurement transducers. Two commonly used bearing models are used to illustrate the process. It is found that the measurement uncertainty is dominated by the transducer uncertainties, particularly speed measurement and repeated experimental results corroborate this trend. However, experimental experience also suggests that the uncertainty derived using GUM Method B is conservative compared to the actual system. The research reported also highlights the importance of validating models of parasitic losses for measurement hardware.

Green friction: Exploring the evolution and potential of natural fibers and other brake pad ingredients in sustainable automotive engineering—A review

AbstractWith the growing environmental concerns and strict regulations, the shift from traditional brake pad materials such as copper, asbestos to eco‐friendly materials has become necessary. This review paper presents an in‐depth analysis of natural fibers (fibers obtained from plants) brake pads, an emerging sustainable alternative in automotive technology. Natural fibers such as jute, kenaf, flax etc. offers a good solution due to their renewable nature, low cost and reduced environmental impact. This paper explores the properties of natural fibers that make them suitable for use in brake pads including their thermal stability, wear resistance and ability to withstand high‐friction environments. It highlights the advantages and challenges of natural fiber composites particularly in terms of performance, durability, and noise reduction. The environmental benefits including biodegradability and reduced carbon footprint are discussed underscoring the role of natural fiber brake pads in promoting sustainable automotive practices. This paper aims to provide a comprehensive overview of the current and future potential of natural fiber brake pads offering valuable insights for researchers and industries in the field of sustainable automotive engineering.Highlights Shift to natural fibers from asbestos and copper for eco‐friendly alternatives. Natural fibers offer biodegradability, low cost, and good mechanical properties. Fiber variability and moisture absorption pose challenges, needing treatments. Natural fibers reduce environmental impact, supporting sustainable practices. More research is needed on fiber treatment and standardized testing protocols.