2013 proceedings - the Virginia Modeling, Analysis and Simulation ...

2013 proceedings - the Virginia Modeling, Analysis and Simulation ... 2013 proceedings - the Virginia Modeling, Analysis and Simulation ...

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1.0 t⩵2498.1 1.0 t⩵2498.1 0.9 0.8 0.7 0.6 0.20 0.15 0.10 0.05 0.8 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.70 0.75 0.80 0.85 0.90 (a) t⩵2498.1 0.0 0.2 0.4 0.6 0.8 1.0 0.1 0.2 0.3 (c) 0.6 0.4 0.2 0.0 CosΘv,n 1.0 0.5 0.0 0.5 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.02 0.04 0.06 (b) 2490 2492 2494 2496 2498 2500 Fig. 1: The simulation on a 1 × 1 square domain with N = 1, and ζ a = −4. (a) Velocity field superimposed to the density plot of concentration. (b) Polarity director. (c) Nematic orientation. (d) The domain averaged cosine angle between the particle polarity director and the fluid velocity. director goes against the flow direction (indicated by the value -1 of the cosine angle). Another simulation is performed in a rectangular region for N = 0.5 and ζ a = −2.5. Strongly oscillatory spatio-temporal structure is also observed. Figure 2 shows the snapshots of the flow field (superimposed to the local concentration) and the polarity director field. There are two shear layers (roughly the top half and the bottom half) with opposite flow and polarity directions. At the concentration valley, the flow field is strong, while at the concentration peak, the velocity is almost quiescent. A movie of this structure shows rapid flow reversal when these two layers change direction. V. CONCLUSION This research conducts 2D numerical simulations of active nematic polymers using the kinetic model [1]. Local concentration, velocity field, polar direction, and nematic orientational order are explored in the dilute regime where the stable passive isotopic phase is driven out of the equilibrium. Due to the active stress, rapid concentration fluctuation is observed in the physical domain for some concentration and active parameters. Polarity and the orientational directors are closely correlated with the flow field in the period of structure oscillations. These findings are in agreement with reports in [5] when a linear model is used. Time (d) t⩵334.4 1.0 0.8 0.6 (a) 0.4 0.2 0.0 0 1 2 3 4 Fig. 2: The polarization direction on a 4 × 1 rectangle domain with N = 0.5, and ζ a = −2.5. (a) Velocity field superimposed to the density plot of the concentration. (b) Polarity director. t⩵334.4 1.0 REFERENCES [1] M.G. Forest, Q. Wang, R. Zhou, Kinetic Theory and Simulations of Active Nematic Polymers, Soft Matter (accepted 2013) [2] A. J. Chorin, Numerical solution of the Navier-Stokes equations, Math. Comp., 22 (1968), 745762. [3] C. Hirt, B. Nichols, N. Romero, A Numerical Solution Algorithm for Transient Fluid Flows, Technical Report (1975) [4] C. M. Tome, S. McKee, GENSMAC: A Computational marker and cell method for free surface flows in general domains, J. Comput. Phys.,110 (1994), 171-186 [5] D. Saintillan and M. J. Shelly, Instabilities, Pattern Formation and Mixing in Active Suspensions, Phys. Fluids 20, 123304 (2008) This work is supported by NSF grant DMS-0908409 and Modeling & Simulation Scholarships from Old Dominion University. 0.8 0.6 (b) 0.4 0.2 0.0 0 1 2 3 4

1 Measuring Success of Separatists’ Demands: Development of the Tool Jan Nalaskowski Abstract—The paper examines question of independence and separatist demands formulated by regional entities. It introduces the Monte Carlo model and simulation in order to check under what conditions a mother-state is more prone to accept these demands. The system is described with the real world data, serving as a rationale for random variables generators. Using Selectorate Theory as a behavior mechanism, the model provides a nucleus which can be further developed as an analytical and visual tool. Index Terms—Authoritarianism and democracy, Selectorate Theory, separatist and independence movements, public and private goods provision. I. INTRODUCTION ONCEPTUALLY, this model aims to catch and operationalize an important, real world phenomenon. C Independence and separatist movements are both significant and difficult to assess. Due to their nature, social sciences usually employ descriptive and quantitative analysis of these processes, but resulting conclusions are often ambiguous. Some point out domestic explanations, other prefer to acknowledge global, international tendencies. The Selectorate Theory offers a parsimonious, quantitative analysis of internal policy-making and foreign outcomes. The logic of this model is to introduce this theory and project it to the sphere of independence and separatist movements. Despite the fact that explanations are by definition internal, the model points out the future need to implement international factors. Therefore, the main goal of this model is to introduce a parsimonious, quantitative framework in inherently descriptive sphere of independence and separatist tendencies. It is to become a nucleus for further improvements. Data gathering, case studies, game theoretic component and implementation of international factors will make this model the useful tool for scholars and policymakers. First, the simuland is described. Then, the Selectorate Theory is introduced. Both conceptual and operational models are thoroughly explained. The results of Monte Carlo simulation are discussed and model’s implications assessed. Verification and validation proceedings are sketched, revealing certain limitations and the need of further development. Manuscript submitted on March 21, 2013. Jan Nalaskowski is with Graduate Program in International Studies, Old Dominion University, Norfolk, VA 23529 USA (e-mail: jnalasko@odu.edu). II. SIMULAND Many nation states consist of regions which reveal certain ambitions for greater autonomy. This model acknowledges these tendencies without paying attention to their motives. The ambition itself is assumed to be present and it is mother-state’s reaction towards it that is important. Ultimately, a motherstate decides whether to grant independence to its region or not. It is this particular moment that model tries to catch. Further implications in the form of civil wars or international intervention, as for example in cases of Abkhazia, South Ossetia or even Kosovo, are beyond the scope of this model. Therefore the question is – will mother-state itself grant independence to a region or not. The Selectorate Theory is further explained, but the main idea is that every state’s political decision derives from its leader’s willingness to remain in office. This analytical parsimony makes it possible to explain state’s attitude towards independence ambitions of a region from internal level. The model consists of many random generators which were created basing on the real-world data. Their purpose is to provide simulation results to empower Selectorate Theory with synthetic, yet vital variables. The model is innovative but its main role is to execute multiple operations, saving time and energy of researchers. It should be coupled with human intelligence and problem solving abilities to become the usable tool. In short, simuland here is the real-world phenomenon of ambition for independence, revealed by many regions being parts of certain mother-states, which then express their answers to these requests. Some answers are positive, like the cases of Greenland or Faroe Islands, but most are negative, for example Catalonia or Tibet. All these regions have certain characteristics which are included into this model. Population variable is a fundament on which modeling, simulation and visualization is based. Assessment of country’s wealth is proposed as a vital factor for Selectorate Theory’s explanation. Its link with the outcomes has to be further developed but the model sets the underlying, technical fundament. As it was mentioned, simuland does not include international factors but the need of their future introduction is extremely vital. Monte Carlo simulation serves the purpose of setting the background and providing a snapshot of the system which could be further extended to include game-theoretic component. With this in mind, future discrete-time simulation introduction is expected. III. THEORETICAL BACKGROUND This chapter provides theoretical foundations for the conceptual model of the simuland. Selectorate Theory, introduced by Bruce Bueno de Mesquita and colleagues in The Logic of Political Survival, proposes an innovative way of thinking about choices being made by decision-makers of various levels. For the use of this model, it provides parsimony needed to simulate behavior which is social in

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2490 2492 2494 2496 2498 2500<br />

Fig. 1: The simulation on a 1 × 1 square domain with N = 1,<br />

<strong>and</strong> ζ a = −4. (a) Velocity field superimposed to <strong>the</strong> density plot of<br />

concentration. (b) Polarity director. (c) Nematic orientation. (d) The<br />

domain averaged cosine angle between <strong>the</strong> particle polarity director<br />

<strong>and</strong> <strong>the</strong> fluid velocity.<br />

director goes against <strong>the</strong> flow direction (indicated by <strong>the</strong> value<br />

-1 of <strong>the</strong> cosine angle).<br />

Ano<strong>the</strong>r simulation is performed in a rectangular region for<br />

N = 0.5 <strong>and</strong> ζ a = −2.5. Strongly oscillatory spatio-temporal<br />

structure is also observed. Figure 2 shows <strong>the</strong> snapshots of <strong>the</strong><br />

flow field (superimposed to <strong>the</strong> local concentration) <strong>and</strong> <strong>the</strong><br />

polarity director field. There are two shear layers (roughly<br />

<strong>the</strong> top half <strong>and</strong> <strong>the</strong> bottom half) with opposite flow <strong>and</strong><br />

polarity directions. At <strong>the</strong> concentration valley, <strong>the</strong> flow field is<br />

strong, while at <strong>the</strong> concentration peak, <strong>the</strong> velocity is almost<br />

quiescent. A movie of this structure shows rapid flow reversal<br />

when <strong>the</strong>se two layers change direction.<br />

V. CONCLUSION<br />

This research conducts 2D numerical simulations of active<br />

nematic polymers using <strong>the</strong> kinetic model [1]. Local<br />

concentration, velocity field, polar direction, <strong>and</strong> nematic<br />

orientational order are explored in <strong>the</strong> dilute regime where <strong>the</strong><br />

stable passive isotopic phase is driven out of <strong>the</strong> equilibrium.<br />

Due to <strong>the</strong> active stress, rapid concentration fluctuation is<br />

observed in <strong>the</strong> physical domain for some concentration <strong>and</strong><br />

active parameters. Polarity <strong>and</strong> <strong>the</strong> orientational directors are<br />

closely correlated with <strong>the</strong> flow field in <strong>the</strong> period of structure<br />

oscillations. These findings are in agreement with reports in<br />

[5] when a linear model is used.<br />

Time<br />

(d)<br />

t⩵334.4<br />

1.0<br />

0.8<br />

0.6<br />

(a)<br />

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0.2<br />

0.0<br />

0 1 2 3 4<br />

Fig. 2: The polarization direction on a 4 × 1 rectangle domain with<br />

N = 0.5, <strong>and</strong> ζ a = −2.5. (a) Velocity field superimposed to <strong>the</strong><br />

density plot of <strong>the</strong> concentration. (b) Polarity director.<br />

t⩵334.4<br />

1.0<br />

REFERENCES<br />

[1] M.G. Forest, Q. Wang, R. Zhou, Kinetic Theory <strong>and</strong> <strong>Simulation</strong>s<br />

of Active Nematic Polymers, Soft Matter (accepted <strong>2013</strong>)<br />

[2] A. J. Chorin, Numerical solution of <strong>the</strong> Navier-Stokes equations,<br />

Math. Comp., 22 (1968), 745762.<br />

[3] C. Hirt, B. Nichols, N. Romero, A Numerical Solution Algorithm<br />

for Transient Fluid Flows, Technical Report (1975)<br />

[4] C. M. Tome, S. McKee, GENSMAC: A Computational marker<br />

<strong>and</strong> cell method for free surface flows in general domains, J.<br />

Comput. Phys.,110 (1994), 171-186<br />

[5] D. Saintillan <strong>and</strong> M. J. Shelly, Instabilities, Pattern Formation<br />

<strong>and</strong> Mixing in Active Suspensions, Phys. Fluids 20, 123304<br />

(2008)<br />

This work is supported by NSF grant DMS-0908409 <strong>and</strong> <strong>Modeling</strong> &<br />

<strong>Simulation</strong> Scholarships from Old Dominion University.<br />

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