Large Dynamic Systems Research Articles

On Entropy Dynamics for Active “Living” Particles

This paper presents a modeling approach, followed by entropy calculations of the dynamics of large systems of interacting active particles viewed as living—hence, complex—systems. Active particles are partitioned into functional subsystems, while their state is modeled by a discrete scalar variable, while the state of the overall system is defined by a probability distribution function over the state of the particles. The aim of this paper consists of contributing to a further development of the mathematical kinetic theory of active particles.

Modeling Optical Spectra of Large Organic Systems Using Real-Time Propagation of Semiempirical Effective Hamiltonians.

We present an implementation of a time-dependent semiempirical method (INDO/S) in NWChem using real-time (RT) propagation to address, in principle, the entire spectrum of valence electronic excitations. Adopting this model, we study the UV/vis spectra of medium-sized systems such as P3B2 and f-coronene, and in addition much larger systems such as ubiquitin in the gas phase and the betanin chromophore in the presence of two explicit solvents (water and methanol). RT-INDO/S provides qualitatively and often quantitatively accurate results when compared with RT- TDDFT or experimental spectra. Even though we only consider the INDO/S Hamiltonian in this work, our implementation provides a framework for performing electron dynamics in large systems using semiempirical Hartree-Fock Hamiltonians in general.

May–Wigner transition in large random dynamical systems

We consider stability in a class of random non-linear dynamical systems characterised by a relaxation rate together with a Gaussian random vector field which is white-in-time and spatial homogeneous and isotropic. We will show that in the limit of large dimension there is a stability-complexity phase transition analogue to the so-called May–Wigner transition known from linear models. Our approach uses an explicit derivation of a stochastic description of the finite-time Lyapunov exponents. These exponents are given as a system of coupled Brownian motions with hyperbolic repulsion called geometric Dyson Brownian motions. We compare our results with known models from the literature.

Computational framework for model updating of large scale linear and nonlinear finite element models using state of the art evolution strategy

In this work, a computational framework applying-finite element model updating techniques is presented for identifying the linear and nonlinear parts of large scale dynamic systems using vibration measurements of their components. The measurements are taken to be, response time histories and frequency response functions of nonlinear and linear components of the system. Covariance Matrix Adaptation – Evolution Strategy (CMA-ES) a state of the art optimization algorithm was coupled with robust and accurate finite element analysis software in order to effectively produce optimal computational results. The developed framework is applied to a geometrically complex and lightweight experimental bicycle frame with nonlinear suspension fork components. The identification of modal characteristics of the frame (linear part) is based on an experimental investigation of its dynamic response. The modal characteristics are then used to update the finite element model. The nonlinear suspension components are identified using the experimentally obtained response spectra for each of the components tested separately. Single objective structural identification methods without the need of substructuring methods, are used for estimating the parameters (material properties, shell thickness properties and nonlinear properties) of the finite element models, based on minimizing the deviations between the experimental and analytical dynamic characteristics. Finally, the numerical results of the complete system assembly were compared to the experimental results of the equivalent physical structure of the bike.

Scalable Design of Heterogeneous Networks

A systematic approach to the analysis and design of a class of large dynamical systems is presented. The approach allows decentralized control laws to be designed independently using only local subsystem models. Design can be conducted using standard techniques, including loopshaping based on Nyquist and Popov plots, $\mathbf{H}_\infty$ methods, and $\mu$ -synthesis procedures. The approach is applied to a range of network models, including those for consensus, congestion control, electrical power systems, and distributed optimization algorithms subject to delays.

Persistent model order reduction for complex dynamical systems using smooth orthogonal decomposition

Full-scale complex dynamic models are not effective for parametric studies due to the inherent constraints on available computational power and storage resources. A persistent reduced order model (ROM) that is robust, stable, and provides high-fidelity simulations for a relatively wide range of parameters and operating conditions can provide a solution to this problem. The fidelity of a new framework for persistent model order reduction of large and complex dynamical systems is investigated. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the framework is used for identifying the robust subspaces obtained from both proper and smooth orthogonal decompositions (POD and SOD, respectively), the results show that SOD outperforms POD in terms of stability, accuracy, and robustness.

SIGEVO plenary lecture in memory of John Holland

Biological design principles can potentially change the way we study, engineer, maintain, and develop large dynamic software systems. For example, computer programmers like to think of software as the product of intelligent design, carefully crafted to meet well-specified goals. In reality, large software systems evolve inadvertently through the actions of many individual programmers, often leading to unanticipated consequences. Because software is subject to constraints similar to those faced by evolving biological systems, we have much to gain by viewing software through the lens of biology. The talk will highlight how abstractions of biological processes can lead to new computational algorithms and engineering principles using examples from my own research. Specifically, it will show how the biological concepts of Darwinian evolution and immunology can be applied to problems such as repairing software bugs and cybersecurity.

A Blueprint for Causal Inference in Implementation Systems

Background: Following a decade of significant progress in implementation science, research efforts are increasingly focused on the investigation of implementation mechanisms and the adoption of multiple EBPs in service delivery systems. This calls for the development of a systems approach to learn about causal mechanisms in implementation systems and a formal methodology for evaluation of systems of care. Methods: We develop a multilevel decision juncture approach to study implementation systems based on advances in implementation research and methods developed in the literature on causal inference and program evaluation. Implementation phases are linked through a sequence of decisions made by relevant agents in the systems at various levels. Formulating the decision juncture as a system of structural equations, each describing a causal relationship between variables, causal mechanisms can be identified throughout the phases of implementation. We use a hypothetical case study of a parenting program in the child welfare sector to illustrate the approach following a staged process analysis model taken from the econometric literature. Following the research on causal inference in recursive structural models, we also demonstrate how graphical models can be used to identify treatment effects at different levels of the system. Results: The models presented make contributions to implementation research in two areas. First, structural systems focus on the causal mechanisms of implementation. This allows efficient selection of covariates and optimal choice of implementation strategies based on identified leverage points in the system. Second, structural models provide a rigorous approach to the evaluation of implementation success. Taking a system approach, implementation, systems and effectiveness outcomes can be investigated as causally linked parts of an implementation system. Conclusions: As implementation science shifts its focus towards large dynamic systems of care, structural causal models connecting actors’ choices and implementation phases across levels of the system provide a rigorous scientific approach to investigate implementation and intervention effectiveness as causally linked parts of the system. Furthermore, using this approach it is straight forward to extend static models of implementation to dynamic systems that can account for sustainment and provide an avenue to integrate continuous quality improvement cycles into implementation research.

Analysis of the performance of a tankless water heating combo system: Simultaneous space heating and domestic hot water operation

In Canada, the overall energy usage in residences for space and water heating has increased year over year despite the introduction of higher efficiency furnaces and water heaters, creating an opportunity for alternative space and water heating systems. A tankless water heater, coupled with a hydronic air handling unit, functions as a novel system known as a combination system that provides water and space heating as a unified system. Previous studies have quantified combo system performance for space heating only; but currently, no studies have quantified simultaneous space and water heating operation. An experimental method was developed in order to quantify the effect of the outlet water temperature from a tankless water heater and domestic hot water draw rate on the performance of a combo system. With the experimental assembly, it was found for outlet water temperature settings between 38°C and 60°C, the thermal efficiency was between 88% and 94% for water draw of 7.6L/min and between 74% and 94% for water draw of 15.1L/min. An exergy analysis of the air heater unit was performed and the results are presented. The results suggest that a tankless water heater combination system can be an efficient configuration for residential space and water heating though there are challenges in managing the large dynamic range such combo systems face.

Identifying online user reputation of user–object bipartite networks

Abstract Identifying online user reputation based on the rating information of the user–object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems.

Multi-agent acoustic noise field characterization using optimal control

Investigations of acoustic-driven control schemes have stayed away from traditional Optimal Control methods because of the computational load and lack of direct solution methods. The method presented here overcomes these challenges and learns a spatially varying noise field by applying the Pontryagin Maximum Principle (PMP) to solve for optimal trajectories over a changing estimate of the noise field. The resulting control drives the agents to the regions of the estimated field with the most uncertainty. This optimization is conducted with multiple agents interested in the noise field for a single frequency as well as for multiple agents simultaneously optimizing over multiple frequencies of interest. The challenges of finding a solution to the mixed boundary value problems is achieved through an eigenvalue decomposition method. The selected cost function is designed to minimize time and thus bound error introduced by the open loop control derived through the PMP methodology. Simulations and preliminary experimental results are presented to demonstrate the effectiveness of this method. The resulting information from utilizing this method are useful in optimized sensor location problems, which are especially relevant for the large dynamic array system used to do the initial sensing. [Research supported by NAVSEA and Raytheon Company.]

Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human

BackgroundGene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. ResultsControllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. ConclusionsThe gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Управление по выходу спектром больших динамических систем

Управление по выходу спектром больших динамических систем

Balanced truncation-rational Krylov methods for model reduction in large scale dynamical systems

In this paper, we consider the balanced truncation method for model reductions in large-scale linear and time-independent dynamical systems with multi-inputs and multi-outputs. The method is based on the solutions of two large coupled Lyapunov matrix equations when the system is stable or on the computation of stabilizing positive and semi-definite solutions of some continuous-time algebraic Riccati equations when the dynamical system is not stable. Using the rational block Arnoldi, we show how to compute approximate solutions to these large Lyapunov or algebraic Riccati equations. The obtained approximate solutions are given in a factored form and used to build the reduced order model. We give some theoretical results and present numerical examples with some benchmark models.

Influence of free water on dynamic behavior of dam concrete under biaxial compression

The effect of saturation coupled with loading rate on the behavior of dam concrete was investigated under the biaxial compression. Dynamic biaxial compressive experiments on dam concrete cubes with an edge length of 250mm (dry and saturated) were carried out using a large static and dynamic triaxial electro-hydraulic servo multiaxial testing system. The specimens were loaded in biaxial compressive stress states (with the stress ratios of 0:1, 0.25:1, 0.5:1, 0.75:1 and 1:1 respectively) under static and a series of dynamic loading velocities (with strain rates ranging from 10−5/s to 10−2/s). The ultimate strengths of dry and saturated concretes were found to increase with the increase of strain rate, while the damage pattern and ultimate strength are closely related to the magnitude of lateral pressure exerted on the specimen. In addition, the dynamic failure criterion is proposed to characterize both the effects of strain rate and water content on the ultimate strength of dam concrete under biaxial compressive stress states. By testing dry and saturated specimens, the effect of water content on concrete strength was also examined. The experimental results indicate that the static compressive strengths of saturated concrete are lower than those of concrete in dry state, but on the contrary the dynamic strengths of saturated concrete is higher than those of concrete in dry state. The strain rate effect on strength of saturated concrete is more significant than that of dry concrete in lateral confining pressure, indicating that the saturated concrete is more rate-sensitive than dry concrete. As explained through a basic mechanical analysis, this dissimilarity is mainly attributed to the inertia effect and the viscosity of pore-water inside the saturated concrete during fast (quasi-static or dynamic) loading.

Numerical algorithms for stationary statistical properties of dissipative dynamical systems

It is well-known that physical laws for large chaotic dynamical systems are revealed statistically. The main concern of this manuscript is numerical methods for dissipative chaotic infinite dimensional dynamical systems that are able to capture the stationary statistical properties of the underlying dynamical systems. We first survey results on temporal and spatial approximations that enjoy the desired properties. We then present a new result on fully discretized approximations of infinite dimensional dissipative chaotic dynamical systems that are able to capture asymptotically the stationary statistical properties. The main ingredients in ensuring the convergence of the long time statistical properties of the numerical schemes are: (1) uniform dissipativity of the scheme in the sense that the union of the global attractors of the numerical approximations is pre-compact in the phase space; (2) convergence of the solutions of the numerical scheme to the solution of the continuous system on the unit time interval $[0,1]$ modulo an initial layer, uniformly with respect to initial data from the union of the global attractors. The two conditions are reminiscent of the Lax equivalence theorem where stability and consistency are needed for the convergence of a numerical scheme. Applications to the complex Ginzburg-Landau equation and the two-dimensional Navier-Stokes equations in a periodic box are discussed.

A novel reliability evaluation method for large engineering systems

Abstract A novel reliability evaluation method for large nonlinear engineering systems excited by dynamic loading applied in time domain is presented. For this class of problems, the performance functions are expected to be function of time and implicit in nature. Available first- or second-order reliability method (FORM/SORM) will be challenging to estimate reliability of such systems. Because of its inefficiency, the classical Monte Carlo simulation (MCS) method also cannot be used for large nonlinear dynamic systems. In the proposed approach, only tens instead of hundreds or thousands of deterministic evaluations at intelligently selected points are used to extract the reliability information. A hybrid approach, consisting of the stochastic finite element method (SFEM) developed by the author and his research team using FORM, response surface method (RSM), an interpolation scheme, and advanced factorial schemes, is proposed. The method is clarified with the help of several numerical examples.

Nonlinear stability and ergodicity of ensemble based Kalman filters

The ensemble Kalman filter (EnKF) and ensemble square root filter (ESRF) are data assimilation methods used to combine high dimensional, nonlinear dynamical models with observed data. Despite their widespread usage in climate science and oil reservoir simulation, very little is known about the long-time behavior of these methods and why they are effective when applied with modest ensemble sizes in large dimensional turbulent dynamical systems. By following the basic principles of energy dissipation and controllability of filters, this paper establishes a simple, systematic and rigorous framework for the nonlinear analysis of EnKF and ESRF with arbitrary ensemble size, focusing on the dynamical properties of boundedness and geometric ergodicity. The time uniform boundedness guarantees that the filter estimate will not diverge to machine infinity in finite time, which is a potential threat for EnKF and ESQF known as the catastrophic filter divergence. Geometric ergodicity ensures in addition that the filter has a unique invariant measure and that initialization errors will dissipate exponentially in time. We establish these results by introducing a natural notion of observable energy dissipation. The time uniform bound is achieved through a simple Lyapunov function argument, this result applies to systems with complete observations and strong kinetic energy dissipation, but also to concrete examples with incomplete observations. With the Lyapunov function argument established, the geometric ergodicity is obtained by verifying the controllability of the filter processes; in particular, such analysis for ESQF relies on a careful multivariate perturbation analysis of the covariance eigen-structure.

Object Oriented Bayesian Network for complex system risk assessment

Abstract: In this communication, we present a new way of modelling risk management process in a large scale, complex and dynamic system using an extended object oriented Bayesian network (EOOBN) as the underlying mathematical tool. The communication begins by presenting the proposed extension that permits to take into account the necessity of varying parameters in object oriented Bayesian network (OOBN) and dynamics for practical purposes. The second part is devoted to adaption of existing information propagation algorithms in classical Bayesian network (BN) and dynamic Bayesian network (DBN) to this extended model. A small practical case study is used along the communication to illustrate each modelling step.

Methodological Aspect of the Logistics Technologies Formation in Reforming Processes on the Railways

Reforming processes of a railway transport based on those positive tendencies, which were recently observed in the transport market. They are caused by competitive rising of all railways in CIS countries and Baltic States. Experience of the countries with developed economy shows us, that reforming processes should be lead with receiving possibility system-wide effect from the individual interconnected subsystems functioning (units of rail transport) like unified system.Systematic approach to the justification the process of reform and development of the railway sector provides the formation of a dynamic model of the individual transport units, consisting of a single organizational – technological structure. This enables the system to be generally aimed at achieving the objectives of its functioning. So, such structure can be considered a major transport system for which is characterized by new properties and characteristics and all separate rail divisions acquire the status of system elements.In order to improve the quality characteristics of the transport process and the efficiency of cargo service, solving management task and logistic system rationalization of the goods delivery can be carried out the basis of multi-criteria methods, taking into account conservation approach. General industry logistic system should consist of the railways, their units, some large railway stations in conjunction with the relevant subsystems cargo. In process of the logistic system centers formation appears a question of managing their organizational structure. In our opinion one of the main factors which must be taken into account in shaping the organizational structure of logistics centers is an opportunity to achieve a high service level with taking in account availability of internal and external constraints.Modern conditions needs to formalize the functioning of large dynamic rail system from the system approach position, which is directed on system-wide optimization (synergistic) effect and takes into account the complex of all participants interests in the transportation process. One of the main limits for this system is a providing of the resources cost reduction (rolling stock, amount of cargo on the route to the consumer, staff etc.) for an assimilation given traffic volumes.From the logistics view point customers needs main services connected with transportation, storage, loading and unloading, warehousing, order processing, inventory management, herewith, services are seen as interrelated and interactive. On the other hand, in this complex are especially important logistics service indicators (service quality, compliance is “just in time”, etc.). Herewith logistic system should take on additional functions, which are accompany transportation, such as packaging, labeling, sorting goods, transportation design, determining the optimal transportation options, control over the movement of goods in transit.To do this, you first need to ensure the absolute safety of cargo; offer the full range of transport services; simplification of goods carriage and tariff system; reducing the delivery time; creation of a powerful and modern information network; clear interaction organization with a motor companies or obtaining of own one for delivery ensuring.