Approach For Large-scale Systems Research Articles

Large-scale first-principles quantum transport simulations using plane wave basis set on high performance computing platforms

As the characteristic lengths of advanced electronic devices are approaching the atomic scale, ab initio simulation method, with full consideration of quantum mechanical effects, becomes essential to study the quantum transport phenomenon in them. The widely used non-equilibrium Green’s function (NEGF) combined with the density functional theory (DFT) approach prefers a localized basis set. As many states of the art DFT calculations for solid state systems are carried out in plane waves, it is thus worth to investigate the feasibility of using the plane wave basis set for large-scale quantum transport calculations. Here we present a plane wave method for large-scale transport calculations based on a previously developed scattering state calculation approach (Wang, 2005). We address the unique computational challenges of applying that approach for large-scale systems where it is too expensive to calculate all the occupied eigenstates of the system as in conventional DFT calculations. By applying several high-efficiency parallel algorithms, including linear-scaling DFT algorithm, folded spectrum method, and Chebyshev filter technique, we demonstrate that it is possible to use this approach to simulate a system with several thousand atoms on high performance computing platforms. This method is not only used to study nanowire interconnects, showing how the shape and point defect affects their transport properties, but also used to study nanoscale Si transistor. Such quantum transport simulation method will be useful for investigating and designing nanoscale devices.

An Optimal Distributed Fault Detection Scheme for Large-Scale Systems with Deterministic Disturbances

The main objective of this paper is to develop an optimal distributed fault detection (FD) approach for large-scale systems in the presence of unknown deterministic disturbances using the measurement of sensor networks. To be specific, the design approach consists of two phases: the distributed offline training phase and the online implementation phase. The offline training phase includes distributed iterative learning and average consensus algorithm. It is worth mentioning that, the distributed approach avoids enormous computational costs and complex information exchanges. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task.

An adaptive quasicontinuum approach for modeling fracture in networked materials: Application to modeling of polymer networks

Materials with network-like microstructure, including polymers, are the backbone for many natural and human-made materials such as gels, biological tissues, metamaterials, and rubbers. Fracture processes in these networked materials are intrinsically multiscale, and it is computationally prohibitive to adopt a fully discrete approach for large scale systems. To overcome such a challenge, we introduce an adaptive numerical algorithm for modeling fracture in this class of materials, with a primary application to polymer networks, using an extended version of the Quasicontinuum method that accounts for both material and geometric nonlinearities. In regions of high interest, for example near crack tips, explicit representation of the local topology is retained where each polymer chain is idealized using the worm like chain model. Away from these imperfections, the degrees of freedom are limited to a fraction of the network nodes and the network structure is computationally homogenized, using the micro-macro energy consistency condition, to yield an anisotropic material tensor consistent with the underlying network structure. A nonlinear finite element framework including both material and geometric nonlinearities is used to solve the system where dynamic adaptivity allows transition between the continuum and discrete scales. The method enables accurate modelling of crack propagation without a priori constraint on the fracture energy while maintaining the influence of large-scale elastic loading in the bulk. We demonstrate the accuracy and efficiency of the method by applying it to study the fracture in different examples of network structures. We further use the method to investigate the effects of network topology and disorder on its fracture characteristics. We discuss the implications of our method for multiscale analysis of fracture in networked material as they arise in different applications in biology and engineering.

Time delays in numerical modeling of frontal thoracic blast pressure wave responses

In this work a lumped mass mechanical model of a thorax subject to a blast pressure wave is taken into consideration. A thorax spring-dashpot model developed by Lobdell is implemented in numerical modeling of dynamics of the multibody system. The five degrees of freedom mechanical model of a chest adjacent to the elastic backrest is subject to an impulse loading generated by the blast pressure wave released by an explosion. The so-called coupling of the pressure wave to the thorax is reconsidered. With respect to the evident existence of inherent time delays of displacements, the system of coupled bodies is described by a time delay differential equations that are derived from the large-scale systems approach. Numerical solutions present interesting dynamical behavior of the bio-inspired system resulting from inherent time delays and a time of arrival of the blast pressure wave. There is pointed out that the inherent state time delays change dynamical response of the multibody system. Proper time of deployment of the foam-based armor plate reduces relative compression of the thorax, which is to be protected by a bullet-proof waistcoat.

Efficient simulation of gas–liquid pipe flows using a generalized population balance equation coupled with the algebraic slip model

The inhomogeneous generalized population balance equation, which is discretized with the direct quadrature method of moment (DQMOM), is solved to predict the bubble size distribution (BSD) in a vertical pipe flow. The proposed model is compared with a more classical approach where bubbles are characterized with a constant mean size. The turbulent two-phase flow field, which is modeled using a Reynolds-Averaged Navier–Stokes equation approach, is assumed to be in local equilibrium, thus the relative gas and liquid (slip) velocities can be calculated with the algebraic slip model, thereby accounting for the drag, lift, and lubrication forces. The complex relationship between the bubble size distribution and the resulting forces is described accurately by the DQMOM. Each quadrature node and weight represents a class of bubbles with characteristic size and number density, which change dynamically in time and space to preserve the first moments of the BSD. The predictions obtained are validated against previously published experimental data, thereby demonstrating the advantages of this approach for large-scale systems as well as suggesting future extensions to long piping systems and more complex geometries.

An optimal repair approach for large-scale systems using queuing theory

An optimal repair approach for large-scale systems is proposed based on queuing theory. In this approach, system steady states corresponding to the number of failed components are obtained by analysing the transition probabilities between states. Because the transition probabilities of two adjacent states are not zero, the transition process, as one of Markov processes, is further determined as the birth-death process which is an important part of queuing theory. According to the queue theory, the long-run expected number and probability distribution function of failed components are derived. On the basis of above analysis, a constrained optimisation problem in terms of cost utility is formulated. Finally, the numerical examples are given to demonstrate the effectiveness of our optimal model.

Plastid Proteome Assembly without Toc159: Photosynthetic Protein Import and Accumulation of N-Acetylated Plastid Precursor Proteins

Import of nuclear-encoded precursor proteins from the cytosol is an essential step in chloroplast biogenesis that is mediated by protein translocon complexes at the inner and outer envelope membrane (TOC). Toc159 is thought to be the main receptor for photosynthetic proteins, but lacking a large-scale systems approach, this hypothesis has only been tested for a handful of photosynthetic and nonphotosynthetic proteins. To assess Toc159 precursor specificity, we quantitatively analyzed the accumulation of plastid proteins in two mutant lines deficient in this receptor. Parallel genome-wide transcript profiling allowed us to discern the consequences of impaired protein import from systemic transcriptional responses that contribute to the loss of photosynthetic capacity. On this basis, we defined putative Toc159-independent and Toc159-dependent precursor proteins. Many photosynthetic proteins accumulate in Toc159-deficient plastids, and, surprisingly, several distinct metabolic pathways are negatively affected by Toc159 depletion. Lack of Toc159 furthermore affects several proteins that accumulate as unprocessed N-acetylated precursor proteins outside of plastids. Together, our data show an unexpected client protein promiscuity of Toc159 that requires a far more differentiated view of Toc159 receptor function and regulation of plastid protein import, in which cytosolic Met removal followed by N-terminal acetylation of precursors emerges as an additional regulatory step.

Fast calculation approach to semi-empirical molecular orbital method using real space division method

The authors have developed a new approach for large-scale systems including over 100,000 atoms to obtain physical strength from the viewpoint of atom–atom bonding energy. Combining the semi-empirical molecular orbital method with real space division method makes it possible to estimate structural parameters, electronic structures and bonding energy for various large systems. With this method, various quantum physical properties can be obtained quickly using the semi-empirical molecular orbital method, while adopting real space division improves the computational efficiency of parallelization. In this study, the authors applied this method to SiH4 molecule and crystalline silica system, and carried out bond order and bonding energy analyses. In this analysis, the developed method offered almost the same analytical accuracy as the first principle method, while its calculation speed was much faster than that of the latter. The developed method was also suitable for parallel computing.

Partitioning approach oriented to the decentralised predictive control of large-scale systems

In this paper, a partitioning approach for large-scale systems based on graph-theory is presented. The algorithm starts with the translation of the system model into a graph representation. Once the system graph is obtained, the problem of graph partitioning is then solved. The resultant partition consists in a set of non-overlapping subgraphs whose number of vertices is as similar as possible and the number of interconnecting edges between them is minimal. To achieve this goal, the proposed algorithm applies a set of procedures based on identifying the highly connected subgraphs with balanced number of internal and external connections. In order to illustrate the use and application of the proposed partitioning approach, it is used to decompose a dynamical model of the Barcelona drinking water network (DWN). Moreover, a hierarchical-like DMPC strategy is designed and applied over the resultant set of partitions in order to assess the closed-loop performance. Results obtained when used several simulation scenarios show the effectiveness of both the partitioning approach and the DMPC strategy in terms of the reduced computational burden and, at the same time, of the admissible loss of performance in contrast to a centralised MPC strategy.

STATUS OF SOIL ELECTRICAL CONDUCTIVITY STUDIES BY CENTRAL STATE RESEARCHERS

Practical tools are needed to identify and advance sustainable management practices to optimize economic return,conserve soil, and minimize negative off-site environmental effects. The objective of this article is to review current researchin non-saline soils of the central U.S. to consider bulk soil electrical conductivity (ECa) as an assessment tool for: (1) trackingN dynamics, (2) identifying management zones, (3) monitoring soil quality trends, and (4) designing and evaluatingfield-scale experiments. The interpretation and utility of ECa are highly location and soil specific; soil properties contributingto measured ECa must be clearly understood. In soils where ECa is driven by NO3-N, ECa has been used to track spatial andtemporal variations in crop-available N (manure, compost, commercial fertilizer, and cover crop treatments) and rapidlyassess N mineralization early in the growing season to calculate fertilizer rates for site-specific management (SSM). Selectionof appropriate ECa sensors (direct contact, electromagnetic induction, or time domain reflectometry) may improve sensitivityto N fluctuations at specific soil depths. In a dryland cropping system where clay content dominates measured ECa, ECa-basedmanagement zones delineated soil productivity characteristics and crop yields. These results provided a framework effectivefor SSM, monitoring management-induced trends in soil quality, and appraising and statistically evaluating field-scaleexperiments. Use of ECa may foster a large-scale systems approach to research that encourages farmer involvement.Additional research is needed to investigate the interactive effects of soil, weather, and management on ECa as an assessmenttool, and the geographic extent to which specific applications of this technology can be applied.

An incorporated algorithm for combined heat and power economic dispatch

This paper presents an improved genetic algorithm with multiplier updating (IGA_MU) to solve the combined heat and power economic dispatch (CHPED) problem. The improved genetic algorithm (IGA) equipped with an improved evolutionary direction operator (IEDO) and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed approach integrates the IGA and the MU such that it has the merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the CHPED problem. Extensive simulations using the proposed method are carried out on various-size systems, and the results are compared with that of the previous methods. Numerical results indicate that the proposed approach has more advantages than other methods in application. Moreover, the proposed algorithm provides an efficacious approach for large-scale systems of the CHPED problem.

Diagonal stability in the large-scale system approach

Diagonal type Lyapunov functions are ubiquitous in interconnected systems analysis and design, either by the so called vector or scalar Lyapunov function approaches. Clear justification for this phenomenon has not been given in the extensive literature on the field. This paper explains the reasons for this ubiquity, actually justifying the necessity of diagonal stability in this context.

Special Issue on Fuzzy Control

This special issue is devoted to the study of Fuzzy Control applied to robotics and mechatronics. In particular, it contains a collection of fuzzy-neural network approaches, together with the conventional fuzzy reasoning or new approaches. Since the first pioneering work on fuzzy sets and fuzzy logic reported in 1965 by Zadeh, many control application papers have been published with the fundamental fuzzy controllers based on the so-called Mamdani's min-max centroidal method, the TakagiSugeno's functional reasoning, and the simplified reasoning. However, it is recognized that much trial and error is necessary in the design of the conventional fuzzy controller, because the fuzzy reasoning methods mentioned above are not fundamentally related to any control or system theory. In addition, it should be noted that the total number of control rules grows exponentially as the number of input variables to the conventional fuzzy reasoning increases. Thus, in order to improve the conventional approach and develop the new approach for large-scale systems, most current work on fuzzy control is concerned with an effective design, construction, or analysis of the fuzzy controller by invoking the neural network theory, genetic algorithm, and other control or system theories. Although the literature, both in Japanese and in English, on fuzzy control and applications is now very rich, I believe that this special issue provides an important impact on the advanced fuzzy control. This issue would not have been possible without the enthusiastic support of the contributors. I am indebted to all of them for their up-to-date contributions and to the editorial staff for care throughout the editorial and printing process.

Asynchronous block-iterative methods for almost linear equations

This paper gives convergence conditions for asynchronous block-iterative methods for the solution of the almost linear equation Ax= F( x), where A is a linear operator, F a block-diagonal Lipschitz-continuous operator, and x a vector, in terms of a splitting of A and the Lipschitz constant of F. The methods used are a combination of the contraction-mapping approach using a vectorial norm and a large-scale systems approach using vector difference inequalities. The load-flow equations for a power system are almost linear in the above sense, and considerable speedup can be obtained on a four transputer machine.

Computer Aided Design of the Stochastic Controller for a Large Scale River System

In a recent paper Hassan et al have developed a new algorithm for solving the large scale systems filter design problem for the case of linear interconnected subsystems with gaussian white noise disturbance sequences. Their algorithm has desirable numerical properties which makes it superior to the global Kalman filter approach for large scale systems. Parallel to this work, Singh has developed a hierarchical algorithm for computing the feedback gains for large scale linear interconnected dynamical systems with quadratic cost functions. Since both the filter of Hassan et al and the controller of Singh provide the optimal solution and use a classical information pattern (in the sense of Witsenhausen) it should be possible to combine the two structures in order to produce stochastic optimal control.In this paper we see how this isdone for a large river pollution control example. The overall system is of order 52 and it is divided into 6 subsystems. Each subsystem problem can be treated as a Linear-Quadratic-Gaussian subproblem. We use the controller algorithm as well as the filter algorithm in order to provide simulation results for stochastic optimal control for this practical large scale problem.

Optimization and Control Problems in Large-Scale Systems

Large-scale system approach to modern technical, organizational, economical and social problems is constantly spreading out. Industrial, energy, water, ecology, urban, medical, agriculture, transport and other systems, are in urgent need of theoretical as well as applied results on optimization and control of large-scale systems. Unfortunately, the progress in this field is not satisfactory due to many reasons.Optimization and suboptimization approach to problems of large-scale system has remained an important field of research. New results on structure optimization and decomposition have been obtained. Several motivied supervisory control algorithms, mostly for two-level systems, have been given.The paper present a survey of recent results on control problems and optimization of large-scale systems. Possible future trends in this area are considered.