Large-scale simulations

Watanabe, M., Karplus, M. Dynamics of Molecules with Internal Degrees of Freedom by Multiple Time-Step Methods. J. Chem. Phys. 99 (1995) 8063-8074 Figueirido, F., Levy, R. M., Zhou, R., Berne, B. J. Large Scale Simulation of Macromolecules in Solution Combining the Periodic Fast Multiple Method with Multiple Time Step Integrators. J. Chem. Phys. 106 (1997) 9835-9849 Derreumaux, P., Zhang, G., Schlick, T, Brooks, B.R. A Truncated Newton Minimizer Adapted for CHARMM and Biomolecular Applications. J. Comp. Chem. 15 (1994) 532-555... 

While CAM-6 is somewhat limited in its ability to perform large-scale simulations of physical systems (it is a much less capable system than its follow-on, the CAM-8, for example see discussion below), its fundamental historical importance cannot be overstated. CAM-6 allowed researchers to directly experience, for the first time and in real time, the evolution of CA systems theretofore undertsood only as purely conceptual models. Margolus and Toffoli recall that when Pomeau, one of... 

Boundary layer similarity solution treatments have been used extensively to develop analytical models for CVD processes (2fl.). These have been useful In correlating experimental observations (e.g. fi.). However, because of the oversimplified fiow description they cannot be used to extrapolate to new process conditions or for reactor design. Moreover, they cannot predict transverse variations In film thickness which may occur even In the absence of secondary fiows because of the presence of side walls. Two-dimensional fully parabolized transport equations have been used to predict velocity, concentration and temperature profiles along the length of horizontal reactors for SI CVD (17,30- 32). Although these models are detailed, they can neither capture the effect of buoyancy driven secondary fiows or transverse thickness variations caused by the side walls. Thus, large scale simulation of 3D models are needed to obtain a realistic picture of horizontal reactor performance. 

The effective potentials described in this chapter, on the other hand, are suited to relatively weak intermolecular quantum effects and require only a slight additional computational overhead - more terms in the potential - relative to routine classical simulations. Therefore, systems with tens of thousands of atoms can readily be modeled. This makes possible the large-scale simulation of biomolecule solvation with... 

Yang et al. (1995) described the application of this scale-up approach. Comprehensive testing programs were performed on two relatively large-scale simulation units for a period of several years a 30-cm diameter (semicircular) Plexiglas cold model and a 3-m diameter (semicircular) Plexiglas cold model, both operated at atmospheric pressure. 

Computer simulations of confined polymers have been popular for several reasons. For one, they provide exact results for the given model. In addition, computer simulations provide molecular information that is not available from either theory or experiment. Finally, advances in computers and simulation algorithms have made reasonably large-scale simulations of polymers possible in the last decade. In this section I describe computer simulations of polymers at surfaces with an emphasis on the density profiles and conformational properties of polymers at single flat surfaces. 

The methods used for modeling pure granular flow are essentially borrowed from that of a molecular gas. Similarly, there are two main types of models the continuous (Eulerian) models (Dufty, 2000) and discrete particle (Lagrangian) models (Herrmann and Luding, 1998 Luding, 1998 Walton, 2004). The continuum models are developed for large-scale simulations, where the controlling equations resemble the Navier-Stokes equations for an ordinary gas flow. The discrete particle models (DPMs) are typically used in small-scale simulations or... 

Truncation does not increase number of taxon members in the sample, but it increases their base rate. Meehl suggested that having a base rate above. 10 is important for the adequate performance of MAXCOV, even when the total number of taxon members is fixed. This claim has not been thoroughly evaluated, but results of a recent large-scale simulation study by Beauchaine and Beauchaine (2002) appear to be consistent with this assertion. Beauchaine and Beauchaine found that when the taxon base rate is low, MAXCOV has... 

We believe that only large scale simulation studies can truly advance the discipline by helping us establish acceptable tolerance intervals. However, individual (parallel) simulations such as those just described can also be useful. These simulations can serve as suitability tests that is, they can tell the researcher whether a particular research data set can, in principle, answer the questions of interest. In other words, if taxonic and dimensional data are generated to simulate the research data and the researcher finds few differences between the simulated sets (e.g., they yielded the same number of taxonic plots), then there is little sense in analyzing the research data because it is unlikely to give a clear answer. With suitability testing, a modest simulation study (e.g., 20 taxonic and 20 continuous data sets) is preferred to individual simulations because it would yield clearer and more reliable results. 

Furthermore we have to prove the influence of obstacles nearby the emission source. Such obstacles will induce mechanical turbulence to the wind turbulence. In large scale simulation such effects are parameterized the turbulence influence is collected in general diffusion coefficients. In the small scale of the source vicinity this method is still questionable. 

Other work involving large-scale simulation of PEFCs was due to Meng and Wang and Ju and... 

James, S., and F. A. Jaberi. 2000. Large-scale simulations of two-dimensional non-premixed methane jet flames. Combustion Flame 123 465-87. 

As discussed in Chapter 2, most force fields are validated based primarily on comparisons to small molecule data and moreover most comparisons involve what might be called static properties, i.e., structural or spectral data for computed fixed conformations. There are a few noteworthy exceptions the OPLS and TraPPE force fields were, at least for molecular solvents, optimized to reproduce bulk solvent properties derived from simulations, e.g., density, boiling point, and dielectric constant. In most instances, however, one is left with the question of whether force fields optimized for small molecules or molecular fragments will perform with acceptable accuracy in large-scale simulations. 

While ab initio molecular dynamics simulations of condensed phase system hold great promise for accurate modeling of condensed phase processes, we anticipate that their use in large-scale simulations of reactions of energetic materials will not be feasible for several years. Therefore, until the computational limitations are eased, then molecular dynamics simulations of energetic materials in the condensed phase will be restricted to classical descriptions of reactions. 

Thus a total of 100 + (100)(10) = 1100 seconds of computational time is required for the large scale simulation, a cost at least approaching our original naive estimate of 100 seconds. 

Kleijnen JPC, Helton JC (1999) Statistical analyses of scatterplots to identify important factors in large-scale simulations. 1 Review and comparison of techniques. Reliability Engineering System Safety, 65(2) 147-185. 

Keith Gubbins I want to add a comment about ab initio calculations I m certainly not an expert in that, but there is a lot of industrial interest now in combining electronic structure calculations with large-scale simulations. There was a conference on this in Britain in January that I went to, called Industrial Applications of Computer Simulation, and I was surprised to see many more people from industry than academia. As a result of the industrial interest, their next conference in January will get together the electronic structure people with the simulators, but I wouldn t hazard a guess as to when this would be sufficiently practical, say, to design a new catalyst. Whether we re close to that in the next five or ten years, I m not really certain. 

Gail (2002, 2004) has set up a series of large-scale simulations predicting the abundances of minerals formed in the early Solar System as based on assumptions on the input dust materials from the interstellar medium (ISM), and condensation,... 

While the majority of these concepts are introduced and illustrated based on single-substrate single-product Michaelis-Menten-like reaction mechanisms, the final section details examples of mechanisms for multi-substrate multi-product reactions. Such mechanisms are the backbone for the simulation and analysis of biochemical systems, from small-scale systems of Chapter 5 to the large-scale simulations considered in Chapter 6. Hence we are about to embark on an entire chapter devoted to the theory of enzyme kinetics. Yet before delving into the subject, it is worthwhile to point out that the entire theory of enzymes is based on the simplification that proteins acting as enzymes may be effectively represented as existing in a finite number of discrete states (substrate-bound states and/or distinct conformational states). These states are assumed to inter-convert based on the law of mass action. The set of states for an enzyme and associated biochemical reaction is known as an enzyme mechanism. In this chapter we will explore how the kinetics of a given enzyme mechanism depend on the concentrations of reactants and enzyme states and the values of the mass action rate constants associated with the mechanism. 

Now, because computer resources are, after all, finite, all phase space points can be found in the first octant, below and to the left of a plane. Acknowledging this state of affairs it is appropriate to call this plane the horizon of the MD simulation world [21], or simply the computational horizon. The major part of all production calculations are still extended over a moderate number of time-steps (corresponding to a few hundred picoseconds), using empirical pair potentials-on systems with sizes, much smaller than what could maximally be possible. So, in fact, the vast majority of the points should be simply found in a region to the left of a plane we call the average computational horizon. The truly large scale simulations can be found between the two planes. 

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