smart-mobility-boosting-using-high-fidelity-magnetorheological-fluid

Smart Mobility Boosting Using High-Fidelity Magnetorheological Fluid Modeling for Adaptive Damper Control Development

With the rise of high-performance electric vehicles (EVs) which require advanced suspension systems capable of delivering precision, high force output, ...

Tsinghua University

A numerical approach for the stress concentration of periodic collinear holes in an infinite plate in tension is presented. It involves the fictitious stress method and a generalization of Bueckner's principle. Numerical examples are concluded to show that the numerical approach is very efficient and accurate for analyzing the stress concentration of periodic collinear holes in an infinite plate in tension. The stress concentration of periodic collinear square holes in an infinite plate in tension is studied in detail by using the numerical approach. The calculated stress concentration factor is proven to be accurate.

Stress Concentration of Periodic Collinear Square Holes in an Infinite Plate in Tension.

The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The friction level, material, and unevenness properties of each road image are annotated in detail. This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. Also, deep-learning experts can regard this dataset as a comparing benchmark for their algorithms. The dataset is available at [1].

A road surface image dataset with detailed annotations for driving assistance applications.

Magnetorheological fluid (MRF) is a widely used smart material that suffers from sedimentation. Since sedimentation is unavoidable, it is crucial to study and improve the redispersibility of MRFs. However, previous redispersibility testing methods have problems, such as complicated operation and low precision. Simultaneously, a simple and effective method is urgently needed for high-precision modeling of MRF sedimentation to test the rheological properties of settled MRFs at different depths. After systematically analyzing the redispersion problem, this paper proposes decoupling the energy required for redispersing settled MRFs into two parts, which are related to different factors. These two parts are the energy required to separate the agglomerated particles (related to the MRF formula) and that to redisperse the settled MRF uniformly vertically against gravity (related to the solid concentration and packing limit). The energy that separates the agglomerated particles is proportional to the shear stress of slowly shearing the corresponding agglomerated samples, i.e., the yield stress. Thus, this paper proposes a simple microdamage quasi-static indentation method to measure the yield stresses of settled MRFs at different depths to characterize the redispersibility of the corresponding MRFs. Herein, this method is applied to study the mechanisms of the influences of surfactants, thixotropic agents, and their networks on the redispersibility of MRFs. The results indicate that a well-dispersed plate-like thixotropic agent network can effectively improve redispersibility, while surfactants with poor compatibility degrade redispersibility. In summary, this redispersibility test method will greatly facilitate studies of MRFs, such as optimizing the formulas and establishing sedimentation models.

A new method for measuring magnetorheological fluid redispersibility by testing yield stresses of sediments at different depths.

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Article	Year
Stress Concentration of Periodic Collinear Square Holes in an Infinite Plate in Tension. Journal of pressure vessel technology\| PubMed ID: 26424900	2015
A road surface image dataset with detailed annotations for driving assistance applications. Data in brief\| PubMed ID: 35928344	2022
A new method for measuring magnetorheological fluid redispersibility by testing yield stresses of sediments at different depths. The Review of scientific instruments\| PubMed ID: 36725614	2023
A road surface reconstruction dataset for autonomous driving. Scientific data\| PubMed ID: 38710687	2024

Yintao Wei

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