Evaluation of methods of reducing community noise impact around San Jose municipal airport

A computer simulation of the airport noise impact on the surrounding communities was used to evaluate alternate operational procedures, improved technology, and land use conversion as methods of reducing community noise impact in the airport vicinity. In addition, a constant density population distribution was analyzed for possible application to other airport communities with fairly uniform population densities and similar aircraft operational patterns. The introduction of sound absorption material (SAM) was found to reduce community noise annoyance by over 25 percent, and the introduction of refan was found to reduce community annoyance by over 60 percent. Replacing the present aircraft was found to reduce the noise problem to very small proportions, and the introduction of an advanced technology twin was found to essentially eliminate the community noise problem

Phase separation on a hyperbolic lattice

We report a preliminary numerical study by kinetic Monte Carlo simulation of the dynamics of phase separation following a quench from high to low temperature in a system with a single, conserved, scalar order parameter (a kinetic Ising ferromagnet) confined to a hyperbolic lattice. The results are compared with simulations of the same system on two different, Euclidean lattices, in which cases we observe power-law domain growth with an exponent near the theoretically known value of 1/3. For the hyperbolic lattice we observe much slower domain growth, consistent to within our current accuracy with power-law growth with a much smaller exponent near 0.13. The paper also includes a brief introduction to non-Euclidean lattices and their mapping to the Euclidean plane.Comment: Computer Simulation Studies in Condensed Matter Physics 26 (CSP13), Edited by D.P. Landau, S.P. Lewis, H.-B. Schuttle

Parallel computations and control of adaptive structures

The equations of motion for structures with adaptive elements for vibration control are presented for parallel computations to be used as a software package for real-time control of flexible space structures. A brief introduction of the state-of-the-art parallel computational capability is also presented. Time marching strategies are developed for an effective use of massive parallel mapping, partitioning, and the necessary arithmetic operations. An example is offered for the simulation of control-structure interaction on a parallel computer and the impact of the approach presented for applications in other disciplines than aerospace industry is assessed

Textile Forms’ Computer Simulation Techniques

Computer simulation techniques of textile forms already represent an important tool for textile and garment designers, since they offer numerous advantages, such as quick and simple introduction of changes while developing a model in comparison with conventional techniques. Therefore, the modeling and simulation of textile forms will always be an important issue and challenge for the researchers, since close‐to‐reality models are essential for understanding the performance and behavior of textile materials. This chapter deals with computer simulation of different textile forms. In the introductory part, it reviews the development of complex modeling and simulation techniques related to different textile forms. The main part of the chapter focuses on study of the fabric and fused panel drape by using the finite element method and on development of some representative textile forms, above all, on functional and protective clothing for persons who are sitting during performing different activities. Computer simulation techniques and scanned 3D body models in a sitting posture are used for this purpose. Engineering approaches to textile forms’ design for particular purposes, presented in this chapter, show benefits and limitations of specific 3D body scanning and computer simulation techniques and outline the future research challenges

Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62

This article uses a sequentialized experimental design to select simulation input com- binations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code). This design and analysis adapt the clas- sic "expected improvement" (EI) in "efficient global optimization" (EGO) through the introduction of an unbiased estimator of the Kriging predictor variance; this estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are com- pared through various test functions, including the six-hump camel-back and several Hartmann functions. These empirical results demonstrate that in some applications bootstrapped EI finds the global optimum faster than classic EI does; in general, however, the classic EI may be considered to be a robust global optimizer.Simulation;Optimization;Kriging;Bootstrap

Escuela Practica: an introduction to vegetation sampling using computer simulation

The fact that vegetation varies from location to location and with time is common knowledge. The description of this spatial and temporal variation, or pattern, is a prelude to seeking an ecological explanation for the observed variation. It is essential, therefore, that students in ecology receive a grounding in the methods used to describe vegetation. The elements of vegetation description are outlined in any of the large number of available standard texts (e.g., Greig-Smith 1983; Causton 1988). These elements include sampling design, measures of plant abundance, type of distribution, identification of association between species, and the scale of any observed pattern. Prior to 1987 third year plant ecology students at The University of Western Australia were given their introduction to the methods of vegetation description in the field. A set of four artificial populations were occasionally used in the laboratory, principally when the weather was too wet for field work, to illustrate the effects of plant distribution and quadrat size on estimates of frequency and density. It was found that these exercises offered several advantages over conventional field work. Compared with the field work they could be completed relatively quickly; the students did not have to cope with problems of species identification; and, because they were able to compare their sample estimates with the known parameters of the artificial populations they had greater confidence in the methods being demonstrated. In 1987 the four artificial populations were designated as separate species and combined into a single map. To introduce more variation additional species exhibiting a range of distributions (random, clumped and regular) and associations (positive, independent, and negative) were added. In an attempt at further realism separate overlays were generated containing information representing soil type and topography. Students were now able to investigate not only aspects of vegetation description but the next ecological step of how the discovered species patterns might be related to abiotic factors of the environment. An Apple University Development Fund award in 1990 of $3000 enabled the transfer of the map information to the Macintosh microcomputer format

Artificiality in Social Sciences

This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.artificial societies; multi-agent systems; distributed artificial intelligence; complexity