The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.
The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.
The increasing awareness on the harmful effects on the environment of traditional Internal Combustion Engines (ICE) is driving the industry toward cleaner powertrain technologies such as battery-driven Electric Vehicles. Nonetheless, the high energy density of Li-Ion batteries can cause strong exothermic reactions under certain conditions that can lead to catastrophic results, called Thermal Runaway (TR). Hence, a strong effort is being placed on understanding this phenomena and increase battery safety. Specifically, the vented gases and their ignition can cause the propagation of this phenomenon to adjancent batteries in a pack. In this work, Computational Fluid Dynamics (CFD) are employed to predict this venting process in a LG18650 cylindrical battery. The ejection of the generated gases was considered to analyze its dispersion in the surrounding volume through a Reynolds-Averaged Navier-Stokes (RANS) approach.
This work puts forward an original autonomous planning and control framework addressing inherent modeling complexity limit through efficient heterosis between latency-connective graph estimation and generative exploration with an aim to enhance trajectory quality and resiliency in unpredicted conditions. The holistic approach encompasses state and cost prediction facilitated via morphable signature mechanism utilizing anti-cloak characteristics derived from environmental graph. In principle, a dynamic graph neural network is proposed with regards to adaptively capture essential influence caused by interactive agents and reciprocal belief augmentation. Moreover, high efficiency exploration is concerted with signature-enhanced prediction system for non-ideal perception conditions. The exploration scheme takes advantage of confidence optimization function to generate trajectory refinement over non-conventional operating circumstances.
Churning loss is an important energy loss term for rolling bearings at high speed condition. However, it is quite challenging to accurately calculate the churning loss. A CFD study based on unsteady Reynolds-Averaged-Navier-Stokes that resolves the gas-liquid interface was performed to examine the unsteady multiphase flow in a roller/ball bearing. In this study, the rotating motion of the cage, races, rollers/balls about the shaft as well as self-rotation of rollers/balls about their own axis were accounted to accurately predict the oil distribution in various parts of the bearings. A novel meshing strategy is presented to resolve thin gaps between the roller/balls and the races/cage while preserving the shape of balls/rollers, races and cage. Seven and five rotational speeds of the shaft have been examined for roller bearing and ball bearing respectively.
In the field of automotive aerodynamics, there's a consistent need for tools that effectively manage both rapid design changes and comprehensive simulations. The recent GPU code update to the PowerFLOW, Lattice Boltzmann simulation tool is an attempt to meet this need. An important feature of this update is the inclusion of the Sliding Mesh rotating reference frame, which improves rim modeling accuracy. This modification provides a clearer depiction of vehicle aerodynamics, aiming for balanced and efficient designs. The updated GPU solver has been tested with two main resolutions. First, a low-resolution aerodynamics scheme which can assist designers and stylists in their initial stages of design. This setup aims to offer a rapid iterative design process. In addition, for more detailed analysis, full-scale resolution simulation setups are possible with the NVIDIA A100's 80GB memory capacity.
The adoption of Electric Vehicles (EVs) is primarily limited by their dependence on batteries, which have lesser power density as compared to conventional fossil fuels as well as its ageing deterioration issues over time. Therefore, there is an urgent need to understand the modifications in battery performance characteristics with respect to changes in temperature, charging behaviour and usage pattern, low and high charge states, current variations etc. To resolve such issues, this work proposes the development of a battery digital twin model to accurately reflect battery dynamics during run time. A digital twin is a virtual model replicating a physical system's characteristics. The digital twin is developed using a physics and machine learning model trained with bench-level and vehicle level actual test data. It uses an equivalent circuit model (ECM) to predict the battery's internal resistance and polarization effect due to ionic diffusion process in the cell.
The proliferation of electric vehicles (EVs) is making big transition in the automotive industry, promising reduced greenhouse gas emissions and improved energy efficiency. The architectural configurations and power distribution strategies necessitate the optimization of their drivability performance, all-electric ranges, and overall efficiency. This paper reports the efforts of the University of California at Riverside (UCR) EcoCAR team in EV architecture selection to match the EcoCAR EV Challenge theme of shared mobility for disadvantaged communities. The UCR EcoCAR team conducted a comprehensive analysis of various EV architectures (including rear-wheel drive, front-wheel drive, and all-wheel drive) and motor parameters, considering a spectrum of targeted vehicle technology specifications such as acceleration and braking performance, fuel economy, and cargo/passenger capacity.
In this paper, water droplet dynamics in FC channels were investigated by applying numerical and experimental methodologies. Specifically, digital imaging with high-spatial resolution was applied for characterising the micro-channel surface and defining the texture of the Gas Diffusion Layer (GDL) of a Membrane electrode assembly (MEA). The optical results allowed the definition of a 3D geometry of the GDL to use in CFD simulations. Moreover, a custom procedure of image processing permitted the estimation of the contact angles of droplets deposited on the GDL (123°) and channel walls (50°-60°) for a wide range of droplet size (0.3-1.2mm). The determined specifications were used as boundary conditions for a 3D CFD two phase simulation employing the Volume of Fluid (VOF) model. Droplets were initialized on the walls and their dynamics were studied under increasing air flow, up to 20 m/s.