902,277 research outputs found
The Role of Environmental Fluid Mechanics in Water System Management
Water has several unique physical and chemical properties that have influenced Life as it has evolved. Indeed the very concept of Life on the Earth is dependent on water supply. Although the total volume of fresh water on Earth is only a small fraction of that in the oceans, the biological and environmental role of freshwater systems is considerable. Environmental management of water systems (rivers, lakes, seas) is therefore a necessity to sustain economical development despite the technical challenges. Scientific methodology is based primarily upon the application of basic fluid mechanics to complex systems involving two- and possibly three-phase flows : e.g., sediment transport (watersolid), air-sea interactions (air-water). The writer proves the role of environmental fluid mechanics in gaining a sound understanding of water systems and in solving environmental problems. This is illustrated for a complete catchment system. That is, from upstream to downstream, the problem of reservoir sedimentation, stream re-oxygenation with instream aeration cascades, the impact of tidal bores on estuarine systems and the contribution of breaking waves to airsea mass transfer. In each case, the complexity of the problem is shown and new technological advances are described highlighting the essential role of environmental fluid mechanics. The management of water systems is extremely complex because of a combination of factors including Man's dependence on water, the geometric scale of water systems (up to 1E+7 km2), the range of relevant time scales (from less than 1-s to over 1 E+9-s), and the complexity of the governing equations (non-linearity)
Efficient FPGA implementation of high-throughput mixed radix multipath delay commutator FFT processor for MIMO-OFDM
This article presents and evaluates pipelined architecture designs for an improved high-frequency Fast Fourier
Transform (FFT) processor implemented on Field Programmable Gate Arrays (FPGA) for Multiple Input Multiple Output
Orthogonal Frequency Division Multiplexing (MIMO-OFDM). The architecture presented is a Mixed-Radix Multipath Delay
Commutator. The presented parallel architecture utilizes fewer hardware resources compared to Radix-2 architecture,
while maintaining simple control and butterfly structures inherent to Radix-2 implementations. The high-frequency
design presented allows enhancing system throughput without requiring additional parallel data paths common in
other current approaches, the presented design can process two and four independent data streams in parallel
and is suitable for scaling to any power of two FFT size N. FPGA implementation of the architecture demonstrated
significant resource efficiency and high-throughput in comparison to relevant current approaches within
literature. The proposed architecture designs were realized with Xilinx System Generator (XSG) and evaluated
on both Virtex-5 and Virtex-7 FPGA devices. Post place and route results demonstrated maximum frequency
values over 400 MHz and 470 MHz for Virtex-5 and Virtex-7 FPGA devices respectively
Method maximizing the spread of influence in directed signed weighted graphs
We propose a new method for maximizing the spread of influence, based on the identification of significant factors of the total energy of a control system. The model of a socio-economic system can be represented in the form of cognitive maps that are directed signed weighted graphs with cause-and-effect relationships and cycles. Identification and selection of target factors and effective control factors of a system is carried out as a solution to the optimal control problem. The influences are determined by the solution to optimization problem of maximizing the objective function, leading to matrix symmetrization. The gear-ratio symmetrization is based on computing the similarity extent of fan-beam structures of the influence spread of vertices v_i and v_j to all other vertices. This approach provides the real computational domain and correctness of solving the optimal control problem. In addition, it does not impose requirements for graphs to be ordering relationships, to have a matrix of special type or to fulfill stability conditions. In this paper, determination of new metrics of vertices, indicating and estimating the extent and the ability to effectively control, are likewise offered. Additionally, we provide experimental results over real cognitive models in support
The integrated use of enterprise and system dynamics modelling techniques in support of business decisions
Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite
bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use
of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are
then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink
Recent advances in the evolution of interfaces: thermodynamics, upscaling, and universality
We consider the evolution of interfaces in binary mixtures permeating
strongly heterogeneous systems such as porous media. To this end, we first
review available thermodynamic formulations for binary mixtures based on
\emph{general reversible-irreversible couplings} and the associated
mathematical attempts to formulate a \emph{non-equilibrium variational
principle} in which these non-equilibrium couplings can be identified as
minimizers.
Based on this, we investigate two microscopic binary mixture formulations
fully resolving heterogeneous/perforated domains: (a) a flux-driven immiscible
fluid formulation without fluid flow; (b) a momentum-driven formulation for
quasi-static and incompressible velocity fields. In both cases we state two
novel, reliably upscaled equations for binary mixtures/multiphase fluids in
strongly heterogeneous systems by systematically taking thermodynamic features
such as free energies into account as well as the system's heterogeneity
defined on the microscale such as geometry and materials (e.g. wetting
properties). In the context of (a), we unravel a \emph{universality} with
respect to the coarsening rate due to its independence of the system's
heterogeneity, i.e. the well-known -behaviour for
homogeneous systems holds also for perforated domains.
Finally, the versatility of phase field equations and their
\emph{thermodynamic foundation} relying on free energies, make the collected
recent developments here highly promising for scientific, engineering and
industrial applications for which we provide an example for lithium batteries
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres
Interactions between clouds and radiation are at the root of many
difficulties in numerically predicting future weather and climate and in
retrieving the state of the atmosphere from remote sensing observations. The
large range of issues related to these interactions, and in particular to
three-dimensional interactions, motivated the development of accurate radiative
tools able to compute all types of radiative metrics, from monochromatic, local
and directional observables, to integrated energetic quantities. In the
continuity of this community effort, we propose here an open-source library for
general use in Monte Carlo algorithms. This library is devoted to the
acceleration of path-tracing in complex data, typically high-resolution
large-domain grounds and clouds. The main algorithmic advances embedded in the
library are those related to the construction and traversal of hierarchical
grids accelerating the tracing of paths through heterogeneous fields in
null-collision (maximum cross-section) algorithms. We show that with these
hierarchical grids, the computing time is only weakly sensitivive to the
refinement of the volumetric data. The library is tested with a rendering
algorithm that produces synthetic images of cloud radiances. Two other examples
are given as illustrations, that are respectively used to analyse the
transmission of solar radiation under a cloud together with its sensitivity to
an optical parameter, and to assess a parametrization of 3D radiative effects
of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2
Sketch of Big Data Real-Time Analytics Model
Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction
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