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The combustion of hydrogen (H2) as a fuel is attractive due to its clean combustion or combustion-enhancing properties when used as a supplement to other fuels. However, the challenge of using H2 as a fuel for transportation applications is the difficulty of onboard storage. Cracking onboard stored ammonia (NH3) into H2 can also improve combustion performance and emissions in mobile applications fuelled with zero and carbon-neutral fuels. However, the reforming process is not always 100 % efficient which can lead to the presence of NH3 in the combustion process. The presence of NH3 can influence engine performance, combustion and emissions. Therefore, this experimental study reports the effect of H2 and H2/NH3/N2 fuel blends added to gasoline in a dual-fuel operation under both stoichiometric (λ=1.0) and lean-burn (λ>1.0) operating conditions in a spark ignition (SI) engine.

In today’s competitive automotive market, customers are now looking for system efficiency as one of the important design parameters of system performance along with durability and reliability. It is essential to ensure products are designed to utilize maximum input power and have better system efficiency. In automotives, transmission and axle systems are power transmitting elements from prime mover to wheels and are one of the main contributors to overall vehicle efficiency. Hence, predicting and assessing overall system efficiency of these aggregates is of paramount importance. System efficiency is driven by component power losses for various speeds and torques, which are arising out of component design parameters, complex interaction within system, operating conditions, lubrication, temperatures etc. To capture multi-physics of speed and torque dependent losses of automotive axle, multidisciplinary and integrated approach is proposed in this paper.

Combustion in conventional and advanced diesel engines is an intricate process that encompasses interaction among fuel injection, fuel-air mixing, combustion, heat transfer, and engine geometry. Manipulation of fuel injection strategies has been recognized as a promising approach for optimizing diesel engine combustion. Although numerous studies have investigated this topic, the underlying physics behind flame interactions from multiple fuel injections, spray-flame-wall interaction and their effects on reaction zones, and NOx/soot emissions are still not well understood. To this end, a computational fluid dynamics (CFD) study is performed to investigate the effects of pilot and post injections on in-cylinder combustion process and emissions (NOx and soot) formation in a heavy-duty (HD) diesel engine.

Hydrogen as a chemical energy carrier is considered as one of the most promising options to achieve effective decarbonization of the transportation sector, due to its carbon-free chemical composition. This is particularly true for applications that rely on internal combustion engines (ICEs), although much research is still needed to achieve stable, reliable, and safe operations of the engine. To this purpose, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential to avoid backfire and largely reduce preignition issues, as opposed to port-fuel injection. Recently, much research has been dedicated, both experimentally and numerically, to understanding the physics and chemistry connected with hydrogen’s mixing and combustion processes in ICEs. This work presents a computational fluid dynamics (CFD) study of the hydrogen DI process in an optical engine operating at relatively low tumble conditions.

To realize a super-leanburn SI engine with a very-high compression ratio, it is required to design a new fuel which could have low ignitability at a low temperature for antiknocking, but high ignitability at a high temperature for stable combustion. Ethane shows a long ignition delay time at a low temperature close to that of methane, but a short ignition delay time at a high temperature close to that of gasoline. In the present study, the antiknocking effect of adding methane with the RON of 120, ethane with the RON of 108, or propane with the RON of 112 to a regular gasoline surrogate fuel with the RON of 90.8 has been investigated. Adding each gaseous fuel by less than 0.4 in heat fraction advances knocking limit in the descending order of SI timing advance of ethane, methane, and propane, and in the descending order of CA 50 advance of ethane, propane, and methane. Adding methane extends combustion duration slightly, but adding ethane or propane shortens it considerably.

Inadequately designed flow field layouts in bipolar plates within Proton Exchange Membrane fuel cells (PEMFCs) may lead to ineffective water removal and impede reactant transport. This work examines the typical flow channel designs found in bipolar plates of fuel cells and implements modifications to certain designs to alleviate pressure drops within the flow channels using computational fluid dynamics (CFD) analysis. These designs are optimized by changing different parameters such as size of the channel and rib width utilizing Taguchi L27 standard orthogonal array. The resultant reduction in pressure drop is anticipated to enhance the overall performance of the fuel cell. The optimal flow field design of bipolar plates (Graphite and Aluminum) are manufactured using CNC milling. Tests evaluating flexural strength, surface roughness, hardness, contact angle, and corrosion resistance are conducted to assess and compare the performance of these plates.

To ensure proper airflow distribution inside the cabin, the AC duct vanes' ability to direct airflow must be evaluated. Objective of this work is to propose a methodology for developing the vane design of AC system duct. CFD based factorial analysis was conducted using three components at three levels. The impact of number of horizontal vanes, number of vertical vanes and distance between them on the pressure drop and face level velocity are investigated. It was observed that when the number of vertical vanes are increased, the vane's ability to direct airflow rises. In this situation, the pressure drop increases as well. When the number of horizontal vanes exceeds a specific threshold, the vane's capacity to steer airflow declines. In literature, it can be noted that a greater number of research are available that focus on the relationship between human thermal comfort and vent position.

This paper investigates the condensation within a two wheeler instrument cluster in different weather conditions. Instrument cluster have high heating components within its assembly particularly over Printed Circuit Board (PCB) which leads to formation of condensation. Air breathers are important component that can be utilized to reduce the condensation in the cluster. Location and orientation of air breather and air vents plays the vital role in the air flow through the instrument cluster. In this study, number of breather and their location and orientation is optimized to reduce the condensation or film thickness on the crystal (transparent body) of cluster. Transient Computational Fluid Dynamics (CFD) based Eulerian Wall Film approach is utilized to investigate the physics administering the condensation phenomenon in the instrument cluster. Experimental tests are conducted to investigate condensation phenomenon actually occurring in the model.

Most of the heavy commercial vehicles are installed with Pneumatic brake system where the medium is a pressurized pneumatic air generated with the reciprocating air compressor. Heating is an undesirable effect of the compression process during loading cycles as reciprocating air compressors are concerned. There fore it is necessary to reduce the delivery air temperature of compressor for safer operation of down stream products. The present investigation deals with the measurement of the delivery air tempearture of a typical 318 cc water cooled compressor. A through steady state conjugate heat transfer analysis is conducted for different speed and different cooling water flow rate to compare the delivery air temperature. Pressure drop across the cooling water flow path has been measured and optimum flow rate is arrived to meet the design requirement.

Electrification is driving the use of batteries for a range of automotive applications, including propulsion systems. Effective management of thermal energy in lithium-ion battery pack is essential for both performance and safety. In automotive applications especially, understanding and managing thermal energy becomes a critical factor. Cells in the propulsion battery pack dissipate heat at high discharge rates. Cooling performance of battery can be realized by optimizing the various parameters. Computational Fluid Dynamics (CFD) model build and simulations are resource intensive and demands high performance computing. Traditionally, evaluating thermal performance involves time-consuming CFD simulations. To address this challenge, the proposed novel approach using Generalized Neural Network Regression (GNNR) eliminates complex CFD model building and significantly reduce simulation time. GNNR achieves up to 85% accuracy in predicting heat transfer coefficient.

In the context of Battery Electric Vehicles (BEVs), airborne noise from Heating, Ventilation and Air Conditioning (HVAC) ducts becomes a prominent concern in the view of passenger comfort. The automotive industry traditionally leverages Computational Fluid Dynamic (CFD) simulation to refine HVAC duct design and physical testing to validate acoustic performance. Optimization of the duct geometry using CFD simulation is a time-consuming process as various design configurations of the duct have to be studied for best acoustic performance. To address this issue effectively, the proposed a novel methodology uses Gaussian Process Regression (GPR) to minimize duct noise. Present solution demonstrates the power of machine learning (ML) algorithms in selecting the optimal duct configuration to minimize noise. Utilizing both real test data and CFD results, GPR achieves remarkable accuracy in design validation, especially for HVAC air ducts.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged as transformative forces across various domains, revolutionizing industries, and reshaping societal paradigms. In this work, we show how a CNN model was used in the field of CFD to predict drag coefficient of a full vehicle profile. A brief description is also provided of the data set used for training and fitting the prediction model. This was done using the integrated AI/ ML technology in the DEP MeshWorks tool focusing on quick design iterations and results generation. The design advisor function within MeshWorks is an intelligent system that comprehends the real-time prediction of the responses such as model stiffness, frequency and others related to durability, NVH or CFD domain, on a model building phase without needing to run the post-processing for every design iteration.

Battery powered vehicles have gained attention globally due to conventional fuels becoming expensive and increasing pollution levels in the environment. Automobile manufacturers are striving to develop efficient Li-ion battery management system using enhanced and fast analysis procedures. Temperature plays an important role in the performance of the Li-ion battery which includes charge output, distance covered, mechanical life of the battery, chemical compositions and reactions inside the cells etc. Initially, batteries were cooled by flow of air over the cell surfaces but as the power output demand from EV’s has increased, air based cooling was not sufficient. Then, to control the temperatures of such battery packs, liquid based cell cooling design has been introduced in EV’s which are more complex in design.

Permanent magnet synchronous (PMS) motors are commonly used in electric vehicles because of their high power density, stable output torque and low noise. During the operation of an electric motor, some of the electrical energy is converted into heat. The rise in motor temperature hampers motor performance (power output, demagnetization, breakdown of winding insulation, efficiency and component lifespan). The losses occurring in electric motors during operation mainly include: winding copper loss, stator and rotor iron loss, permanent magnet eddy current loss and mechanical losses. The life and operating reliability of a motor depends on the thermal performance of the motor. This paper describes a detailed procedure for an indirect coupled analysis between Ansys Maxwell and Ansys Fluent, in order to predict critical thermal characteristics of the motor and cooling jacket.

With the growing popularity of electric vehicles (EVs) due to environmental degradation and rising carbon emissions there is also a need of arising for a battery thermal management system (BTMS) particularly for Lithium-ion batteries exhibiting unique characteristics such as long life, high specific energy, significant storage capacity, and remarkable energy density. The continual difficulty temperature non-uniformity over the cell surface, module surface, and inside the whole battery pack, on the other hand, remains a major barrier in battery technology, significantly contributing to the tendency towards Thermal Runaway (TR) and the accompanying severe hazards including battery fires and explosions. Addressing TR effectively is crucial for promising the safety and reliability of battery systems, and encouraging ongoing research and developments in this critical field.

This research study investigates the influence of undercover design on three critical aspects of vehicle performance: water entering into air intake filter, Aerodynamic performance, thermal performance on vehicle engine room components (Condenser, Radiator and Air Intake System). Undercover serves the purpose of protecting Engine, underhood components and also improves aerodynamics of the vehicle. Through CFD simulations, various undercover design configurations: Full Undercover, no undercover and half undercover cases are evaluated to assess their effectiveness in mitigating the water ingress into the air intake system. Additionally, we explore the implications of these design alterations on the thermal performance and aerodynamic drag.

In today's fast-paced lifestyle, people spend a significant amount of time for traveling which leads to a heightened demand for thermal comfort. Automotive Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in providing conditioned air to ensure comfort during travel. To evaluate HVAC performance, the parameters like heat exchanger efficiency, air thermal mixing zones, and temperature distribution are essential to maintain the comfort, fuel economy & styling. However, accurately predicting cooling/heating performance using Computational Fluid Dynamics (CFD) simulations poses challenges due to the complex nature of heat exchanger modeling, which demands substantial computational resources and time. This paper presents the development of CFD modeling capabilities for predicting HVAC temperature distribution at duct outlet grills for different operating modes. Additionally, it assesses heater performance under maximum hot conditions.