Optimization of Excitation in FDTD Method and Corresponding Source Modeling

Source and excitation modeling in FDTD formulation has a significant impact on the method performance and the required simulation time. Since the abrupt source introduction yields intensive numerical variations in whole computational domain, a generally accepted solution is to slowly introduce the source, using appropriate shaping functions in time. The main goal of the optimization presented in this paper is to find balance between two opposite demands: minimal required computation time and acceptable degradation of simulation performance. Reducing the time necessary for source activation and deactivation is an important issue, especially in design of microwave structures, when the simulation is intensively repeated in the process of device parameter optimization. Here proposed optimized source models are realized and tested within an own developed FDTD simulation environment

Modeling of Radiation Damage Effects in Silicon Detectors at High Fluences HL-LHC with Sentaurus TCAD

In this work we propose the application of an enhanced radiation damage model based on the introduction of deep level traps / recombination centers suitable for device level numerical simulation of silicon detectors at very high fluences (e.g. 2.0x10E16 1 MeV equivalent neutrons/cm2). We present the comparison between simulation results and experimental data for p-type substrate structures in different operating conditions (temperature and biasing voltages) for fluences up to 2.2x10E16 neutrons/cm2. The good agreement between simulation findings and experimental measurements fosters the application of this modeling scheme to the optimization of the next silicon detectors to be used at HL-LHC.Comment: Supported by the H2020 project AIDA-2020, GA no. 65416

An introduction to enterprise modeling and simulation

As part of an ongoing effort to continuously improve productivity, quality, and efficiency of both industry and Department of Energy enterprises, Los Alamos National Laboratory is investigating various manufacturing and business enterprise simulation methods. A number of enterprise simulation software models are being developed to enable engineering analysis of enterprise activities. In this document the authors define the scope of enterprise modeling and simulation efforts, and review recent work in enterprise simulation at Los Alamos National Laboratory as well as at other industrial, academic, and research institutions. References of enterprise modeling and simulation methods and a glossary of enterprise-related terms are provided

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Introducing Modeling to First-Year Engineering Students for Effective Implementation in the Engineering Design Process

Introducing Modeling to First-Year Engineering Students for Effective Implementation in the Engineering Design Process Carolina Barriento and Juan Francisco Granizo Embry-Riddle Aeronautical University Abstract Modeling and Simulation are fundamental skills for all engineering students [1]. However, students are usually introduced to these concepts during their junior or even as late as their senior year. Our research aims at familiarizing students with Modeling and Simulation tools in their first year of engineering studies. We consider the fact that proper learning involves using long-term memory (in contrast with working memory) and this is accomplished by the gradual introduction and practice of the concepts to learn [2]. The concept of Scaffolding is applied for this purpose. Scaffolding is a teaching method where the instructor gradually decreases the assistance provided as students increase their understanding of the material presented to them [3]. Our methodology involves a project-based approach, where students enrolled in an Introduction to Engineering course explore the Engineering Design Process by completing a semester-long design project where they work in teams to design, build, and test a prototype. Modeling and Simulation concepts are illustrated by using MATLAB and Simulink software, providing numerous opportunities to analyze connections between software models and physical models, and integrating guided mathematical modeling instruction. CAD modeling and 3D printing is also explored by having students design, model, and 3D-print a small part to be used in their prototype. The course material is designed so that students begin to make connections between virtual, mathematical, and physical models starting early in their academic career, solidifying these concepts in their long-term memory to facilitate and improve learning of engineering fundamentals in the years to come [4,5]. Our methodology involves a project-based approach, where students enrolled in an Introduction to Engineering course explore the Engineering Design Process by completing a semester-long design project where they work in teams to design, build, and test a prototype. Modeling and Simulation concepts are illustrated by using MATLAB and Simulink software, providing numerous opportunities to analyze connections between software models and physical models, and integrating guided mathematical modeling instruction. CAD modeling and 3D printing is also explored by having students design, model, and 3D-print a small part to be used in their prototype. The course material is designed so that students begin to make connections between virtual, mathematical, and physical models starting early in their academic career, solidifying these concepts in their long-term memory to facilitate and improve learning of engineering fundamentals in the years to come [4,5. Keywords: engineering fundamentals education, introduction to modeling, teaching first-year engineering students, introduction to modeling methods, introduction to the Engineering Design proces

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

Nonlinear physics of electrical wave propagation in the heart: a review

The beating of the heart is a synchronized contraction of muscle cells (myocytes) that are triggered by a periodic sequence of electrical waves (action potentials) originating in the sino-atrial node and propagating over the atria and the ventricles. Cardiac arrhythmias like atrial and ventricular fibrillation (AF,VF) or ventricular tachycardia (VT) are caused by disruptions and instabilities of these electrical excitations, that lead to the emergence of rotating waves (VT) and turbulent wave patterns (AF,VF). Numerous simulation and experimental studies during the last 20 years have addressed these topics. In this review we focus on the nonlinear dynamics of wave propagation in the heart with an emphasis on the theory of pulses, spirals and scroll waves and their instabilities in excitable media and their application to cardiac modeling. After an introduction into electrophysiological models for action potential propagation, the modeling and analysis of spatiotemporal alternans, spiral and scroll meandering, spiral breakup and scroll wave instabilities like negative line tension and sproing are reviewed in depth and discussed with emphasis on their impact in cardiac arrhythmias.Peer ReviewedPreprin