Browse Topic: Powertrains
This paper reports on the development of a simulation model to predict engine blowby flow rates for a common rail DI diesel engine. The model is a transient, three-dimensional computational fluid dynamics (CFD) model. Managing blowby flow rates is beneficial for managing fuel economy and oil consumption. In doing so, an improved understanding of the blowby phenomenon is also possible. A mesh for the sub-micron level clearances (up to 0.5 microns) within the piston ring pack is created using a novel approach. Commercial CFD software is used to solve the pressure, velocity, and temperature distributions within the fluid domain. Ring motions within the piston grooves are predicted by a rigorous force balance. This model is the first of its kind for predicting engine blowby using a three-dimensional simulation model while solving the complete set of governing transport equations, without neglecting any terms in the equations. The predicted blowby flow rate has been validated with
Structural topology optimization for vehicle structures under static loading is a well-established practice. Unfortunately, extending these methods to components subjected to dynamic loading is challenged by the absence of sensitivity coefficients: analytical expressions are unavailable and numerical approximations are computationally impractical. To alleviate this problem, researchers have proposed methods such as hybrid cellular automata (HCA) and equivalent static load (ESL). This work introduces a new approach based on equivalent static displacement (ESD). The proposed ESD method uses a set of prescribed nodal displacements, simulating the resultant reaction forces of a body subjected to dynamic loading, at different simulation time steps to establish the boundary conditions for each corresponding model—one model for each simulation time. A scalarized multi-objective function is defined considering all the models. A gradient-based optimizer is incorporated to find the optimal
This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the
Internal combustion engines (ICEs) will continue to be critical propulsion systems for certain applications in the coming decades. It is, therefore, extremely important to further develop environmentally friendly and sustainable internal combustion engines. These developments include, but are not limited to, improved tribology and reduced mechanical losses, higher mean effective pressures, compatibility with carbon-free or -neutral fuels, improved exhaust gas aftertreatment systems, and condition-based maintenance. Due to the increased stress on engine components associated with these changes, accurate, online data with high temporal resolution is required from inside the engine. Acquisition of this data can be achieved with a wireless telemetry system in order to minimize the influence of measurement devices on the measurement itself. This paper describes challenges in the development of telemetry systems for internal combustion engines. Systems for measuring the piston temperature
This paper describes an optimal control method utilizing a Linear Quadratic Regulator (LQR) to control the torque during the gear shift on a multispeed electrified transmission to optimize for clutch actuator durability and shift performance. The dynamic state-space model of the system has been obtained using System-Identification. An LQR controller is formulated to minimize driveline oscillations and transmission-input-torque using the model by manipulating the electrical torque applied by the traction motor at the transmission input. The LQR controller is implemented in a simulation framework wherein the impact of vehicle parameters on the shift quality metrics is also assessed. Subjective and objective requirements are considered in the tuning process for the LQR controller. The LQR controller is utilized to generate profiled torque table calibrations. These calibrations are then deployed onto a production ready Transmission Control Unit and experimentally validated on a Class-8
In cost- effective P2 hybrid vehicles with low voltage electric machines connected to the engine, an interesting control problem arises during the transition to a locked driveline state. This occurs when the engine connects to the wheels via a separation clutch. The two primary torque sources, the engine and the clutch, are traditionally imperfect estimators of applied and transferred torques. The Hybrid Supervisor’s feedforward constraints model relies on these imperfect inputs to determine torque and acceleration limits for the engine’s desired acceleration profiles and to specify engine feedforward commands, aiming for synchronization speed. Due to the inaccuracies in the torque estimates of the engine and clutch, the Hybrid Supervisor is susceptible to control windup, increased jerk to the driveline during synchronization, and inaccurate computation of its target acceleration profile, speed, and torque targets for the engine to achieve synchronization speed. This paper presents a
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