Modeling and simulation

About: Modeling and simulation is a research topic. Over the lifetime, 10273 publications have been published within this topic receiving 111550 citations.

Simulations of inorganic–bioorganic interfaces to discover new materials: insights, comparisons to experiment, challenges, and opportunities

Abstract: Natural and man-made materials often rely on functional interfaces between inorganic and organic compounds. Examples include skeletal tissues and biominerals, drug delivery systems, catalysts, sensors, separation media, energy conversion devices, and polymer nanocomposites. Current laboratory techniques are limited to monitor and manipulate assembly on the 1 to 100 nm scale, time-consuming, and costly. Computational methods have become increasingly reliable to understand materials assembly and performance. This review explores the merit of simulations in comparison to experiment at the 1 to 100 nm scale, including connections to smaller length scales of quantum mechanics and larger length scales of coarse-grain models. First, current simulation methods, advances in the understanding of chemical bonding, in the development of force fields, and in the development of chemically realistic models are described. Then, the recognition mechanisms of biomolecules on nanostructured metals, semimetals, oxides, phosphates, carbonates, sulfides, and other inorganic materials are explained, including extensive comparisons between modeling and laboratory measurements. Depending on the substrate, the role of soft epitaxial binding mechanisms, ion pairing, hydrogen bonds, hydrophobic interactions, and conformation effects is described. Applications of the knowledge from simulation to predict binding of ligands and drug molecules to the inorganic surfaces, crystal growth and shape development, catalyst performance, as well as electrical properties at interfaces are examined. The quality of estimates from molecular dynamics and Monte Carlo simulations is validated in comparison to measurements and design rules described where available. The review further describes applications of simulation methods to polymer composite materials, surface modification of nanofillers, and interfacial interactions in building materials. The complexity of functional multiphase materials creates opportunities to further develop accurate force fields, including reactive force fields, and chemically realistic surface models, to enable materials discovery at a million times lower computational cost compared to quantum mechanical methods. The impact of modeling and simulation could further be increased by the advancement of a uniform simulation platform for organic and inorganic compounds across the periodic table and new simulation methods to evaluate system performance in silico.

Modelica - a general object-oriented language for continuous and discrete-event system modeling and simulation

Abstract: Modelica is a general equation-based object-oriented language for continuous and discrete-event modeling of physical systems for the purpose of efficient simulation. The language unifies and generalizes previous object-oriented modeling languages. It is bringing about a revolution in this area, based on its ease of use, visual design of models with combination of Lego-like predefined model building blocks, its ability to define model libraries with re-usable components and its support for modeling and simulation of complex applications involving parts from several application domains. In this paper we present the Modelica language with emphasis on its language features and one of the associated simulation environments. Simulation models can be developed in an integrated problem-solving environment by using a graphical editor for connection diagrams. Connections are established just by drawing lines between objects picked from a class library. The principles of object oriented physical systems modeling and the multi-domain capabilities of the language are presented in the paper by several examples.

Modeling and simulation fundamentals : theoretical underpinnings and practical domains

Abstract: Preface. Contributors. 1 Introduction to Modeling and Simulation (Catherine M. Banks). M&S. M&S Characteristics and Descriptors. M&S Categories. Conclusion. References. 2 Statistical Concepts for Discrete Event Simulation (Roland R. Mielke). Probability. Simulation Basics. Input Data Modeling. Output Data Analysis. Conclusion. References. 3 Discrete-Event Simulation (Rafael Diaz and Joshua G. Behr). Queuing System Model Components. Simulation Methodology. DES Example. Hand Simulation Spreadsheet Implementation. Arena Simulation. Conclusion. References. 4 Modeling Continuous Systems (Wesley N. Colley). System Class. Modeling and Simulation (M&S) Strategy. Modeling Approach. Model Examples. Simulating Continuous Systems. Simulation Implementation. Conclusion. References. 5 Monte Carlo Simulation (John A. Sokolowski). The Monte Carlo Method. Sensitivity Analysis. Conclusion. References. 6 Systems Modeling: Analysis and Operations Research (Frederic D. McKenziei). System Model Types. Modeling Methodologies and Tools. Analysis of Modeling and Simulation (M&S). OR Methods. Conclusion. References. Further Readings. 7 Visualization (Yuzhong Shen). Computer Graphics Fundamentals. Visualization Software and Tools. Case Studies. Conclusion. References. 8 M&S Methodologies: A Systems Approach to the Social Sciences (Barry G. Silverman, Gnana K. Bharathy, Benjamin Nye, G. Jiyun Kim, Mark Roddy, and Mjumbe Poe). Simulating State and Substate Actors with CountrySim: Synthesizing Theories Across the Social Sciences. The CountrySim Application and Sociocultural Game Results. Conclusions and the Way Forward. References. 9 Modeling Human Behavior (Yiannis Papelis and Poornima Madhavan). Behavioral Modeling at the Physical Level. Behavioral Modeling at the Tactical and Strategic Level. Techniques for Human Behavior Modeling. Human Factors. Human Computer Interaction. Conclusion. References. 10 Verifi cation, Validation, and Accreditation (Mikel D. Petty). Motivation. Background Defi nitions. VV&A Defi nitions. V&V as Comparisons. Performing VV&A. V&V Methods. VV&A Case Studies. Conclusion. Acknowledgments. References. 11 An Introduction to Distributed Simulation (Gabriel A. Wainer and Khaldoon Al-Zoubi). Trends and Challenges of Distributed Simulation. A Brief History of Distributed Simulation. Synchronization Algorithms for Parallel and Distributed Simulation. Distributed Simulation Middleware. Conclusion. References. 12 Interoperability and Composability (Andreas Tolk). Defining Interoperability and Composability. Current Interoperability Standard Solutions. Engineering Methods Supporting Interoperation and Composition. Conclusion. References. Further Readings. Index.

IFC BIM-Based Methodology for Semi-Automated Building Energy Performance Simulation

Abstract: Building energy performance (BEP) simulation is still rarely used in building design, commissioning and operations. The process is too costly and too labor intensive, and it takes too long to deliver results. Its quantitative results are not reproducible due to arbitrary decisions and assumptions made in simulation model definition, and can be trusted only under special circumstances. A methodology to semi-automate BEP simulation preparation and execution makes this process much more effective. It incorporates principles of information science and aims to eliminate inappropriate human intervention that results in subjective and arbitrary decisions. This is achieved by automating every part of the BEP modeling and simulation process that can be automated, by relying on data from original sources, and by making any necessary data transformation rule-based and automated. This paper describes the new methodology and its relationship to IFC-based BIM and software interoperability. It identifies five steps that are critical to its implementation, and shows what part of the methodology can be applied today. The paper concludes with a discussion of application to simulation with EnergyPlus, and describes data transformation rules embedded in the new Geometry Simplification Tool (GST).

Computational Materials Science: The Simulation of Materials Microstructures and Properties

Abstract: INTRODUCTION AND FUNDAMENTALS. Modeling and Simulation in Materials Science. Fundamentals and Solution of Differential Equations. SIMULATION TECHNIQUES AT THE NANO-MICRO SALE. Fundamentals. Statistical Mechanics in Atomic-Scale Simulations. Monte Carlo Simulation and Integration. Molecular Dynamics. SIMULATION TECHNIQUES AT THE MICRO-MESO SCALE. Discrete Dislocation Statics and Dynamics. Ginzburg--Landau-Type Phase Field Kinetic Models. Cellular Automata. Mesoscale Kinetic Monte Carlo and Potts Models. Geometrical and Component Models. Topological Network and Vertex Models. SIMULATION TECHNIQUES AT THE MESO-MACRO SCALE. Finite Element and Difference Methods at the Meso-Macroscale. Polycrystal Elasticity and Plasticity Models. INTEGRATED MODELING AND SIMULATION. Fundamentals. Space and Time Scales in Microstructure Simulation. Appendices.