Browse Topic: Vehicle acceleration
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in
This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness
In pursuit of a distinct sporty interior sound character, the present study explores an innovative strategy for designing intake systems in passenger vehicles. While most existing literature primarily emphasizes exhaust system tuning for enhancing vehicle sound quality, the current work shifts the focus toward the intake system’s critical role in shaping the perceived acoustic signature within the vehicle cabin. In this research work, target cascading and settings were derived through a combination of benchmark and structured subjective evaluation study and aligning with literature review. Quantitative targets for intake orifice noise was defined to achieve the desired sporty character inside cabin. Intake orifice targets were engineered based on signature and sound quality parameter required at cabin. Systems were designed by using advanced NVH techniques, Specific identified acoustic orders were enhanced in the intake system to reinforce the required signature in acceleration as well
Safety improvements in vehicle crashworthiness remain a primary concern for automotive manufacturers due to the increasing complexity of traffic and the rising number of vehicles on roads globally. Enhancing structural integrity and energy absorption capabilities during collisions is paramount for passenger protection. In this context, longitudinal rails play a critical role in vehicle crashworthiness, particularly in mitigating the effects of rear collisions. This study evaluates the structural performance of a rear longitudinal rail extender, characterized by a U-shaped, asymmetric cross-section, subjected to rear-impact scenarios. Seventy-two finite-element models were systematically developed from a baseline configuration, exploring variations in material yield conditions, sheet thickness, and targeted geometric modifications, including deformation initiators at three distinct positions or maintaining the original geometry. Each model was simulated according to ECE R32 regulation
Battery technology is at the center of global innovation. From electric vehicles and off-highway machinery to consumer electronics and grid storage, demand for high-performing, reliable batteries has never been higher. This acceleration creates pressure on manufacturers to scale production while safeguarding quality and throughput.
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
A road simulator reproduction method was developed to reproduce the off-road conditions of utility vehicles in a laboratory setting. Off-road running behavior can be reproduced by considering the effects of inertial forces from jump landings owing to uneven terrain and slow-speed navigation. However, extremely low-frequency components and behaviors, including inertial forces from jumps, vehicle acceleration and deceleration, are difficult to reproduce with a normal road simulator in the limited test space of a laboratory. Therefore, it is common practice to intentionally remove input components below 1 Hz. Alternatively, inertial forces can be reproduced by adding a restraining device to the sprung mass of the vehicle along the wheel-axle inputs. Therefore, the former method excludes extremely low-frequency components, whereas the effects between actuators, which increase the test complexity and time required, should be canceled in the latter method. Furthermore, the restraining device
The growing demand for improved air quality and reduced impact on human health along with progress in vehicle electrification has led to an increased focus on accurate Emission Factors (EFs) for non-exhaust emission sources, like tyres. Tyre wear arises through mechanical and thermal processes owing to the interaction with the road surface, generating Tyre Road Wear Particles (TRWP) composed of rubber polymers, fillers, and road particles. This research aims to establish precise TRWP airborne EFs for real-world conditions, emphasizing in an efficient collection system to generate accurate PM10 and PM2.5 EFs from passenger car tyres. Particle generation replicates typical driving on asphalt road for a wide selection of tyres (different manufacturers, price ranges, fuel economy rating). Factors such as tyre load, speed and vehicle acceleration are also considered to cover various driving characteristics. The collection phase focuses on separating tyre wear particles from potential
To define a test procedure that will provide repeatable measurements of a vehicle’s maximum acceleration performance for launch and passing maneuvers and standardize time zero used in reported results.
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