A fictitious domain decomposition method for the solution of partially axisymmetric acoustic scattering problems. Part I: Dirichlet boundary conditions

We present a fictitious domain decomposition method for the fast solution of high‐frequency acoustic scattering problems characterized by a partially axisymmetric sound‐soft scatterer. We apply this method to the solution of various mockup submarine problems, and highlight its computational advantages and intrinsic parallelism. Copyright © 2002 John Wiley & Sons, Ltd.     

A fictitious domain decomposition method for the solution of partially axisymmetric acoustic scattering problems. Part 2: Neumann boundary conditions

We present a fictitious domain decomposition method for the fast solution of acoustic scattering problems characterized by a partially axisymmetric sound‐hard scatterer. We apply this method to the solution of a mock‐up submarine problem, and highlight its computational advantages and intrinsic parallelism. A key component of our method is an original idea for addressing a Neumann boundary condition in the general framework of a fictitious domain method. This idea is applicable to many other linear partial differential equations besides the Helmholtz equation. Copyright © 2003 John Wiley & Sons, Ltd.     

Automatic partitioning of unstructured meshes for the parallel solution of problems in computational mechanics

Most of the recently proposed computational methods for solving partial differential equations on multiprocessor architectures stem from the 'divide and conquer' paradigm and involve some form of domain decomposition. For those methods which also require grids of points or patches of elements, it is often necessary to explicitly partition the underlying mesh, especially when working with local memory parallel processors. In this paper, a family of cost-effective algorithms for the automatic partitioning of arbitrary two- and three-dimensional finite element and finite difference meshes is presented and discussed in view of a domain decomposed solution procedure and parallel processing. The influence of the algorithmic aspects of a solution method (implicit/explicit computations), and the architectural specifics of a multiprocessor (SIMD/MIMD, startup/transmission time), on the design of a mesh partitioning algorithm are discussed. The impact of the partitioning strategy on load balancing, operation count, operator conditioning, rate of convergence and processor mapping is also addressed. Finally, the proposed mesh decomposition algorithms are demonstrated with realistic examples of finite element, finite volume, and finite difference meshes associated with the parallel solution of solid and fluid mechanics problems on the iPSC/2 and iPSC/860 multiprocessors.     

Depollution of atmospheric emissions of wood pyrolysis furnaces

The wood carbonization in Tunisia consists essentially of traditional activity using charcoaling stacks and pits characterized by high atmospheric pollution and poor energy conversion. Indeed, 70% of the initial mass of anhydrous wood are found in the vapor as aerosols, polluting and toxic gases and complex condensable organic compounds that can cause a substantial pollution of air, ground and water. Several processes of treatment and energy valorization of such effluents were proposed, but the incineration remains at present the most promising technique of depollution. The results show that the incineration, at about 1000°C, of wood carbonization smokes allows the destruction of 99% of the mass of pollutants except CO₂ and the reduction of polluting gas emission. The possible valorization of the smoke’s energy in the exit of the incinerator enhances the thermal efficiency of the process.     

Microwave diversity imaging and automated target identificationbased on models of neural networks

Radar targets can be identified by either forming images with sufficient resolution to be recognized by the human observer or by forming signatures or representations of the target for automated machine recognition. Tomographic microwave diversity imaging techniques that combine angular (aspect), spectral, and polarization degrees of freedom have been shown, as summarized in the first part of this paper, to be capable of producing images of the scattering centers of a target with near optical resolution. In the second part of the paper the author shows that collective nonlinear signal processing based on models of neural networks combined with the use of suitable target signatures (here sinogram representations) offer the promise of robust super-resolved target identification from partial information. Results presented are of numerical simulations for a neuromorphic processor where the neural net performs simultaneously the functions of data storage, processing, and recognition by automatically generating an identifying object label, and fast optoelectronic architectures and hardware implementations are briefly mentioned. Practical considerations and extensions to real systems are briefly discussed     

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly...     

Age-Related Differences in the use of Hypertext: Experiment and Design Guidelines

Hypertext systems can be targeted for many different user populations, yet little experimental research has been done to provide design guidelines on how to match the system to the user. An experiment tested whether age relates to the dimensions of performance, navigation strategies and perceptions in use of a hypertext library card catalog. While adults were superior to children in speed and accuracy, there were no indications that the children were qualitatively different than the adults in navigation patterns or perceptions of the system. Some children exhibited more exploratory problem-solving behaviors that the adults. A number of design guidelines are offered, based on the empirical results.     

Local calibration of rigid pavement performance models using resampling methods

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance programme. The nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local design and construction practices. Therefore, before implementing the new M-E design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when limited number of pavement sections are available. Consequently, there is a need to employ statistical and resampling methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The main objectives of the paper are to demonstrate the local calibration of rigid pavement performance models and compare the calibration results based on different resampling techniques. The bootstrap is a non-parametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping and jackknifing to locally calibrate the transverse cracking and IRI performance models for newly constructed and rehabilitated rigid pavements. The results of the calibration show that the standard error of estimate and bias are lower compared to the traditional sampling methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the IRI model, which indicates robustness of the local model coefficients.     

Suppressing the coalescence in the multi-component lattice Boltzmann method

This article discusses a novel phenomenological approach for suppressing the coalescence in the Gunstensen multi-component lattice Boltzmann method (LBM). The suppression of coalescence is achieved by perturbing the terminal nodes of the ambient fluid’s thin layer trapped between the approaching droplets. This additional perturbation creates a local high pressure fluid layer which eventually leads to suppressing the coalescence of the neighboring droplets while maintaining a suitable qualitative force balance representative of the physical intermolecular forces which act between them.     

Multidisciplinary Simulation of the Maneuvering of an Aircraft

A computational methodology for the simulation of the transient aeroelastic response of an unrestrained and flexible aircraft during high-G maneuvers is presented. The key components of this methodology are: (a) a three-field formulation for coupled fluid/structure interaction problems; (b) a second-order time-accurate and geometrically conservative flow solver for CFD computations on unstructured dynamic meshes; (c) a corotational finite element method for the solution of geometrically nonlinear and unrestrained structural dynamics problems; (d) a robust method for updating an unrestrained and unstructured moving fluid mesh; and (e) a second-order time-accurate staggered algorithm for time-integrating the coupled fluid/structure semi-discrete equations of motion. This computational methodology is illustrated with the simulation on a parallel processor of several three-dimensional high-G pullup maneuvers of the Langley Fighter in the transonic regime, using a detailed finite element aeroelastic model.