Dynamic Modeling Method Research Articles

Research on the Influence of Depth and Breadth of Equity Reform on the Performance of State Owned Enterprises Based on Dynamic Panel Data Model

In order to explore the necessity of mixed ownership reform of state-owned enterprises and clarify the direction of mixed ownership reform of state-owned enterprises in China, this paper uses dynamic panel data model and bootstrap-DEA method to study the impact of “depth” and “breadth” of equity reform of state-owned enterprises in China on the performance of state-owned enterprise. The results show that there is an inverted U-shaped relationship between performance and the level of equitable reform of state-owned enterprises. When the depth of equity reform is 36.6%, the performance of mixed reform enterprises is the best. There is a positive U-shaped relationship between the breadth of equity reform and the performance of state-owned enterprises. When the breadth of equity reform is 12.47%, the performance of mixed reform enterprises is the worst. At the same time, it is found that the average performance of the sample enterprises shows an obvious downward trend, while the performance variance of the sample enterprises shows an obvious upward trend, and this trend has not changed significantly after China launched a new round of state-owned enterprise reform in 2015. According to the above research findings, this paper believes that the intervention of government forces is conducive to promoting the survival of the fittest of state-owned enterprises and the overall optimal allocation of social resources, but we should pay attention to the adoption of market-oriented means and should not directly interfere with the mixed reform of state-owned enterprises. At the same time, the mixed reform of state-owned enterprises in China is either relatively thorough, giving up the dominance of the largest state-owned shareholder, giving other shareholders a greater say, or not to carry out mixed reform, or symbolic mixed reform, otherwise it may aggravate the internal friction of the mixed reform state-owned enterprises, resulting in worse performance.

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

AbstractData‐driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low‐volume and high value‐added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data‐driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.

Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot

Space robots have been playing an important role in space on-orbit operation missions. However, the traditional configuration of space robots only has a single function and cannot meet the requirements of different space missions, and the launch cost of space robots is very high. Thus, the reconfigurable modular space robot system that can carry multiple loads and own mission adaptability is of great significance. Based on the analysis of a robot space mission, combined with the existing reconfigurable robots, this paper develops a configuration design scheme for a modular reconfigurable space robot, and carries out the prototype design. According to the configuration characteristics of the module, the dynamic modeling of the space robot is based on the graph theory analysis and principle of virtual work. Related application scenarios are set up. Function and feasibility of the dynamic modeling methods are verified through assembly experimentation and dynamic simulation.

A hybrid rotordynamic modeling method for a rotor system with flexible foundation and nonlinear support force: Numerical and experimental investigation

The dynamic characteristics of a flexible foundation would greatly affect the dynamic behavior of the rotor system. In this paper, a hybrid dynamic model method in time domain combines the finite element (FE) method and the subspace-based identification method to model a rotor system with the consideration of a flexible foundation and nonlinear support forces. The state-space model of flexible foundation is identified by the subspace state-space system identification algorithm with measured frequency response functions (FRFs). To solve the calculation problem of the hybrid dynamic model in time domain, a general algorithm named the hybrid model fusion solution (HMFS) method is proposed, which combines the linear and nonlinear nodes separation method used in the FE model and the trapezoidal rule used in the state-space model, improving the calculation efficiency of solving transient response with nonlinear forces. Simulations for the hybrid dynamic model under constant speed and speed-up cases were carried out, resulting that the dynamic characteristics of the entire system can be represented when the nonlinear factors of rolling bearings and gravity were also taken into consideration. Additionally, owe to the consideration of tested dynamic behaviors of flexible base, experimental results in the laboratory test rig are well agreed with the numerical ones under two different test configurations. The investigation provides a complete modeling and highly efficient solution framework for the rotor system with a flexible foundation and nonlinear rolling bearings, with the abilities of further consideration of more nonlinear components such as squeeze film dampers and rubbing forces.

Study of the influence of added water and contained water on structural vibrations and acoustic radiation using different dynamic modeling methods

Study of the influence of added water and contained water on structural vibrations and acoustic radiation using different dynamic modeling methods

Vibration control for the solar panels of spacecraft: Innovation methods and potential approaches

AbstractSolar panels on spacecraft are typical kinds of flexible structures. Low‐frequency and large‐amplitude vibrations usually occur due to the inevitable disturbances of deployment impact, attitude/orbit maneuver, separation/docking impact, and so forth. These vibrations degrade the stability of the spacecraft platform, leading to a reduction in imaging quality and pointing direction accuracy. Vibration control is obligatory during flight missions. Here, we summarize the researches on vibration control of the solar panels. First, typical solar panels used in spacecraft and the specific difficulties in dynamic modeling and control design are introduced. Next, the researches on dynamic modeling methods, decentralized vibration control strategy, and in‐orbit vibration controller design technologies are presented sequentially. Finally, a practical example where our method was successfully applied in‐orbit is described. In conclusion, the theories, methods, and technologies presented in this review hold significant value for achieving high‐precision performance in large spacecraft.

Deployment dynamics of thick panel Miura-origami

Origami and kirigami folding methods applied to deployable structures have been investigated in many studies, and most of them use approximately zero-thickness materials to follow the paper-folding movement. However, the rigidity and thickness of materials cannot be neglected in aerospace engineering applications, resulting in different kinematic and dynamic characteristics of deployment. This work deals with the deployment dynamics of a typical thick panel Miura origami (Miura-ori). The kinematics of the Miura-ori unit and array are discussed considering the tessellated extension of the Miura-ori pattern, which can be described by the recursively relative movement. Based on the Lagrange's equation, the dynamic equation is determined by generalized force and potential energy. Furthermore, prototypes for Miura-ori unit and array are fabricated, respectively. To measure the dynamic behaviours during deployment, a gravity compensation apparatus is designed and constructed, which can effectively reduce the gravity effect of Miura-ori prototypes. The comparison of the numerical and experimental results validates our dynamic model. Our work provides a recursive dynamic modelling method and experiment apparatus for analysing and testing the deploying dynamic behaviours of tessellated thick-panel origami deployable structures, and the findings can be developed in the other thick-panel origami and kirigami concepts of actual engineering applications in the future.

Review on Dynamic Modeling and Vibration Characteristics of Rotating Cracked Blades

As one of the most important parts in the engine, the structure and state of the rotating blade directly affect the normal performance of the aero engine. In order to monitor engine crack failure and ensure flight safety, it is necessary to carry out research on the dynamic modeling of the cracked blade and breathing crack induced vibration mechanisms. This paper summarizes the current research status on the dynamics of cracked blade and the related topics mainly include four aspects: crack propagation path, mechanical model of open and breathing cracks, dynamic modeling methods of cracked blades such as lumped mass model, semi-analytical model and finite element model, and dynamic characteristics of cracked blades. The review will provide valuable references for future studies on dynamics and fault diagnosis of cracked blade in aero engine.

The Impact of Taxation in the Tourism Industry: Dynamic Approach

Tourism is becoming one of the essential pillars of any economy. Therefore, the best way to grow and upgrade this industry is to position the best tax rate that encourages tourism development. The Government should adjust the economies' tax system based on developing their tourism industry to improve this area's investment. Therefore, this study's main objective is to investigate the effect of the tax rate on the tourism industry in selected countries using the dynamic panel modeling (SGMM) method in the range of 2011 to 2016. The results indicate that the position of a robust tax system will harm the tourism industry. The high tax rate will hurt competition, and a major disaster will be toward competition and revenues. So, the hypothesis of this research indicates the negative impact of the tax rate on tourism revenues is accepted. Other variables such as per capita income and trade openness have a positive effect on tourism revenues

Research into a Marine Helicopter Traction System and Its Dynamic Energy Consumption Characteristics

As countries attach great importance to the ocean-going navigation capability of ships, the energy consumption of shipborne equipment has attracted much attention. Although energy consumption analysis is a guiding method to improve energy efficiency, it often ignores the dynamic characteristics of the system. However, the traditional dynamic analysis method hardly considers the energy consumption characteristics of the system. In this paper, a new type of electric-driven helicopter traction system is designed based on the ASIST system. Combined with power bond graph theory, a system dynamic modeling method that considers both dynamic and energy consumption characteristics is proposed, and simulation analysis is carried out. The results indicate that the designed traction system in this study displays high responsiveness, robust, steady-state characteristics, and superior energy efficiency. When it engages with helicopter-borne aircraft, it swiftly transitions to a stable state within 0.2 s while preserving an efficient speed tracking effect under substantial load force, and no significant fluctuations are detected in the motor rotation rate or the helicopter movement velocity. Moreover, it presents a high energy utilization rate, achieving an impressive energy utilization rate of 84% per single working cycle. Simultaneously, the proposed modeling methodology is validated as sound and effective, particularly apt for the dynamic and power consumption analysis of marine complex machinery systems, guiding the high-efficiency design of the transmission system.

Modeling and Disturbance Analysis of Spinning Satellites with Inflatable Protective Structures

The escalating proliferation of space debris poses an increasing risk to spinning satellites, elevating the probability of hazardous collisions that can result in severe damage or total loss of functionality. To address this concern, a pioneering inflatable protective structure is employed to ensure the optimal functionality of spinning satellites. Additionally, a multi-body dynamic modeling method based on spring hinge unfolding/spring expansion is proposed to tackle the complex dynamics of spinning satellites with inflatable protective structures during flight. This method enables analysis of the motion parameters of spinning satellites. First, the structural composition of a spinning satellite with inflatable protective structures is introduced and its flight process is analyzed. Then, an articulated spring hinge unfolding model or a spring expansion model using the Newton–Euler method is established to describe the unfolding or expansion of the spinning satellite with inflatable protective structures during flight. Finally, the effects on the motion parameters of a spinning satellite are analyzed through simulation under various working conditions.

Real-time monitoring of CO2 transport pipelines using deep learning

Real-time pipeline monitoring is important for the safe transportation of captured CO2. A dynamic modeling method, which is one of pipeline monitoring methods, can provide reliable diagnostic results for various anomalies. In the dynamic modeling method, anomalies are detected by comparing the predictions and observations of pipeline monitoring variables. However, the licensing costs associated with the use of pipeline flow simulators that provides the predictions are high. In this study, we developed a real-time deep-learning-based pipeline monitoring method that can save the licensing cost of the pipeline flow simulators. The predictions are obtained using deep-learning models where the pipeline flow simulator is required only in the training step. Two improvements were made to enhance both the prediction and anomaly detection accuracies. First, the prediction accuracy for the monitoring variables can be improved by considering a delay time interval between inlet and outlet points in pairing training input and output data. Second, the anomaly detection accuracy can be also improved by conditionally choosing observations based on the normal operation ranges of the observations. As part of a field demonstration, the proposed deep-learning-based pipeline monitoring method was applied to the monitoring of a CO2 transport pipeline located in the East Sea gas field. The results showed the anomaly detection accuracy of the proposed method was improved by more than 25 %.

Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling

Multitasking is a common element in complex human-computer interactions and is known to impose deleterious mental workload demands. High mental workload is known to involve bilateral hemisphere activation, but the patterns of effective connectivity (directed causal influence or communication) among brain regions in such a context remain unclear. This study investigated the effect of mental workload on the causal influence brain regions exert over each other under a multitasking scenario. The Dynamic Causal Modeling (DCM) method was implemented to infer the flow of information and allocation of attentional resources. Thirty participants performed four subtasks with varying levels of workload on a computer-based multitasking program, simulating a pilot cockpit. Using eight brain regions commonly identified to be activated in multitasking conditions, nine candidate models were developed. Bayesian model averaging was then used to quantify the connectivity strengths among the brain regions. Linear regression was conducted to study the relationships between connection strengths and subtask performances. The results showed that the causal connections shifted from the left to both sides of the brain with increased workload. Linear regression analysis showed that the subtask performance could be predicted by connectivity strengths. Thus, by studying the brain dynamics of mental workload, we may be able to develop a predictor that supplements subjective self-report measures.

Closed-Form Dynamic Modeling and Performance Evaluation of a 4-Degrees-of-Freedom Parallel Driving Mechanism

Abstract Kinematic estimations and dynamic performance assessments are fundamental theoretical issues to realize the mechanism from conceptual design to engineering application. In this article, the closed-form dynamic formulations of a 4-degrees-of-freedom (DoFs) parallel driving mechanism are derived by combining the Lagrange method and the virtual work principle. The selection principle of generalized coordinates and the steps for inverse dynamics modeling of the manipulator are proposed. Simulation results verify the correctness of the dynamic model, and a physical prototype has been built. Based on the dynamic modeling, the concise algebraic expression of the operational space inertia matrix of the parallel driving mechanism is deduced. Because the translation and rotation degrees-of-freedom are inconsistent in the operational space, the Jacobian matrix is adopted to map the inertia matrix from the operational space to the joint space. Based on the inertia matrix in joint space, the average energy transfer efficiency (AETE) index is proposed. Finally, two control techniques for the manipulator implementable in joint space are compared. The AETE index and dynamic modeling method suggested in this article can be further used in other manipulators for dynamic analysis and motion system design.

Wetting and spreading of AgCuTi on Fe substrate at high temperatures: A molecular dynamics study

In this study, the wetting and spreading behavior of AgCuTi ternary alloys on Fe substrate was investigate using a molecular dynamics (MD) modeling method. Modeling indicates that precursor films consisted of single-layer atoms are formed and that the wetting and spreading morphologies of the molten droplets on various crystal surfaces are distinct. Diffusive processes play a key role in the formation of the precursor film, and the droplet spreading morphology is correlated critically with the atomic arrangement of the substrate. The droplet spreading process can be divided into two stages of rapid and slow spreading, and there is an obvious interdiffusion phenomenon close to the solid-liquid contact line. The rate at which droplets spread quickens as the temperature rises. Furthermore, the spreading and diffusion capacities of various elements vary, and these properties are closely related to their chemical reactivity.

Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment

In recent years, magnetoactive soft continuum robots (MSCRs) with multimodal locomotion capabilities have emerged for various biomedical applications. Developments in nonlinear dynamic models and effective control methods for MSCRs are deemed vital not only to gain a better understanding of their coupled magneto-mechanical behavior but also to accurately steer the MSCRs inside the human body. This study presents a novel dynamic model and model-based AI-driven control method to guide an MSCR in a fluidic environment. The MSCR is fully exposed to fluid flows at different rates to simulate the biofluidic environment within the body. A novel nonlinear dynamic model considering the effect of damping and drag force attributed to fluidic flows is first developed to accurately and efficiently predict the response of the MSCR under varying magnetic and mechanical loading. Fairly accurate correlations were observed between the theoretical responses based on the developed magneto-viscoelastic model and the experimental data for various scenarios. A novel model-based control algorithm based on a fractional-order sliding surface and deep reinforcement learning algorithm (DRL-FOSMC) is subsequently developed to accurately steer the magnetoactive soft robot on predefined trajectories considering varying fluid flow rates. A fractional-order sliding surface and a compensator, trained using the deep deterministic policy gradient algorithm, are designed to mitigate the amount of chattering and enhance the tracking performance of the closed-loop system. The stability proof of the developed control algorithm is also presented. A hardware-in-the-loop experimental framework has been designed to assess the effectiveness of the proposed control algorithm through various case studies. The performance of the proposed DRL-FOSMC algorithm is rigorously assessed and found to be superior when compared with other control methods.

Discrete event simulation for dynamic thermal modelling of district heating pipe

Optimizing district heating (DH) systems in a holistic manner is often impeded by the computational complexities associated with network modeling. This study introduces a novel, efficient and theoretically accurate method for dynamic thermal modelling of DH pipes. The approach is to track water frontiers traveling along the pipe using discrete event simulation (DES) paradigm. As the DES method is based on variable time steps, the computational effort is significantly reduced compared to earlier methods. The proposed model can compute outlet water temperature, temperature profile along the pipe, and heat loss based on variable inlet temperature and flow rate. The DES model was validated by comparison with real measurements of a long DH pipe. Four variants of the model with different temperature profile assumptions and interpolation methods were compared. Numerical results show that the DES model can accurately predict outlet water temperature with a maximum discrepancy of 0.52 °C. The mean error of simulated outlet temperature was −0.01 ± 0.02 °C. Average computation time for 24-h simulation was 59 μs. Overall, this study shows that the DES method is appropriate for variable time step simulation for DH pipe, potentially, for network simulation. Our study may also inspire variable time step implementation in other energy applications.

Control of coupled interaction between the flexible cantilever link and airflow

Airflow-induced vibrations of the flexible structure are large, especially when the structure is made of lightweight material. Therefore, the interaction between the flexible structure and airflow should be considered. Although significant work has been placed on the flexible structures, less effort has been directed at the interaction between flexible structures and airflows. A dynamic equation of the motion of interaction between the flexible cantilever link and airflow has been derived in this article. The complicated link-airflow interaction has been captured by the analytical model. Furthermore, a new method has been presented to control coupled vibrations induced by the interaction. Numerous experiments have been performed on a Cartesian coordinate manipulator moving a polyurethane cantilever link to validate the effectiveness of the dynamic model and vibration-control method. The investigative findings may also acceptable for other types of flexible structures interacting with airflows.

Organic ternary mixture separation and purification using chromatography: Design, operation, and optimization

BackgroundFrom the improvement of raw material production and process, environmental friendliness has been silently packaged into commodities and integrated into society. Traditional batch chromatography methods are often used for separating succinic acid but incur large wastewater treatment costs and low efficiency. MethodsThe process design of simulated-moving-bed cascades (SMBC) for separating organic ternary mixture is proposed to address environmentally friendly raw material production methods with a more resource-saving process. Through dynamic modeling, numerical analysis, separation theory, and optimization methods, SMBC model is validated and scale-up by using Aspen Chromatography. FindingAccording to the adsorptivity and selectivity of different adsorbents, two scenarios of the SMBC for separating the organic ternary mixture of succinic acid, formic acid, and acetic acid are investigated. The results show that the SMBC using Amberchrom-CG300C and Reillex 425 resins could achieve the purity of each organic acid up to 96.8∼99.9%, and the corresponding recovery ratio is around 97.7∼99.9%.

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

AbstractWith a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the online monitored samples after data augmentation, so that the corresponding inconsistency within the given consecutive samples could be timely and adaptively uncovered. Instead of expressing the time‐serial relationship that generally accepted by the normal samples in the given dataset, solving the objective function designed for JITLAR2G in a just‐in‐time manner can adaptively and correspondingly seek but only one projecting vector as well as coefficient vector to generate residual, which points to the potential inconsistency inherited in the monitored samples, for the sole purpose of fault detection. As demonstrated through comparisons, the proposed JITLAR2G model can consistently guarantee its effectiveness, in terms of reducing both false alarm rate and missed alarm rate, for dynamic process monitoring, the salient performance achieved by the proposed JITLAR2G‐based method in contrast to the counterparts can be always confirmed.