Fluidization of spherical versus elongated particles: Experimental investigation using magnetic particle tracking

In biomass processing fluidized beds are used to process granular materials where particles typically possess elongated shapes. However, for simplicity, in computer simulations particles are often considered spherical, even though elongated particles experience more complex particle– particle interactions as well as different hydrodynamic forces. The exact effect of these more complex interactions in dense fluidized suspensions is still not well understood. In this study we use the magnetic particle tracking technique to compare the fluidization behavior of spherical particles to that of elongated particles. We found a considerable difference between fluidization behavior of spherical versus elongated particles in the time-averaged particle velocity field as well as in the time-averaged particle rotational velocity profile. Moreover, we studied the effect of fluid velocity and the particle's aspect ratio on the particle's preferred orientation in different parts of the bed, which provides new insight in the fluidization behavior of elongated particles.     

Tools for designing conceptual schemata of databases

CSDL, a language for designing conceptual schemata, is described. The language provides tools to ease the translation of knowledge about data into a formal conceptual schema.     

ADMS: a testbed for incremental access methods

ADMS is an advanced database management system developed-to experiment with incremental access methods for large and distributed databases. It has been developed over the past eight years at the University of Maryland. The paper provides an overview of ADMS, and describes its capabilities and the performance attained by its incremental access methods. This paper also describes an enhanced client-server architecture that allows an incremental gateway access to multiple heterogeneous commercial database management systems     

Performance comparison of three modern DBMS architectures

The introduction of powerful workstations connected through local area networks (LANs) inspired new database management system (DBMS) architectures that offer high performance characteristics. The authors examine three such software architecture configurations: client-server (CS), the RAD-UNIFY type of DBMS (RU), and enhanced client-server (ECS). Their specific functional components and design rationales are discussed. Three simulation models are used to provide a performance comparison under different job workloads. Simulation results show that the RU almost always performs slightly better than the CS, especially under light workloads, and that ECS offers significant performance improvement over both CS and RU. Under reasonable update rates, the ECS over CS (or RU) performance ratio is almost proportional to the number of participating clients (for less than 32 clients). The authors also examine the impact of certain key parameters on the performance of the three architectures and show that ECS is more scalable that the other two     

TORUS: A step towards bridging the gap between data bases and the casual user

This paper describes TORUS, a natural language understanding system that serves as a front end to a data base management system in order to facilitate communication with casual users. The system employs a semantic network to store knowledge about a data base of student files. This knowledge is used to find the meaning of each input statement, to decide what action to take with respect to the data base, and to select information that must be output in response to the input statement. A prototype version of TORUS has been implemented.     

Incrementally maintained network→relational database mapping

An incrementally maintained mapping from a network to a relational database is presented. This mapping may be established either to support the efficient retrieval of data from a network database through a relational interface, or as the first step in a gradual conversion of data and applications from a network to a relational database system. After the mapping has been established, the only data mapped from the network to the relational database are the increments resulting from updates on the network database. The mapping is therefore an efficient alternative to mappings that repeatedly map the results of retrievals through the relational interface from the network database to the relational database. This is in particular the case when the two databases reside on different hosts. Applications on the network database may, under certain restrictions, gradually be moved to the relational database, while the mapping incrementally maintains the relational database for the applications that remain on the network database. A detailed, but generic, account of how to build such a mapping from a network to a relational database is given, including all the algorithms needed and examples of their use.     

Practical load balancing for content requests in peer-to-peer networks

This paper studies the problem of balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentralized load balancing techniques in distributed systems base their decisions on periodic updates containing information about load or available capacity observed at the serving entities. We show that these techniques do not work well in the peer-to-peer context; either they do not address peer node heterogeneity, or they suffer from significant load oscillations which result in unutilized capacity. We propose a new decentralized algorithm, Max-Cap, based on the maximum inherent capacities of the replica nodes. We show that unlike previous algorithms, it is not tied to the timeliness or frequency of updates, and consequently requires significantly less update overhead. Yet, Max-Cap can handle the heterogeneity of a peer-to-peer environment without suffering from load oscillations. Mema Roussopoulos is an Assistant Professor of Computer Science on the Gordon McKay Endowment at Harvard University. Before joining Harvard, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University. She received her PhD and Master’s degrees in Computer Science from Stanford, and her Bachelor’s degree in Computer Science from the University of Maryland at College Park. Her interests are in the areas of distributed systems, networking, and mobile and wireless computing. Mary Baker is a Senior Research Scientist at HP Labs. Her research interests include distributed systems, networks, mobile systems, security, and digital preservation. Before joining HP Labs she was on the faculty of the computer science department at Stanford University where she ran the MosquitoNet project. She received her PhD from the University of California at Berkeley.     

Bandwidth-constrained queries in sensor networks

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth- constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.     

Fluidization of elongated particles—Effect of multi-particle correlations for drag,lift, and torque in CFD-DEM

Having proper correlations for hydrodynamic forces is essential for successful CFD-DEM simulations of a fluidized bed. For spherical particles in a fluidized bed, efficient correlations for predicting the drag force, including the crowding effect caused by surrounding particles, are already available and well tested. However, for elongated particles, next to the drag force, the lift force, and hydrodynamic torque also gain importance. In this work, we apply recently developed multi-particle correlations for drag, lift and torque in CFD-DEM simulations of a fluidized bed with spherocylindrical particles of aspect ratio 4 and compare them to simulations with widely used single-particle correlations for elongated particles. Simulation results are compared with previous magnetic particle tracking experimental results. We show that multi-particle correlations improve the prediction of particle orientation and vertical velocity. We also show the importance of including hydrodynamic torque.