【标题】Effect of Material Models on Rolling Resistance of Non-pneumatic Tires with Hexagonal Spokes

【参考中译】材料模型对六角形非充气轮胎滚动阻力的影响

【类型】 期刊

【关键词】 Non-pneumatic tire (NPT); ABAQUS; Ogden material model; Neo-Hookean material model; Mooney-Rivlin material model; Contact pressure; Rolling resistance; Slip ratio

【参考中译】 非充气轮胎；ABAQUS；Ogden材料模型；Neo-Hookean材料模型；Mooney-Rivlin材料模型；接触压力；滚动阻力；滑移率

【作者】 M. Kiran; M. Aswath; D. Athreya Shishir; Babu Rao Ponangi; Rammohan Bhanumurthy

【摘要】 A non-pneumatic tire (NPT) has a lot of applications and is a viable option for the future, as they do not possess the problem of blowouts and air pressure maintenance. In these NPTs, the air-filled part is replaced by a flexible structure capable of withstanding the weight of the vehicle and delivering optimum performance. In the present study, endeavors have been made to analyze the rolling performance of NPTs by considering a light commercial vehicle as an application. The NPTs with three different configurations are studied by considering three hyperelastic material models for the hexagonal spoke structure and shear band under various loading conditions. Initially, static analysis for the models is conducted in two dimension (2D) and three dimension (3D) to validate the results, and these models were further extended to rolling analysis. The rolling resistance and slip ratios are obtained and compared in both 2D and 3D analyses. From the results, the least rolling resistance was observed for Type-A with the Mooney-Rivlin material model for polyurethane. The present study also includes the effect of various tread patterns on NPT Type-A with the Mooney-Rivlin material model on rolling resistance, contact patch, and contact pressure.

【参考中译】 非充气轮胎(NPT)有很多应用，是未来可行的选择，因为它们不存在爆胎和维持气压的问题。在这些NPT中，充气部分被灵活的结构取代，能够承受车辆的重量并提供最佳性能。本文以某轻型商用车为研究对象，对其滚动性能进行了分析。通过考虑六角轮辐结构和剪切带的三种超弹性材料模型，研究了三种不同构型的NPT在不同加载条件下的性能。首先对模型进行了二维(2D)和三维(3D)的静力分析，以验证结果，并将这些模型进一步扩展到轧制分析。得到了滚压阻力和滑移率，并在二维和三维分析中进行了比较。结果表明，采用Mooney-Rivlin材料模型时，A类材料的滚动阻力最小。采用Mooney-Rivlin材料模型，研究了不同胎面花纹对NPT-A型轮胎的滚动阻力、接触补丁和接触压力的影响。

【来源】 SAE International Journal of Commercial Vehicles 2023, vol.16, no.1

【入库时间】 2023/6/29

【标题】MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE

【参考中译】基于网格的图卷积神经网络在微结构材料建模中的应用

【类型】 期刊

【关键词】 Graph neural network; Material microstructure; Homogenization

【参考中译】 图神经网络；材料微观结构；均质化

【作者】 Ari Frankel; Cosmin Safta; Coleman Alleman; Reese Jones

【摘要】 Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. Compared to feature-based and pixel-based convolutional neural network models, the proposed method has a number of advantages: (a) it is deep in that it does not require featurization but can benefit from it, (b) it has a simple implementation with standard convolutional filters and layers, (c) it works natively on unstructured and structured grid data without interpolation (unlike pixel-based convolutional neural networks), and (d) it preserves rotational invariance like other graph-based convolutional neural networks. We demonstrate the performance of the proposed network and compare it to traditional pixel-based convolution neural network models and feature-based graph convolutional neural networks on multiple large datasets.

【参考中译】 预测具有微观结构材料的代表性样品的演化是均匀化中的一个基本问题。在这项工作中，我们提出了一种图卷积神经网络，它直接利用初始微结构的离散化表示，而不需要分割或聚类。与基于特征和基于像素的卷积神经网络模型相比，该方法具有许多优点：(A)它不需要特征化，但可以从中受益；(B)它具有标准的卷积过滤器和层的简单实现；(C)它无需内插即可处理非结构化和结构化的网格数据(不同于基于像素的卷积神经网络)；(D)它像其他基于图的卷积神经网络一样保持旋转不变性。我们在多个大数据集上验证了该网络的性能，并与传统的基于像素的卷积神经网络模型和基于特征的图卷积神经网络进行了比较。

【来源】 Journal of Machine Learning for Modeling and Computing 2022, vol.3, no.1

【入库时间】 2023/6/29

【标题】DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK

【参考中译】数据驱动的脆性材料失效预测：一种基于相场的机器学习框架

【类型】 期刊

【关键词】 Finite element method; Virtual sensing nodes; Pattern recognition; Artificial neural networks; K-nearest neighbor; Confusion matrix; Failure location/pattern

【参考中译】 有限元方法；虚拟传感节点；模式识别；人工神经网络；K近邻；混淆矩阵；故障位置/模式

【作者】 Eduardo A. Barros de Moraes; Hadi Salehi; Mohsen Zayernouri

【摘要】 Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor node responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework and the applicability of artificial intelligence and ML to a practical engineering problem, i.e., data-driven failure prediction in brittle materials.

【参考中译】 脆性材料在重复或不断增加的载荷下，由微裂纹到宏观裂纹的演化导致的破坏往往是灾难性的，没有明显的塑性来预示断裂的开始。具有各自位置的早期故障检测在任何实际应用中都是非常重要的功能，这两个功能都可以使用人工智能有效地解决。在本文中，我们发展了一个有监督的机器学习(ML)框架，用于预测脆性材料损伤和疲劳的等温、线弹性和各向同性相场模型中的失效。相场模型的时间序列数据是从几何上不同位置的虚拟传感节点提取的。提出了一种模式识别方案，将时序数据/传感器节点响应表示为具有相应标签的模式，并与ML算法相结合，用于识别模式的损伤分类。我们通过将随机噪声叠加到时间序列数据中进行不确定性分析，以评估该框架在噪声污染数据下的稳健性。结果表明，即使在高噪声水平下，所提出的框架也能够以可接受的精度预测故障。结果表明，有监督最大似然框架的性能令人满意，以及人工智能和最大似然方法在实际工程问题中的适用性，即脆性材料的数据驱动失效预测。

【来源】 Journal of Machine Learning for Modeling and Computing 2021, vol.2, no.1

【入库时间】 2023/6/29