Model Of Mechanism Research Articles

Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample

Gear transmission systems are key components in rotating machinery, and its remaining useful life (RUL) prediction can provide adequate leading time for well-timed maintenance. Existing RUL prediction models usually rely on sufficient training data. However, the acquisition of full life cycle dataset of gear transmission system is difficult or impossible. To solve the problem on the difficult startup and poor generalization of prediction model under zero environment, a dual-drive prediction method based on dynamic model of gear transmission system and unsupervised domain adaptation is proposed in this paper. Firstly, a dynamic model of gear transmission system and growth mechanism of local defect is established to generate full-life cycle simulation data. Then, the multi-scale modulation features are extracted based on simulated and measured data. Furthermore, multi-scale temporal convolution operations are introduced into dual-channel unsupervised domain adaptation model. Besides, a compound principle of reverse truncation and forward expansion principle is investigated to determine the first prediction time. Finally, the validity of the proposed model is verified by two kinds of gearbox data. Ablation experiments are carried out to evaluate the contribution of each module in proposed model. In addition, the effectiveness and generalization ability of proposed model are verified when compared with other advanced transfer learning methods.

A novel molecular structure parameter determination method for biomass thermochemical conversion mechanism investigation

The low-grade energy properties and high O content of biomass limit its efficient energy utilization. Torrefaction is known to be one of the most promising thermochemical conversion methods for upgrading biomass. 250 °C Gas-pressurized (GP) torrefaction realizes deep decomposition of hemicellulose in biomass to as high as 99.7% by deoxygenation and aromatization. However, the above thermochemical conversion mechanism at molecular-level is currently scant. Here, a novel molecular structural parameter method and pure hemicellulose torrefaction were employed to investigate the thermochemical conversion mechanism. Results demonstrate that O content of 250 °C GP torrefied hemicellulose was as low as 28.8 wt%, which was considerably lower than 44.6 wt% of traditional torrefied hemicellulose. There are five deoxygenation mechanisms of hemicellulose during the GP torrefaction: deacetylation, decarbonylation, dehydroxylation, ring opening and cyclization reactions to produce CO2/CH3COOH, CO, H2O, ketones/aldehydes and furans. The deoxygenation and aromatization reactions cause the chemical structure of hemicellulose evolution significantly, these mechanisms are: active hemicellulose undergo aromatization reaction through cyclization and deoxidation to generate preliminary 2-ring furans polymer, followed by stepwise polymerization reactions to form 4-ring and 6-ring furans polymers. These polymerization reactions further greatly enhance deoxygenation reaction. A thermochemical conversion mechanism model is developed based on the above investigation. This work provides a novel method for determining the molecular structure and reaction mechanism of thermochemical converted biomass.

Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach

An efficient energy management system (EMS) enhances microgrid performance in terms of stability, safety, and economy. Traditional centralized or decentralized energy management systems are unable to meet the increasing demands for autonomous decision-making, privacy protection, global optimization, and rapid collaboration simultaneously. This paper proposes a hierarchical multi-layer EMS for microgrid, comprising supply layer, demand layer, and neutral scheduling layer. Additionally, common mathematical optimization methods struggle with microgrid scheduling decision problem due to challenges in mechanism modeling, supply–demand uncertainty, and high real-time and autonomy requirements. Therefore, an improved proximal policy optimization (PPO) approach is proposed for the multi-layer EMS. Specifically, in the centrally managed supply layer, a centralized PPO algorithm is utilized to determine the optimal power generation strategy. In the decentralized demand layer, an auction market is established, and multi-agent proximal policy optimization (MAPPO) algorithm with an action-guidance-based mechanism is employed for each consumer, to implement individual auction strategy. The neutral scheduling layer interacts with other layers, manages information, and protects participant privacy. Numerical results validate the effectiveness of the proposed multi-layer EMS framework and the PPO-based optimization methods.

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

CONSTRUCTION METHOD AND VERIFICATION OF DIGITAL TWIN MECHANISM MODEL OF PULMONARY VENTILATION

The establishment of a fully functional and accurate pulmonary ventilation mechanism model is of great significance to simulate the real respiratory states of the specified human and configure respiratory therapeutic equipment (such as ventilators and simulated lungs). This paper proposed an alternative method to establish the digital twin mechanism model of pulmonary ventilation, which is a composition of the geometric and physical model mapping to the real respiratory process of humans. Through simulation, it could more intuitively reflect respiratory states and generate reliable respiratory data. The consistency of the digital twin of pulmonary ventilation was verified by the respiratory experiments. According to the verification results, the correlation coefficient and goodness of fit were 98.4% and 96.8%, respectively, in different respiratory patterns; 98.4% and 96.2%, respectively, in different examinees. It can be concluded that the digital twin of pulmonary ventilation could well mimic various respiratory patterns of different human bodies. Then it can generate reliable data to support the parameter settings of the respiratory therapeutic devices and auxiliary respiratory devices such as simulated lungs and ventilators.

Higher or lower? Interpersonal behavioral and neural synchronization of movement imitation in autistic children.

How well autistic children can imitate movements and how their brain activity synchronizes with the person they are imitating have been understudied. The current study adopted functional near-infrared spectroscopy (fNIRS) hyperscanning and employed a task involving real interactions involving meaningful and meaningless movement imitation to explore the fundamental nature of imitation as a dynamic and interactive process. Experiment 1 explored meaningful and meaningless gesture imitation. The results revealed that autistic children exhibited lower imitation accuracy and behavioral synchrony than non-autistic children when imitating both meaningful and meaningless gestures. Specifically, compared to non-autistic children, autistic children displayed significantly higher interpersonal neural synchronization (INS) in the right inferior parietal lobule (r-IPL) (channel 12) when imitating meaningful gestures but lower INS when imitating meaningless gestures. Experiment 2 further investigated the imitation of four types of meaningless movements (orofacial movements, transitive movements, limb movements, and gestures). The results revealed that across all four movement types, autistic children exhibited significantly lower imitation accuracy, behavioral synchrony, and INS in the r-IPL (channel 12) than non-autistic children. This study is the first to identify INS as a biomarker of movement imitation difficulties in autistic individuals. Furthermore, an intra- and interindividual imitation mechanism model was proposed to explain the underlying causes of movement imitation difficulties in autistic individuals.

Effects of Intervention Timing on Health-Related Fake News: Simulation Study.

Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. This study investigated the appropriate timing of interventions to minimize the side effects of fake news. A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response.

Dynamic modeling and analysis of 3-RRCPR parallel pointing mechanism with clearance

In order to study the influence of kinematic pair clearance on the dynamic characteristics and pointing accuracy of 3-RRCPR parallel pointing mechanism, the dynamic model of spatial mechanism with three-dimensional revolute pair clearance is established. Firstly, the revolute pair mode considering three-dimensional clearance is established, and the normal and tangential contact force models between the kinematic pair elements are established based on Flores contact model and modified Coulomb friction model. Secondly, the dynamic model of parallel mechanism with clearance is established according to Lagrange multiplier method. Finally, the effects of different clearance sizes and loads on the dynamic characteristics and pointing accuracy of the mechanism are analyzed by numerical simulation. The results show that the increase of clearance size and load will reduce the pointing accuracy, and the decrease of clearance size and the increase of load will enhance the motion stability of the mechanism. Reasonable clearance and load matching is conducive to improving the accuracy of the mechanism. This study provides a theoretical basis for the study of the nonlinear dynamic characteristics and accuracy of the mechanism considering the clearance.

Formation mechanism of interface layer of ZTA particle reinforced iron matrix composite

ZTA particle modified by Ti–Ni powder(Ti–Ni@ZTAp) reinforced iron matrix composite (Ti–Ni@ZTAp-VCp/Fe45) was fabricated by powder metallurgy. The microstructure, scratch properties and formation mechanism of the interface were investigated. An interface layer of 10 μm–20 μm was formed between ZTAp and VCp/Fe45 matrix, which reduced the defects at the interface and played a good transition role. The phases in the interface layer were TiO, Ti6O11 and ZrO2. Metallized layer was formed on the surface of ZTAp after the vacuum sintering of Ti–Ni powder modified ZTAp at 1500 °C. Elemental diffusion occurred between the Ti–Ni powder and ZTAp, which resulted that the ZrO2 phase migrated from ZTAp to the coating layer and reacted with the Ti–Ni powder to form new phases of Ni3Ti, TiO and Ti6O11. The models of the formation mechanisms of the metallized layer, interface layer between ZTAp and VCp/Fe45 matrix were established. Ni3Ti in the metallized layer on the surface of ZTAp was further decomposed duing the vacuum sintering of Ti–Ni@ZTAp-VCp/Fe45 composite, and then the elements Ni and Ti diffused with elements Fe, Cr and V of the VCp/Fe45 matrix. The V element diffusing to the interface layer can further displaced the Ti element in ZTAp and realized the diffusion from iron matrix to ZTAp. The metallized layer on the surface of ZTAp was transformed into an interface layer between ZTAp and VCp/Fe45.

Heterogeneous components removal mechanism and grinding force model from energy aspect in ultrasonic grinding continuous fiber reinforced metal matrix composites

Continuous fiber reinforced metal matrix composites (CFMMCs) offer higher specific strength, specific modulus, and operating temperature than matrix metals due to the unique enforcement mechanism of the one-to-one scale arrangement of matrix and reinforced phases. Due to the heterogeneous characteristics between the plastic matrix and brittle fibers, the removal mechanism of CFMMCs during processing is exceptionally complex. Ultrasonic vibration-assisted grinding (UVAG) shows great advantages in machining difficult-to-cut materials (i.e., ceramics and composites) by changing the motion trajectory between grains and workpieces, effectively reducing grinding force and improving machining quality. However, little is known about the removal mechanism of UVAG for CFMMCs composed of the ductile (i.e., metal matrix) and brittle (i.e., SiC fiber) phases with highly anisotropic structure characteristics. This raises the question of how CFMMCs with heterogeneous components perform under abrasive processing and how to predict their processing forces. Hence, UVAG and conventional grinding (CG) experiments with single CBN grain were carried out on SiC fiber reinforced TC17 matrix composites (SiCf/TC17) in this work. A grinding force model considering both phases and materials structure from energy aspect was proposed. A theoretical model for suppressing SiC fiber damage has been proposed, which is expected to guide low-damage processing of brittle materials. According to the results, the removal models of CFMMCs are revealed including: i) macro fracture of SiC fiber, ii) neat fracture of SiC fiber, and iii) TC17 matrix massive adhesion on the SiC fiber. Besides, no cracks crossing fibers are observed on the subsurface of SiC fiber under both UVAG and CG due to the good support of the TC17 matrix on SiC fibers. The grinding force predicted model error decreases as ap increases. When ap is 50 μm, the errors between predicted and experimental values are 7.8 % and 9.1 % for normal forces (Fn) and tangential forces (Ft), respectively. Ultrasound suppresses the severe wear behavior of grains, thereby improving the tool life. This paper aims to comprehensively reveal the characteristics of abrasive processing of CFMMCs from various aspects (surface morphology, subsurface features, grinding force prediction, and tool wear), which will promote the industrial application of CFMMCs.

Model for the Failure Prediction Mechanism of In-Service Pipelines Based on IoT Technology

With the rapid increase in pipeline mileage in China, the accurate prediction of corrosion issues in in-service pipelines has become crucial for ensuring safe pipeline operation. Traditional pipeline leakage monitoring methods are significantly limited by human factors and equipment precision, making it challenging to predict and identify leakage points accurately. Therefore, aligned with the trend of intelligent pipeline development, this study aims to construct a failure pressure prediction mechanism model for corroded pipelines based on IoT technology. This model leverages intelligent sensing and prediction to assess the safety status of corroded pipeline sections. Ultrasonic phased array technology detects specific corrosion points and detailed defect parameters within pipeline sections. The parameters are then utilized in the Simdroid domestic finite element analysis model to simulate the ultimate burst pressure of the pipeline. A single-variable approach is employed to analyze the sensitivity of different parameters to the pipeline’s ultimate burst pressure, with the minimum burst pressure point of multi-point corroded sections selected as the overall segment failure pressure. Finite element simulation data are integrated into a neural network database to predict the pipeline failure pressure. The real-time operational data of the pipeline are monitored using negative-pressure wave sensing. The operational pressure of the corroded points is compared with the algorithm-predicted failure pressure; if the values approach a critical threshold, an alarm is triggered. Moreover, the remote control terminals evaluate the pipeline’s self-rescue time, providing a buffer for pipeline leakage self-rescue. The failure prediction mechanism model for in-service pipelines was applied to the Fujian–Guangdong branch of the West–East Gas Pipeline III to verify its accuracy and feasibility. The research results offer technical support for the maintenance and emergency repair of pipeline leakage scenarios, leveraging intelligent pipeline technology to reduce costs and increase the efficiency of pipeline operations, thereby supporting the sustainable development of China’s oil and gas pipelines with theoretical and technical backing.

Intensified effect of nitrogen forms on dominant phytoplankton species succession by climate change

Nutrient proportion, light intensity, and temperature affect the succession of dominant phytoplankton species. Despite these insights, this transformation mechanism in highly turbid lakes remains a research gap, especially in response to climate change. To fill this gap, we investigated the mechanism by which multi-environmental factors influence the succession of dominant phytoplankton species in Lake Chagan. This investigation deployed the structural equation model (SEM) and the hydrodynamic-water quality-water ecology mechanism model. Results demonstrated that the dominant phytoplankton species in Lake Chagan transformed from diatom to cyanobacteria during 2012 and 2022. Notably, Microcystis was detected in 2022. SEM revealed the primary environment variables for this succession, including water temperature (Tw), nutrients (total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH4N)), and total suspended solids (TSS). Moreover, this event was not the consequence of zooplankton grazing. An integrated hydrodynamic-water quality-bloom mechanism model was built to explore the mechanism driving phytoplankton succession and its response to climate change. Nutrients determined the phytoplankton biomass and dominant species succession based on various proportions. High NH4N:NO3N ratios favored cyanobacteria and inhibited diatom under high TSS. Additionally, the biomass proportions of diatom (30.77 % vs. 22.28 %) and green (30.56 % vs. 23.30 %) decreased dramatically. In contrast, cyanobacteria abundance remarkably increased (35.78 % to 51.71 %) with the increasing NH4-N:NO3-N ratios. In addition, the proportion of non-nitrogen-fixing cyanobacteria was higher than that of the nitrogen-fixing cyanobacteria counterparts when TN:TP≥20 and NH4N:NO3N ≥ 10. Light-limitation phenotypes also experienced an increase with the rising NH4N:NO3N ratios. Notably, the cyanobacterial biomass reached 3–6 times that in the baseline scenario when the air temperature escalated by 3.0 °C until 2061 under the SSP585 scenario. We highlighted the effect of nitrogen forms on the succession of dominant phytoplankton species. Climate warming will increase nitrogen proportion, providing an insightful reference for controlling cyanobacterial blooms.

Triple Templates Directed Synthesis of Nitrogen-Doped Hierarchically Porous Carbons from Pyridine Rich Monomer as Efficient and Reversible SO2 Adsorbents.

Herein, a variety of 2,6-diaminopyridine (DAP) derived nitrogen-doped hierarchically porous carbon (DAP-NHPC-T) prepared from carbonization-induced structure transformation of DAP-Zn-SiO2-P123 nanocomposites are reported, which are facilely prepared from solvent-free co-assembly of block copolymer templates P123 with pyridine-rich monomer of DAP, Zn(NO3)2 and tetramethoxysilane. In the pyrolysis process, P123 and SiO2 templates promote the formation of mesoporous and supermicroporous structures in the DAP-NHPC-T, while high-temperature volatilization of Zn contributed to generation of micropores. The DAP-NHPC-T possess large BET surface areas (≈956-1126 m2g-1), hierarchical porosity with micro-supermicro-mesoporous feature and high nitrogen contents (≈10.44-5.99 at%) with tunable density of pyridine-based nitrogen sites (≈5.99-3.32 at%), exhibiting good accessibility and reinforced interaction with SO2. Consequently, the DAP-NHPC-T show high SO2 capacity (14.7mmolg-1, 25°C and 1.0bar) and SO2/CO2/N2 IAST selectivities, extraordinary dynamic breakthrough separation efficiency and cycling stability, far beyond any other reported nitrogen-doped metal-free carbon. As verified by in situ spectroscopy and theoretical calculations, the pyridine-based nitrogen sites of the DAP-NHPC-T boost SO2 adsorption via the unique charge transfer, the adsorption mechanism and reaction model have been finally clarified.

Purification of crude glycerol derived from biodiesel production process using ceramic membranes: Experimental studies and modeling of flux behavior

Large-scale biodiesel production generates large quantities of glycerol with high impurities levels, and new purification processes are needed to add value to this by-product of biodiesel production. Thus, this study evaluated the application of ceramic membranes for glycerol purification from biodiesel production and the influence of the mean pore diameter of the membrane, pressure, temperature, and the addition of acidified water to the mixture to be purified. The experiments were carried out in a tangential filtration module with ceramic membranes of 5 kDa, 20 kDa, 0.05 μm, and 0.2 μm, varying the pressure by 1, 2, and 3 bar and the temperature by 25, 40, and 60 °C. The results showed that increasing the pressure, average pore diameter and temperature caused an increase in the permeate flux. Furthermore, adding acidified water increased permeate fluxes and glycerol concentrations for all membranes and pressures used. A higher glycerol content (91.13 %) was obtained with the 5 kDa membrane at 3 bar and 60 °C. The traditional fouling mechanism models failed to fit most of the experimental data. On the other hand, the proposed generalized model adequately predicted the experimental permeate flux data for the different types of fouling mechanisms observed.

Modeling and motion simulation study of a three-dimensional sewing device

Abstract In response to the sewing demands of steering wheels and their unique shapes, a multi-robot arm cooperative three-dimensional sewing device was designed to achieve efficient and precise sewing operations. Utilizing SolidWorks, a collaborative three-dimensional sewing device involving multiple robotic arms was engineered, and its mechanism model was established in ADAMS software for motion simulation analysis to evaluate the device’s performance and motion characteristics. This three-dimensional sewing device is capable of fulfilling the sewing requirements of special curved surface items such as steering wheels, enhancing production efficiency and saving labor. Taking the constructed physical prototype as the research subject, motion experiments simulating the sewing process were conducted. Combined with the simulation results, curves, and three-dimensional motion effects, the feasibility of the design was successfully verified, providing a foundation for subsequent sewing trajectory planning and motion control of the device.

Mechanism modeling and expert system design of radar cold plate cleaning process

Mechanism modeling and expert system design of radar cold plate cleaning process

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

Prediction of packing performance of pneumatic control valve based on stem mechanism model and VMD-SSA-LSTM

Abstract Packing performance is one of the most important parts affecting the control operation of pneumatic control valves. In order to avoid the failure caused by packing wear and aging of pneumatic control valves after long-term use, this paper constructs a valve stem mechanism model and combines the advantage of deep learning to predict the packing performance of pneumatic control valves based on Long Short-term Memory (LSTM) and Variable Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA). Firstly, the historical data of the actuator is calculated based on the mechanism model of the valve stem. The time-friction force obtained by the calculation is the parameter set. After the parameter set is subject to the variational mode decomposition, the parameter set is imported into the SSA-LSTM prediction model for prediction, and the prediction data is obtained.

Development and problems of the national journalistic model of Kazakhstan, considering social, ethical, managerial, and marketing mechanisms of the media environment

ABSTRACT Journalistic activity in Kazakhstan is a complex system that occupies an important place in the development and promotion of a national modernized model of public consciousness. The purpose of the study is to analyze the features of the development of the communicative model of the periodical press of Kazakhstan. The following methods were used to achieve the objectives: systematization, survey, and comparative. The results of the study determined that journalistic activity manifests itself in both professional and civil contexts, while outlining the saturation of content on the Internet. The survey of correspondents and journalists determined that the most influential topic that forms the public consciousness of Kazakhs is politics. Due to the active promotion of innovative technologies and the Internet, the media market is overflowing with various information, including fake, which negatively affects the consciousness of society. In order to effectively create a national model of society, representatives of the mass media must adhere to the principles of ethics, and actively promote the fight against fake information together with government agencies and society. The practical significance lies in the use of the results of the study by academics and specialists in journalism and communication.