Browse Topic: Finite element analysis
ABSTRACT FBS Inc. is working with the TARDEC Electrified Armor Lab to develop a nondestructive structural health monitoring technology for composite armor panels that utilizes an array of embedded ultrasonic sensors for guided wave tomographic imaging. This technology would allow for periodic or real-time monitoring of armor integrity while being minimally intrusive and adding negligible weight. The technology is currently being developed and tested in pseudo composite armor panels and efforts are focused on reducing sensor array density, improving sensor integration procedures, and maximizing system sensitivity to damage. In addition to experimental testing and development, FBS is developing a highly-automated finite element model generation and analysis program to be used in conjunction with Abaqus/Explicit commercial finite element software. This program is specifically dedicated to modeling guided wave propagation in pseudo composite armor panels between embedded ultrasonic sensors
ABSTRACT Researchers at Caterpillar have been using Finite Element Analysis or Method (FEA or FEM), Mesh Free Models (MFM) and Discrete Element Models (DEM) extensively to model different earthmoving operations. Multi-body dynamics models using both flexible and rigid body have been used to model the machine dynamics. The proper soil and machine models along with the operator model can be coupled to numerically model an earthmoving operation. The soil – machine interaction phenomenon has been a challenging matter for many researchers. Different approaches, such as FEA, MFM and DEM are available nowadays to model the dynamic soil behavior; each of these approaches has its own limitations and applications. To apply FEA, MFM or DEM for analyzing earthmoving operations the model must reproduce the mechanical behavior of the granular material. In practice this macro level mechanical behavior is not achieved by modeling the exact physics of the microfabric structure but rather by
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly changed various industries. This study demonstrates the application of a Convolutional Neural Network (CNN) model in Computational Fluid Dynamics (CFD) to predict the drag coefficient of a complete vehicle profile. We have developed a design advisor that uses a custom 3D CNN with a U-net architecture in the DEP MeshWorks environment to predict drag coefficients (Cd) based on car shapes. This model understands the relationship between car shapes and air drag coefficients calculated using computational fluid dynamics (CFD). The AI/ML-based design advisor feature has the potential to significantly decrease the time required for predicting drag coefficients by conducting CFD calculations. During the initial development phase, it will serve as an efficient tool for analyzing the correlation between multiple design proposals and aerodynamic drag forces within a short time frame
Tire/Road noise is a dominant contribution to a vehicle interior noise and requires significant engineering resources during vehicle development. A process has been developed to support automotive OEMs with road noise engineering during vehicle design and development which has test as its basis but takes advantage of simulation to virtually accelerate road noise improvement. The process uses noise sources measured on a single tire installed on a test stand in a chassis dynamometer. The measured sources are then combined with vehicle level transfer functions calculated using a Finite-Element model for structure-borne noise and a Statistical Energy Analysis (SEA) model for airborne noise to predict the total sound at the driver’s ears. The process can be applied from the initial stages of a vehicle development program and allows the evaluation of vehicle road noise performance as perceived by the driver long before the first prototype is available. This process is also extensible to
To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing. Simulation is a key supporting tool to guide location, thickness, and properties of
With the increasing importance of electrified powertrains, electric motors and gear boxes become an important Noise Vibration & Harshness (NVH) source especially regarding whining noises in the high frequency range. Engine encapsulation noise treatments become often necessary and present some implementation, modeling as well as optimization issues due to complex environments with contact uncertainties, pass-throughs and critical uncovered areas. Relying purely on mass spring systems is often a too massive and relatively unefficient solution whenever the uncovered areas are dominant. Coverage is key and often a combination of hybrid backfoamed porous stiff shells with integral foams for highly complex shapes offer an optimized trade-off between acoustic performance, weight and costs. A dedicated experimental set-up has been designed in order to measure both structureborne and airborne NVH performances of engine encapsulation insulators applied on an engine casing placed in a coupled
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