Dynamic System Model Research Articles

East Asian international doctoral students’ role identity development in the United States

The U.S. attracts a significant number of international doctoral students each year. As these students navigate cultural and academic systems, understanding their unique experiences and challenges becomes crucial. This study explores the intricate nuances of motivation and identity negotiation in the lived experiences of six East Asian international doctoral students in the U.S. using collaborative autoethnography. We examined how role identities explain our experiences by employing the Dynamic Systems Model of Role Identity framework. Our reflections revealed (1) the salience of role identity components and structural relations and the impact of other role identities on resolving tensions; (2) the development of our researcher and teacher role identities within doctoral student role identity; and (3) resilience as both a cultural disposition and a dynamic process that evolves through the constant negotiation and renegotiation of our identities.

Editorial. The 10th yearly volume of JPOR is complete

The first issue of Journal for Person-Oriented Research (JPOR) was published in 2015, representing the first international journal specifically focused on person-oriented research. With this issue, the JPOR completes its tenth yearly volume. The status of JPOR as a truly international journal is reflected in the number of published articles from different countries. The number of submissions to JPOR has been steadily increasing, as has also its CiteScore as measured by Scopus. A call is made for papers on the application of non-linear dynamic system modelling.

Holistic Dynamic Modeling and Simulation of Alkaline Water Electrolysis Systems Based on Heat Current Method

Hydrogen production technology is becoming increasingly important with the rapid development of hydrogen energy. Among existing hydrogen production technologies, alkaline water electrolysis (AWE) is drawing wide attention due to its advantages such as high maturity and low cost, and its performance analysis and optimization are important for applications. However, the AWE system contains processes with different physical and mathematical properties such as electrochemical reaction and heat transport processes, bringing difficulties to the system modeling. Moreover, the electrical and thermal processes have different characteristic time scales, and the system shows a sophisticated dynamic behavior, which has not been well studied yet. Here, a homomorphic dynamic model of the AWE system in the form of electrical circuit is built to describe the thermal and electrochemical processes uniformly, where the two parts are integrated via the energy conservation seamlessly. The model is verified by comparing with the experimental data and shows a high accuracy. The dynamic simulation analysis is conducted to investigate the dynamic response characteristics of the system under current step changes and fluctuations. The temperature overshoot and oscillation phenomena caused by delays in heat transport processes are studied. Results show that the time delay yields a maximum temperature overshoot of 10 °C, which would reduce the lifespan of the stack. This also highlights the importance of dynamic system analysis.

Dynamic Systems Model of Organizational Performance in Improving Public Service Quality of BPS In Bandung Raya Region

Initial analysis shows that the performance of BPS in Bandung Raya has not provided maximum results. This has an impact on the non-optimal quality of BPS public services in Bandung Raya. By using descriptive analysis, SEM, and system dynamics, this research seeks to determine the implementation of BPS performance in Bandung Raya, analyze the factors that hinder the quality of public services, and analyze what performance models are effective in improving the quality of BPS public services in Bandung Raya. The results showed that there are still several things that need to be improved related to the strategy, process, and capability of BPS in Bandung Raya. From these three aspects, it was found that the process and organizational capability had a positive and significant effect on the quality of BPS services in Bandung Raya, while the business strategy did not have a significant effect. Several factors that hinder the performance and effectiveness of BPS services in Bandung Raya were also found. These factors are related to transformational leadership, training for human resources, technology and information, and data integrity. Therefore, this research provides 5 scenarios obtained based on the results of system dynamics simulation to be used as policy proposals. Of the 5 scenarios, it is found that scenario V, namely data integrity, is the best scenario because it is able to improve organizational performance up to 76.01% and service quality up to 70.82%. By applying the novelty of this research, it is expected to improve the quality of BPS public services in Bandung Raya.

Model-Free Adaptive Control of an Active Half-Vehicle-Seat System Coupled with a Nonlinear Energy Sink Inerter (NESI)

In order to reduce vehicle vibration and improve vehicle ride comfort and handling stability, a nonlinear energy sink inerter (NESI) is designed by combing an inerter and nonlinear energy sink (NES) for use in the seat suspension and vehicle suspension for the half-vehicle-seat (HVS) system; furthermore, a model-free adaptive control (MFAC) method based on the genetic algorithm is proposed to enhance the dynamic performance of the passive HVS system. The dynamic model of the active HVS system coupled with NESI using the MFAC method is established; its dynamic responses under pavement random and shock excitations are acquired using the numerical method and the dynamic performance is evaluated by seven evaluation indicators. The efficacy of the MFAC method is demonstrated through comparative analysis with the original passive HVS system, the HVS system coupled with NESI, and the active HVS system coupled with NESI using the proportional integral derivative (PID) control method. In addition, the influence of the installed position of MFAC on the dynamic performance of the active HVS system coupled with NESI is examined. The results show that for the active HVS system coupled with NESI using the MFAC method, compared with the other three HVS systems, the root mean square (RMS) values of the vehicle body vertical acceleration, vehicle body pitch acceleration, seat vertical acceleration, and front and rear suspension dynamic travel under pavement random excitation are smaller, the corresponding peak amplitudes under pavement shock excitation reduce, and the vibration attenuation time shortens; the RMS values of the front and rear dynamic tire loading under pavement random excitation are slightly smaller, the corresponding peak amplitudes under pavement shock excitation increase, and the vibration attenuation time decreases, which reflects the best dynamic performance among the four HVS systems and shows the effectiveness of the MFAC method. Furthermore, the control effect of the MFAC method is the best when it acts both on the seat and vehicle suspensions.

Dynamic model and performance assessment of inter-story isolation system with supplement inerter (IISI)

The hybrid control combining the conventional seismic isolation and tuned mass damper inerter is recently gaining popularity for the seismic protective strategy. This paper presents a simplified dynamic model of the inter-story isolation system with a supplement inerter (IISI) for seismic performance assessment. The dynamic characteristics, such as modal frequencies, mode shapes, damping ratios, and participation factors, as well as the seismic response spectrum, were studied considering the importance of the equivalent linear model for seismic design. The vibration characteristic formula of the IISI was derived using a simplified 2-degree-of-freedom (2DOF) model. The influence of the calculated parameters of the model on the vibration characteristics was analyzed. To understand the isolation control mechanism, the response transfer function in the frequency domain was derived, and the effects of model parameters on the deformation amplitude amplification factor of the superstructure and substructure were analyzed. Based on equivalent linear properties, a seismic response prediction formula based on the response spectrum method is provided and verified by time history analysis (THA). The effect of soil-structure interaction (SSI) on the simplified 4-DOF model was also studied. The seismic responses and effectiveness of the IISI were evaluated using a Multi-degree-of-freedom (MDOF) model example. The research results demonstrate that the addition of an inerter changes the vibration characteristics of the traditional isolation system. The supplement inerter is effective in controlling the seismic response of the IISI, and the established analysis method can be used to analyze the seismic response of the IISI by incorporating additional inerter mounted in the isolation layer. The hybrid control strategy shows better effectiveness in reducing displacement in the isolation layer.

Dynamic characteristics of pumped thermal-liquid air energy storage system: Modeling, analysis, and optimization

Pumped thermal-liquid air energy storage (PTLAES) is a novel energy storage technology that combines pumped thermal- and liquid air energy storage and eliminates the need for cold storage. However, existing studies on this system are all based on steady-state assumption, lacking dynamic analysis and optimization to better understand the system’s performance under cyclic operation. To fill this gap, the mainbody-linearized cyclic dynamic model of the PTLAES system with packed bed thermal energy storage (TES) was first developed. Then, the dynamic characteristics of the baseline system were investigated. Sensitivity analyses were carried out on TES parameters. Minimal values of levelized cost of storage (LCOS) were observed for all parameters in the range of interest. Subsequently, the TES circuit was optimized, and a triple improvement of efficiency and energy density enhancement, discharge stabilization, and cost reduction was achieved. The optimized system's round-trip efficiency and energy density increased from 61.7% to 63.1% and from 141.9 kWh/m³ to 159.2 kWh/m³, and the LCOS decreased from 163.2 $/MWh to 159.4 $/MWh. A power offset ratio lower than 3% was reached, which is the lowest value ever reported in the literature. This study provides reference for future design and operation of the PTLAES system.

A fractional adaptive type-2 fuzzy structural control system: Theoretical/experimental study

A new modified sliding mode control (SMC) method based on type-2 fuzzy neural networks (T2FNNs) is introduced for the active mass damper (AMD) systems. An adaptive T2FNN is used in the switching part, and another adaptive T2FNN is used to estimate the uncertainty of the AMD system. T2FNNs are adopted to predict the system’s uncertainties and establish a dynamic model of the AMD system, independent of the mathematical model. The chattering phenomenon is also taken into account and analyzed. The stability is studied by using the Lyapunov approach to derive training rules for both T2FNNs in dynamic modeling and switching part. Numerical simulation and experimental verifications confirm the feasibility and effectiveness of the designed T2FNN based SMC. The results reveal that the designed controller outperforms the conventional controllers in reducing the vibration peak and reducing the root mean square (RMS) value of structural displacement and acceleration. It also exhibits good robustness against external disturbances and structural dynamic perturbations. The suggested algorithm combines the advantages of adaptive control and fuzzy logic, addressing the issue of chattering in control and overcoming the lack of self-learning capability in conventional SMCs.

A computational dynamic systems model for in silico prediction of neural tube closure defects

A computational dynamic systems model for in silico prediction of neural tube closure defects

An adaptive dynamical system model for development of schizophrenia: Epigenetics and false memories

This paper introduces a fifth-order adaptive dynamical system model represented as a self-modelling network model to understand the role of epigenetic factors in developing schizophrenia, particularly about the occurrence of false memories. Schizophrenia, affecting a significant portion of the global population, presents complex challenges due to its symptoms across cognitive, emotional, and social domains. Central to this paper is the exploration of epigenetic influences on the development of schizophrenia, with a focus on how these epigenetic factors contribute to the generation of false memories, a notable symptom of the disorder. The model assumes that early adverse experiences significantly affect hippocampal development, which is crucial for memory processes. By employing a fifth-order adaptive dynamical system model represented as a self-modelling network, this paper examines the intricate dynamics between genetic predispositions, environmental factors, and epigenetic modifications in schizophrenia. The model’s sophistication allows for a nuanced understanding of the gene-environment interactions, particularly highlighting the role of early-life trauma in shaping lifelong cognitive outcomes. This approach offers a comprehensive framework for analyzing the multifaceted nature of schizophrenia, contributing to a deeper understanding of its etiology and progression.

Dynamic Model and PD Control with Forces Compensation of Dual-Stage Gough-Stewart Platform

This paper investigates a dual-stage Gough-Stewart platform. The lower platform is responsible for simulating the oscillations of moving vehicles such as cars, ships, and airplanes. The upper platform is connected to devices that require either balance stabilization or motion stabilization according to specific requirements. The dynamic model of the robot system is derived in a general form based on the Lagrange equations of motion with Lagrange multipliers. Using these equations in a compact form, a PD controller with forces compensation in task space is designed for the robot system. Oscillation generation and balance stabilization are computed and simulated using the kinematic and dynamic parameters of two Bosch Rexroth robots. The computation and simulation results demonstrate the dynamic model's accuracy and the controller's effectiveness.

Adaptive Sliding Mode Control of Quadrotor System with Elastic Load Connection of Unknown Mass

During quadrotor load transport, the cable’s elasticity exacerbates load fluctuations, which may result in platform instability or a potential crash. This paper introduced a model of the connecting cable as a spring-damper system and established the dynamic model of the suspension system based on Newton’s law. Nonsingular fast terminal sliding mode control (NFTSMC) was employed for attitude, position, and anti-swing controller design. Adaptive controllers were integrated into altitude control to address uncertainties related to load mass and cable length. The inclusion of an anti-swing controller into the position control loop effectively dampens load oscillations while ensuring accurate position tracking. Numerical simulations demonstrated that the proposed controller outperforms both the energy-based controller and the conventional linear sliding mode controller.

Design and modeling of three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester for harvesting multi-band vibration energy on the freight wagon axle box

Abstract With the rapid development of rail transit system, it is becoming more demanding for structural health monitoring (SHM) of the train. It is crucial to supply power for sensing devices on the freight wagon to ensure the safe operation of rail transit systems. Three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester (TNHVEH) with a three-layer pickup system has been designed and applied to efficiently harvest vibration energy on the axle box of freight wagons for providing the power of the sensors for online SHM. Dynamic coupling model of the vehicle-harvester system is built to devote the relationship between the operation bandwidth and the nonlinear degree of TNHVEH for broadening the operation frequency band. Simulation and experimental results demonstrate that the resonant frequencies of the three pickup systems of TNHVEH are concentrated around 27, 70, and 120 Hz, matching the representative frequencies of axle box vibrations. A maximum output voltage of 2.97 V and output power of 29.4 μW under an optimal load of 300 kΩ at 0.5 g acceleration is achieved. It successfully lights up 53 LEDs with ‘ECJTU’ patterns, providing a solution to the power supply problem of microelectronic devices on freight wagons.

Influence of dynamic complexity on serial robots with joint clearance based on a nonlinearity measure

Conditional monitoring and diagnosis of serial robots depend not only on the method used but also on the dynamic complexity of the robotic system itself. Previously, researchers have focused mainly on the analysis of dynamic behaviors with different parameters of a mechanical system with clearance. Although the indicators such as Root Mean Square (RMS), spectrum kurtosis (SK), Entropy have the potentiality to express the specific trend by different parameters, it can be noticed that they mainly depend on the values of the dynamic responses and neglect the information of the nonlinear characteristics in the responses of mechanical systems. In this paper, a nonlinearity measure-based method is proposed to analyze the impact of dynamic complexity on serial robots with joint clearance. The kinetic equations of a serial robot with joint clearance are established by considering the contact forces in the joint. The dynamic model of the robotic system is validated by experiments. A quantization method for dynamic complexity analysis is established based on the nonlinearity measure, and the influences of parameters on the dynamic complexity are analyzed via numerical computations for the validated dynamical models of the robotic system. A relationship is developed among the clearance radius, friction coefficient, and dynamic complexity. This shows that a larger clearance radius will lead to a more complex system and that friction coefficients within a certain range can reduce the dynamic complexity of the system. These results can guide methodological choices for condition monitoring and diagnosis of serial robotic systems.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

Abstract 4145826: Atrial Fibrillation Screening During Sinus Rhythm Periods by Interrelated Systems Dynamics Analysis

Background: Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate. The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system. Methods: Long (>10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals' datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups. Results: Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity. Conclusion: In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

Dynamical system model of gentrification: Exploring a simple rent control strategy.

Motivated by the need to understand the factors driving gentrification, we introduce and analyze two simple dynamical systems that model the interplay between three potential drivers of the phenomenon. The constructed systems are based on the assumption that three canonical drivers exist: a subpopulation that increases the desirability of a neighborhood, the desirability of a neighborhood, and the average price of real estate in a neighborhood. The second model modifies the first and implements a simple rent control scheme. For both models, we investigate the linear stability of equilibria and numerically determine the characteristics of oscillatory solutions as a function of system parameters. Introducing a rent control scheme stabilizes the system, in the sense that the parameter regime under which solutions approach equilibrium is expanded. However, oscillatory time series generated by the rent control model are generally more disorganized than those generated by the nonrent control model; in fact, long-lived transient chaos was observed under certain conditions in the rent control case. Our results illustrate that even simple models of urban gentrification can lead to complex temporal behavior.

Negative Mechanical Characteristics of Self‐Similar Star‐Like Hinged Grilles

Ferroelastic textures called star patterns are transformable into grilles whose rigid bars replace domain boundaries and are connected with hinges at their junctions. The grilles show several levels of self‐similarity. Exact formulae for the scaling factors as functions of the shape and number N of the arms are provided. The minimal dynamic model of such systems involves force constants at only one level (generation) of self‐similarity. The minimal set of force constants ensures stability of the stars with N = 2 and N = 3. At N = 4 the minimal model exhibits exactly one mode of zero frequency. The reaction of the four‐arm star to a tensile stress applied at the level of the force constants depends on the self‐similarity scaling factor and on the separation between the applied stress and the generation where the observation is made. In some regions of these parameters the reaction is analogous to auxetics but it also shows negative tensile characteristics, that is, a shrinking in the direction of the stress axis. With increasing distance from the external perturbation toward outer generations of self‐similarity the system shows a surprisingly fast restoration of the symmetry broken by the stress.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Transient energy transfer of wind-photovoltaic-storage grid-connected system under virtual synchronous coupling

In the new power system, the efficient capture of transient energy by a virtual synchronous generator (VSG) will be the key to improve the grid-connected stability of wind-photovoltaic-storage. This paper first analyzes the virtual synchronous coupling relationship between the VSG and the synchronous generator (SG), and constructs the dynamic model of grid-connected system. Secondly, based on the Hamiltonian theory, the transient energy transfer characteristics between the VSG and the SG under the virtual synchronous coupling are analyzed, and the conditions for the VSG to completely capture the transient energy is obtained, and a virtual inertia control strategy based on the efficient transfer of transient energy is proposed. Finally, a 9-node simulation system is built to verify that the proposed control strategy can significantly improve the performance of VSG in suppressing power oscillations and frequency changes.