Holdings Information

Bibliographic Record Display

• Title:Statistical thermodynamics and stochastic kinetics : an introduction for engineers / Yiannis N. Kaznessis.
  
•    
• Author/Creator:Kaznessis, Yiannis Nikolaos, 1971-
  
• Published/Created:Cambridge ; New York : Cambridge University Press, 2012.
  

• Holdings

  Holdings Record Display

  • Location:WOODWARD LIBRARY stacksWhere is this?
    
  • Call Number: TP155.2.T45 K39 2012
    
  • Number of Items:1
    
  • Status:Available
    
  • Links:Donor bookplate
    

   
• Library of Congress Subjects:Statistical thermodynamics.
  Stochastic processes.
  Molucular dynamics--Simulation methods.
  
• Description:xii, 314 p. : ill. ; 24 cm.
  
• Summary:"Presenting the key principles of thermodynamics from a microscopic point of view, this book provides engineers with the knowledge they need to apply thermodynamics and solve engineering challenges at the molecular level. It clearly explains the concerns of entropy and free energy, emphasising key concepts used in equilibrium applications, whilst stochastic processes, such as stochastic reaction kinetics, are also covered. It provides a classical microscopic interpretation of thermodynamic concepts which is key for engineers, rather than focusing on more esoteric concepts of statistical thermodynamics and quantum mechanics. Coverage of molecular dynamics and Monte Carlo simulations as natural extensions of the theoretical treatment of statistical thermodynamics is also included, teaching readers how to use computer simulations and thus enabling them to understand and engineer the microcosm. Featuring many worked examples and over 100 end-of-chapter exercises, it is ideal for use in the classroom as well as for self-study"--Provided by publisher.
  
• Notes:Includes bibliographical references and index.
  
• ISBN:9780521765619
  0521765617
  
• Contents:Machine generated contents note: 1. Introduction
  1.1. Prologue
  1.2. If we had only a single lecture in statistical thermodynamics
  2. Elements of probability and combinatorial theory
  2.1. Probability theory
  2.1.1. Useful definitions
  2.1.2. Probability distributions
  2.1.3. Mathematical expectation
  2.1.4. Moments of probability distributions
  2.1.5. Gaussian probability distribution
  2.2. Elements of combinatorial analysis
  2.2.1. Arrangements
  2.2.2. Permutations
  2.2.3. Combinations
  2.3. Distinguishable and indistinguishable particles
  2.4. Stirling's approximation
  2.5. Binomial distribution
  2.6. Multinomial distribution
  2.7. Exponential and Poisson distributions
  2.8. One-dimensional random walk
  2.9. Law of large numbers
  2.10. Central limit theorem
  2.11. Further reading
  2.12. Exercises
  3. Phase spaces, from classical to quantum mechanics, and back
  3.1. Classical mechanics
  3.1.1. Newtonian mechanics
  3.1.2. Generalized coordinates
  3.1.3. Lagrangian mechanics
  3.1.4. Hamiltonian mechanics
  3.2. Phase space
  3.2.1. Conservative systems
  3.3. Quantum mechanics
  3.3.1. Particle-wave duality
  3.3.2. Heisenberg's uncertainty principle
  3.4. From quantum mechanical to classical mechanical phase spaces
  3.4.1. Born-Oppenheimer approximation
  3.5. Further reading
  3.6. Exercises
  4. Ensemble theory
  4.1. Distribution function and probability density in phase space
  4.2. Ensemble average of thermodynamic properties
  4.3. Ergodic hypothesis
  4.4. Partition function
  4.5. Microcanonical ensemble
  4.6. Thermodynamics from ensembles
  4.7. S = kB In Ω, or entropy understood
  4.8. Ω for ideal gases
  4.9. Ω with quantum uncertainty
  4.10. Liouville's equation
  4.11. Further reading
  4.12. Exercises
  5. Canonical ensemble
  5.1. Probability density in phase space
  5.2. NVT ensemble thermodynamics
  5.3. Entropy of an NVT system
  5.4. Thermodynamics of NVT ideal gases
  5.5. Calculation of absolute partition functions is impossible and unnecessary
  5.6. Maxwell-Boltzmann velocity distribution
  5.7. Further reading
  5.8. Exercises
  6. Fluctuations and other ensembles
  6.1. Fluctuations and equivalence of different ensembles
  6.2. Statistical derivation of the NVT partition function
  6.3. Grand-canonical and isothermal-isobaric ensembles
  6.4. Maxima and minima at equilibrium
  6.5. Reversibility and the second law of thermodynamics
  6.6. Further reading
  6.7. Exercises
  7. Molecules
  7.1. Molecular degrees of freedom
  7.2. Diatomic molecules
  7.2.1. Rigid rotation
  7.2.2. Vibrations included
  7.2.3. Subatomic degrees of freedom
  7.3. Equipartition theorem
  7.4. Further reading
  7.5. Exercises
  8. Non-ideal gases
  8.1. virial theorem
  8.1.1. Application of the virial theorem: equation of state for non-ideal systems
  8.2. Pairwise interaction potentials
  8.2.1. Lennard-Jones potential
  8.2.2. Electrostatic interactions
  8.2.3. Total intermolecular potential energy
  8.3. Virial equation of state
  8.4. van der Waals equation of state
  8.5. Further reading
  8.6. Exercises
  9. Liquids and crystals
  9.1. Liquids
  9.2. Molecular distributions
  9.3. Physical interpretation of pair distribution functions
  9.4. Thermodynamic properties from pair distribution functions
  9.5. Solids
  9.5.1. Heat capacity of monoatomic crystals
  9.5.2. Einstein model of the specific heat of crystals
  9.5.3. Debye model of the specific heat of crystals
  9.6. Further reading
  9.7. Exercises
  10. Beyond pure, single-component systems
  10.1. Ideal mixtures
  10.1.1. Properties of mixing for ideal mixtures
  10.2. Phase behavior
  10.2.1. law of corresponding states
  10.3. Regular solution theory
  10.3.1. Binary vapor-liquid equilibria
  10.4. Chemical reaction equilibria
  10.5. Further reading
  10.6. Exercises
  11. Polymers - Brownian dynamics
  11.1. Polymers
  11.1.1. Macromolecular dimensions
  11.1.2. Rubber elasticity
  11.1.3. Dynamic models of macromolecules
  11.2. Brownian dynamics
  11.3. Further reading
  11.4. Exercises
  12. Non-equilibrium thermodynamics
  12.1. Linear response theory
  12.2. Time correlation functions
  12.3. Fluctuation-dissipation theorem
  12.4. Dielectric relaxation of polymer chains
  12.5. Further reading
  12.6. Exercises
  13. Stochastic processes
  13.1. Continuous-deterministic reaction kinetics
  13.2. Away from the thermodynamic limit - chemical master equation
  13.2.1. Analytic solution of the chemical master equation
  13.3. Derivation of the master equation for any stochastic process
  13.3.1. Chapman-Kolmogorov equation
  13.3.2. Master equation
  13.3.3. Fokker-Planck equation
  13.3.4. Langevin equation
  13.3.5. Chemical Langevin equations
  13.4. Further reading
  13.5. Exercises
  14. Molecular simulations
  14.1. Tractable exploration of phase space
  14.2. Computer simulations are tractable mathematics
  14.3. Introduction to molecular simulation techniques
  14.3.1. Construction of the molecular model
  14.3.2. Semi-empirical force field potential
  14.3.3. System size and geometry
  14.3.4. Periodic boundary conditions
  14.3.5. Fortran code for periodic boundary conditions
  14.3.6. Minimum image convection
  14.4. How to start a simulation
  14.5. Non-dimensional simulation parameters
  14.6. Neighbor lists: a time-saving trick
  14.7. Further reading
  14.8. Exercises
  15. Monte Carlo simulations
  15.1. Sampling of probability distribution functions
  15.2. Uniformly random sampling of phase space
  15.3. Markov chains in Monte Carlo
  15.4. Importance sampling
  15.4.1. How to generate states
  15.4.2. How to accept states
  15.4.3. Metropolis Monte Carlo pseudo-code
  15.4.4. Importance sampling with a coin and a die
  15.4.5. Biased Monte Carlo
  15.5. Grand canonical Monte Carlo
  15.6. Gibbs ensemble Monte Carlo for phase equilibria
  15.7. Further reading
  15.8. Exercises
  16. Molecular dynamics simulations
  16.1. Molecular dynamics simulation of simple fluids
  16.2. Numerical integration algorithms
  16.2.1. Predictor-corrector algorithms
  16.2.2. Verlet algorithms
  16.3. Selecting the size of the time step
  16.4. How long to run the simulation?
  16.5. Molecular dynamics in other ensembles
  16.5.1. Canonical ensemble molecular dynamics simulations
  16.6. Constrained and multiple time step dynamics
  16.7. Further reading
  16.8. Exercises
  17. Properties of matter from simulation results
  17.1. Structural properties
  17.2. Dynamical information
  17.2.1. Diffusion coefficient
  17.2.2. Correlation functions
  17.2.3. Time correlation functions
  17.3. Free energy calculations
  17.3.1. Free energy perturbation methods
  17.3.2. Histogram methods
  17.3.3. Thermodynamic integration methods
  17.4. Further reading
  17.5. Exercises
  18. Stochastic simulations of chemical reaction kinetics
  18.1. Stochastic simulation algorithm
  18.2. Multiscale algorithms for chemical kinetics
  18.2.1. Slow-discrete region (I)
  18.2.2. Slow-continuous region (II)
  18.2.3. Fast-discrete region (III)
  18.2.4. Fast-continuous stochastic region (IV)
  18.2.5. Fast-continuous deterministic region (V)
  18.3. Hybrid algorithms
  18.4. Hybrid stochastic algorithm
  18.4.1. System partitioning
  18.4.2. Propagation of the fast subsystem - chemical Langevin equations
  18.4.3. Propagation of the slow subsystem - jump equations
  18.5. Hy3S - Hybrid stochastic simulations for supercomputers
  18.6. Multikin - Multiscale kinetics
  18.7. Further reading
  18.8. Exercises
  Appendices
  A. Physical constants and conversion factors
  A.1. Physical constants
  A.2. Conversion factors
  B. Elements of classical thermodynamics
  B.1. Systems, properties, and states in thermodynamics
  B.2. Fundamental thermodynamic relations.