Thorsten Windmann

ms2: A molecular simulation tool for thermodynamic properties, new version release

Abstract A new version release (2.0) of the molecular simulation tool ms2 [S. Deublein et al., Comput. Phys. Commun. 182 (2011) 2350] is presented. Version 2.0 of ms2 features a hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation to achieve higher scalability. Furthermore, the formalism by Lustig [R. Lustig, Mol. Phys. 110 (2012) 3041] is implemented, allowing for a systematic sampling of Massieu potential derivatives in a single simulation run. Moreover, the Green–Kubo formalism is extended for the sampling of the electric conductivity and the residence time. To remove the restriction of the preceding version to electro-neutral molecules, Ewald summation is implemented to consider ionic long range interactions. Finally, the sampling of the radial distribution function is added. Program summary Program title: m s 2 Catalogue identifier: AEJF_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJF_v2_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 50375 No. of bytes in distributed program, including test data, etc.: 345786 Distribution format: tar.gz Programming language: Fortran90. Computer: The simulation program m s 2 is usable on a wide variety of platforms, from single processor machines to modern supercomputers. Operating system: Unix/Linux. Has the code been vectorized or parallelized?: Yes: Message Passing Interface (MPI) protocol and OpenMP Scalability is up to 2000 cores. RAM: m s 2 runs on single cores with 512 MB RAM. The memory demand rises with increasing number of cores used per node and increasing number of molecules. Classification: 7.7, 7.9, 12. External routines: Message Passing Interface (MPI) Catalogue identifier of previous version: AEJF_v1_0 Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 2350 Does the new version supersede the previous version?: Yes. Nature of problem: Calculation of application oriented thermodynamic properties for fluids consisting of rigid molecules: vapor–liquid equilibria of pure fluids and multi-component mixtures, thermal and caloric data as well as transport properties. Solution method: Molecular dynamics, Monte Carlo, various classical ensembles, grand equilibrium method, Green–Kubo formalism, Lustig formalism Reasons for new version: The source code was extended to introduce new features. Summary of revisions: The new features of Version 2.0 include: Hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation; Ewald summation for long range interactions; sampling of Massieu potential derivatives; extended Green–Kubo formalism for the sampling of the electric conductivity and the residence time; radial distribution function. Restrictions: None. The system size is user-defined. Typical problems addressed by m s 2 can be solved by simulating systems containing typically 1000–4000 molecules. Unusual features: Auxiliary feature tools are available for creating input files, analyzing simulation results and visualizing molecular trajectories. Additional comments: Sample makefiles for multiple operation platforms are provided. Documentation is provided with the installation package and is available at http://www.ms-2.de . Running time: The running time of m s 2 depends on the specified problem, the system size and the number of processes used in the simulation. E.g. running four processes on a “Nehalem” processor, simulations calculating vapor–liquid equilibrium data take between two and 12 hours, calculating transport properties between six and 24 hours. Note that the examples given above stand for the total running time as there is no post-processing of any kind involved in property calculations.

The air pressure effect on the homogeneous nucleation of carbon dioxide by molecular simulation

Abstract Vapour–liquid equilibria (VLE) and the influence of an inert carrier gas on homogeneous vapour to liquid nucleation are investigated by molecular simulation for quaternary mixtures of carbon dioxide, nitrogen, oxygen, and argon. Canonical ensemble molecular dynamics simulation using the Yasuoka–Matsumoto method is applied to nucleation in supersaturated vapours that contain more carbon dioxide than in the saturated state at the dew line. Established molecular models are employed that are known to accurately reproduce the VLE of the pure fluids as well as their binary and ternary mixtures. On the basis of these models, also the quaternary VLE properties of the bulk fluid are determined with the Grand Equilibrium method. Simulation results for the carrier gas influence on the nucleation rate are compared with the classical nucleation theory (CNT) considering the ‘pressure effect’ [Phys. Rev. Lett. 101: 125703 (2008)]. It is found that the presence of air as a carrier gas decreases the nucleation rate only slightly and, in particular, to a significantly lower extent than predicted by CNT. The nucleation rate of carbon dioxide is generally underestimated by CNT, leading to a deviation between one and two orders of magnitude for pure carbon dioxide in the vicinity of the spinodal line and up to three orders of magnitude in the presence of air as a carrier gas. Furthermore, CNT predicts a temperature dependence of the nucleation rate in the spinodal limit, which cannot be confirmed by molecular simulation.

Simulation of Liquid-Liquid Equilibria with Molecular Models Optimized to Vapor-Liquid Equilibria and Model Development for Hydrazine and Two of Its Derivatives

In the chemical industry, knowledge on fluid phase equilibria is crucial for design and optimization of many technical processes. In a chemical plant, the costs for separation facilities constitute one of the highest investment outlays, typically in the order of 40–80% [15]. Not only vapor-liquid equilibrium(VLE) data are of interest, e.g. for distillation columns, but also other types of phase equilibria. For example, liquid-liquid equilibrium (LLE) data provide the basis for extraction processes. Classically, thermodynamic data for the design of such processes have to be measured experimentally and have to be aggregated by empirical correlations. For practical applications this leads to problems. For example, it is not possible to describe the entire fluid phase behavior consistently with a single model and set of parameters. Thus LLE data cannot be predicted reliably from VLE data (or vice versa) based on such correlations. Furthermore, the effort for measurements in the laboratory is very high, because every single fluid system of interest has to be measured individually. This approach particularly reaches its limits when multicomponent fluids or systems with multiple phases are of interest due to the sheer amount of independent variables. In a recent study by Hendriks et al. [10] about the demand of thermodynamic and transport properties in the chemical industry, the urgent need for a reliable and predictive approach to describe VLE as well as LLE with a single model and parameter set is pointed out.