Browse Topic: Frames
The weave mode of a motorcycle is known to be affected by the flexibility of the vehicle frame. The weave mode has been shown to be more unstable in the 10-DOF model than in the 4-DOF model. However, it is not clear why the weave mode would be unstable, given the six different frame flexibilities. In this study, the authors analyzed the stability of the weave mode in a 4-DOF model when the same was integrated with two types of frame flexibilities. In the vehicle specifications used in the analysis, the combination of the bending flexibility of the front forks and the torsional flexibility of the main frame destabilizes the weave mode. The analysis results show that the phase delay of the front tire lateral force is caused by the phase delay of the steering angle. The combined bending flexibility of the front forks and the torsional flexibility of the main frame results in a large phase lag in the steering angle.
The stiffness and positioning of engine mounts are crucial in determining the powertrain rigid body modes and kinetic energy distribution. Therefore, optimizing these mounts is essential in the automotive industry to separate the torque roll axis (TRA) and minimize vibration. This study aims to enhance mount locations by isolating the engine rigid body modes and predicting the inter-component force (ICF) and transfer function of the vehicle. The individual ICFs for engine mountings are calculated by applying a unit force at the bearing location. Critical frequencies are identified where the amplification exceeds the unit force at the mounting interface between the engine and the frame. The transfer function approach is utilized to assess the vibration at the handlebar. Both ICF and transfer functions analyze the source and path characteristics linked to critical response frequencies. This understanding aids in enhancing mounting positions to minimize vibration levels, thereby enhancing
Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The method is based on non-linear theories in the frequency domain. It uses the Harmonic Balance Method (HBM) in combination with the Alternating Frequency/Time Domain Method (AFT) to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked. In the second step, the
Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras. A time resolution of less than 1ms eventually allows for the true localization of the initial and subsequent sound events as well as a clear separation of direct from
REE Automotive is aiming to be a major disruptor in the medium-duty truck space with the rollout of its P7 EV chassis. The P7 frame is built around its “REEcorners” suspension, which are modular suspension units featuring REE's x-by-wire design. By packaging components into the area between the chassis and the wheel, REE claims that it was able to design the P7 with a completely flat chassis with up to 35% more interior volume for passengers, cargo and batteries. “The REEcorners suspension system is the core of the technology that we built this truck around,” Peter Dow, VP of engineering for REE Automotive, said during an interview with Truck & Off-Highway Engineering. “It also allows us to achieve the level of vehicle dynamics we were looking for. We were trying to make a truck that was very exciting and easy to drive.”
In the early stages of vehicle development, it is critical to establish performance goals for the major systems. The fundamental modes of body and chassis frames are typically assessed using FE models that are discretized using shell elements. However, the use of the shell-based FE method is problematic in terms of fast analysis and quick decision-making, especially during the concept phase of a vehicle design because it takes much time and effort for detailed modeling. To overcome this weakness, a one-dimensional (1D) method based on beam elements has been extensively studied over several decades, but it was not successful because of low accuracy for thin-walled beam structures. This investigation proposes a 1D method based on thin-walled beam theory with comparable accuracy to shell models. Most body pillars and chassis frame members are composed of thin-walled beam structures because of the high stiffness-to-mass ratio of thin-walled cross sections. However, thin-walled cross
Electric vehicles (EV) are much quieter than IC engine powered vehicles due to less mechanical components and absence of combustion. The lower cabin noise in electric vehicles make customers sensitive to even small noise disturbances in vehicle. Road boom noise is one of such major concerns to which the customers are sensitive in electric vehicles. The test vehicle is a front wheel driven compact SUV powered by electric motor. On normal plain road, noise levels are acceptable but when the vehicle has been driven on coarse road, the boom noise is perceived, and the levels are objectionable. Multi reference Transfer Path Analysis (MTPA) is conducted to identify the path through which maximum forces are entering the body. Based on MTPA, modifications are proposed on the suspension bushes and the noise levels were assessed. Operational Deflection Shape (ODS) analysis is conducted on entire vehicle components like suspension links, sub frame, floor, roof, and doors to identify the
NVH is of prime importance in buses as passengers prefer comfort. Traditionally vehicle NVH is analysed post completion of proto built however this leads to modifications, increases cost & development time. In modern approach physical validation is replaced by CAE. There are many sources of NVH in vehicle however this article is focused about the methodology to improve NVH performance of bus by analysing and improving the stiffness and mobility of various chassis frame attachment points on which source of vibrations are mounted or attached. In this study chassis frame attachment stiffness of Engine mounts and propeller shafts is focused.
The current approach of hybrid RLDA is typically incapable of providing accurate dynamic loads coming on cab at chassis-to-cab load transfer locations, primarily due to the following two reasons. Firstly, all of the model parameters of the vehicle, which is put on the 4-post, are not known. Secondly, MBS (Multi-body System) Cab model is multi degree of freedom with rigid bodies, flex bodies, contacts and non-linear force elements. Therefore, if the system identification is to be performed manually it becomes an arduous and humanly impossible task. Towards generating accurate dynamic loads on cab, an approach using FEMFAT LAB - VI & MI has been developed which involves a two-step process: a) Generating MBS excitation by back calculating from measured frame response – VI (Virtual iterations). b) Fine tuning modelling parameters to match measured cab response – MI (Model Improvement). To execute the VI & MI approach for calculating dynamic loads on cab through 4-poster & 7-poster
A research team has developed a robotic system that can be unobtrusively built into the frame of a standard honeybee hive. Composed of an array of thermal sensors and actuators, the system measures and modulates honeybee behavior through localized temperature variations.
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