Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

Leveraging graph neural networks and neural operator techniques for high-fidelity mesh-based physics simulations

Developing fast and accurate computational models to simulate intricate physical phenomena has been a persistent research challenge. Recent studies have demonstrated remarkable capabilities in predicting various physical outcomes through machine learning-assisted approaches. However, it remains challenging to generalize current methods, usually crafted for a specific problem, to other more complex or broader scenarios. To address this challenge, we developed graph neural network (GNN) models with enhanced generalizability derived from the distinct GNN architecture and neural operator techniques. As a proof of concept, we employ our GNN models to predict finite element (FE) simulation results for three-dimensional solid mechanics problems with varying boundary conditions. Results show that our GNN model achieves accurate and robust performance in predicting the stress and deformation profiles of structures compared with FE simulations. Furthermore, the neural operator embedded GNN approach enables learning and predicting various solid mechanics problems in a generalizable fashion, making it a promising approach for surrogate modeling

An exposure time calculator for the Maunakea Spectroscopic Explorer

International audienceThe Maunakea Spectroscopic Explorer (MSE) will convert the 3.6-m Canada-France-Hawaii Telescope (CFHT) into an 11.25-m primary aperture telescope with a 1.5 square degrees field-of-view at the prime focus. It will produce multi-object spectroscopy with a suite of low (R∼3,000), moderate (R∼6,000), and high (R∼40,000) spectral resolution spectrographs in optical and near-infrared bands that are capable of detecting over 4,000 objects per pointing. Generally, an exposure time calculator (ETC) should simulate a system performance by computing a signal-to-noise ratio (SNR) and exposure time based on parameters such as a target magnitude, a total throughput of the system, and sky conditions, etc. The ETC that we have developed for MSE has individual computation modes for SNR, exposure time, SNR as a function of AB magnitude, and SNR as a function of wavelength. The code is based on an agile development methodology and allows for a variety of user input. Users must select either LR, MR, or HR spectral resolution settings in order to pull the associated MSE instrument parameters. Additionally, users must specify the target and background sky magnitudes (and have the ability to alter the default airmass and water vapor values). The software is developed with Python 3.7, and Tkinter graphical user interface is implemented to facilitate cross-platform use. In this paper, we present the logic structure and various functionalities of our MSE-ETC, including a software design and a demonstration