Dynamic Analysis Model Research Articles

Simplified Tunnel–Soil Model Based on Thin-Layer Method–Volume Method–Perfectly Matched Layer Method

In order to analyze the ground vibration responses induced by the dynamic loads in a tunnel, this paper proposes a new simplified tunnel–soil model. Specifically, based on the basic theory of the thin-layer method (TLM), the basic solution of three-dimensional layered foundation soil displacement was derived in the cylindrical coordinate system. The perfectly matched layer (PML) boundary condition was applied to the TLM. Subsequently, a tunnel–soil dynamic interaction analysis model was established using the volume method (VM) in conjunction with the TLM-PML method. The displacement frequency response function of the foundation soil around the tunnel foundation was derived. Finally, a ground vibration test under an impact load in a tunnel was carried out. The test and calculated results were compared. The comparison results show that the ground vibration acceleration response values within 25 m from the load are similar. Compared with the test results, the theoretical calculation results exhibit a decreasing trend in the range of 40–80 Hz between 25 and 60 m, with the maximum reduction being approximately one order of magnitude. In addition, the experimental comparison demonstrates that the model can be used to analyze the ground vibrations caused by underground loads.

Development of fatigue prediction system for bogie frame using a dynamic analysis model based on high‐speed and high‐precision stress estimation method

AbstractDespite ensuring the integrity of bogie frames for railway vehicles via nondestructive inspections during periodic maintenance, the possibility of fatigue cracks occurring at locations other than the predetermined inspection points cannot be dismissed. Therefore, fatigue cracks can be prevented more efficiently by assessing the overall degree of fatigue damage to the entire bogie frame and determining the results via nondestructive inspections. In this study, dynamic stresses in the bogie frame during running were estimated via finite element dynamic analysis by using the axle box acceleration as input, and the degree of fatigue damage and life were calculated from the waveform of the estimated stresses. Furthermore, we developed a bogie frame fatigue prediction system based on a high‐speed and high‐precision stress calculation method. The developed system visualized the overall relative life.

Developing an efficiency evaluation model for the circular economy in Europe

ABSTRACT The aim of this work is to develop a model for evaluating the efficiency of the Circular Economy (CE) in Europe. In this context, we have developed a dynamic efficiency model of the CE, the Dynamic Network Data Envelopment Analysis (DNDEA) model (Tone and Tsutsui 2014), with the purpose of it being utilized to determine the degree of progress of the policies on the CE, the degree of compliance with those policies, as well as to determine the factors that are associated with a greater or lesser efficiency. The results obtained indicate that the factors which have most affected the dynamic inefficiency have been: the Imports from the EU countries, the Exports to the non-EU countries, and the Gross Value Added. The patents that are related to Waste Management, Recycling, and Persons Employed in the CE sector have been the factors that have added the greatest efficiency. The contribution of this work lies in the development of a new model for the measurement of the dynamic efficiency of the CE at the macroeconomic level in Europe, while considering the relationships between the results and the resources that are allocated to the CE, and establishing a period of implementation and execution.

Numerical Simulation Study on the Hydrothermal Characteristics and Dynamic Response of the Embankment–Bridge Transition Zone in Cold Regions

The chain reaction of bridge abutment structure caused by frost heave of the embankment–bridge transition zone filler in cold regions has a great impact on the smoothness of railway lines, which will bring great challenges to the normal operation, safety, and maintenance of rail transit in cold regions under the action of long-term dynamic load. In this paper, the hydrothermal model and dynamic analysis model of the embankment–bridge transition zone are established, and the frost heave deformation rule and dynamic response characteristics of the embankment–bridge transition zone under frost heave conditions are studied. The results show that the effect of the bridge abutment structure on vertical frost heave is mainly concentrated in the range of the bridge abutment, and the vertical frost heave near the abutment is much smaller than other parts away from the abutment. The dynamic response and wheel–rail contact force of the embankment–bridge transition zone are significantly different under non-frost heave and frost heave conditions. Frost heave may cause a higher risk of derailment. This study provides a scientific reference for the design, construction, and safe operation and maintenance of embankment–bridge transition zones in cold regions, and has certain practical engineering significance.

Bayesian structural model updating with multimodal variational autoencoder

A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications.

Effects of technical advance on cobalt supply and its supply trends: A perspective of the whole industrial chains

By coupling dynamic material flow analysis and econometric models, the effects of technical advance on cobalt supply in the whole industrial chains are explored. Then, its supply trends are investigated at the global and national levels. The results show that: (1) the impact of technical advance on cobalt supply in the mining stage is inverted U-shape, while in the other stages, they are linear promotions. (2) Technical advance could increase global primary supply by up to 10 % from current production capacity. By 2030, secondary and total supplies will reach 79 kt and 251 kt or even more. (3) Refined cobalt supply share of China is expected to be 76 % by 2030. The European Union will produce the largest secondary supply, followed by China, the United States and Japan. (4) With the simultaneous development of technologies in each stage, supply-demand balance could be realized under some ideal scenarios. The above findings help intuitively recognize the roles of technical advance and strengthen the determination to do it.

Impact of Corporate Capital Structure on Corporate Performance: An Empirical Study of Emerging Market Using GMM Estimation Technique

This study aims to check the impact of capital structure on nonfinancial firm's performance, which are listed on the Pakistan Stock Exchange. This study used panel data from 06 years of 289 nonfinancial firms from 2017 to 2022 to achieve the goal. This study used two-step Generalized Methods of Moments (GMM), a dynamic analysis model. Five measures are used for firm performance, while two are used for capital structure. This study proved that a mixed result, such as TDR, has a significant and inverse relation with four measures of firm performance, such as ROA, GPM, NPM, and SP, while statistically, there is no relation with ROE. LTDR is only significant with NPM at a 5% level and has an inverse impact on NPM. This study is significant because it examines, in a single investigation, the effects of leverage on various performance metrics. This study is particularly well-weighed because it has many ramifications for potential stakeholders. The study's practical implications will benefit top-level management in strategic management, the government in enacting laws and other regulations, and investors in making investment decisions based on various environmental and factor considerations. A lower leverage ratio, for instance, is a sign of improved performance because managers will strategically plan to keep it low. On the other hand, lower leverage ratios are also advantageous to stockholders because they allow them to invest in companies with lower debt-to-equity ratios and increase their wealth.

High-precision identification and compensation of nonlinear error in hemispherical resonator gyro

This paper presents a novel nonlinear error identification and compensation method for the Hemispherical Resonator Gyro (HRG) from the signal processing perspective. Firstly, the paper establishes an HRG nonlinear error model that reveals the quantitative correlation between angle measurement error and the amplitude-gap ratio. Subsequently, a dynamic analysis model for gyro drift is developed using nonlinear motion equations. Based on this, a scalar factor nonlinear identification model encompassing assembly and nonlinear errors is proposed. Nonlinear optimization is then used to identify error parameters accurately. Finally, a joint compensation method is devised to mitigate assembly and nonlinear error issues. Empirical findings provide concrete validation for the precision of the identification and compensation method. The experimental results demonstrate that reductions of 97%, 89%, and 40% after the joint compensation are achieved in the fourth harmonic component, scale factor nonlinearity, and bias instability of the gyro, respectively.

Dynamic fault analysis model development for vacuum circuit breaker action

Dynamic fault analysis model development for vacuum circuit breaker action

Enhanced Methods for Evaluating Water-inrush Risk from Underlying Aquifers: Incorporating Dynamic Weight Theory and Uncertainty Analysis Model

Enhanced Methods for Evaluating Water-inrush Risk from Underlying Aquifers: Incorporating Dynamic Weight Theory and Uncertainty Analysis Model

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.

Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Gear Train Optimization of High Speed Machinery by using CATIA and ANSYS

This project aims to optimize gear trains for high-speed machinery using CATIA and ANSYS. Gear trains are crucial in various mechanical systems, especially in high-speed applications where efficiency and reliability are essential. The objective is to combine CATIA for designing and modelling gear trains with ANSYS for analysing their performance under high rotational speeds. Helical gear train use for Reverses Motion design as per requiment of power and motor speed. Same design check for maximium load by load on pinion taken as step of 13K, 15K, 20K, 25K and 30K Newtons are applied on helical gear and check the stress and other parameters material High speed steel. Dynamic analysis model also be carried for mode frequency Keywords: Gear trains, Finite element analysis, CATIA, ANSYS.

Vibration control and stroke evaluation of bi-directional bistable nonlinear energy sinks applied in monopile offshore wind turbines during emergency shutdown

Monopile offshore wind turbines (OWTs) in complex wind and wave environments can generate more excessive vibrations in the fore-aft (FA) and side-to-side (SS) directions when emergency shutdowns occur, and therefore damping techniques to alleviate such vibrations are essential. Due to the potential frequency detuning (reduced damping effectiveness) of traditional tuned mass dampers (TMDs) and stroke issues (such as large strokes of TMDs and limited maximum strokes of unidirectional nonlinear energy sinks (NESs)) in existing absorbers, this paper proposes a bi-directional bistable nonlinear energy sink (BBNES) to mitigate the vibration of the OWT in the FA and SS directions during the emergency shutdown. First, the proposed device and its operation mechanism are elaborated, and an optimization method for the device is introduced. Then, the dynamic analysis model of the BBNES is developed for vibration control of the OWT and incorporated into a fully coupled numerical simulation tool with high fidelity, FAST v8. Subsequently, the responses of the OWT without and with the proposed BBNES and the bi-directional tuned mass damper (BTMD) are exhaustively investigated under diverse conditions (including mass ratios, wind-wave loadings, emergency shutdown times, wind-wave misalignments, and 1st mode natural frequencies of the OWT) to reveal their performances and compare them. The research results show that under most conditions, the BBNES and BTMD have a similar damping performance in the FA direction, whereas the BBNES has a slightly lower damping performance in the SS direction than the BTMD. In addition, except for the wind velocity of 8 m/s at the hub and the wind-wave misalignment of 90°, the FA stroke of the BBNES with a small mass ratio (i.e., 0.01∼0.035) is significantly smaller than that of the BTMD with the same mass ratio by more than 1.1 m, and even up to about 4 m. The SS stroke of the BBNES is slightly greater than that of the BTMD, and in most cases, the difference between both is less than 0.52 m. Therefore, the BBNES is superior to the BTMD in this regard. When the 1st mode natural frequency of the OWT decreases, the BTMD can lose its tuning efficacy and makes the tower more susceptible to resonance caused by 1p and wave loadings, while the BBNES is not affected by the 1st mode natural frequency variation of the OWT, demonstrating its stronger robustness.

Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors.

Relationship between recycling, circular economy and eco‐innovation in Europe. Application of a dynamic network data envelopment analysis model

AbstractMonitoring of European‐level circular economy (CE) efficiency requires more research than does currently exists in the body of literature on CE. In this regard, this research aims to answer two research questions. Firstly, to be efficient in the implementation of CE at a macroeconomic level, should there be a relationship between the efficiency of the three phases of CE (efficiency in recycling, efficiency in the implementation of CE and efficiency of eco‐innovation)? To answer this question, we use decoupling theory. Secondly, are the three phases of CE related from the point of view of dynamic efficiency? To answer this question, we use dynamic efficiency theory.In accordance with both of these theories, we establish a circular relationship between recycling, CE and eco‐innovation. We test this circular relationship through the application of the dynamic network data envelopment analysis (DNDEA) model of Tone and Tsutsui. The analysis is based on Eurostat data for the period of 2016 to 2022 for 27 European countries.This work contributes to the advancement of the research via (1) the development of a model for measuring the transversal and longitudinal efficiency of the CE and (2) the findings of positive and significant relationships between the efficiency ratios of the three fundamental phases (recycling, CE and eco‐innovation). This leads us to affirm that the countries studied that have managed to be efficient in recycling have also been in the implementation phase of CE and eco‐innovation.

An improved mathematical model for static and dynamic analysis of functionally graded doubly-curved shells

An improved mathematical model for static and dynamic analysis of functionally graded doubly-curved shells

Comparative analysis of dynamic load characteristics of artillery steering gear based on different MBD methods

The multi-body dynamics analysis model of the steering gear of a certain type of artillery was established. The dynamic load characteristics of the output gear of the steering gear motor and its driven gear under the given speed and load conditions were calculated based on the three different MBD analysis methods, such as the speed, meshing force, and contact stress. Through the comparative analysis, it was concluded that the calculation results of rigid-flexible coupling analysis were closer to the theoretical value and the fluctuation range was smaller, which provides reference and basis for the prediction of gear fatigue life and the optimization design under dynamic load conditions.

Dynamic damage evolution of double-block ballastless track structure under train derailment impact

Train derailment is a rare event, which seriously threatens the safety of personnel and the service status of track structures along the line. Aiming at the impact damage of high-speed train on ballastless track structure, a nonlinear dynamic analysis model of vehicle-ballastless track-bridge was established based on the theory of concrete plastic damage and impact dynamics. Based on the energy conversion characteristics, the damage evolution and energy dissipation characteristics of ballastless track are studied by using the plastic damage and failure constitutive model. The results show that the contact force during sleeper collision is mainly composed of longitudinal resistance and vertical pressure. At this time, the tensile damage of ballastless track develops rapidly and the shoulder is almost destroyed. When the track bed is collided, the contact force is characterized by a single peak. At this time, the edge of the track bed is seriously damaged by tension, and the shoulder falls off due to tensile damage. At the peak position of the collision contact force, the damage of the sleeper and the track slab is the largest. Compared with the collision track bed, the ballastless track has a larger damage volume when colliding with the sleeper, and can dissipate the kinetic energy of the train faster, and the dissipated energy is 5.07 times that of the former. The research results can provide help for the repair and protection design of the early structure of the accident.

Nonlinear model for assessing seismic resilience in nuclear fuel assemblies

Seismic events may lead to vibrations of fuel assemblies in the nuclear reactor cores, thus the dynamic analysis of such vibration of fuel assemblies is essential for the safety design of nuclear reactor plant. In this paper, by considering the friction between fuel pins, we proposed a nonlinear mechanical model for both static and dynamic analysis of fuel assemblies for pressurized water reactor (PWR). The proposed model used a two-beam structure with frictional units to mimic friction and possible contacts in the pin bundle during the static and dynamic loading in the air. By using this approach, we could characterize not only the hysteresis loop during the static bending test (the static slingshot experiment), but also the decrease of resonance frequency with increase of seismic load. Further, maximum contact forces during the seismic test are compared with experiment at various impact locations, and the predictions closely match the experimental findings, affirming the scientific validity and accuracy of the proposed model. The proposed model can be further used to analyzing seismic behavior of the entire reactor core structure, providing valuable insights for assessing reactor structural safety and predictive hazard assessments.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.