Simple Dynamic Model Research Articles

Strategies for Reducing Booming Noise Generated by the Tailgate of an Electric Sport Utility Vehicle

This article investigates the source of booming noise emanating from the tailgate of an electric sport utility vehicle (SUV), along with proposed strategies to mitigate it. This annoying low-frequency booming noise, which significantly impacts interior sound quality, is less perceptible in conventional internal combustion engine vehicles. However, this noise is more readily detected in electric SUVs, highlighting the necessity for focused measures to reduce it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, ODSs were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes, resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite-element analysis software to propose measures for reducing booming noise. Experimental findings validated this model’s accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to reduce the booming noise resulting from tailgate motion. Under random force inputs, the analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness. This study identifies the mechanism generating booming noise, establishes a practical and simple dynamic model, and proposes improvement measures aimed at reducing the booming noise.

Central vs peripheral vascular factors determining risk of aortic dissection

Hypertension is the commonest cause of “central” aortic dissection, despite being an “peripheral” arteriole disease. We hypothesised that known regional variations in peripheral organ perfusion, due to variation in arteriole tone, via its effect on differential head and neck vessel flows can alter the risk and location of central aortic dissection. This might help to explain why some hypertensive patients dissect their aorta and others don't despite having similar aortic geometry. We utilised a simple 2D computational fluid dynamic model to test our hypothesis. We hypothesise (1) that variations in “peripheral” organ flow can alter the site of “central” aortic dissection, despite the blood pressure and cardiac output being identical, (2) variations in cardiac output at rest may also help to explain the risk of dissection but not the site of the initial intimal tear, and (3) atrial fibrillation, a known factor for aortic dissection, increases the risk via stroke volume variation. MRI scanning, via regional aortic branch flow quantification, may help to identify individuals who have a family history of dissection/aneurysm, who are normotensive with normal aortic anatomy that have a high potential risk for dissection due to calculated high wall velocity/shear stresses. This may allow selective beta blocker prescriptions in an asymptomatic patient population that is at a predicted high risk of aortic dissection – a hither to unidentified patient subpopulation.

Dynamical Approach to the Jamming Problem.

A simple dynamical model, biased random organization (BRO), appears to produce configurations known as random close packing (RCP) as BRO's densest critical point in dimension d=3. We conjecture that BRO likewise produces RCP in any dimension; if so, then RCP does not exist in d=1-2 (where BRO dynamics lead to crystalline order). In d=3-5, BRO produces isostatic configurations and previously estimated RCP volume fractions 0.64, 0.46, and 0.30, respectively. For all investigated dimensions (d=2-5), we find that BRO belongs to the Manna universality class of dynamical phase transitions by measuring critical exponents associated with the steady-state activity and the long-range density fluctuations. Additionally, BRO's distribution of near contacts (gaps) displays behavior consistent with the infinite-dimensional theoretical treatment of RCP when d≥4. The association of BRO's densest critical configurations with random close packing implies that RCP's upper-critical dimension is consistent with the Manna class d_{uc}=4.

Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this article, we propose to integrate safety constraints into constrained task-space imitation learning, endowing a humanoid robot with adaptive walking capability. Specifically, unlike previous work using nonlinear and coupled capturability dynamics, we first linearize the 3-D capture conditions using appropriate extreme values and then seamlessly incorporate them into constrained imitation learning. Furthermore, we propose novel heuristic rules to define control points, enabling adaptive locomotion learning. The resulting framework allows robots to learn locomotion skills from a few demonstrations efficiently and apply the learned skills to unseen 3-D scenarios while satisfying the constraints for passive safety. Unlike deep enforcement learning, our framework avoids the need of a large number of iterations or sim-to-real transfer. By virtue of the task-space adaptability, the proposed imitation learning framework can reuse collected demonstrations in a new robot platform. We validate our method by hardware experiments on Walker2 robot and simulations on COMAN robot.

Dynamics of information networks

AbstractWe explore a simple model of network dynamics which has previously been applied to the study of information flow in the context of epidemic spreading. A random rooted network is constructed that evolves according to the following rule: at a constant rate, pairs of nodes (i, j) are randomly chosen to interact, with an edge drawn from i to j (and any other out-edge from i deleted) if j is strictly closer to the root with respect to graph distance. We characterise the dynamics of this random network in the limit of large size, showing that it instantaneously forms a tree with long branches that immediately collapse to depth two, then it slowly rearranges itself to a star-like configuration. This curious behaviour has consequences for the study of the epidemic models in which this information network was first proposed.

Use of new means for frequency support of power system in emergency state

Emergency state of the power system occurs when operational limits are violated, and actions must be taken immediately to avert the further spread of disturbance. This paper deals with the use of new means for frequency support in such an emergency state. Specifically, the role of electric vehicles charging stations, electrolysers and batteries is studied on a simplified dynamic simulation model, which is described in detail. The aim of the paper is to show how the support of these modern means, being connected to the power system, can help with the frequency control in emergency state, and thus to reduce the negative effects of under-frequency. Simulation cases were selected as the separations of interconnected synchronous area of Continental Europe corresponding to the situation in the year 2030.

Granger causality based on vector time series and quaternion algebra with possible applications to molecular dynamics data analysis.

Causal analysis plays a significant role in physics, chemistry, and biology. Dynamics of complex (bio)molecular and nanosystems, from the microscopic to the macroscopic scale, are characterized by time-dependent vectors such as positions, forces, momenta, angular momenta, or torques. Identification and analysis of causal relationships between these time-dependent signals is an important problem in the multidimensional time-series analysis and is of great practical importance in describing the properties of such dynamical systems, and to understanding their functionality. For linear stochastic systems characterized by multidimensional scalar signals, Granger proposed a simple procedure to detect causal relationships, called Granger causality. In this study we extended this formalism to vector signals representing physical vector quantities. For this purpose, we used quaternion algebra, where vector signals are treated as time-dependent quaternions. The developed analytical model is based on the autoregressive formalism. This formalism (Q-MVAR) and its numerical implementation were validated using two simple dynamic models: a rigid body model represented by a benzenelike molecular fragment, interacting with a short-range harmonic potential with a wall, as well as a system of three model atomic balls moving inside a soft spherical surface and interacting with long range electrostatic forces. Although the motivation to these studies was the analysis of classical motions in complex (bio)molecular systems, described with a mechanical model and based on molecular dynamics (MD) simulations, in particular coarse-grained ones, it should be noted that the developed extended formalism can be applied to any system composed of many rigid elements that interact with arbitrary potentials and are characterized by complex internal motions. A description of the detailed procedure for calculating causality measures is provided in the Appendicesof the Supplemental Material. This formalism and the prototype of its numerical implementation can be further developed and applied in many different fields of physical, natural, and engineering sciences.

2284. SARS-CoV-2 Transmission in Utah Nursing Homes

Abstract Background The goal of this study was to apply a dynamic modeling approach to assess the impact of COVID-19 control measures in nursing homes. Utah's strategy for COVID control in nursing homes included serial point prevalence surveys, triggered when a staff or resident case was detected. However, implementation of the policy varied across nursing homes. Methods We used state-collected testing data from 70 different Utah nursing homes from March 2020 to April 2021 to estimate a facility specific transmission. Data included facility id, COVID-19 test results, number of days since the first covid test administration, and a December 2020 population census. We constructed a stochastic Reed Frost based model, using the stan library in R using the Hamiltonian Monte Carlo algorithm to estimate parameter values. Results COVID attack rates tended to peak between 50-100 days from the onset of included data, averaging 14.2% (Figure 1). A high degree of day-to-day variability in SARS-CoV-2 test collection was observed, consistent with uneven implementation of serial point prevalence surveys. Nonetheless, we were able to achieve excellent model calibration and estimate the basic reproductive number (R0) in all included facilities. The stochastic model captured differences between facilities; the estimated facility R0 ranged from 1.4 to 2.5 (Figure 2). We did not find evidence that a delay in collection of point prevalence survey was associated with an increased R0. Conclusion Fitting simple dynamic models to COVID surveillance data enables a comparison of nursing homes with respect to facility R0 and an evaluation of the effect of different control policies. More complex models are needed to yield insights about the relative importance of alternative transmission pathways, such as combinations between staff and residents. Disclosures All Authors: No reported disclosures

A dynamic soil freezing characteristic curve model for frozen soil

The soil freezing characteristic curve (SFCC) plays a fundamental role in comprehending thermo-hydraulic behavior and numerical simulation of frozen soil. This study proposes a dynamic model to uniformly express SFCCs amidst varying total water contents throughout the freezing-thawing process. Firstly, a general model is proposed, wherein the unfrozen water content at arbitrary temperature is determined as the lesser of the current total water content and the reference value derived from saturated SFCC. The dynamic performance of this model is verified through test data. Subsequently, in accordance with electric double layer (EDL) theory, the theoretical residual and minimum temperatures in SFCC are calculated to be −14.5 °C to −20 °C for clay particles and −260 °C, respectively. To ensure that the SFCC curve ends at minimum temperature, a correction function is introduced into the general model. Furthermore, a simplified dynamic model is proposed and investigated, necessitating only three parameters inherited from the general model. Additionally, both general and simplified models are evaluated based on a test database and proven to fit the test data exactly across the entire temperature range. Typical recommended parameter values for various types of soils are summarized. Overall, this study provides not only a theoretical basis for most empirical equations but also proposes a new and more general equation to describe the SFCC.

A novel tool path smoothing algorithm of 6R manipulator considering pose-dependent dynamics by designing asymmetrical FIR filters

The joint dynamic constraints of robot manipulators are normally set to conservative constant values to meet the dynamic tolerances at different poses, which leads to incomplete utilization of the joint drive capability. To fully utilize the drive capability of joint drives, this paper proposes an asymmetrical finite impulse response (FIR) filter-based tool path smoothing algorithm, which considers the pose-dependent dynamics of the robot with 6 rotational (6R) joints. To analyze the pose-dependent motor drive capabilities, a simplified dynamics model is established by considering both the tangential and joint dynamic constraints. An asymmetrical FIR filter-based tool path smoothing algorithm is also proposed to realize different constraints of the acceleration and the deceleration limits at different positions of the linear segments. The tool tip position and the tool orientation commands are respectively smoothed in the Cartesian coordinate system and spherical coordinate system, with the constraints of the tool path deviations in the task frame. The synchronization of the position and the orientation is realized by adjusting the parameters of FIR filters. The simulation and experimental results show that the proposed algorithm guarantees the corner error tolerances of the tool tip position and the tool orientation. Moreover, the proposed method improves the robot motion efficiency by more than 10 % through the full utilization of the joint drive capability compared with the traditional tool path smoothing algorithm based on symmetrical FIR filters.

A simple model of panic buying

We study a simple dynamic model to rationalize episodes of excess demand that resemble “panic buying” episodes in actual markets. In such episodes, consumers compete for a scarce good. Scarcity is triggered by an anticipated negative supply shock that takes place in a future period with positive probability. To avoid the risk of non-consumption, consumers can stockpile the good in earlier periods. We demonstrate that these stockpiling decisions can reinforce each other, creating a cascade of excess demand in several periods similar to “panic buying” episodes observed, for instance, during the Corona crisis. In our model, stockpiling is always detrimental to welfare, and we develop a suitable policy response.

Transport Barriers in Geophysical Flows: A Review

In the Lagrangian approach, the transport processes in the ocean and atmosphere are studied by tracking water or air parcels, each of which may carry different tracers. In the ocean, they are salt, nutrients, heat, and particulate matter, such as plankters, oil, radionuclides, and microplastics. In the atmosphere, the tracers are water vapor, ozone, and various chemicals. The observation and simulation reveal highly complex patterns of advection of tracers in turbulent-like geophysical flows. Transport barriers are material surfaces across which the transport is minimal. They can be classified into elliptic, parabolic, and hyperbolic barriers. Different diagnostics in detecting transport barriers and the analysis of their role in the dynamics of oceanic and atmospheric flows are reviewed. We discuss the mathematical tools, borrowed from dynamical systems theory, for detecting transport barriers in simple kinematic and dynamic models of vortical and jet-like flows. We show how the ideas and methods, developed for simple model flows, can be successfully applied for studying the role of barriers in oceanic and atmospheric flows. Special attention is placed on the significance of transport barriers in important practical issues: anthropogenic and natural pollution, advection of plankton, cross-shelf exchange, and propagation of upwelling fronts in coastal zones.

Complex nonmonotonic responses of biodiversity to habitat destruction.

It has typically been assumed that habitat destruction, characterized by habitat loss and fragmentation, has consistently negative effects on biodiversity. While numerous empirical studies have shown the detrimental effects of habitat loss, debate continues as to whether habitat fragmentation has universally negative effects. To explore the effects of habitat fragmentation, we developed a simple model for site-occupancy dynamics in fragmented landscapes. With the model, we demonstrate that a competition-colonization trade-off can result in nonlinear oscillatory responses in biodiversity to both habitat loss and fragmentation. However, the overall pattern of habitat loss reducing species richness is still established, in line with empirical observations. Interestingly, the existence of localized oscillations in biodiversity can explain the mixed responses of species richness to habitat fragmentation per se observed in nature, thereby reconciling the debate on the fragmentation-diversity relationship. Therefore, this study offers a parsimonious mechanistic explanation for empirically observed biodiversity patterns in response to habitat destruction.

Dynamic Rupture Models of the 2016 ML 5.8 Gyeongju, South Korea, Earthquake, Constrained by a Kinematic Rupture Model and Seismic Waveform Data

ABSTRACT The ML 5.8 earthquake that jolted Gyeongju in southeastern Korea in 2016 was the country’s largest inland event since instrumental seismic monitoring began in 1978. We developed dynamic rupture models of the Gyeongju event constrained by near-source ground-motion data using full 3D spontaneous dynamic rupture modeling with the slip-weakening friction law. Based on our results, we propose two simple dynamic rupture models with constant strength excess (SE) and slip-weakening distance (Dc) that produce near-source ground-motion waveforms compatible with recorded ones in the low-frequency band. Both dynamic models exhibit relatively large stress-drop values, consistent with previous estimates constrained by source spectrum analyses. The fracture energy estimates were also larger than those predicted by a scaling relationship with the seismic moment. The dynamic features constrained in this study by spontaneous rupture modeling and waveform comparison may help understand the source and ground-motion characteristics of future large events in southeastern Korea and thus the seismic hazard of the region.

A simple model to control the wild mosquito with sterile release

This article studies a simple population dynamics model for interaction between wild and sterile mosquitoes. The model has at most two positive equilibria. Local stability of the positive equilibria is analyzed with respect to the total number of offspring production and the natural death rate. Numerical studies show the bi-stability of the positive equilibrium and the extinction equilibrium, as there always exist solutions that converge to the extinction equilibrium. We also discuss the threshold release rate of the sterile population to achieve the extinction of wild mosquitoes and perform sensitivity analysis with respect to the release rate.

When do traffic reports make traffic better?

We present a simple dynamic traffic model to study whether public information about road conditions increases or decreases travel times. We find that the effect depends on comparing (1) the increase in travel time when a road with a pre-existing delay becomes congested, and (2) the corresponding increase on a road with no pre-existing delay. Traffic reports are helpful when the first value is large relative to the second. Surprisingly, they are less useful when total road capacity is sufficient to potentially accommodate all drivers without congestion. In that case, traffic reports are always harmful in the nonatomic limit as individual drivers become negligible.

Single-compartment model of a pyramidal neuron, fitted to recordings with current and conductance injection.

For single neuron models, reproducing characteristics of neuronal activity such as the firing rate, amplitude of spikes, and threshold potentials as functions of both synaptic current and conductance is a challenging task. In the present work, we measure these characteristics of regular spiking cortical neurons using the dynamic patch-clamp technique, compare the data with predictions from the standard Hodgkin-Huxley and Izhikevich models, and propose a relatively simple five-dimensional dynamical system model, based on threshold criteria. The model contains a single sodium channel with slow inactivation, fast activation and moderate deactivation, as well as, two fast repolarizing and slow shunting potassium channels. The model quantitatively reproduces characteristics of steady-state activity that are typical for a cortical pyramidal neuron, namely firing rate not exceeding 30Hz; critical values of the stimulating current and conductance which induce the depolarization block not exceeding 80mV and 3, respectively (both values are scaled by the resting input conductance); extremum of hyperpolarization close to the midpoint between spikes. The analysis of the model reveals that the spiking regime appears through a saddle-node-on-invariant-circle bifurcation, and the depolarization block is reached through a saddle-node bifurcation of cycles. The model can be used for realistic network simulations, and it can also be implemented within the so-called mean-field, refractory density framework.

A Unified Numerical Approach to the Dynamics of Beams with Longitudinally Varying Cross-Sections, Materials, Foundations, and Loads Using Chebyshev Spectral Approximation

Structures with inhomogeneous materials, non-uniform cross-sections, non-uniform supports, and subject to non-uniform loads are increasingly common in aerospace applications. This paper presents a simple and unified numerical dynamics model for all beams with arbitrarily axially varying cross-sections, materials, foundations, loads, and general boundary conditions. These spatially varying properties are all approximated by high-order Chebyshev expansions, and discretized by Gauss–Lobatto sampling. The discrete governing equation of non-uniform axially functionally graded beams resting on variable Winkler–Pasternak foundations subjected to non-uniformly distributed loads is derived based on the Euler–Bernoulli beam theory. A projection matrix method is employed to simultaneously assemble spectral elements and impose general boundary conditions. Numerical experiments are performed to validate the proposed method, considering different inhomogeneous materials, boundary conditions, foundations, cross-sections, and loads. The results are compared with those reported in the literature and obtained by the finite element method, and excellent agreement is observed. The convergence, accuracy, and efficiency of the proposed method are demonstrated.

A simple model of edit activity in Wikipedia

A simple dynamical model of collective edit activity of Wikipedia articles and their content evolution is introduced. Based on the recent empirical findings, each editor in the model is characterized by an ability to make content edit, i.e., improving the article by adding content and a tendency to make maintenance edit, i.e., dealing with formal aspects and maintaining the edit flow. In addition, each article is characterized by a level of maturity as compared to a potential quality needed to comprehensively cover its topic. This model is found to reproduce the basic structure of the bipartite network between editors and articles of Wikipedia. Furthermore, the relation between the model parameters of editors and articles and the metrics of those calculated from the emergent network turns out to be robust, i.e. depending only on the rate of the introduction of new articles to the editing activity. This results provides us a way to relate observations in the real data to the hidden characteristics of editors and articles. For the nestedness of the networks, systems with weighted parameter distribution gives better match to the empirical one. This suggests the importance of high-dimensional nature of the ability of editors and quality of articles in the real system.

How does facilitation influence the outcome of species interactions?

Abstract Research on facilitation has not had the advantage of being explicitly underpinned by mathematical population dynamic theory. This has limited understanding of the role of facilitation on ecological dynamics and the integration of facilitation into general theory. I show that facilitative interactions can be classified into four categories likely to be dynamically similar to interaction types for which population‐dynamic theory does exist. This classification provides a useful population‐dynamic foundation for facilitation studies and reveals several fundamental questions unanswered by existing empirical work. While facilitation has typically been excluded from studies of ‘competitive’ coexistence, I also describe how coexistence theory does not always default to the assumption that species only compete; recent developments in coexistence theory fully accommodate the potentially important influence of facilitation. Simple models of multispecies population dynamics emphasize the importance of reciprocal interspecific interactions, intraspecific interactions, and density dependence for understanding the outcome of facilitative interactions. I point to approaches for aligning future empirical studies of facilitation with these features. Synthesis: Closely aligning at least some future empirical work on facilitation with existing mathematical population dynamic theory will further improve understanding of the effects of facilitation on ecological dynamics.