Locally recurrent neural networks for wind speed prediction using spatial correlation

This paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. Owing to the temporal complexity of the problem, we employ local recurrent neural networks with internal dynamics, as advanced forecast models. To improve the prediction performance, the training task is accomplished using on-line learning algorithms based on the recursive prediction error (RPE) approach. A global RPE (GRPE) learning scheme is first developed where all adjustable weights are simultaneously updated. In the following, through weight grouping we devise a simplified method, the decoupled RPE (DRPE), with reduced computational demands. The partial derivatives required by the learning algorithms are derived using the adjoint model approach, adapted to the architecture of the networks being used. The efficiency of the proposed approach is tested on a real-world wind farm problem, where multi-step ahead wind speed estimates from 15 min to 3 h are sought. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that the suggested learning algorithms outperform three gradient descent algorithms, in training of the recurrent forecast models.     

A system integration framework for coupled multiphysics simulations

Multiphysics simulations are playing an increasingly important role in computational science and engineering for applications ranging from aircraft design to medical treatments. These simulations require integration of techniques and tools from multiple disciplines, and in turn demand new advanced technologies to integrate independently developed physics solvers effectively. In this paper, we describe some numerical, geometrical, and system software components required by such integration, with a concrete case study of detailed, three-dimensional, parallel rocket simulations involving system-level interactions among fluid, solid, and combustion, as well as subsystem-level interactions. We package these components into a software framework that provides common-refinement based methods for transferring data between potentially non-matching meshes, novel and robust face-offsetting methods for tracking Lagrangian surface meshes, as well as integrated support for parallel mesh optimization, remeshing, algebraic manipulations, performance monitoring, and high-level data management and I/O. From these general, reusable framework components we construct domain-specific building blocks to facilitate integration of parallel, multiphysics simulations from high-level specifications that are easy to read and can also be visualized graphically. These reusable building blocks are integrated with independently developed physics codes to perform various multiphysics simulations.     

A spatial temporal decision framework for adaptation to sea level rise

There is a strong link between decision making and environmental stresses. Two dilemmas confront decision makers: how and when to adapt to sea level rise, due to complexities of environmental systems and the changing nature of the decision making process. This process is inherently complex and often involves many stakeholders with conflicting views. Considering the complexity and dynamic nature of coastal systems, this paper introduces a Spatial Temporal Decision framework to assess coastal vulnerability, and the adaptation alternatives to SLR. The STD is based upon a combination of: System Dynamics modelling; Geographical Information Systems modelling; and multicriteria analyses of stakeholders' views using the Analytical Hierarchy Process. For case study analyses, the City of the Gold Coast located in Southeast Queensland, Australia has been selected. The results of the vulnerability assessment indicate that, at the end of a 100 year simulation period, approximately 6% of the landscape in the study area will be gradually inundated over time, with 0.5 cm rise per year. However, the percentage of the vulnerable area leapt to about 34% for Scenario 2, and 56% for Scenario 3, which represent 1 cm and 1.5 cm rise per year. Using the information obtained from vulnerability assessments, three stakeholder groups (Politicians, Experts and Residents) were consulted to determine the goal, criteria and adaptation alternatives for the multicriteria analyses. Analyses of survey data reveal that across the three stakeholder groups, Effectiveness and Sustainability are the criteria of highest priority.     

A flexible high‐performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries

We describe the porting of the Lattice Boltzmann component of MUPHY, a multi‐physics/scale simulation software, to multiple graphics processing units using the Compute Unified Device Architecture. The novelty of this work is the development of ad hoc techniques for optimizing the indirect addressing that MUPHY uses for efficient simulations of irregular domains. Copyright © 2009 John Wiley & Sons, Ltd.     

A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments

As crowd simulation in micro-spatial environment is more widely applied in urban planning and management, the construction of an appropriate spatial data model that supports such applications becomes essential. To address the requirements necessary to building a model of crowd simulation and people–place relationship analysis in micro-spatial environments, the concept of the grid as a basic unit of people–place data association is presented in this article. Subsequently, a grid-based spatial data model is developed for modelling spatial data using Geographic Information System (GIS). The application of the model for crowd simulations in indoor and outdoor spatial environments is described. There are four advantages of this model: first, both the geometrical characteristics of geographic entities and behaviour characteristics of individuals within micro-spatial environments are involved; second, the object-oriented model and spatial topological relationships are fused; third, the integrated expression of indoor and outdoor environments can be realised; and fourth, crowd simulation models, such as Multi-agent System (MAS) and Cellular Automata (CA), can be further fused for intelligent simulation and the analysis of individual behaviours. Lastly, this article presents an experimental implementation of the data model, individual behaviours are simulated and analysed to illustrate the potential of the proposed model.     

A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs

The mechanical product design process involves much experiential reasoning which relies extensively on accumulated experience knowledge and ambiguous synthetic decision of experts (ASDE). This makes it hard to achieve the automated, intelligent and rapid design of mechanical products. Furthermore, due to the lack of consideration of experts' cognition of product functions and structures in the application of the current case-based reasoning (CBR) method in the field of automated experiential reasoning (AER), the parameter solving process is separated from ASDE. Aiming at improving the accuracy and intelligence level of AER in mechanical product design, this paper proposed a parameter-extended CBR (PECBR) method based on a functional basis by integrating ASDE into AER. The PECBR method mainly contains two parts: firstly, in order to acquire and quantitatively describe expert experiential knowledge to provide an effective basis for AER, a knowledge representation method integrating a function-flow-parameter matrix set (FFP-MS) using functional bases and a parameter experiential correlation matrix (PEC-M) extracted from FFP-MS were presented for mechanical products, where the FFP-MS characterized the operation of function and energy flow during the working process of products. An acquisition rule for FFP-MS was designed to extract the degree of correlation between each two parameters, in which the implicit knowledge hiding among functions, flows and parameters was mined to form PEC-M; secondly, to cope with the difficulty in integrating ASDE into AER, a feature-weighted case adaptation (FCA) method was proposed by adopting a presented weighted kernel support vector machine (WK-SVM) and dynamic particle swarm optimization (DPSO). The FCA method can achieve the intelligent and automated solving of product parameters through identifying PEC-M during the case adaptation process. Two case studies on two-stage reducers and corn huskers were carried out to demonstrate the validity of the PECBR method. Compared with other conventional CBR methods, PECBR method can derive a more accurate value of parameters in mechanical product designs especially in the case of limited similar cases.