Behavior Of Typical Systems Research Articles

A Thermodynamically Consistent, Microscopically-Based, Model of the Rheology of Aggregating Particles Suspensions.

In this work, we outline the development of a thermodynamically consistent microscopic model for a suspension of aggregating particles under arbitrary, inertia-less deformation. As a proof-of-concept, we show how the combination of a simplified population-balance-based description of the aggregating particle microstructure along with the use of the single-generator bracket description of nonequilibrium thermodynamics, which leads naturally to the formulation of the model equations. Notable elements of the model are a lognormal distribution for the aggregate size population, a population balance-based model of the aggregation and breakup processes and a conformation tensor-based viscoelastic description of the elastic network of the particle aggregates. The resulting example model is evaluated in steady and transient shear forces and elongational flows and shown to offer predictions that are consistent with observed rheological behavior of typical systems of aggregating particles. Additionally, an expression for the total entropy production is also provided that allows one to judge the thermodynamic consistency and to evaluate the importance of the various dissipative phenomena involved in given flow processes.

Interarea Power System Oscillations Damping via AI-based Referential Integrity Variable-Structure Control

Abstract The design of power system stabilizer (PSS) is load-dependent and needs continuous adjustment at each operating condition. This paper aims at introducing a robust non-fragile PSS for interconnected power systems. The proposed controller has the capability of adaptively tuning online its rule-base through a variable-structure direct adaptive control algorithm in order to rigorously attain the desired objectives. The PSS controller acts on damping the electromechanical modes of oscillations not only through a wide range of operating conditions but also in presence of different disturbances. Using MATLABTM-Simulink, simulation results significantly verify that the proposed controller provides favorable performance and efficiently contributes towards enhancing the system dynamic behavior when applied to the four machines two-area power system that mimics the typical system behavior in actual operation. The interaction between the variable-structure adaptive fuzzy-based power system stabilizer (VS-AFPSS) and the existed typical ones inside the interconnected power systems has been explicitly discussed. Compared to other conventional controllers, VS-AFPSS enables better damping characteristics to both local and inter-area oscillation modes considering different operating conditions and sever disturbances.

Random $\mathbb{Z}^d$-shifts of finite type

In this work we consider an ensemble of random $\mathbb{Z}^d$-shifts of finite type ($\mathbb{Z}^d$-SFTs) and prove several results concerning the behavior of typical systems with respect to emptiness, entropy, and periodic points. These results generalize statements made in [26] regarding the case $d=1$. &nbsp Let $\mathcal{A}$ be a finite set, and let $d \geq 1$. For $n$ in $\mathbb{N}$ and $\alpha$ in $[0,1]$, define a random subset $\omega$ of $\mathcal{A}^{[1,n]^d}$ by independently including each pattern in $\mathcal{A}^{[1,n]^d}$ with probability $\alpha$. Let $X_{\omega}$ be the (random) $\mathbb{Z}^d$-SFT built from the set $\omega$. For each $\alpha \in [0,1]$ and $n$ tending to infinity, we compute the limit of the probability that $X_{\omega}$ is empty, as well as the limiting distribution of entropy of $X_{\omega}$. Furthermore, we show that the probability of obtaining a nonempty system without periodic points tends to zero. &nbsp For $d>1$, the class of $\mathbb{Z}^d$-SFTs is known to contain strikingly different behavior than is possible within the class of $\mathbb{Z}$-SFTs. Nonetheless, the results of this work suggest a new heuristic: typical $\mathbb{Z}^d$-SFTs have similar properties to their $\mathbb{Z}$-SFT counterparts.

A NETWORK-THEORETICAL APPROACH TO UNDERSTANDING INTERSTELLAR CHEMISTRY

Recent years have seen dramatic advances in computational models of chemical processes in the interstellar medium (ISM). Typically, these models have been used to calculate changes in chemical abundances with time; the calculated abundances can then be compared with chemical abundances derived from observations. In this study, the output from an astrochemical simulation has been used to generate directed graphs with weighted edges; these have been analyzed with the tools of network theory to uncover whole-network properties of reaction systems in dark molecular clouds. The results allow the development of a model in which global network properties can be rationalized in terms of the basic physical properties of the reaction system. The ISM network exhibits an exponential degree distribution, which is likely to be a generic feature of chemical networks involving a broad range of reaction rate constants. While species abundances span several orders of magnitude, the formation and destruction rates for most species are approximately balanced—departures from this rule indicate species (such as CO) that play a critical role in shaping the dynamics of the system. Future theoretical or observational studies focusing on individual molecular species will be able to situate them in terms of their role in the complete system or quantify the degree to which they deviate from the typical system behavior.

Monte Carlo simulations of systems with complex energy landscapes

Non-traditional Monte Carlo simulations are a powerful approach to the study of systems with complex energy landscapes. After reviewing several of these specialized algorithms we shall describe the behavior of typical systems including spin glasses, lattice proteins, and models for “real” proteins. In the Edwards–Anderson spin glass it is now possible to produce probability distributions in the canonical ensemble and thermodynamic results of high numerical quality. In the hydrophobic–polar (HP) lattice protein model Wang–Landau sampling with an improved move set (pull-moves) produces results of very high quality. These can be compared with the results of other methods of statistical physics. A more realistic membrane protein model for Glycophorin A is also examined. Wang–Landau sampling allows the study of the dimerization process including an elucidation of the nature of the process.

Long-term load forecasting for fast developing utility using a knowledge-based expert system

The application of the classical forecasting methods, when applied to a fast developing utility with a period characterized by fast and dynamic changes, are insufficient and may provide an invaluable dimension to the decision making process. In this paper, a knowledge-based expert system (ES) is implemented to support the choice of the most suitable load forecasting model for medium/long term power system planning. In the proposed ES, the detailed problem statement including forecasting algorithms and the key variables (electrical and nonelectrical variables) that affect the demand forecasts are firstly identified. A set of decision rules relating these variables are then obtained and stored in the knowledge base. Afterwards, the best model that will reflect accurately the typical system behavior over other models is suggested to produce the annual load forecast. A practical application is given to demonstrate the usefulness of the developed prototype system.

Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System

The application of the classical forecasting methods, when applied to a fast-developing utility with a period characterized by fast and dynamic changes, are insufficient and may provide an invaluable dimension to the decision-making process. In this paper, a knowledge-based expert system (ES) is implemented to support the choice of the most suitable load forecasting model for medium/long-term power system planning. In the proposed ES, the detailed problem statement including forecasting algorithms and the key variables (electrical and non-electrical variables) that affect the demand forecasts are first identified. A set of decision rules relating these variables are then obtained and stored in the knowledge base. Afterwards, the best model that will reflect accurately the typical system behavior over other models is suggested to produce the annual load forecast. A practical application is given to demonstrate the usefulness of the developed prototype system.

The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II

Abstract In this paper, a knowledge-based expert system (ES) is implemented to support the choice of the most suitable load forecasting model, among traditional mathematical techniques of Part I, for medium/long term power system planning. In the proposed ES, the detailed problem statement including forecasting algorithms and the key variables that affect the demand forecasts are firstly identified. So, system planner establishes a multitude of electrical, non-electrical variables for different areas. A set of decision rules relating these variables are then established and stored in the knowledge base. With the knowledge based at hand, a list of realistic models that can reflect accurately the typical system behavior over other models is emulated. Then, the best one is suggested to produce the annual load forecast. A practical application is given to demonstrate the usefulness of the developed prototype.

Posbist reliability behavior of typical systems with two types of failure

The posbist reliability theory is based on the possibility assumption and the binary-state assumption. In this paper we study the posbist reliability behavior, in the presence of two failure modes, for various classes of typical systems, including series systems, parallel systems, k-out-of-n systems, series-parallel systems, and parallel-series systems. It is shown that for each typical system, the posbist system reliability is bounded by the minimum and the maximum of the posbist component reliabilities.