Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

The model complexity reduction problem of large chemical reaction networks under isobaric and isothermal conditions is considered. With a given detailed kinetic mechanism and measured data of the key species over a finite time horizon, the complexity reduction is formulated in the form of a mixed-integer quadratic optimization problem where the objective function is derived from the parametric sensitivity matrix. The proposed method sequentially eliminates reactions from the mechanism and simultaneously tunes the remaining parameters until the pre-specified tolerance limit in the species concentration space is reached. The computational efficiency and numerical stability of the optimization are improved by a pre-reduction step followed by suitable scaling and initial conditioning of the Hessian involved. The proposed complexity reduction method is illustrated using three well-known case studies taken from reaction kinetics literature.     

Oxygen permeation of various archetypes of oxygen membranes based on BSCF

Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3‐δ tubes, capillaries, capillary modules, and asymmetric membranes were prepared and tested for oxygen permeation in a dead‐end vacuum operation mode at temperatures up to 850^°C. The capillary module was built up by reactive air brazing using seven capillaries and a supply tube. Two machined discs were used as an end cap and as a connector plate. The oxygen permeation behaves according to Wagner at small driving forces, but significant negative deviations were observed for asymmetric membranes and single capillaries at higher ones. This is caused by pressure drops at the vacuum side for single capillaries. The highest oxygen flux was revealed for the capillary module with 175.5 mL(STP)/min at a low‐vacuum pressure of 0.042 bar at 850^°C, but the asymmetric membrane showing a little bit higher flux at moderate vacuum pressures above 0.07 bar. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3195–3202, 2012     

Relaxation Pathways in Metallic Glasses

At temperatures below the glass transition temperature, physical properties of metallic glasses, such as density, viscosity, electrical resistivity or enthalpy, slowly evolve with time. This is the process of physical aging that occurs among all types of glasses and leads to structural changes at the microscopic level. Even though the relaxation pathways are ruled by thermodynamics as the glass attempts to re-attain thermodynamic equilibrium, they are steered by sluggish kinetics at the microscopic level. Understanding the structural and dynamic pathways of the relaxing glassy state is still one of the grand challenges in materials physics. We review some of the recent experimental advances made in understanding the nature of the relaxation phenomenon in metallic glasses and its implications to the macroscopic and microscopic properties changes of the relaxing glass.     

Using internal domain-specific languages to inherit tool support and modularity for model transformations

Software and Systems Modeling - Model-driven engineering (MDE) has proved to be a useful approach to cope with today’s ever-growing complexity in the development of software systems;...     

House Prices as a Result of Trading Activities: A Patient Trader Model

Computational Economics - We present a new modeling approach for house price movements as a consequence of the trading behavior of market agents. In our modeling approach, all agents are assumed to...     

Dependency solving: A separate concern in component evolution management

Maintenance of component-based software platforms often has to face rapid evolution of software components. Component dependencies, conflicts, and package managers with dependency solving capabilities are the key ingredients of prevalent software maintenance technologies that have been proposed to keep software installations synchronized with evolving component repositories. We review state-of-the-art package managers and their ability to keep up with evolution at the current growth rate of popular component-based platforms, and conclude that their dependency solving abilities are not up to the task.We show that the complexity of the underlying upgrade planning problem is NP-complete even for seemingly simple component models, and argue that the principal source of complexity lies in multiple available versions of components. We then discuss the need of expressive languages for user preferences, which makes the problem even more challenging.We propose to establish dependency solving as a separate concern from other upgrade aspects, and present CUDF as a formalism to describe upgrade scenarios. By analyzing the result of an international dependency solving competition, we provide evidence that the proposed approach is viable.