Non-equilibrium statistical mechanics and applications to transport modelling. Rosemary Harris

Transcription

1 Non-equilibrium statistical mechanics and applications to transport modelling Rosemary Harris Goldsmiths Company Maths Course, July 24th 2008

2 Transport processes

3 Outline Framework Stochastic Markovian dynamics Toy example Asymmetric Simple Exclusion Process (ASEP) Definition of model Fundamental diagram for periodic boundary conditions Phase diagram for open boundary conditions Applications to traffic Cars Ants Molecular motors Summary and perspectives

4 Stochastic Markovian dynamics Interacting particles Discrete space, configurations σ(t) Dynamics Memoryless Markov Inherently random Stochastic Continuous time Transition rates (probabilities per unit time) k σ,σ Deterministic evolution for probability distribution P(σ,t) Master Equation: d dt P(σ,t) = [ kσ,σ P(σ,t) k σ,σp(σ,t) ] σ σ Aside: Can also be written in matrix formulation d P(t) = HP(t) dt

5 Equilibrium versus non-equilibrium Master equation again d dt P(σ,t) = [ kσ,σ P(σ,t) k σ,σp(σ,t) ] σ σ Conservation of probability P(σ,t) = 1 Long-time/stationary distribution d dt P (σ,t) = 0 Equilibrium, detailed balance Non-equilibrium Broken detailed balance σ k σ,σ P (σ ) = k σ,σp (σ) Stationary state characterized by non-zero currents (So far) no general framework for non-equilibrium statistical mechanics......insight from toy models, e.g., asymmetric simple exclusion process

6 Asymmetric Simple Exclusion Process Model defined in continuous time: Site occupancies n l = 1 (particle) 0 (hole) Results qualitatively same for all p q Totally Asymmetric case (TASEP) has q = 0 Importance of boundary conditions...

7 Periodic boundary conditions Number of particles conserved Average density = n l How does current depend on density? Average current j = p n l (1 n l+1 ) Assume no correlations, i.e., n l and n l+1 independent j = p n l (1 n l+1 ) = p (1 )

8 Fundamental diagram How does current depend on density? j = p (1 ) j p

9 Open boundary conditions Model has phase transitions β 1 Low density High current p 2 High density 0 0 p 2 1 α

10 Modelling of transport Models are a simplification of reality Onion picture, build up layers: Simplest possible toy model Progressively add more details Use computer! ASEP used as starting point for various transport processes...

11 Vehicular traffic ASEP is toy model for single-lane traffic Phase diagram already shows some features of real traffic β 1 Empty road High flow p 2 Jammed 0 0 p 2 1 α

12 Adding details Variation in road-surface, add disorder j 2 C p min Different speeds, rules for acceleration/deceleration (e.g., Nagel-Schreckenberg) For computational simplicity often work in discrete time cellular automata Different geometries More lanes Road networks 1

13 Experiments on real traffic Not usually periodic boundary conditions... Fundamental diagram Note metastable states Can construct simple models with similar effects

14 Real life: measurement

15 Real life: prediction

16 Real life: validation

17 Ant-trail model Modify model to include chemical signals (pheromones)

18 Ant fundamental diagrams Uni-directional versus bi-directional movement Flux Density Flux Density [Schadschneider et al. 03] Model predicts formation of platoons...

19 Intracellular transport Kinesin on axonal microtubule... [from Klumpp & Lipowsky] Key features: Preferred direction Discrete steps Exclusion Attachment and detachment At mesoscopic scale can model by stochastic exclusion process...

20 Breakdown of intracellular transport Higher than usual tau concentration in Alzheimer s patients Experiments, e.g., [Trinczek et al.]: Doesn t affect speed of motors on microtubules. But reduces their absorption probability Analytics for simple model, simulation for more complicated model [Grzeschik, Harris & Santen 08]: System robust to low concentration of tau but at higher densities Mean current strongly reduced Fluctuations increased...

21 Current fluctuations Often (relatively) easy to calculate mean current But finite-time observations can yield average current larger or smaller than mean Characterizing these fluctuations is important in applications Also of theoretical importance Current large deviations analogous to free energy in equilibrium p(j,t) e ê(j)t Exhibit particular symmetry (fluctuation theorem) Insight into structure of non-equilibrium statistical mechanics

22 Summary Science: Non-equilibrium physics typically very different to equilibrium physics ASEP is a simple test model allowing exact calculations Society: Use tools from maths/physics to model real-life transport situations Some examples of critical importance: Transport failure in Alzheimer s disease Pedestrian dynamics at the Hajj

23 Acknowledgments / References Thanks to Andreas Schadschneider (Cologne University) for supplying the videos Some interesting websites: [traffic state in NRW] [simulations/videos, articles on Hajj]