Monte Carlo simulation strategy

We have pursued a plug-and-play strategy with two different energy functions, a molecular mechanics AMBER force field [131,132] and a simplified energy function, along with two different sampling techniques, evolutionary programming [91] and Monte Carlo simulations [118,119,127,128]. 

The best strategy to be followed in order to get accurate sets of A values has not been defined, so at present more or less complex statistical elaborations of some data are used. Among the numerical data that have been used we mention solvation and solvent transfer energies, intrinsic solute properties (electron isodensity surfaces, isopotential electronic surfaces, multipole expansions of local charge distribution), isoenergy surfaces for the interaction with selected probes (water, helium atoms), Monte Carlo simulations with solutes of various nature. All these sets of data deserve comments, that are here severely limited not to unduly extend this Section. 

N.B. Wilding Generic Sampling Strategies for Monte Carlo Simulation of Phase Behaviour, Lect. Notes Phys. 703, 39—66 (2006)... 

Fig. 2. Schematic of the Monte Carlo library design and redesign strategy (from Falcioni and Deem, 2000). (a) One Monte Carlo round with 10 samples an initial set of samples, modification of the samples, measurement of the new figures of merit, and the Metropolis criterion for acceptance or rejection of the new samples, (b) One parallel tempering round with five samples at and five samples at f>2- In parallel tempering, several Monte Carlo simulations are performed at different temperatures, with the additional possibility of sample exchange between the simulations at different temperatures.

This simulation strategy has been widely used in MD simulations [117, 118, 131, 152, 153] and to a lesser extent in Monte Carlo simulations [128, 129, 154]. Nonetheless, the SCF procedure is limited and computationally demanding, because any nonconverged SCF calculation (i.e., energy minimization in the case of the Drude model) introduces systematic drag forces on the physical atoms that considerably affect energy conservation and the stability of the temperature [118,132, 155]. Therefore, this approach is not ideal for MD simulations. 

In this paper we will present an improved methodology for the modelling and analysis of complex production systems by using a combination of extended coloured stochastic Petri net (ECSPN) and a reliability block diagram (RDB). This enables us to model condition based maintenance strategies as well as dynamically grouped maintenance actions. The models are analyzed by applying a Monte Carlo simulation. 

Monte Carlo simulation has been presented as a technique that is able to effectively model the apphcation of TLD to systems (Prescott Andrews 2005, 2006, 2008a,b,c). It is a flexible modelling technique that can easily handle the different possible maintenance strategies and the multiple fault occurrences described in Section 2. Figure 5 shows a Monte Carlo simulation algorithm for modelling the apphcation of TLD, which is briefly described here and described in more detail in previous papers (Prescott Andrews 2005, 2006). 

The structural, thermal and mechanical characterization of the interlamellar domain and of the 201 crystal-melt interface of semicrystalline PE was performed, and compared with experimental data where available. Monte Carlo simulations complete with three-fold torsional potential were used with a united atom representation of polyethylene. We have employed two different strategies to assess the properties of the interface. 

Bhatagar et al. [44] raised a question about coordination of multi-plants production in a vertically integrated firm, and had a thorough review of conventional coordination problems and multi-plants coordination problems. Chien [45] smdied a similar problem in [43] considering stochastic demand conditions. Based on the stable and independent weekly demand and the known probability distribution, they derived the average profit function of the unit product production and transportation and then got the optimal production-transport strategy by means of the analytic method. The numerical example verified the validity of the model based on Monte Carlo (Monte Carlo) simulation and sensitivity analysis. 

Beyond the above computation, and with the help of a Monte Carlo simulation (see, also, Section 3), the degree to which the individual component contributes to a system failure was determined. The above method also permits taking into account specific maintenance strategies. The life histories of the individual components are created over a period of time from 5 x 10 h and thereby it is also determined whether the system failure can be traced to corresponding component failures at any specific point in time. Here the failure-prone components are recorded in connection with each individual failure. Results demonstrate that position transmission (2) is involved in 100% of all system failures, which is natural because of the system s logical structure (Figure 5.23). The mechanical system of the valve is involved in 12%, the hydraulic control element in 29%, and the control pulse in 59% of all cases of system failure. 

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