Theory and simulation in neuroscience.

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.

Analysis and simulation of combustion oscillations in a laboratory spray combustor

The unstable combustion that can occur in combustion chambers is a major problem for aeroengines and ground-based industrial gas turbines. Nowadays, CFD provides a flexible, low cost tool to supplement direct measurement. This paper presents simulations of combustion oscillations in a liquid-fuelled experimental rig at the University of Cambridge. Linear acoustic theory was used to describe the acoustic waves propagating upstream and downstream of the combustion zone and to develop inlet and outlet boundary conditions just upstream and downstream of the combustion region enabling the CFD calculation to be efficiently concentrated on the combustion zone. A combustion oscillation was found to occur with its predicted frequency in good agreement with experimental measurements. More details about the unstable combustion can be obtained from the simulation results. The approach developed here is expected to provide a powerful tool for the design and operation of stable combustion systems. Copyright © 2009 by ASME.

Reynolds number influence on turbulent blocking effects of a rigid plane boundary (2nd report, direct numerical simulation using townsend's eddy forcing)

The Reynolds number influence on turbulent blocking effects by a rigid plane boundary is studied using direct numerical simulation (DNS). A new forcing method using 'simple model eddies' (Townsend 1976) for DNS of stationary homogeneous isotropic turbulence is proposed. A force field is obtained in real space by sprinkling many space-filling 'simple model eddies' whose centers are randomly but uniformly distributed in space and whose axes of rotation are random. The method is applied to a shear-free turbulent boundary layer over a rigid plane boundary and the blocking effects are investigated. The results show that stationary homogeneous isotropic turbulence is generated in real space using the present method. By using different model eddies with different sizes and rotation speeds, we could change the turbulence properties such as the integral and micro scales, the turbulent Reynolds number and the isotropy of turbulence. Turbulence intensities near the wall showed good agreements with the previous measurement and the linear analysis based on a rapid distortion theory (RDT). The splat effect (i.e., turbulence intensities of the components parallel to the boundary are amplified) occurs near the boundary and the viscous effect prohibits the splat effect at the quasi steady state at low Reynolds number.

Reynolds number influence on turbulent blocking effects of a rigid plane boundary (3rd report, direct numerical simulation on wall blocking effects for anisotropic turbulence)

The Reynolds number influence on turbulent blocking effects by a rigid plane boundary is studied using direct numerical simulation (DNS). A new forcing method proposed in the second report using Townsend's "simple model eddies" for DNS was extended to generate axisymmetric anisotropic turbulence. A force field is obtained in real space by sprinkling many space-filling "simple model eddies" whose centers are randomly but uniformly distributed in space. The axes of rotation are controlled in this study to generate axisymmetric anisotropic turbulence. The method is applied to a shear-free turbulent boundary layer over a rigid plane boundary and the blocking effects for anisotropic turbulence are investigated. The results show that stationary axisymmetric anisotropic turbulence is generated using the present method. Turbulence intensities near the wall showed good agreements with the rapid distortion theory (RDT) for small t (t ≪ TL), where TL. is the eddy turnover time. The splat effect (i. e. turbulence intensities of the components parallel to the surface are amplified) occurs near the boundary and the viscous effect attenuates the splat effect at the quasi steady state at low Reynolds number as for Isotropic turbulence. Prandtl's secondary flow of the second kind does not occur for low Reynolds number flows, which qualitatively agrees with previous observetion in a mixing-box.

Thermal building simulation using the UKCP09 probabilistic climate projections

This article investigates how to use UK probabilistic climate-change projections (UKCP09) in rigorous building energy analysis. Two office buildings (deep plan and shallow plan) are used as case studies to demonstrate the application of UKCP09. Three different methods for reducing the computational demands are explored: statistical reduction (Finkelstein-Schafer [F-S] statistics), simplification using degree-day theory and the use of metamodels. The first method, which is based on an established technique, can be used as reference because it provides the most accurate information. However, it is necessary to automatically choose weather files based on F-S statistic by using computer programming language because thousands of weather files created from UKCP09 weather generator need to be processed. A combination of the second (degree-day theory) and third method (metamodels) requires only a relatively small number of simulation runs, but still provides valuable information to further implement the uncertainty and sensitivity analyses. The article also demonstrates how grid computing can be used to speed up the calculation for many independent EnergyPlus models by harnessing the processing power of idle desktop computers. © 2011 International Building Performance Simulation Association (IBPSA).

International Workshop on Fluid-Structure Interaction. Theory, Numerics and Applications

... Fluid-Structure Interaction Simulation of Viscoplastic and Fracturing Thin- Shells Subjected to Underwater Shock Loading Ralf Deiterding, Fehmi Cirak, ...

QM/MM simulation of liquid water with an adaptive quantum region.

The simulation of complex chemical systems often requires a multi-level description, in which a region of special interest is treated using a computationally expensive quantum mechanical (QM) model while its environment is described by a faster, simpler molecular mechanical (MM) model. Furthermore, studying dynamic effects in solvated systems or bio-molecules requires a variable definition of the two regions, so that atoms or molecules can be dynamically re-assigned between the QM and MM descriptions during the course of the simulation. Such reassignments pose a problem for traditional QM/MM schemes by exacerbating the errors that stem from switching the model at the boundary. Here we show that stable, long adaptive simulations can be carried out using density functional theory with the BLYP exchange-correlation functional for the QM model and a flexible TIP3P force field for the MM model without requiring adjustments of either. Using a primary benchmark system of pure water, we investigate the convergence of the liquid structure with the size of the QM region, and demonstrate that by using a sufficiently large QM region (with radius 6 Å) it is possible to obtain radial and angular distributions that, in the QM region, match the results of fully quantum mechanical calculations with periodic boundary conditions, and, after a smooth transition, also agree with fully MM calculations in the MM region. The key ingredient is the accurate evaluation of forces in the QM subsystem which we achieve by including an extended buffer region in the QM calculations. We also show that our buffered-force QM/MM scheme is transferable by simulating the solvated Cl(-) ion.

Influencing factors of successful transitions towards product-service systems: A simulation approach

Product-Service Systems (PSS) are new business strategies moving and extending the product value towards its functional usage and related required services. From a theoretical point of view the PSS concept is known since a decade and many Authors reported reasonable possible success factors: higher profits over the entire life-cycle, diminished environmental burden, and localization of required services. Nevertheless the PSS promises remain quantitatively unproven relaying on a simple theory that involves a few constructs with some empirical grounding, but that is limited by weak conceptualization, few propositions, and/or rough underlying theoretical logic. A plausible interpretation to analyze the possible evolution of a PSS strategy could be considering it as a new business proposition competing on a traditional Product-Oriented (PO) market, assumed at its own equilibrium state at a given time. The analysis of the dynamics associated to a possible transition from a traditional PO to a PSS strategy allows investigating the main parameters and variables influencing an eventual successful adoption. This research is worthwhile because organizations undergoing fundamental PSS strategy are concerned about change and inertia key processes which, despite equilibrium theory and because of negative feedback loops, could undermine, economically, the return of their PSS proposition. In this paper Authors propose a qualitative System Dynamics (SD) approach by considering the PSS as a perturbation of an existing PO market featured by a set of known parameters. The proposed model incorporates several PSS factors able to influence the success of a PSS proposition under a set of given and justified assumptions, attempting to place this business strategy in a dynamic framework.

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.