Conventional Dynamic Model Research Articles

A novel ‘3D + digital twin + 3D’ upscaling strategy for predicting the detailed multi-physics distributions in a commercial-size proton exchange membrane fuel cell stack

With the rapid development of proton exchange membrane fuel cell (PEMFC) commercialization, a comprehensive knowledge of multi-physics fields in large-scale PEMFC stacks has become ever more critical. Although conventional three-dimensional computational fluid dynamic (CFD) models have achieved great success, the application in the commercial-size stack-scale simulation remains inapplicable due to enormous computational resource requirements. Herein, based on the latest 3D CFD model, multi-physics digital twin (DT) technology and 3D stack flow distribution prediction model, a novel multi-scale upscaling prediction model is proposed. The voltage, water and thermal management characteristics of a 164-cell PEMFC stack with an active electrode area of 292.5 cm2 are studied and analyzed in details. For the analysis of commercial-size PEMFC stacks, the most comprehensive multi-physics fields are covered in this paper to date. And the results suggest that by introducing the DT technology, the time requirement of the multi-physics field prediction for unit scale prediction can be reduced by hundreds of thousands of times with a maximum global relative deviation of 1% under 10 groups of random test conditions, giving a solution from the cell scale to stack scale performance prediction, design, heat and thermal management in the PEMFC research and application.

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Abstract Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis. We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem, which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model. We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023. We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.

Mechanism Study of Symmetric Mechanism-Rolling Bearing Dynamic System Considering the Influence of Temperature Factor

Due to the symmetry of their structure and the way they rotate, rolling bearings are often used at high temperatures and high speeds. When the temperature changes, the material properties of the rolling bearing change, which in turn causes the dynamic model equation of the rolling bearing to change. Given the problem in that the conventional dynamic model equation does not consider temperature changes, but because temperature changes cause changes in material performance parameters resulting in differences between the dynamic simulation signals and the actual signals, this paper fully considers three factors, namely, the thermal expansion coefficient, the hardness value, and the friction coefficient. With the influence of temperature changes, a study on the mechanism of the symmetric mechanism-rolling bearing dynamic system considering the influence of temperature factors is proposed. First, combined with the material properties of the rolling bearing, the changes in thermal expansion coefficient, hardness value, and friction coefficient caused by temperature changes were analyzed. Second, the functional relationship expressions obtained above were substituted into the existing conventional dynamic model. Finally, the comprehensive functional relationship formula was substituted into the conventional dynamic model to obtain the rolling bearing dynamic model under temperature difference changes. By studying the mechanism of the symmetric mechanism-rolling bearing dynamic system that considers the influence of temperature factors, this method can undertake a more comprehensive consideration of the dynamic analysis research into rolling bearing fault diagnosis, thereby verifying the effectiveness of the method.

Nonlinear dynamics of spur gear systems with time-varying misalignment errors

The mesh stiffness excitation of conventional dynamic models for misaligned spur gear systems is obtained under constant misalignment errors, which inevitably neglects the additional fluctuation of mesh stiffness caused by the influence of dynamic response on misalignment errors. The uneven load distribution and contact state alteration resulting from misalignment errors will produce nonlinear tilting moments that cause pendular vibration in gears. The pendular vibration of the gears, in turn, contributes to the time-varying property of misalignment errors. In this study, misalignment errors consist of an initial constant component that may originate from mounting errors and a time-varying component caused by the pendular vibration of gears. Subsequently, a nonlinear coupled lateral-torsional-axial-pendular vibration model for spur gear systems with time-varying misalignment errors (TVME) is constructed. Parametric analyses reveal that the dynamic behavior of the proposed TVME model and the conventional model differ due to the presence of an initial misalignment error. In contrast to the conventional model, the proposed model exhibits richer nonlinear characteristics, mainly in the high-speed zone, and is more prone to sub-harmonic resonance and contact loss owing to the time-varying property of misalignment errors. At 11,400 r/min, the proposed model undergoes sub-harmonic resonance, and its root-mean-square (RMS) value of dynamic transmission error (DTE) increases from 0.27 to 1.15 and 2.96 as the initial misalignment error increases from 0 to 0.005 ° and 0.01 ° , whereas the corresponding values of the conventional model remain almost unchanged at 0.27.

Dynamic modeling and analysis of traction operation process for the shipboard helicopter

The traction operation of a shipboard helicopter on the deck is a complex force-coupling process involving multiple moving entities. To more efficiently simulate the traction process, this study introduces a dynamic model based on the independent modeling of moving subjects (IMMS) method. Initially, the forces on the tire and the articulation part are analyzed, followed by the separate establishment of dynamic models for the helicopter and tractor using the Lagrange equation. Considering the articulated states among the ship, helicopter, and tractor, and according to the active–passive relationships of each entity, a unified solution method is introduced to solve the motion equations sequentially. The model's reliability and real-time performance are subsequently assessed and validated, effectively simulating the helicopter's traction operation procedure. The results reveal that the IMMS dynamic model exhibits high reliability and requires, on average, only 0.5% of the CPU time compared to the conventional dynamic model. The IMMS approach will be useful in advancing research into the modeling of shipboard helicopter traction processes.

Investigation of the Feasibility of the Dynamic Equivalent Model of Large Photovoltaic Power Plants in a Harmonic Resonance Study

In recent years, there have been several harmonic resonance accidents around the world that involve renewable energy power plants. The frequency scanning method is the most widely used technique in engineering practice to evaluate the severity of resonance due to its simple operation and clear physical meaning. However, when establishing electromagnetic transient simulation models and conducting frequency scans, a single generation unit or a few renewable generation units are usually used to represent the original power plant for the purpose of reducing model complexity and improving the simulation efficiency. Such a practice has been found to be effective in dynamic studies around the fundamental frequency. However, its feasibility in harmonic resonance studies has not yet been fully investigated. Because of this research gap, a feasibility study is conducted in this paper by using a real-life photovoltaic power plant. The detailed harmonic model of the plant is first established using the harmonic linearization method, and the equivalent harmonic model is then developed using the power loss conservation method. The feasibility of the equivalent model was investigated in detail, and the impact of the different impedance models on the resonance analysis was analyzed. The results indicate that the conventional dynamic equivalent model can effectively reflect the harmonic resonance characteristics of photovoltaic power plants. Furthermore, a more simplified model that ignores the inductance of the collector line is recommended in this paper to further reduce the modeling complexity.

Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

The online version contains supplementary material available at 10.1007/s00181-022-02356-9.

A hybrid dynamic hysteresis model based on the Preisach model and stacked auto-encoder

With the increase in power electronic equipment in power system, the excitation of ferromagnetic materials often involves a large number of harmonics. Therefore, it is necessary to construct an accurate dynamic hysteresis model to adapt to this complicated operating state of electrical equipment. In this paper, a Hybrid Dynamic Hysteresis Model (HDHM), which can effectively characterize the harmonic excitation of materials is studied based on the Preisach model and Stacked Auto-Encoder (SAE) model. The static part of this model takes the form of the inverse Preisach model. And the Multiple Dynamic Hysteresis Model Set (MDHMS) is constructed by multiple dynamic models of eddy currents and excess characteristics of the ferromagnetic materials. The dynamic part of the HDHM takes the form of the model structure combining the Stacked Auto-encoder and the MDHMS. The calculation results of the hysteresis loop and ferromagnetic loss in the harmonic condition of silicon steel sheet proves the validity of this model. Moreover, compared with the conventional dynamic hysteresis model, the HDHM has better accuracy and generalization ability.

Effects of gear flexibility on the dynamic characteristics of spur and helical gear system

The gear foundation is commonly assumed to be a rigid body in analyzing the dynamic characteristics of the gear system. This modeling approach may not be appropriate for building the gear model with lightweight features. Therefore, this paper proposes a gear dynamic model considering the flexible gear. The shell element with gyroscopic effect is employed to establish the gear finite element model, retaining the gear foundation and teeth structure. The fixed interface modal synthesis method is used to improve computational efficiency. The proposed model is verified by comparing it with the results calculated by Ansys. Based on the proposed model, the effects of gear flexibility on the dynamic characteristics of the spur and helical gear system are investigated. Numerical results show that the developed model can degenerate into the classical gear dynamic model. Due to the assumption of the gear as rigid discs, the predicted resonance speed by the conventional gear dynamic model is larger than the proposed model. Gear flexibility affects the dynamic characteristics of the spur and helical gear system differently. It's worth noting that the dynamic axial meshing force can excite the nodal diameter vibration in the helical gear system. The proposed model provides the theoretical basis and optimization tools for the high-performance design and vibration and noise reduction of high-speed lightweight gear transmission systems.

Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method.

A concurrent locality-preserving dynamic latent variable (CLDLV) method is proposed to extract the correlation between process variables and quality variables for quality-related dynamic process monitoring. Given that dynamic process data can easily be contaminated by noise and outliers and conventional dynamic latent variable models lack robustness, a low-rank autoregressive model is developed to deal with autocorrelation and cross-correlation properties among the data. Then neighborhood structure information is integrated into the partial least squares model, which can better reveal the essential structure of the data. The final concurrent projection of the latent structures is employed to monitor output-related faults and input-related process faults that affect quality. The Tennessee Eastman process and hot strip mill process are used to demonstrate the effectiveness of CLDLV-based detection and diagnostic methods.

Transition in collective motion decision making.

Collective decision making in a biological motion group requires fast and robust transmission of information. Typically, directional switching information propagation across the whole group obeys a linear dispersion law. However, conventional dynamic collective motion models, such as the Vicsek model and the Couzin model did not take into account ultrafast directional synchronous motions. In the present paper, a multiparticle model is proposed based on inertial spin self-propel action, which can provide adequate description of such group motion. By considering both spin mechanism and collision avoidance, the proposed self-propelled particle spin model can nicely describe collective motion with fast directional switching. By analyzing the order parameter of the group-velocity synchronization, a mechanism of group decision making is revealed, which is based on the difference between two clusters of divergent leaders, showing a transition from the compromising phase (i.e., following the group average) to the preferred phase (i.e., aligning to a leader cluster). The finding provides new insight to the decision-making process of followers when they face with divergent leaders in group motion.

Modeling and analysis of dynamic characteristics of rubber isolators for electric vehicles under high-frequency excitation

The rubber isolator is a key component connecting the vehicle body and the powertrain, which plays a significant role in reducing the vibration transmission from the engine to the vehicle body. With the development of electric vehicles, the excitation frequency generated by the powertrain can exceed 2 kHz. The conventional dynamic models are usually used to simulate the low-frequency dynamic characteristics of rubber isolators, in the range of 0–200 Hz, which is the main excitation frequency range of the traditional internal combustion engine vehicle. However, they cannot simulate the high-frequency dynamic characteristics accurately since the inertial force caused by the mass of rubber isolators increases greatly under high frequencies. And the high-frequency dynamic characteristics of rubber isolators is of great significance for isolator fatigue life prediction, powertrain system vibration isolation design and noise control caused by the high-frequency resonance of the isolator. In this paper, a general model with mass elements is proposed to describe the effect of the inertial force on high-frequency dynamic stiffness. Based on the proposed model and considering the different expressions of the viscoelastic properties of rubber isolators, three improved dynamic models of rubber isolators are established, and used to analyze the high-frequency dynamic characteristics of rubber isolators. It has been demonstrated that the inertial force plays a non-negligible role in the dynamic characteristics of rubber isolators under high-frequency excitation. The proposed model can be used to analyze the effect of the inertial force on the dynamic characteristics at high frequencies accurately. The high-frequency dynamic characteristic modeling method of rubber isolators presented in this paper is helpful to the vibration and noise analysis of electric vehicle powertrain systems under high-frequency excitation.

Variability of Spectral Estimates of Stress Drop Reconciled by Radiated Energy

ABSTRACTA review of a collection of theoretical source spectral models revealed: (1) Despite the well-known variation in predicting static stress drop Δσs from the seismic moment and corner frequency, all models, especially the three conventional models, suggest that earthquakes radiate about half of the available strain energy into the surrounding medium. This similarity justifies a less model-dependent approach to estimate Δσs, though estimates for natural earthquakes rely on apparent seismic radiation efficiency (=2σa/Δσs; σa is apparent stress of an earthquake). (2) When one attempts to use Δσs and spectral models to make predictions, such as apparent stress σa, there is a model-dependent discrepancy between the σa inferred from theoretical energy partitioning and the σa predicted using spherical mean corner frequency. Their ratio cp varies significantly from 1.0 for the Brune (1970, 1971) model to 6.38 for the Madariaga (1976) model. If one uses spectral models to predict the ground motion, cp must be considered. (3) We infer that the constancy of the “stress parameter” (Δσ˜) found in engineering seismology (e.g., Boore, 1983; Atkinson and Beresnev, 1998) is similar to having constant apparent stress, σa (e.g., Ide and Beroza, 2001). The observation that Δσ˜ is generally larger than the average static stress drop Δσs for global M >5.5 shallow crustal earthquakes in active tectonic regions implies that these earthquakes radiate, on average, more seismic energy than predicted from the conventional dynamic crack models.

Endogenous dynamic inefficiency and optimal resource allocation: An application to the European Dietetic Food Industry

The conventional dynamic cost inefficiency model relies on the directional distance function with an exogenous directional vector to measure technical and allocative inefficiency. However, this approach may lead to contradictory recommendations for firms to become technically and allocatively efficient. By definition, the conventional model forces firms to reduce their inputs and increase their investments in order to become technically efficient; for some firms this is followed by the reverse recommendation to become allocatively efficient. This paper proposes a model that endogenizes the directional vector to solve for the cost minimizing combination of inputs and investments. In contrast to the conventional model with an exogenous directional vector, our model provides managers with monotonic prescriptions. We illustrate the superiority of the endogenous directional vector model over its conventional counterpart using a dataset of EU firms in the dietetic food industry. The differences in the managerial prescriptions are striking, with the conventional model wrongly recommending reductions in inputs that are underused with respect to their optimal amounts minimizing cost.

3D dynamic modelling of a rigid manipulator mounted on a rectangular plate based on Hamilton principle

This paper proposes a comprehensive model to predict the dynamical coupling behaviors of a rotating rigid manipulator mounted on a rectangular plate, which represents real applications such as on-orbit array installation of solar panels. The three-dimensional relationship of displacement field between the end-effector, with arbitrary installation position and azimuth, and the rectangular plate is first established based on spatial descriptions and transformations method. Galerkin’s method is employed and extended to treat the spatial partial derivatives. Then, the governing equation of motion is derived using the Hamilton principle. The dynamic behavior of the system with respect to torque input, the variation of structural parameters as well as installation position and azimuth of the manipulator, is analyzed by simulation, which may provide some principles to control the vibration amplitude of plate and oscillating value of end-effector. Simulation results show that the presented three-dimensional dynamic model is more proper and accurate to describe the dynamic behaviors of system through comparison with two conventional dynamic models, and especially the large structure parameter manipulator is mounted on a rectangular plate.

Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling

Data-driven building energy load prediction models should be updated dynamically to adapt to building performance degradation and changes of outdoor environment. Conventional dynamic modeling methods usually have to either increase the costs of model training and data storage for obtaining high accuracy, or decrease accuracy for reducing the costs of model training and data storage. Therefore, this paper presented an elastic weight consolidation-based sliding window fine-tuning method for dynamic building energy load prediction modeling with high accuracy, low computational costs, and low data storage costs. It adopted elastic weight consolidation to make a model be able to remember historical knowledge and learn new knowledge without storing historical data. Three common dynamic modeling methods (sliding window retraining, accumulative retraining, and sliding window fine-tuning) and a static modeling method were adopted for performance comparisons with the proposed method using the five-year operational data of a public building. According to the results, the mean absolute error of the proposed method decreased by 66.58%, 9.06%, and 8.70% on average compared with sliding window retraining, sliding window fine-tuning, and static modeling, respectively. It was further demonstrated that the proposed method had better stability than sliding window fine-tuning. Moreover, the proposed method showed similar accuracy to accumulative retraining, while it had lower costs of model training and data storage than accumulative retraining.

Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of “ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Autonomous navigation for Mars probes using only satellite-to-satellite tracking measurements by singularity-avoiding orbit elements

AbstractThis paper proposes a novel autonomous navigation method for Mars-orbiting probes. Satellite-to-satellite tracking (SST) between two probes is generally deemed to involve autonomous measurements with no dependence on any external observation sites on the Earth. For the conventional two-body dynamic model, it is well known that the orbit states cannot be estimated by merely using such SST measurements. Considering the effects of third-body gravitation perturbation and the weak Mars tesseral harmonics perturbation, autonomous navigation with SST measurements alone becomes weakly observable and may be achieved by some nonlinear filtering techniques. Two significant improvements are made to mitigate the nonlinearity brought by the dynamic models. First, singularity-avoiding orbit elements are selected to represent the dynamic models in order to reduce the intensity of the nonlinearity which cannot be overcome by the traditional position–velocity state expression. Second, the unscented Kalman filter method is effectively utilised to avoid the linearised errors calculated by its extended Kalman filter counterpart which may exceed the tesseral harmonics perturbation. A constellation, consisting of one low-orbit probe and one high-orbit probe, is designed to realise the autonomous orbit determination of both participating Mars probes. A reliable navigation solution is successfully obtained by Monte Carlo simulation runs. It shows that the errors of the semimajor axes of the two Mars probes are less than 10 m and the position errors are less than 1 km.

An extended LuGre model for estimating nonlinear frictions in feed drive systems of machine tools

Nonlinear frictions in feed drive axes affect the control accuracy and dynamic of the machine tools. Conventional dynamic friction models neglect the position-dependent friction, caused by manufacturing errors and misalignment as well as the environment. This paper proposes an extended LuGre friction model for estimating the dynamic and position-dependent friction effects. The conventional LuGre model is modified by a position-dependent term, which describes the nonlinear friction behavior of machine axes with long travel distance. An online data-driven identification approach is developed to fast parametrize and update the model. The suggested method is validated on a real machine tool.

Dynamic effective connectivity network based on change points detection

Human brain networks can be modeled as a system of interconnected brain regions which are recorded by time-dependent observations with functional magnetic resonance imaging (fMRI). In order to spot trends, detect anomalies, and interpret the temporal dynamics, it is essential to understand the connections among distinct brain regions, and how these connections evolve over time. However, the change points of dynamic reorganization in brain connectivity are unknown, which may occur frequently during the scanning session. In this paper, we introduce a fused lasso regression approach to detect the number and position of rapid connectivity changes of subject and subsequently estimate the brain effective connectivity networks within each state phase lying between consecutive change points by conditional Granger causality method from fMRI time series data. The performance of the method is verified via numerical simulations and the obtained classification accuracy with support vector machine (SVM) was 86.24% in 140 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared with static EC model and conventional dynamic EC model based on sliding window technique, the experimental results show that the fused lasso achieved better classification effect, which probably due to better dynamic description. The result shows that the dynamic effective connectivity based on change points detected by fused lasso method is a better feature for classification.