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Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.

The continuous improvement of spark-ignition direct-injection (SIDI) engines is largely attributed to the enhanced understanding of air-fuel mixing and combustion processes. The intricate interaction between transient spray behavior and the ambient flow field is important to unveil the airflow dynamics during the spray injection process. This study investigates the fuel-spray boundary interactions under different superheated conditions by analyzing the ambient flow field pattern with constraint-based modeling (CBM). In the experimental setup, superheated conditions are facilitated by adjusting different fuel temperatures and ambient pressures. By adding the tracer particles containing Rhodamine 6G to the ambient air, the combined diagnostic of fluorescent particle image velocimetry (FPIV) and Mie-scattering is implemented to measure the velocity distribution and flow trajectory of the air surrounding the spray formation and propagation.

The environmental and sustainable energy concerns in transport are being addressed through the decarbonisation path and the potential of hydrogen as a zero-carbon alternative fuel. Using hydrogen to replace fossil fuels in various internal combustion engines shows promise in enhancing efficiency and achieving carbon-neutral outcomes. This study presents an experimental investigation of hydrogen (H2) combustion and engine performance in a boosted spark ignition (SI) engine. The H2 engine incorporates both port fuel injection (PFI) and direct injection (DI) hydrogen fuel systems, capable of injecting hydrogen at pressures of up to 4000 kPa in the DI system and 1000 kPa in the PFI operations. This setup enables a direct comparison of the performance and emissions of the PFI and DI operations. The study involves varying the relative air-to-hydrogen ratio (λ) at different speeds to explore combustion and engine limits for categorising and optimising operational regions.

The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles.

The process of assembling the bearing and crimp ring to the steering pinion shaft is intricate. The bearing is pressed into its position via the crimp ring, which is tipped inward and fully fitted into a groove on the pinion shaft. Only when the bearing is pressed to a low surface on the pinion shaft, the caulking force for the crimp ring is achieved. The final caulking distance for the crimp ring confirms the proper bearing position. Simulating this transient fitting process using CAE is a challenging topic. Key factors include controlling applied force, defining contact between bearing and pinion surface, and defining contact between crimp ring and bearing surface from full close to half open transition. The overall CAE process is validated through correlation with testing.

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods.

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

The rise of greenhouse gas emissions has reached historic levels, with 37 billion tons of CO2 released into the atmosphere in 2018 alone. In the European Union, 32% of these emissions come from transportation, with 73.3% of that percentage coming from vehicles. To address this problem, solutions such as cleaner fuels and more efficient engines are necessary. Artificial Intelligence can also play a crucial role in climate analysis and verification to move towards a more sustainable future. By utilizing connected vehicle data, automakers can analyze real-time vehicle performance data to identify opportunities for improvement and reduce carbon emissions. This approach benefits the environment, improves vehicle quality, and reduces engineering work time, making it a win-win solution. Connected vehicle data offers a wealth of information on vehicle performance, such as fuel consumption and carbon emissions.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.

In the present work, five surrogate components (n-Hexadecane, n-Tetradecane, Heptamethylnonane, Decalin, 1-Methylnaphthalene) are proposed to represent liquid phase of diesel fuel, and another different five surrogate components (n-Decane, n-Heptane, iso-Octane, MCH (methylcyclohexane), Toluene) are proposed to represent vapor phase of diesel fuel. For the vapor phase, a 5-component surrogate chemical kinetic mechanism has been developed and validated. In the mechanism, a recently updated H2/O2/CO/C1 detailed sub-mechanism is adopted for accurately predicting the laminar flame speeds over a wide range of operating conditions, also a recently updated C2-C3 detailed sub-mechanism is used due to its potential benefit on accurate flame propagation simulation. For each of the five diesel vapor surrogate components, a skeletal sub-mechanism, which determines the simulation of ignition delay times, is constructed for species C4-Cn.

Disc brake squeal is always a challenging multidisciplinary problem in vehicle noise, vibration, and harshness (NVH) that has been extensively researched. Theoretical analysis has been done to understand the mechanism of disc brake squeal due to small disturbances. Most studies have used linear modal approaches for the harmonic vibration of large models. However, time-domain approaches have been limited, as they are restricted to specific friction models and vibration patterns and are computationally expensive. This research aims to use a time-domain approach to improve the modeling of brake squeal, as it is a dynamic instability issue with a time-dependent friction force. The time-domain approach has been successfully demonstrated through examples and data.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

A sophisticated finite element analysis (FEA) method for modeling interior permanent magnet (IPM) electric motors is presented. Based on this method, a coupled structural-acoustic analysis procedure was developed to simulate the motor dyno vibroacoustic responses with improved accuracy and reliability for NVH (noise, vibration, and harshness) behavior prediction over a wide range of torques and frequencies under the operational electromagnetic forces. The proposed motor modeling and analysis method is detail-oriented with high fidelity in modeling the structure and complex material representation. To effectively deal with the motor stator core constructed with large numbers of electromagnetic laminae, the unit-cell approach was employed to derive the core material properties by homogenizing the laminated core as an equivalent orthotropic material. Meanwhile, the windings were modeled by capturing the precise geometry for accuracy improvement.

The vehicle instrument panel (IP) system has several interactions with the surrounding components such as the Dash, Cowl, Cross Car Beam (CCB), Floor, Body Side etc. With such interactions comes different loadings, usage scenarios, interfaces and design challenges to overcome. For the specific case of the IP to Cowl & Dash interfaces, the position and performance in different load cases, such as, but not limited to, vibration and heat expansion loading as well as the assembly process. A design solution is required to enhance the performance in all these scenarios while maintaining the cost, weight & complexity as low as possible. This paper describes the development process of an optimized solution with a multi-disciplinary approach using advanced computer aided engineering (CAE) optimization tools, which involved performance in multiple virtual evaluations and mass.

Climate change mitigation is the main challenge for the automotive industry, as the government issues legislation to combat CO2 emissions. In addition to electrification and battery electric vehicles, using low-carbon and zero-carbon fuels in Internal Combustion (IC) engines can also be an effective way to reach net zero-carbon transport. This study investigated and compared the combustion characteristics, performance and emissions of a highly boosted spark ignition (SI) engine fuelled with EU VI 95 RON E10 gasoline and blends of second-generation bio-gasoline with different ethanol contents of 5% (E5), 10% (E10), and 20% (E20). The single-cylinder SI engine was equipped with a centrally mounted high-pressure injector and supplied externally boosted air. Engine experiments were conducted at 2000 RPM and 3000 RPM with low and high load operations.

When manufacturing the stators in EV motors, stator wires are first coated with a layer of resin to provide primary insulation. After winding, impregnating varnish fills all voids within the windings and between the windings and lamination. In addition to electrically insulating the copper wires, another function of the varnish fill is to mechanically secure the copper wires from movement. The process is not complicated in terms of physics. In essence, the mechanics of the varnish flow is the balance of inertia force, viscous force, gravity and surface tension. However, understanding the fluid dynamics of the varnish flow is critical to predicting the quality of the varnish fill, which has a tremendous impact on motor performance. With the advancement of computational fluid dynamics (CFD), the industry can benefit greatly if the varnish trickling process can be tuned, without physical tryouts, to achieve optimal fill.

Currently the automotive industry has been under extremely important technological changes. Part of these changes are related to the way that users interact with the vehicle and fundamental components are the new digital cluster and screens. These devices have created a disruption in the way information is transmitted to the user, being essential for vehicle operation, including safety. Due to new operating conditions, multiple evaluations need to be performed, one of them is the solar temperature Load to ensure correct operation without compromising user safety. This test is required to identify the thermal performance on the screens mounted on the instrument panel. The performance identification is performed on both sides, analytical and physical. In regards finite element simulation it represents the solar chamber as the main source of heat and being the main mechanism of transmission the radiation.

In recent years there has been an increase in the development of vehicles that use alternative energy sources, more specifically electric vehicles, intending to establish the transition from combustion engines, bringing to the automotive chain a reduction in the consumption of fossil fuels. Electrified vehicles help to improve air quality by drastically reducing the emission of harmful gases and contributing to a considerable improvement in sound quality, due to the use of their silent electric motors. A material allied to these alternative technologies is graphene, few layers (usually up to 6) of Carbon atoms arranged in a hexagonal and crystalline form in a two-dimensional plane lattice. Its unique chemical structure allows it to share its exceptional properties with other materials, making it a strong candidate to meet the needs and improve products of the automotive sector.