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In applications demanding high performance under extreme conditions of pressure and temperature, a range of Mechanically Attached Fittings (MAFs) is offered by various Multinational Corporations (MNCs). These engineered fittings have been innovatively designed to meet the rigorous requirements of the aerospace industry, offering a cost-effective and lightweight alternative to traditional methods such as brazing, welding, or other mechanically attached tube joints. One prominent method employed for attaching these fittings to tubing is through Internal Swaging, a mechanical technique. This process involves the outward formation of rigid tubing into grooves within the fitting. One of the methods with which this intricate operation is achieved is by using a drawbolt - expander assembly within an elastomeric swaging machine.

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.

With the increasing demand for efficient & clean transport solutions, applications such as road transport vehicles, aerospace and marine are seeing a rise in electrification at a significant rate. Irrespective of industries, the main source of power that enables electrification in mobility applications like electric vehicles (EV), electric ships and electrical vertical take-off & landing (e-VTOL) is primarily a battery making it fundamentally a DC system. Fast charging solutions for EVs & e-VTOLs are also found to be DC in nature because of several advantages like ease of integration, higher efficiency, etc. Likewise, the key drivers of the electric grid are resulting in an energy transition towards renewable sources, that are also essentially DC in nature. Overall, these different business trends with their drivers appear to be converging towards DC power systems, making it pertinent.

The automotive industry has seen accelerating demand for electrified transportation. While the complexity of conventional ICE vehicles has increased, the powertrain still largely consists of a mechanical system. In contrast, vehicle architectures in electrified transportation are a complex integration of power electronics, batteries, control units, and software. This shift in system architecture impacts the entire organization during new product development, with increased focus on high power electronic components, energy management strategies, and complex algorithm development. Additionally, product development impact extends beyond the vehicle and impacts charging networks, electrical infrastructure, and communication protocols. The complex interaction between systems has a significant impact on vehicle safety, development timeline, scope, and cost.

Wet-sump transmissions are widely used in heavy duty and medium duty vehicles. As these transmissions do not have a dedicated forced lubrication system, it is important that the gear train, shafts, and enclosure are designed appropriately so that enough oil splashes to critical locations to ensure sufficient lubrication. The lubrication effectiveness of such transmissions can be studied through detailed tests or numerical simulations. Often, the vehicle, and therefore the transmission, encounters some severe operating conditions, such as climbing on an incline, driving downhill, etc. Studying these conditions through tests is an expensive process and this imposes the need for an analysis first approach. In this paper, the 3D multiphase Volume of Fluid (VOF) method is used to examine two such extreme cases: an 8-degree tilted installation of transmission in a vehicle, and an inclined condition of transmission during a 10-degree uphill climb.

Roots blower is a rotary positive displacement pump which operates by pumping a fluid with a pair of meshing lobes. Recent trends in automotive industry demands high power density solutions for various applications. In comparison with legacy applications, compressors for high power density applications demand continuous operation with harsher duty cycle as well as demand higher pressure ratios. Because of longer duty cycles, it will be subjected to high heat loads which will cause a rise in temperatures of timing gears, bearings, and other components within the assembly. Accurate prediction of thermal performance is critical to design a durable and efficient roots blower for high power density applications. Thermal analysis of an assembly of roots blower involves modelling of multi-physics phenomena. This paper details a coupled CFD analysis approach to predict temperatures of roots blower components and timing gear case oil. Timing gears are lubricated using wet sump lubrication.

The use of electrical contacts in aerospace applications is crucial, particularly in connectors that transmit signal and power. Crimping is a widely preferred method for joining electrical contacts, as it provides a durable connection and can be easily formed. This process involves applying mechanical load to the contact, inducing permanent deformation in the barrel and wire to create a reliable joint with sufficient wire retention force. This study utilizes commercially available Abaqus software to simulate the crimping process using an explicit solver. The methodology developed for this study correlates FEA and testing for critical quality parameters such as structural integrity, mechanical strength, and joint filling percentage. A four-indenter crimping tool CAD model is utilized to form the permanent joint at the barrel-wire contact interfaces, with displacement boundary conditions applied to the jaws of the tool in accordance with MIL-C-22520/1C standard.

With the advent of wide band gap semiconductor devices like SiC based MOSFETs/Diodes, there is a growing demand for utilizing electrical power instead of the conventional fuel-based power generation in both automotive and aerospace industry. In automotive/aerospace industry the focus on electrification has resulted in a need for sub-systems like inverters, power distribution units, motor controllers, DC-DC converters that actively utilize SiC based power electronics devices. To address the growing power density requirements for electronics in next generation product families, more efficient & reliable thermal management solution plays a critical role. The effective thermal management of the power electronics is also critical aspect to ensure overall system reliability. The conventional thermal management system (TMS) optimization targets heat sink/ cold plate design parameters like fin spacing, thickness, height etc. or sizing of the required cooling pump/fan.

The transmission is an integral part of the driveline in an automotive vehicle. Global vehicle pass-by noise regulations are becoming more stringent and transmissions are expected to be very quiet. Typically for an automotive system, engine is the most dominant noise source and transmissions have been considered a secondary noise source but as the trend is shifting towards more electric vehicles where engine noise is absent and overall vehicle is becoming quieter, the transmission can be more of a significant noise contributor. Gear whine is the major concern for sound radiation from the transmission. The gear whine simulation and acoustic radiation analysis of the transmission using traditional methods (FEM and BEM) is a crucial but very time-consuming part of the product development cycle. On top of that, electric vehicle transmissions operate at higher RPM which in turn increases the excitation frequency arising from the gear whine phenomenon.

Layered materials are one of the most commonly used acoustical treatments in the automotive industry, and have gained increased attention, especially owing to the popularity of electric vehicles. Here, a method to model and couple layered systems with various layer types (i.e., poro-elastic layers, solid-elastic layers, stiff panels, and fluid layers) is derived that makes it possible to stably predict their acoustical properties. In contrast with most existing methods, in which an equation system is constructed for the whole structure, the present method involves only the topmost layer and its boundary conditions at two interfaces at a time, which are further simplified into an equivalent interface. As a result, for a multi-layered system, the proposed method splits a complicated system into several smaller systems and so becomes computationally less expensive.

As the vehicle and wind speeds and directions change, the unsteady flow creates non-stationary wind noise. To investigate people’s perceptions of non-stationary wind noise, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary recordings taken at several speeds and directions to create a set of sound pressure level predictions in each one-third octave band that are a function of wind speed and direction. These functions are used to create time-varying filters based on provided wind profiles. A reference wind noise measurement is then filtered to produce the sounds. A drawback of this method is that many stationary wind condition measurements are needed to form accurate sound pressure level functions, which can be time consuming. A method requiring fewer measurements was investigated.

In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization.

Commercial vehicles require fast aftertreatment heat-up to move the SCR catalyst into the most efficient temperature range to meet upcoming NOX regulations while minimizing CO2. The focus of this paper is to identify the technology levers when used independently and also together for the purpose of NOX and CO2 reduction toward achieving 2027 emissions levels while remaining CO2 neutral or better. A series of independent levers including cylinder deactivation, LO-SCR, electric aftertreatment heating and fuel burner technologies were explored. All fell short for meeting the 2027 CARB transient emission targets when used independently. However, the combinations of two of these levers were shown to approach the goal of transient emissions with one configuration meeting the requirement. Finally, the combination of three independent levers were shown to achieve 40% margin for meeting 2027 transient NOx emissions while remaining CO2 neutral.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.

A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations.

The current simulation models of EV and ICE Vehicles are well known in industry for their use in estimating the fuel economy or Range benefits because of controller calibrations and component sizing. However, there is a gap in understanding the behavior of accessories such as HVAC, power steering and other such auxiliary loads and the energy losses associated with them. Impact of thermal behavior of electronics on vehicle range also needs to be studied in detail. These kinds of studies help OEM and tier 1 manufactures in improving their design concepts significantly with minimum cost and development time. Hence, the focus of this study is on building simulation models of thermal, electrical, traction and control circuits of a typical electric vehicle. These models are then integrated, and analysis is performed to understand vehicle system level performance metrics.

In computational fluid dynamic (CFD) and computational aeroacoustics (CAA) simulations, the wall surface is normally treated as a purely reflective wall. However, some surface treatments are usually applied in experiments. Thus, the acoustic simulations cannot be validated by experimental results. One of the major challenges is how to define acoustically boundary conditions in a well-posed way. In aeroacoustics analysis, impedance is a quantity to characterize reflectivity and absorption of an acoustically treated surface, which may be introduced into the numerical models as a frequency-domain boundary condition. However, CFD and CAA simulations are time-domain computations, meaning the frequency-domain impedance boundary condition cannot be adopted directly. Several methods, including the three-parameter model, the z-transform method and the reflection coefficient model, were developed.

High surface area particles have drawn attention in the context of noise control due to their good sound absorption performance at low frequencies, which is an advantage compared with more traditional materials. That observation suggests that there is a good potential to use these particles in various scenarios, especially where low frequency noise is the main concern. To facilitate their application, composite materials are formed by dispersing particles within a fiber matrix, thus giving more flexibility in positioning those particles. In this work, a hybrid model that combines a model for limp porous materials and a model of high surface area particles is proposed to describe the acoustic performance of such composites. Two-microphone standing wave tube test results for several types of composites with different thickness, basis weight, and particle concentration are provided.

Nested planetary gear trains, which consist of two integrated co-axial single-stage planetary gearsets, have recently been widely implemented in automobile transmissions and various other applications. In the current study, a non-destructive vibrational and acoustical monitoring technique is developed to detect a common type of gear grinding defect for a complex nested gear train structure. A nested gear train which has an unground pinion with unpolished teeth profile is used to exemplify the developed methodology. An experimental test stand with an open and vertical mounting configuration has been designed to acquire both vibrational and acoustical data. The measured data are investigated using several signal processing techniques to identify unground pinions in the gear system. A general frequency spectrum analysis is performed initially, which is then followed by a peak finding algorithm to identify the peaks in the spectrum.

Tire/road noise has become a significant issue in the automotive industry, especially for electric vehicles. Among the various tire/road noise sources, the air-cavity mode can amplify the forces transmitted from the tire to the suspension system causing noticeable cabin noise near 200 Hz. Furthermore, when the tire is deformed by loading, the fundamental air-cavity mode separates into two acoustic modes, a fore-aft mode and vertical mode due to the break in geometrical symmetry. This is important because the two components of the split mode can increase force levels at the hub by interacting with neighboring structural modes, thus resulting in increased interior noise levels. In this research, finite element simulations of five commercial tires at rated load were performed with a view to identifying the frequency split and its interaction with structural resonances. These results have been compared with previously obtained empirical results.