Modeling Of Large Systems Research Articles

Open-Shell Formulation of the Fragment Molecular Orbital Method.

Performing accurate calculations on large molecular systems is desirable for closed- and open-shell systems. In this work, the fragment molecular orbital method is extended to open-shell systems and implemented in the GAMESS (General Atomic and Molecular Electronic Structure System) program package. The accuracy of the method is tested, and the ability to reproduce reaction enthalpies is demonstrated. These tests also demonstrate its utility in providing an efficient means to model large open-shell systems.

Methods for analyzing thermal-hydraulic operating conditions of large heat supply systems

Mathematical models of large heat supply systems with arbitrary configuration (highly branched and multiloop) and structure are presented, and methods for calculating their steady and dynamic thermal-hydraulic operating conditions are considered.

The THOMAS architecture in Home Care scenarios: A case study

Today, the need for architectures and computational models for large scale open multi-agent systems is considered a key issue for the success of agent technology in real world scenarios. The main goal of this paper is to describe a case study in Home Care scenarios applying an abstract architecture and a computational model for large scale open multi-agent systems based on a service-oriented approach. The architecture used is THOMAS, which specifically addresses the design of Home Care systems. This paper presents services examples for the management of a dependent home environment, and demonstrates the new features of the proposal.

Reduced-order models for parameter dependent geometries based on shape sensitivity analysis

The proper orthogonal decomposition (POD) is widely used to derive low-dimensional models of large and complex systems. One of the main drawback of this method, however, is that it is based on reference data. When they are obtained for one single set of parameter values, the resulting model can reproduce the reference dynamics very accurately but generally lack of robustness away from the reference state. It is therefore crucial to enlarge the validity range of these models beyond the parameter values for which they were derived. This paper presents two strategies based on shape sensitivity analysis to partially address this limitation of the POD for parameters that define the geometry of the problem at hand (design or shape parameters.) We first detail the methodology to compute both the POD modes and their Lagrangian sensitivities with respect to shape parameters. From them, we derive improved reduced-order bases to approximate a class of solutions over a range of parameter values. Secondly, we demonstrate the efficiency and limitations of these approaches on two typical flow problems: (1) the one-dimensional Burgers’ equation; (2) the two-dimensional flows past a square cylinder over a range of incidence angles.

Consistent integration of models based on views of meta models

Abstract The complexity of large system models in software engineering nowadays is mastered by using different views. View-based modelling aims at creating small, partial models, each one of them describing some aspect of the system. Existing formal techniques supporting view-based visual modelling are based on typed attributed graphs, where views are related by typed attributed graph morphisms. Such morphisms up to now require a meta model given by a fixed type graph, as well as a fixed data signature and domain. This is in general not adequate for view-oriented modeling where only parts of the complete meta model are known and necessary when modelling a partial view of the system. The aim of this paper is to extend the framework of typed attributed graph morphisms to generalized typed attributed graph morphisms, short GAG-morphisms, which involve changes of the type graph, data signature, and domain. This allows the modeller to formulate type hierarchies and views of visual languages defined by GAG-morphisms between type graphs, short GATG-morphisms. In this paper, we study the interaction and integration of views, and the restriction of views along type hierarchies. In the main result, we present suitable conditions for the integration and decomposition of consistent view models (Theorem 4.1) and extend these conditions to view models defined over meta models with constraints (Theorem 5.1). As a running example, we use a visual domain-specific modelling language to model coarse-grained IT components and their connectors in decentralized IT infrastructures. Using constraints, we formulate connection properties as invariants.

Discovery Through the Computational Microscope

All-atom molecular dynamics simulations have become increasingly popular as a tool to investigate protein function and dynamics. However, researchers are concerned about the short time scales covered by simulations, the apparent impossibility to model large and integral biomolecular systems, and the actual predictive power of the molecular dynamics methodology. Here we review simulations that were in the past both hotly disputed and considered key successes, namely of proteins with mainly mechanical functions (titin, fibrinogen, ankyrin, and cadherin). The simulation work covered shows how state-of-the-art modeling alleviates some of the prior concerns and how unrefuted discoveries are made through the "computational microscope."

Generating function approach to reliability analysis of structural systems

The generating function approach is an important tool for performance assessment in multi-state systems. Aiming at strength reliability analysis of structural systems, generating function approach is introduced and developed. Static reliability models of statically determinate, indeterminate systems and fatigue reliability models are built by constructing special generating functions, which are used to describe probability distributions of strength (resistance), stress (load) and fatigue life, by defining composite operators of generating functions and performance structure functions thereof. When composition operators are executed, computational costs can be reduced by a big margin by means of collecting like terms. The results of theoretical analysis and numerical simulation show that the generating function approach can be widely used for probability modeling of large complex systems with hierarchical structures due to the unified form, compact expression, computer program realizability and high universality. Because the new method considers twin loads giving rise to component failure dependency, it can provide a theoretical reference and act as a powerful tool for static, dynamic reliability analysis in civil engineering structures and mechanical equipment systems with multi-mode damage coupling.

A reduced order, test verified component mode synthesis approach for system modeling applications

Component mode synthesis (CMS) is a very common approach used for the generation of large system models. In general, these modeling techniques can be separated into two categories: those utilizing a combination of constraint modes and fixed interface normal modes and those based on a combination of free interface normal modes and residual flexibility terms. The major limitation of the methods utilizing constraint modes and fixed interface normal modes is the inability to easily obtain the required information from testing; the result of this limitation is that constraint mode-based techniques are primarily used with numerical models. An alternate approach is proposed which utilizes frequency and shape information acquired from modal testing to update reduced order finite element models using exact analytical model improvement techniques. The connection degrees of freedom are then rigidly constrained in the test verified, reduced order model to provide the boundary conditions necessary for constraint modes and fixed interface normal modes. The CMS approach is then used with this test verified, reduced order model to generate the system model for further analysis. A laboratory structure is used to show the application of the technique with both numerical and simulated experimental components to describe the system and validate the proposed approach. Actual test data is then used in the approach proposed. Due to typical measurement data contaminants that are always included in any test, the measured data is further processed to remove contaminants and is then used in the proposed approach. The final case using improved data with the reduced order, test verified components is shown to produce very acceptable results from the Craig–Bampton component mode synthesis approach. Use of the technique with its strengths and weaknesses are discussed.

Wind Farms Modeling for Short-Circuit Level Calculations in Large Power Systems

This paper presents a methodology to evaluate the contribution of wind farms (WFs) to the short-circuit level of high-voltage substations. The method relies on simplified equivalent WF models suitable for integration in commercial network-analysis software platforms. The developed WF models are especially suitable for integration in PSS/E and they can be incorporated in models of large power systems. The methodology can be applied to all kinds of WFs regardless of the type of the wind turbines used. Finally, the proposed models are verified through detailed simulations and a case study concerning short-circuit level calculation at all 150-kV and 400-kV buses of the Greek power system is presented. The relevant results are presented and general conclusions are extracted.

Nonlinear multivariate rainfall–stage model for large wetland systems

Wetland restoration is often measured by how close the spatial and temporal water level (stage) patterns are to the pre-drainage conditions. Driven by rainfall, such multivariate conditions are governed by nonstationary, nonlinear, and nonGaussian processes and are often simulated by physically based distributed models which are difficult to run in real time due to extensive data requirements. The objective of this study is to provide the wetland restorationists with a real time rainfall–stage modeling tool of simpler input structure and capability to recognize the wetland system complexity. A dynamic multivariate Nonlinear AutoRegressive network with eXogenous inputs (NARX) combined with Principal Component Analysis (PCA) was developed. An implementation procedure was proposed and an application to Florida Everglade’s wetland systems was presented. Inputs to the model are time lagged rainfall, evapotranspiration and previously simulated stages. Data locations, preliminary time lag selection, spatial and temporal nonstationarity are identified through exploratory data analysis. PCA was used to eliminate input variable interdependence and to reduce the problem dimensions by more than 90% while retaining more than 80% of the process variance. A structured approach to select optimal time lags and network parameters was provided. NARX model results were compared to those of the linear Multivariate AutoRegressive model with eXogenous inputs. While one step ahead prediction shows comparable results, recursive prediction by NARX is far more superior to that of the linear model. Also, NARX testing under drastically different climatic conditions from those used in the development demonstrates a very good and robust performance. Driven by net rainfall, NARX exhibited robust stage prediction with an overall Efficiency Coefficient of 88%, Mean Square Error less than 0.004 m 2 , a standard error less than 0.06 m, a bias close to zero and normal probability plots show that the errors are close to normal distributions.

Modeling Cascading Failure Propagation through Dynamic Bayesian Networks

Capturing dependencies in dependability studies is one of the most challenging tasks and requires suitable modeling techniques. In recent years the growing interest toward complex critical infrastructures and their interdependencies has solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading failures are a typical phenomenon of dependencies of components inside a system or among systems. New research in the area of the analysis of cascading phenomena has been also dictated by the recent occurrence of large scale electrical blackouts both in USA and in Europe that have caused the shortage of electrical power to millions of citizens. The present paper proposes to model cascading failures by means of Dynamic Bayesian Networks (DBN). In contrast with available techniques, DBN offer a good trade-off between the analytical tractability and the representation of the propagation of the cascading event. A failure scenario, taken from the literature, is considered as a final example.

Discrete element parameter calibration and the modelling of dragline bucket filling

The Discrete Element Method (DEM) is useful for modelling granular flow. The accuracy of DEM modelling is dependent upon the model parameter values used. Determining these values remains one of the main challenges. In this study a method for determining the parameters of cohesionless granular material is presented. The particle size and density were directly measured and modelled. The particle shapes were modelled using two to four spheres clumped together. The remaining unknown parameter values were determined using confined compression tests and angle of repose tests. This was done by conducting laboratory experiments followed by equivalent numerical experiments and iteratively changing the parameters until the laboratory results were replicated. The modelling results of the confined compression tests were mainly influenced by the particle stiffness. The modelling results of the angle of repose tests were dependent on both the particle stiffness and the particle friction coefficient. From these observations, the confined compression test could be used to determine the particle stiffness and with the stiffness known, the angle of repose test could be used to determine the particle friction coefficient. Usually DEM codes do not solve the equations of motion for so-called walls (non-granular structural elements). However, in this study a dynamic model of a dragline bucket is developed and implemented in a commercial DEM code which allows the dynamics of the walls to be modelled. The DEM modelling of large systems of particles is still a challenge and procedures to simplify and speed up the modelling of dragline bucket filling are presented. Using the calibrated parameters, numerical results of bucket filling are compared to experimental results. The model accurately predicted the orientation of the bucket. The model also accurately predicted the drag force over the first third of the drag, but predicted drag forces too high for the subsequent part of the drag.

Identification of Low Order Models for Large Scale Systems

Abstract Abstract In this paper we propose a novel procedure for obtaining a low order non-linear model of a large scale multi-phase, non-linear, reactive fluid flow systems. Our approach is based on the combinations of the methods of Proper Orthogonal Decomposition (POD), and non-linear System Identification (SID) techniques. The problem of non-linear model reduction is formulated as parameter estimation problem. In the first step POD is used to separate the spatial and temporal patterns and in the second step a model structure and it's parameters of linear and of non-linear polynomial type are identified to approximate the temporal patterns obtained by the POD in the first step. The proposed model structure treats POD modal coefficients as states rather than outputs of the identified model. The state space matrices which happens to be the parameters of a black-box to be identified, comes linearly in parameter estimation process. For the same reason, Ordinary Least Square (OLS) method is used to estimate the model parameters. The simplicity and reliability of the proposed method gives computationally very efficient linear and non-linear low order models for extremely large scale processes. The method is of generic nature. The efficiency of proposed approach is illustrated on a very large scale benchmark problem depicting Industrial Glass Manufacturing Process (IGMP). The results show good performance of the proposed method.

Identification of Low Order Parameter Varying Models for Large Scale Systems

In this paper we propose a novel procedure for obtaining reduced dimensional models of large scale multi-phase, non-linear, reactive fluid flow systems with geometric parameter uncertainty (corrosion). Our approach is based on the combinations of methods of Proper Orthogonal Decomposition (POD), black box System Identification (SID) techniques and nonlinear spline based blending of local black box models to create Reduced Order Linear Parameter Varying (RO-LPV) model. The proposed method gives computationally very efficient reduced dimension models for processes with parameter uncertainty. The efficiency of proposed approach is illustrated on a benchmark problem depicting industrial Glass Manufacturing Process (GMP) with corrosion of refractory materials as a process parameter uncertainty. The results show good performance of the proposed method.

Model Checking of Real-Time Properties of Resource-Bound Process Algebra

The algebra of communicating shared resources (ACSR) is a timed process algebra which extends classical process algebras with the notion of a resource. In analyzing ACSR models, the existing techniques such as bisimulation checking and Hennessy-Milner Logic (HML) model checking are very important in theory of ACSR, but they are difficult to use for large complex system models in practice. In this paper, we suggest a framework to verify ACSR models against their requirements described in an expressive timed temporal logic. We demonstrate the usefulness of our approach with a real world case study.

Applying marine protected area design models in large estuarine systems

Several types of design models are currently being used to develop marine protected areas (MPAs) for conservation of coastal and pelagic ecosystems. However, few studies have applied these models in large estuaries which have unique characteristics that need to be considered in MPA design, including strong physical, chemical and biological gradients and significant human impacts. We explored how one design model, MARXAN, can be applied to estuarine systems by developing MPA design scenarios for Long Island Sound, an estuary in the northeastern USA. Using sedimentary texture as a proxy for habitats, we modeled and tested several scenarios where conservation goals differed with respect to location and spatial scale but included 10 to 20% representation of target habitats and spatially contiguous MPAs. When the entire estuary was modeled, potentially critical locations were not included in the solutions. Dividing the estuary into regions to account for spatial gradients proved a better approach. Final MPA solutions were tested for effectiveness by comparing benthic species richness and community composition inside and outside solutions. Solutions in the eastern region of Long Island Sound generally contained higher species richness, likely due to the inclusion of highly heterogeneous portions of the sea floor within most solutions, but community com- position varied. In contrast, solutions in the western/central region of the estuary usually had lower species richness but similar community composition. We also made preliminary assessments of how human activities, including dredge disposal, resource extraction and sediment pollution, might affect MPA design in estuaries. Our results illustrate potential conflicts that may arise due to the geographic location of best MPA candidate areas in estuarine regions with environmental impacts and human activities. Several tradeoffs will likely affect MPA design and selection in large estuarine systems, and MPA design models may prove useful in focusing these efforts.

Accurate simplified dynamic model of a metal hydride tank

As proton exchange membrane fuel cell technology advances, the need for hydrogen storage intensifies. Metal hydride alloys offer one potential solution. However, for metal hydride tanks to become a viable hydrogen storage option, the dynamic performance of practical tank geometries and configurations must be understood and incorporated into fuel cell system analyses. A dynamic, axially-symmetric, multi-nodal metal hydride tank model has been created in Matlab–Simulink ® as an initial means of providing insight and analysis capabilities for the dynamic performance of commercially available metal hydride systems. Following the original work of Mayer et al. [Mayer U, Groll M, Supper W. Heat and mass transfer in metal hydride reaction beds: experimental and theoretical results. Journal of the Less-Common Metals 1987;131:235–44], this model employs first principles heat transfer and fluid flow mechanisms together with empirically derived reaction kinetics. Energy and mass balances are solved in cylindrical polar coordinates for a cylindrically shaped tank. The model tank temperature, heat release, and storage volume have been correlated to an actual metal hydride tank for static and transient absorption and desorption processes. A sensitivity analysis of the model was accomplished to identify governing physics and to identify techniques to lessen the computational burden for ease of use in a larger system model. The sensitivity analysis reveals the basis and justification for model simplifications that are selected. Results show that the detailed and simplified models both well predict observed stand-alone metal hydride tank dynamics, and an example of a reversible fuel cell system model incorporating each tank demonstrates the need for model simplification.

Effect of molecular size on the parity-non-conserving contributions to the nuclear magnetic resonance shielding constant

We present density-functional theory studies on the effects of molecular size on the parity-violating contribution to the nuclear magnetic shielding constant. We focus on models with different backbone and side chain lengths, as well as the details of geometry optimization for certain helical polysilylenes and investigate the parity-violating contribution to the shielding constant of the 29Si nucleus of the backbone. Our calculations show that the molecular geometry has a large influence on the magnitude of the parity-violating shielding contribution, a result that is in line with the previous studies on much smaller molecules. In addition, we find convergence in the magnitude of the PV effect with respect to system size, when using geometries that preserve the helical Si backbone structure optimized for the largest of the present systems. This can be interpreted in terms of the non-size-extensive nature of the parity-violating operator influencing the leading-order effect on nuclear magnetic shielding, as opposed to the size-extensive interaction affecting the energy difference between enantiomers. Our molecules are truncated models of large polysilylene systems, for which a difference in the 29Si chemical shift between enantiomers has been observed to be 0.06 ppm (Fujiki in Macromol Rapid Commun 22, 669–674, 2001). As expected based on earlier first principles studies of small molecules, we do not find support for the difference to be of the parity-violating origin. Instead, the predicted parity-violation-induced splitting of the 29Si resonance is found to converge at values around 10−8 ppm with increasingly large Si backbone.

Multiscale and Modular Analysis of Cardiac Energy Metabolism

Computational models of large molecular systems can be assembled from modules representing biological function emerging from interactions among a small subset of molecules. Experimental information on isolated molecules can be integrated with the response of the network as a whole to estimate crucial missing parameters. As an example, a “skeleton” model is analyzed for the module regulating dynamic adaptation of myocardial oxidative phosphorylation (OxPhos) to fluctuating cardiac energy demand. The module contains adenine nucleotides, creatine, and phosphate groups. Enzyme kinetic equations for two creatine kinase (CK) isoforms were combined with the response time of OxPhos (tmito; generalized time constant) to steps in the cardiac pacing rate to identify all module parameters. To obtain tmito, the time course of O2 uptake was measured for the whole heart. An O2 transport model was used to deconvolute the whole‐heart response to the mitochondrial level. By optimizing mitochondrial outer membrane permeability to 21 μm/s the experimental tmito = 3.7 s was reproduced. This in vivo value is about four times larger, or smaller, respectively, than conflicting values obtained from two different in vitro studies. This demonstrates an important rule for multiscale analysis: experimental responses and modeling of the system at the larger scale allow one to estimate essential parameters for the interfaces of components which may have been altered during physical isolation. The model correctly predicts a smaller tmito when CK activity is reduced. The model further predicts a slower response if the muscle CK isoform is overexpressed and a faster response if mitochondrial CK is overexpressed. The CK system is very effective in decreasing maximum levels of ADP during systole and reducing average Pi levels over the whole cardiac cycle.

Application of large scale system model based on particle swarm optimization to optimal allocation of water resources in irrigation areas

Application of large scale system model based on particle swarm optimization to optimal allocation of water resources in irrigation areas