Simulation strategy

Developing effective flow-simulation strategies for the equipment. 

Abstract Description of a multiscale simulation strategy we have developed to attack problems... 

In this case study a simulation strategy, based on a mechanistic PK/PD model, was developed to predict the outcome of the first time in man (FTIM) and proof of concept (POC) study of a new erythropoietin receptor agonist (ERA). A description of the erythropoiesis model, along with the procedures to scale the pharmacokinetics and pharmacodynamics based on preclinical in vivo and in vitro information is presented. The Phase I study design is described and finally the model-based predictions are shown and discussed. 

In this case study a simulation strategy was developed to select the dosing regimen of an antibiotic. A description of the critical factors for the interaction between drug, pathogen and host is presented, along with the procedures to integrate all... 

For single separation duties, Al-Tuwaim and Luyben (1991) provided a shortcut method for simultaneous optimisation of design and operation for binary and ternary separations. Using repetitive simulation strategy they have explained in detail the interaction between design and operation with an objective to maximise a capacity factor (total amount of specified products over unit time). 

Sorensen and Skogestad (1994) developed control strategies for BREAD processes by repetitive simulation strategy using a simple model in SPEEDUP package. Wilson and Martinez (1997) developed EKF (Extended Kalman Filter) based composition estimator to control BREAD processes. The estimator was found to be quite robust and was able to estimate composition within acceptable accuracy, even in the face of process/model mismatches. Balasubramhanya and Doyle III... 

It is very important to be clear on these points in devising simulation strategies, especially (when going on to pdes) the boundary conditions, which must conform to these points as well. 

Mackay, D.H.J., Cross, A.J., and Hagler, A.T. (1989). The role of energy minimization in simulation strategies of biomolecular systems. In Prediction of Protein Structure and the Principles of Protein Conformation. G.D.Fasman, ed. (New York Plenum Press), pp. 317 358. 

In the present work, a simulation strategy is formulated to study the performance of cathode materials in lithium ion batteries. Here micro scale properties, for example, diffusion of spherical electrode particle within the periodic boundaiy condition, 0electrode particles move in each step to its nearest neighbor distance, employing the condition ir j) > e -dLi lds ), where ir represents the random number, dLil is the nearest neighbor distance for the Li ion in the absence of solvent and ds being the thickness of the sohd phase. The MC codes involve macro scale properties, namely, solvation effects, diffusion coefficients and the concentration gradient... 

Dynamic simulation is more computationally intensive than steady-state simulation. Dynamic simulation is usually applied to parts of a process (or even single unit operations) rather than an entire process. Different simulation strategies are needed to give a robust dynamic model. Good introductions to dynamic simulation are given in the books by Luyben (2006), Ingham et al. (2007), Seborg et al. (2003), and Asprey and Machietto (2003) and the paper by Pantelides (1988). 

Maroudas and co-workers have described a hierarchical scheme for atomistic simulations involving the use of electronic structure calculations to develop and test semiempirical potentials that are in turn used for MD simulations. These results can sometimes be used to develop elementary step transition probabilities for use in dynamic Monte Carlo schemes. With Monte Carlo techniques, the well-known length and time scale limitations of MD can be greatly extended. This hierarchical approach appears to have great promise for the development of simulation strategies that will allow studies of a wide range of practical surface and thin-film chemical and physical processes. 

Fig. 10. Simulation strategy for silicon etching with energetic F or Cl. (From Barone and Graves, 1995b.)...

CHEOPS obtains this setup file in XML format from ModKit-l-. Tool wrappers are started according to this XML file. The input files required for the modeling tools Aspen Plus and gPROMS are obtained from the model repository ROME. CHEOPS applies a sequential-modular simulation strategy implemented as a solver component because all tool wrappers are able to provide closed-form model representations. The iterative solution process invokes the model evaluation functionality of each model representation, which refers to the underljdng tool wrapper to invoke the native computation in the modeling tool the model originated from. Finally, the results of all stream variables are written to a Microsoft Excel table when the simulation has terminated. 

The shortcut solution can often be used as the foundation for progressing toward a rigorous solution of the problem. In many applications the two methods are used in conjunction with each other to attain efficient and reliable simulation strategies. 

The solution of the above system of equations can be done following different simulation strategies, as it will be shown in the next example. 

Without subtracting the residual sharp spectral components of spin-labeled ASYN in the presence of SUVs, a multi-component spectral simulation strategy is required in order to describe the experimental data (Fig. 9b, c). Three different contributions featuring different isotropic rotational mobilities can be allocated to free spin labels, labeled residues not bound to SUVs, and residues bound to SUVs by the following approach. The spectra of ASYN in the absence of liposomes are well described by a superposition of two components. Si and S2, where Si corresponds to the spectrum of the free spin label MTSSL measured independently. In the presence of SUVs, an additional component S3 is needed, corresponding to the broadened part of the spectra. The shape of component S3 and the prefactors... 

The practical details of free energy difference calculations depend on the particular system and the particular mutation that is studied. For example, determining the potential of mean force between two small molecules in solvent clearly demands a simulation strategy different from that applied to the calculation of the free energy difference between two stereoisomers of an enzyme-bound inhibitor. 

Even if the software supplier is to be the primary source of training, it is important to establish a comprehensive training plan as an integral part of an overall simulation strategy. The training plan should include the following major areas ... 

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. 

III. POSSIBLE SIMULATION STRATEGY FOR MOLECULAR DYNAMICS INVESTIGATION OF WATER/OIL LIQUID MEMBRANE SYSTEMS... 

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