Dynamics Of Physical Systems Research Articles

A Novel Framework for Simultaneous Topology and Sizing Optimization of Complex, Multi-Domain Systems-of-Systems

Abstract This article presents a novel design framework for topology and component sizing optimization of multi-domain dynamic systems described by conservation laws. Multidisciplinary design optimization (MDO) is a powerful tool for minimizing metrics such as inefficiency and cost for these systems-of-systems (SoS). However, quality of the designs identified from the optimization procedure depends on model accuracy and ability to capture inter-system interactions. This work utilizes a conservation-based, graphical modeling approach to capture physical system dynamics and interactions, and expands it to be used in MDO techniques. This yields three contributions to the literature. First, an augmented graph-based model is provided, expressing continuous and discrete design variable values as changes to vertex size, edge size, and edge connections of the dynamic system graph. Second, a sizing and topology optimization framework is developed using the augmented graph-based model as a basis. Third, analytical and numerical sensitivity functions are derived for a cooling system design problem, stemming from application of the design framework. The design framework is applied to two case studies for cooling subsystem design and electric vehicle (EV) powertrain design, with the goal of optimizing thermal and electrical component sizes, as well as discrete choices in the topology of the system being designed. These case studies provide examples for how the design framework enables analysis of alternatives (AoA) during early design stages.

Welcome to 2020 and the Future of Control

Welcome to 2020 and the Future of Control

Harnessing ambient sensing & naturalistic driving systems to understand links between driving volatility and crash propensity in school zones – A generalized hierarchical mixed logit framework

With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real-world microscopic driving behavior and its relevance to school zone safety – expanding the capability, usability, and safety of dynamic physical systems through data analytics. Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events featuring over 9.4 million temporal samples of real-world driving, a characterization of volatility in microscopic driving decisions is sought at school and non-school zone locations. A big data analytic methodology is proposed for quantifying driving volatility in microscopic real-world driving decisions. Eight different volatility measures are then linked with detailed event-specific characteristics, health history, driving history/experience, and other factors to examine crash propensity at school zones. A comprehensive yet fully flexible state-of-the-art generalized mixed logit framework is employed to fully account for distinct yet related methodological issues of scale and random heterogeneity, containing multinomial logit, random parameter logit, scaled logit, hierarchical scaled logit, and hierarchical generalized mixed logit as special cases. The results reveal that both for school and non-school locations, drivers exhibited greater intentional volatility prior to safety-critical events. Volatility in positive and negative vehicular jerk in longitudinal and lateral directions associates with increases the probability of unsafe outcomes (crashes or near-crashes) at school zones. A one-unit increase in intentional volatility measured by positive vehicular jerk in longitudinal direction associates with a 0.0528 increase in the probability of crash outcome. Importantly, the effect of negative vehicular jerk (braking) in longitudinal direction on the likelihood of crash outcome is almost double. Methodologically, Hierarchical Generalized Mixed Logit model resulted in best-fit, simultaneously accounting for scale and random heterogeneity. When accounted for separately, more parsimonious models accounting for scale heterogeneity performed comparably to the less parsimonious counterparts accounting for random heterogeneity. Importantly, even after accounting for random heterogeneity, substantial heterogeneity due to a “pure scale-effect” is still observed, underscoring the importance of scale effects in influencing the overall contours of variations in the modeled relationships. The study demonstrates the value of observational study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes. Implications for designing personalized school zone behavioral countermeasures are discussed.

Quantum codes from neural networks

We examine the usefulness of applying neural networks as a variational state ansatz for many-body quantum systems in the context of quantum information-processing tasks. In the neural network state ansatz, the complex amplitude function of a quantum state is computed by a neural network. The resulting multipartite entanglement structure captured by this ansatz has proven rich enough to describe the ground states and unitary dynamics of various physical systems of interest. In the present paper, we initiate the study of neural network states in quantum information-processing tasks. We demonstrate that neural network states are capable of efficiently representing quantum codes for quantum information transmission and quantum error correction, supplying further evidence for the usefulness of neural network states to describe multipartite entanglement. In particular, we show the following main results: (a) neural network states yield quantum codes with a high coherent information for two important quantum channels, the generalized amplitude damping channel and the dephrasure channel. These codes outperform all other known codes for these channels, and cannot be found using a direct parametrization of the quantum state. (b) For the depolarizing channel, the neural network state ansatz reliably finds the best known codes given by repetition codes. (c) Neural network states can be used to represent absolutely maximally entangled states, a special type of quantum error-correcting codes. In all three cases, the neural network state ansatz provides an efficient and versatile means as a variational parametrization of these highly entangled states.

Maximal Product of Graphs under Vague Environment

Graph models are found everywhere in natural and human made structures, including process dynamics in physical, biological and social systems. The product of graphs are appropriately used in several combinatorial applications and in the formation of different structural models. In this paper, we present a new product of graphs, namely, maximal product of two vague graphs. Then we describe certain concepts, including strongly, completely, regularity and connectedness on a maximal product of vague graphs. Further, we consider some results of edge regular and totally edge regular in a maximal product of vague graphs. Finally, we present an application for optimization of the biomass based on a maximal product of vague graphs.

Topological mechanics of knots and tangles.

Knots play a fundamental role in the dynamics of biological and physical systems, from DNA to turbulent plasmas, as well as in climbing, weaving, sailing, and surgery. Despite having been studied for centuries, the subtle interplay between topology and mechanics in elastic knots remains poorly understood. Here, we combined optomechanical experiments with theory and simulations to analyze knotted fibers that change their color under mechanical deformations. Exploiting an analogy with long-range ferromagnetic spin systems, we identified simple topological counting rules to predict the relative mechanical stability of knots and tangles, in agreement with simulations and experiments for commonly used climbing and sailing bends. Our results highlight the importance of twist and writhe in unknotting processes, providing guidance for the control of systems with complex entanglements.

Automation of Data Acquisition Strategies in Model Calibration for System Models: Sensor Placement

Model calibration is a challenging process for large-scale system models with a non-trivial number of variables. Although domain heuristics are often called for to guide the model calibration process, in particular, the scheduling of data acquisition on model variables, there lacks a general automatic framework for this strategic task. This problem points to the well-known sensor placement problem in physical dynamic systems, and existing approaches are ready to be translated to system models in social and economic sciences. The sensor placement problem on system models addresses the following question: with the model at hand and a pre-existing data availability, what are the (next) k model variables that would bring the largest utility to model calibration, once their datasets are acquired? In this study, we first translate two established solution approaches of this optimization problem, the information entropy approach and the miss probability approach, from physical dynamic systems to social science system models. Next, based on the idea of Data Availability Partition and drawing on the insights of the two existing solutions, we propose a new objective function for this optimization problem, constructed from the individual utilities of model variables, which could be understood from the entropy perspective. The new solution could essentially be embedded in the broader theoretical framework of the evaluation of side information. On the basis of graph theory, analytical results of the optimal placement solution under the new objective function are derived for binary and multi-ary trees; for a general tree structure with n nodes, an algorithm to determine the optimal placement is devised, with complexity upper bounded by O(nlog2(n)). For arbitrary model structures, approximate solution schemes for this combinatorial problem are pinned down. Our new solution scheme is compared with the two translated approaches on a sample model structure, and results suggest the advantages of our solution. In this study, in-depth discussions on the design of data acquisition strategies for system models are carried out, which may bring important insights for system modeling practice in social and economic sciences.

Numerical Renormalization Group Algorithms for Self-Similar Asymptotics of Partial Differential Equations

We systematically study a numerical procedure that reveals the asymptotically self-similar dynamics of solutions of partial differential equations (PDEs). This procedure, based on the renormalization group (RG) theory for PDEs, appeared initially in a conference proceedings [G. A. Braga, F. Furtado, and V. Isaia, in Proceedings of the Fifth International Conference on Dynamical Systems and Differential Equations, Pomona, CA, 2004, pp. 1--13]. A numerical version of the RG method, dubbed nRG, rescales the temporal and spatial variables in each iteration and drives the solutions to a fixed point exponentially fast, which corresponds to the self-similar dynamics of the equations. In this paper, we carefully examine and validate this class of algorithms by comparing the numerical solutions with either exact or asymptotic solutions of model equations found in the literature. The other contribution of the current paper is that we present several examples to demonstrate that this class of nRG algorithms can be applied to a wide range of PDEs to shed light on long time self-similar dynamics of certain physical systems modeled by PDEs.

The system dynamics modeling of Gayo arabica coffee industry supply chain management

This study aims to design a model of supply chain management of Gayo Arabica coffee industry in an effort to increase value added and profit for smallholder plantation and company. The method applied to achieve this goal is a system dynamics methodology which is a modeling approach based on systemic thinking and uses perspectives based on information feedback and delay to understand the complex behavior dynamics of physical, biological and social systems, which occur in the supply chain management. The results showed that through the development of a hybrid production system and quality engineering in supply chain management of the Gayo Arabica coffee industry has been able to respond to the dynamics of consumer order. Scenario I and II made changes to model parameters which resulted in an increase in business profit obtained by company, while smallholder plantations did not experience changes in the profits of their business. Scenario III carried out a structural change model that was divided into two parts, namely the Scenario IIIA which applied institutional innovation between governance of public plantation relations with agro-industrial cooperatives in the form of coffee bean delivery system, while Scenario IIIB applied the governance of public plantation relations with agroindustry cooperatives in the form of transaction systems coffee bean. The results of Scenario IIIA are able to increase the value added and benefit obtained by smallholder plantation and be able to increase company profits and maintain customer satisfaction simultaneously. Scenario IIIB results are only able to increase company profits and maintain customer satisfaction, while increasing profits and value added of smallholder plantations do not occur. A post-publication change was made to this article on 11 Mar 2020 to correct the conference title in the PDF header.

Complex Vague Graphs and Their Application in Decision-Making Problems

Fuzzy graph models are found everywhere in natural and human made structures, including process dynamics in biological, physical and social systems. Since real-life problems are often uncertain due to inconsistent and indeterminate information, it is very hard for an expert to model those problems using a fuzzy graph. A complex vague model is useful in the field of mathematics which gives more precision, comparability and flexibility to the system as compared to the vague model. In these years, a mathematical approach is a generalized approach of blending different aspects. According to the above mathematical approach, we investigate strong techniques which are based on complex vague graphs. The purpose of this research study is to present and explore the key properties including: Cartesian products, composition, strong product, semi-strong product and direct product of complex vague graphs with examples. Finally, we present application of complex vague graphs in decision-making problems.

Real-time multiple model joint estimation for an urban traffic junction subject to jump dynamics

Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt changes due to unanticipated traffic incidents or network obstructions. These abrupt changing conditions are represented as different regimes or modes where each mode is represented by its own distinct model, forming a set of multiple models. At any instance in time, only one model of the set has the potential of representing the physical system dynamics at that time. However the dynamics may arbitrarily jump over to a different regime when an abnormal condition arises. Furthermore, it might be impossible to identify these models a priori. Hence, a multiple model approach is developed to self-detect these abrupt changes, identify which member of the set best represents the actual system and automatically self-configure and add a new model to the set when a previously unmodelled regime arises. This approach makes use of a real-time joint (dual) estimation algorithm to estimate traffic state variables such as queue lengths and traffic flow, as well as model parameters such as turning ratios, saturation flow values and noise covariance resulting from unmodelled dynamics and measurement errors. The proposed algorithm is validated through simulations on signalized 3-arm and 4-arm junctions with typical day-to-day traffic conditions including several network irregularities occuring at different times of the day such as arm closures as a result of traffic incidents. This work is aimed to form part of adaptive control loops for traffic light systems that are able to autonomously adjust to changing traffic conditions so as to ensure efficient vehicle flows.

Integrating runtime validation and hardware-in-the-loop (HiL) testing with V & V in complex hybrid systems

This study addresses the problem of assuring provably safe and correct behaviour of safety-critical complex hybrid systems (CHS) throughout their life-cycles when physical system dynamics tend to change due to natural causes. Model-based development methods are needed that integrate formal specification, verification, implementation level testing and runtime validation. Scalability limitations of available algorithms/methods dictate a modular approach, but this poses conflicting issues of compositionality, falsetransitivity, soundness and completeness. In this paper a compositional solution approach based on the decomposition and hybridisation of statechart models (into hybrid automata - HA) is demonstrated. Specifically, a compositional formal verification methodology developed earlier for discrete event dynamic systems (DEDS) was elevated to hybrid dynamics, successfully overcoming the risk of false transitivity common to direct abstraction methods of continuous state-space. It was then used to develop a scalable strategy to generate sound and complete (relative to coverage criteria) tests, and configure corresponding compositional HiL tests for different abstraction and functional levels. The same decomposition was used to derive compositional runtime validation tests based on discrete invariants and differential invariants. The study formally proves that (1) the proposed HA based formal verification method is compositional, sound and complete relative to first-order logic of differential equations, (2) the modular tests are compositional and (3) the HA based test generation method is compositional, sound and complete relative to first-order logic of differential equations. To reduce complexity compositionality is rendered parallel than sequential to perform the simpler tasks concurrently. The claims have been validated experimentally on a full scale experimental rig.

An optimal hybrid learning approach for attack detection in linear networked control systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems &#x0028 NCS &#x0029 , wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

Ultrafast twin-peak rogue waves in a vector field

Rogue waves, which were first described as an oceanographic phenomenon, constitute an important factor in the dynamics of many physical systems. Most of these systems were analyzed by scalar fields, while some of them, and specifically in optics, are described by a vector field. Thus, they differ from scalar systems in several crucial aspects. In this work, we study experimentally twin-peak rogue waves with a temporal imaging system capable of measuring the Stokes vectors as a function of time. We found that the two peaks in optical twin-peak rogue waves have orthogonal states of polarization and similar intensities. We observed this with two different systems, however, we do not have a theoretical explanation for this phenomena and could not explain it with current models.

Symmetry Breaking in Stochastic Dynamics and Turbulence

Symmetries play paramount roles in dynamics of physical systems. All theories of quantum physics and microworld including the fundamental Standard Model are constructed on the basis of symmetry principles. In classical physics, the importance and weight of these principles are the same as in quantum physics: dynamics of complex nonlinear statistical systems is straightforwardly dictated by their symmetry or its breaking, as we demonstrate on the example of developed (magneto)hydrodynamic turbulence and the related theoretical models. To simplify the problem, unbounded models are commonly used. However, turbulence is a mesoscopic phenomenon and the size of the system must be taken into account. It turns out that influence of outer length of turbulence is significant and can lead to intermittency. More precisely, we analyze the connection of phenomena such as behavior of statistical correlations of observable quantities, anomalous scaling, and generation of magnetic field by hydrodynamic fluctuations with symmetries such as Galilean symmetry, isotropy, spatial parity and their violation and finite size of the system.

Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding

Pedestrian dynamics is an interdisciplinary field of research. Psychologists, sociologists, traffic engineers, physicists, mathematicians and computer scientists all strive to understand the dynamics of a moving crowd. In principle, computer simulations offer means to further this understanding. Yet, unlike for many classic dynamical systems in physics, there is no universally accepted locomotion model for crowd dynamics. On the contrary, a multitude of approaches, with very different characteristics, compete. Often only the experts in one special model type are able to assess the consequences these characteristics have on a simulation study. Therefore, scientists from all disciplines who wish to use simulations to analyze pedestrian dynamics need a tool to compare competing approaches. Developers, too, would profit from an easy way to get insight into an alternative modeling ansatz. Vadere meets this interdisciplinary demand by offering an open-source simulation framework that is lightweight in its approach and in its user interface while offering pre-implemented versions of the most widely spread models.

A Multiplicative Coordinated Stealthy Attack for Nonlinear Cyber-Physical Systems with Homogeneous Property

Stealthy attacks to cyber-physical systems (CPS) refer to the ones that avoid attack detection mechanisms augmented to the systems typically in the form of anomaly detectors. Various types of stealthy attacks have been reported in the literature. Among the attacks with stealthy property, a recently reported multiplicative coordinated attack is particularly dangerous in that it corrupts sensor and actuator data in a coordinated manner, and it does not require precise system knowledge in order to be stealthy. It must be noted that most of these attacks are applicable to CPS, the physical counterparts of which are of linear dynamics. This could be a limitation since most of the physical dynamic systems that are encountered from CPS perspective are of nonlinear nature. In this work, we present a version of multiplicative coordinated stealthy attack for a class of CPS, the physical counterpart of which possesses nonlinear dynamics. Specifically, for the physical systems with homogeneous property, the attack is constructed and the effect is analyzed. Various simulations are carried out to illustrate the effect of the attack.

Effect of Co substitution on thermal stability and magnetocaloric effects of Gd70Al20Fe10−xCox amorphous alloys

The alloys with great magnetocaloric effect (MCE) are expected to be a good alternative for magnetic refrigerantion. In this work, ribbons were prepared by the melt-spinning method using Gd70Al20Fe10−xCox (x = 0, 2, 4, 6, 8 and 10) alloys. Crystal structure, thermal stable, specific heat and magnetic properties of the ribbons were investigated by x-ray diffraction, Dynamic flow differential scanning calorimetry and Physical Properties Measurement System. Results show all samples are in amorphous state, and their onset crystallization temperatures (595–634 K) are higher than the magnetic transition temperature, which contributes to the stable of amorphous structures in practical application. In applied magnetic field of 5 T, the value of magnetic entropy change ∣ΔSM∣ increases from 7.1 to 8.7 J · kg−1 · K−1 near the Curie temperature (151–114 K) of Gd70Al20Fe10-xCox alloys (x = 0, 2, 4, 6, 8 and 10) , respectively, which is ascribed to the exchange interaction between magnetic atoms Co and Fe; the full width at half maximum (FWHM) of ΔSM and refrigeration capacity (RC) increase by 91% and 56% compared with those of pure Gd, respectively; the adiabatic temperature change are in the range of 3.4–3.7 K for the alloys. In addition, all alloys undergo a second order ferro-paramagnetic phase transition, which is certified by the Arrott plots and universal curves.

How interactive simulations can improve the support of environmental management ‒ lessons from the Dutch peatlands

How interactive simulation systems can improve the support of environmental management is not fully understood. We therefore cross-analyzed questionnaires with logfiles and videos of workshops in which an interactive simulation system for peatland management was applied, to derive an in-depth perspective of its added values. The workshop participants explored the physical system dynamics, implementing measures, and the social system dynamics, brokering deals with other stakeholders. The system enabled capacity building at individual and group level, through iterative exploration of possible measures. As a result, cooperation among the stakeholders was enhanced and their understanding of problems and action perspectives regarding the peatlands was increased. Interventions that stimulated deliberation during the workshops were shown to prevent individualistic strategies, and instead fostered cooperative attitudes. The embeddedness in preceding Science‒Policy Interfaces enhanced the credibility and legitimacy of the system, whereas salience was strengthened by abundant detailed information, realistic visual quality, and short calculation times.

A Laplace Transfer-Based Citation Model and its Application in Detecting the Obsolescence Characteristics of Articles

This study describes a Laplace transfer-based citation model proposed for the actual citation process among journals. The actual citation process is a dynamic physical system which includes two continuous processes of ageing and publishing of cited articles. By deducing the Laplace transfer function for these two processes, we have established the input–output function for the actual citation process. Considering the subject of ‘automation and control systems’ as an example, time parameters associated with the ageing process were identified from the input–output function to examine the obsolescence characteristics for the subject. Experimental results show that the ageing time parameter calculated by the Laplace transferbased citation model is a good expression on the obsolescence of the field. The field shows a moderate ageing rate and a shortened obsolescence tendency on the whole. Most of the articles cited annually in this field are articles published 5–10 years ago. There is a monotonic decreasing dependence between the increment of IF and the increment of time parameter T 1 , although IF always increases regardless of whether the ageing tends to prolong or shorten.