Yield stress in magnetorheological and electrorheological fluids: A comparison between microscopic and macroscopic structural modelsRecently, a series of electrorheological ͑ER͒ fluids with high yield stress up to hundreds of kilopascals, which is named as polar molecule dominated electrorheological ͑PM-ER͒ fluids, has been developed. The mechanism of PM-ER fluids is quite different from that of conventional ER fluids. The normal rheometer cannot be used anymore to measure the shear stress of PM-ER fluid because a slide occurs at the interface between PM-ER fluids and metallic electrodes. In this paper, a proper technique for measuring the yield stress of PM-ER fluids is presented. By using this method, the intrinsic yield stress and the shear modulus of PM-ER fluids can be obtained conveniently, and the boundary effect at electrodes can be eliminated.

ABSTRACT:Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.