Dynamic Law Research Articles

Digital Simulation of Coupled Dynamic Characteristics of Open Rotor and Dynamic Balancing Test Research

An aero engine, as the core power equipment of the aircraft, enables safe and stable operation with a very high reliability index, and is an important guarantee in flight. The open rotor turbine engines (contra-rotating propeller) have stood out as a research hotspot for aviation power equipment in recent years due to their outstanding advantages of low fuel consumption, high airspeed, and strong propulsion efficiency. Aiming at the problems of vibration exceeding the standard generated by imbalance during the operation of the dual-rotor system of aircraft development, the difficulty of identifying the coupled vibration under the micro-differential speed condition, and the complexity of the dynamic characteristic law, a kind of numerical simulation of the dynamics based on the finite element technology is proposed, together with an experimental research method for the fast and accurate identification of the coupled vibration of the dual-rotor system. Based on the existing open rotor engine structure design to build a simulation test bed, establish a double rotor finite element simulation digital twin model, and analyze and calculate the typical working conditions of the dynamic characteristics of parameters. The advanced algorithm of double rotor coupling vibration signal identification is utilized to carry out decoupling and dynamic balancing experimental tests, comparing the simulation results with the measured data to verify the accuracy of the technical means. The results of the study show that the vibration suppression rate of the finite element calculation simulation test carried out for the simulated double rotor is 98%, and the average vibration reduction ratio of the actual field test at 850 rpm, 1000 rpm, and 3000 rpm is over 50%, which achieves a good dynamic balancing effect, and has the merit of practical engineering application.

Research on the spontaneous combustion of the goaf at different advancing distances based on the dynamic evolution of porosity

Spontaneous combustion of residual coal in the goaf is one of the main disasters in coal mines. Understanding the law of spontaneous combustion of residual coal can effectively prevent coal mine fires. This paper establishes a dynamic evolution model of overburden caving and porosity in the goaf using PFC numerical simulation software, and compares and analyzes the dynamic changes in porosity and the spontaneous combustion zone at advancing distances of 50 m, 70 m, and 100 m. The results show that when the working face advances 100 m, the porosity in the caving zone of the goaf decreases from 0–0.6 to 0.16–0.3. The range of the spontaneous combustion zone on the return air side expands from 42m-50 m to 39m–61 m, and on the inlet side, it expands from 0 to 22m–50 m. With increased exposure time, the spontaneous combustion zone range shifts slightly forward to 32m–55 m on the inlet side and 21m–49 m on the return side. This study reveals the dynamic evolution law of the spontaneous combustion zone in the goaf under the influence of different advancing distances and exposure times, which can assist in preventing goaf fires.

A quantum inspired approach to learning dynamical laws from data—block-sparsity and gauge-mediated weight sharing

Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we propose a scalable and numerically robust method for this task, utilizing efficient block-sparse tensor train representations of dynamical laws, inspired by similar approaches in quantum many-body systems. Low-rank tensor train representations have been previously derived for dynamical laws of one-dimensional systems. We extend this result to efficient representations of systems with K-mode interactions and controlled approximations of systems with decaying interactions. We further argue that natural structure assumptions on dynamical laws, such as bounded polynomial degrees, can be exploited in the form of block-sparse support patterns of tensor-train cores. Additional structural similarities between interactions of certain modes can be accounted for by weight sharing within the ansatz. To make use of these structure assumptions, we propose a novel optimization algorithm, block-sparsity restricted alternating least squares with gauge-mediated weight sharing. The algorithm is inspired by similar notions in machine learning and achieves a significant improvement in performance over previous approaches. We demonstrate the performance of the method numerically on three one-dimensional systems—the Fermi–Pasta–Ulam–Tsingou system, rotating magnetic dipoles and point particles interacting via modified Lennard–Jones potentials, observing a highly accurate and noise-robust recovery.

Study on dynamic stress variation of head cover bolt during transient operation of pump turbine

Abstract The head cover bolt is an important connecting part of pump turbine, if it is damaged, it will affect the operation safety of the unit and the power station. During the transient process, such as start-up and load rejection in extreme case, the head cover bolt is subjected to complex load action, and its stress also changes, and extreme cases may occur. The dynamic stress level of the bolt will have a certain influence on its fatigue life, therefore, in order to further study the dynamic stress level and variation law of the bolt, this paper relies on a pumped-storage station, the dynamic stress of the head cover bolt during the start-up and load rejection is measured, and the pressure pulsation between the head cover and the runner and the bladeless zone are collected synchronously. Through the data analysis, the bolt dynamic stress level and variation law and the correlation between stress and pressure fluctuation under the power generation and pumping conditions are summarized.

AI-Based Detection of Surge and Rotating Stall in Axial Compressors via Dynamic Model Parameter Estimation

Compressors are an essential component of aircraft engines. Their design and operation must be extremely reliable as engine safety and performance depend greatly on these elements. Axial compressors exhibit instabilities, such as surge or rotating stall, in a region close to the peak of their performance curves. These fluid dynamic instabilities can cause drops in efficiency, stress on the blades, fatigue, and even failures. Compressors are handled therefore by operating with a safety margin far from the surge line. Moreover, models able to predict onset instabilities and to reproduce them are of great interest. A dynamic system able to describe successfully both surge and rotating stall is the model presented by Moore and Greitzer That model has also been used for developing control laws of the compressor dynamics. The present work aims at developing an artificial neural network (ANN) approach able to predict either the permanence of the system in stable working condition or the onset instabilities from a time sequence of the compressor dynamics. Different solutions were tried to find the most suitable model for identifying the system, as well as the effects of the duration of the time sequence on the accuracy of the predicted compressor working conditions. The network was further tried for sequences with different initial values in order to perform a system analysis that included multiple variations from the initial database. The results show how it is possible to identify with high accuracy both rotating stall and surge with the ANN approach. Moreover, the presence of an underlying fluid dynamic model shares some similarities with physically informed AI procedures.

Measurement and evaluation of agricultural technological innovation efficiency in the Yellow River Basin of China under water resource constraints

Improving the efficiency of agricultural technology innovation is an inevitable requirement for ecological protection and sustainable agricultural development in the Yellow River Basin. Under the background of prominent water resources constraints in the Yellow River Basin, the actual level and regional differences of agricultural technology innovation efficiency in the basin are analyzed, which will provide theoretical basis and data reference for the development plan of agricultural modernization in the Yellow River Basin. The construction of evaluation index system and the choice of method are the main factors that affect the measurement results. Therefore, this paper first selects input indicators such as water-saving technology level and output indicators such as total agricultural output value to construct an innovative evaluation index system of agricultural technology innovation efficiency in the Yellow River Basin under the consideration of water resources constraints. Secondly, the three-stage DEA model was used to eliminate the interference of external environmental variables, and the overall and regional agricultural technology innovation efficiency of the Yellow River Basin during 2011–2020 was measured with the data of prefecture-level cities under the constraint of water resources. Finally, from the perspective of the whole and the region, the spatio-temporal dynamic evolution law is analyzed and evaluated. The results show that: on the whole, the agricultural technology innovation efficiency in the Yellow River Basin is good and rising in a wave trend, the adjusted pure technology efficiency increases, while the scale efficiency decreases. From the regional perspective, although there is regional heterogeneity, the overall development trend is good, and the lower Yellow River has the highest efficiency, followed by the upper reaches and the middle reaches. From the perspective of the change law, the spatio-temporal evolution distribution shows a multi-polarization trend, and the different regions in the upper, middle and lower reaches show great differences. Therefore, promoting the innovation of water-saving technology and the integration of agricultural science and technology, promoting the integration of production, study and research, and strengthening the regional linkage mechanism are helpful to improve the competitiveness of agricultural science and technology and achieve sustainable agricultural development in the Yellow River Basin.

Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification.

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

Design and implementation of adaptive control for industrial robot servo trajectory tracking

Abstract To cope with the complex external environment of the robot, an online adaptive control method for industrial robots is implemented by using robot dynamic parameters as control variables. A Newton-Euler method is implemented to establish a rigid body dynamics model for the robot. Subsequently, we confirm the possibility of dynamic parameter adaptive control by employing the Lyapunov stability theory and derive the dynamic parameter estimation law. Three methods are proposed to realize the observation matrix containing reference information in the adaptive control algorithm. Through physical experiments, it is verified that the dynamic parameter adaptive control is superior to traditional PPI control, and its position tracking error and anti-interference ability are significantly improved.

Effects of Salt Content and Freeze-Thaw Conditions on Dynamic characteristics and its microscopic mechanism of Chloride Silty Clay

Generally, artificial ground freezing (AGF) technology is utilized to guarantee tunnel safety during construction. However, the soil structure changes significantly after freeze-thaw, resulting in uneven deformation of the tunnel under traffic loading from subway vibration. To solve this problem effectively, it is necessary to consider the combined impact of freeze-thaw, salt, and traffic loading damage that marine soft soil must withstand simultaneously. For this reason, cyclic triaxial test and NMR test were performed on the silty clay saturated with NaCl solution in this study. The influence of three main factors on dynamic properties has been thoroughly investigated, namely freeze-thaw, salt content, and confining pressure. According to cyclic triaxial test, the shape of the hysteresis loop of the specimens after freeze-thaw changed more significantly with increasing loading cycles. The dynamic elastic modulus was weakened by freeze-thaw, while improved by the addition of NaCl. Damping ratio was consistent with the dynamic elastic modulus law. It was worth noting that the different freezing temperatures (−10 °C, −20 °C and − 30 °C) had only a slight impact on dynamic elastic modulus, as well as damping ratio. Mathematical models were proposed to forecast the dynamic elastic modulus and damping ratio regarding marine soft clay. NMR test indicated that the addition of salt made the internal pore environment of the specimens tend to be consistent and enhanced the water-solid interaction. The increase in porosity resulted in the decrease in dynamic elastic modulus. The results have provided valuable insights into the mechanical characteristics of marine soft clay when AGF technology is applied.

Dynamic event-triggered synchronization for semi-Markovian switching inertial neural networks with generally uncertain transition rates in finite-time interval

This paper investigates the finite-time synchronization for inertial neural networks with stochastic switching parameters based on dynamic event-triggered protocol. Due to the complexity of network environment, semi-Markovian process is introduced into the modeling of inertial neural networks, in which the transition rates vary with the operating time. The dynamic event-triggered protocol is developed to determine whether the signal is transmitted, in which Zeno phenomenon is eliminated under limited bandwidth resources. The objective is to construct an appropriate dynamic event-triggered control law such that the drive-response system maintains finite-time synchronization under generally uncertain transition rates. Based on the Lyapunov functional theory, finite-time synchronization criterion is proposed for the related inertial neural networks. Furthermore, a dynamic event-triggered controller is constructed in a finite-time interval. A numerical example and an image encryption process are given to show the efficiency of the proposed method.

Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output disturbance. The imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead zone. Because of the presence of output dead zone and the additive disturbance, the states cannot be expected to driven into an arbitrarily small neighborhood of the origin. To solve the global practical stabilization problem, we proposes a low gain-based linear dynamic output feedback law, under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set, both in finite time. Simulation results illustrate the effectiveness of our proposed control method.

Dynamic characteristics and evolution laws of underground brine in Mahai salt lake of Qaidam Basin during mining process

In the late stage of underground brine mining in salt lakes, the method of injecting fresh water is often used to extract the salt from the brine storage medium. This method of freshwater displacement breaks the original water–rock equilibrium and changes the evolution process of the original underground brine. To explore the mechanism of salt release in saline water-bearing media under conditions of relatively fresh lake water dissolution, this paper analyzes the changes in the chemical parameters of brine from 168 sampling points in the Mahai salt lake in the Qaidam Basin at three stages (before exploitation, during exploitation, and late exploitation) by correlation analysis, ion ratio analysis, and other methods and investigate the variations in porosity and the evolution laws of brine. The results show that the changes in the main ion content and brine mineralization during the exploitation process are small. The changes in Ca2+ content are significant due to the low solubility of calcium minerals, the precipitation of gypsum during the mixing process, and the adsorption of cations by alternating with Ca2+. Primary intergranular pore skeletons are easily corroded to form secondary pores, which increase the geological porosity. Na+ and Cl- are the dominant ions in the brine in the study area, but the concentration of Ca2 + decreased significantly under the influence of mining, by 41.7% in the middle period and 24.5% in the late period. The correlation between Ca2+ and salinity changes significantly in different mining stages, and the reason for the decrease of Ca2+ may be due to the influence of mineral dissolution, mixing, and anion-cation exchange. The porosity of the layer in the study area showed the opposite trend of Ca2+, and the porosity increased first and then decreased. The innovation of this paper lies in analyzing the reasons and mechanisms of the disturbance of artificial dissolution mining on stratum structure by comparing the hydrochemical characteristics and porosity of underground brine storage media in three different mining stages. The research in this paper provides a theoretical basis for the calculation of brine resource reserves and the sustainable development of underground brine in salt lake areas.

Theoretical research of impact vibration of multi-section serial pipeline inspection gauge in large drop pipeline

When operating in large drop pipelines, pipeline inspection gauge may experience severe impact vibration at elbows, potentially leading to its failure. This paper focuses on serial pipeline inspection gauges with multiple sections in large drop pipelines, analyzes the force acting on the pipeline inspection gauge during the descent phase, and establishes a multi-body dynamics model of the gauge under pipe-soil coupling conditions. In addition, a correction formula is developed for the pressure changes in the medium at the front and rear of the pipeline inspection gauge. To solve the model, MSC/ADAMS and MATLAB/Simulink are used for the bi-directional fluid–structure-interaction joint simulation. Moreover, the results are compared with the pipeline inspection gauge motion model established by OLGA. The study investigates the dynamic responses, vibration superposition and laws of dynamic evolution at the upper and lower elbows of the large drop pipeline. The results show that the speed of the gauge increases suddenly during the descent in large drop sections and then stabilizes, with different effects of the fluid medium pressure difference at various stages. The vibration responses along the axial, horizontal radial, and vertical directions at the elbows vary, with the second section experiencing stronger vibrations due to superposition effects. Compared to fixed boundaries, the acceleration extremes of the gauge are lower under the constraints of pipe-soil model, indicating significant soil buffering and vibration damping effects.

Does Quantum Mechanics Require "Conspiracy"?

Quantum states containing records of incompatible outcomes of quantum measurements are valid states in the tensor-product Hilbert space. Since they contain false records, they conflict with the Born rule and with our observations. I show that excluding them requires a fine-tuning to an extremely restricted subspace of the Hilbert space that seems "conspiratorial", in the sense that (1) it seems to depend on future events that involve records (including measurement settings) and on the dynamical law (normally thought to be independent of the initial conditions), and (2) it violates Statistical Independence, even when it is valid in the context of Bell's theorem. To solve the puzzle, I build a model in which, by changing the dynamical law, the same initial conditions can lead to different histories in which the validity of records is relative to the new dynamical law. This relative validity of the records may restore causality, but the initial conditions still must depend, at least partially, on the dynamical law. While violations of Statistical Independence are often seen as non-scientific, they turn out to be needed to ensure the validity of records and our own memories and, by this, of science itself. A Past Hypothesis is needed to ensure the existence of records and turns out to require violations of Statistical Independence. It is not excluded that its explanation, still unknown, ensures such violations in the way needed by local interpretations of quantum mechanics. I suggest that an as-yet unknown law or superselection rule may restrict the full tensor-product Hilbert space to the very special subspace required by the validity of records and the Past Hypothesis.

Treatment of phenolic wastewater in an anaerobic fluidized bed microbial fuel cell filled with graphene oxide-macroporous adsorption resin as multifunctional carrier

ABSTRACT A novel graphene oxide-modified resin (graphene oxide-macroporous adsorption resin) was prepared and used as a multifunctional carrier in an anaerobic fluidized bed microbial fuel cell (AFB-MFC) to treat phenolic wastewater (PW). The macroporous adsorption resin (MAR) was used as the carrier, graphene oxide was used as the modified material, the conductive modified resin was prepared by loading graphene oxide (GO) on the resin through chemical reduction. The modified resin particles were characterized by scanning electron microscopy (SEM), Raman spectroscopy (RS), specific surface area and pore structure analysis. Graphene oxide-macroporous adsorption resin special model was established using the Amorphous Cell module in Materials Studio (MS), and the formation mechanism of graphene oxide-macroporous adsorption resin was studied using mean square displacement (MSD) of the force module. Molecular dynamics simulation was used to study the motion law of molecular and atomic dynamics at the interface of graphene oxide-macroporous adsorption resin composites. The strong covalent bond between GO and MAR ensures the stability of GO/MAR. When the modified resin prepared in 3.0 mg/mL GO mixture was used in the AFB-MFC, the COD removal of wastewater was increased by 9.1% to 72.44%, the voltage was increased by 84.04% to 405.8 mV, and power density was increased by 765.44% to 242.67 mW/m2.

Machine Learning Memory Kernels as Closure for Non-Markovian Stochastic Processes.

Finding the dynamical law of observable quantities lies at the core of physics. Within the particular field of statistical mechanics, the generalized Langevin equation (GLE) comprises a general model for the evolution of observables covering a great deal of physical systems with many degrees of freedom and an inherently stochastic nature. Although formally exact, GLE brings its own great challenges. It depends on the complete history of the observables under scrutiny, as well as the microscopic degrees of freedom, all of which are often inaccessible. We show that these drawbacks can be overcome by adopting elements of machine learning from empirical data, in particular coupling a multilayer perceptron (MLP) with the formal structure of GLE and calibrating the MLP with the data. This yields a powerful computational tool capable of describing noisy complex systems beyond the realms of statistical mechanics. It is exemplified with a number of representative examples from different fields: from a single colloidal particle and particle chains in a thermal bath to climatology and finance, showing in all cases excellent agreement with the actual observable dynamics. The new framework offers an alternative perspective for the study of nonequilibrium processes opening also a new route for stochastic modeling.

Coupling Mechanism of Multiple-Thermal-Fluid Multi-Cycle Stimulation in Ultra-Heavy-Oil Reservoirs

Multiple-thermal-fluid (MTF) stimulation technology has been successfully applied in heavy-oil reservoir development, resulting in the significant enhancement of oil production. However, the underlying mechanism of multi-component coupling remains unclear. This paper constructs a coupling model for MTF stimulation, investigates the coupling mechanism of different media in various zones during multiple-cycle stimulation operations, and compares the implementation effect with field results. The findings reveal that (1) based on media distribution, the area from near-wellbore to far well locations can be divided into four zones: high-temperature oil-viscosity-reduction zones, compound action zones, energy-replenishment zones, and unaffected zones. (2) In the high-temperature oil-viscosity-reduction zone, the latent heat of vaporization is released by steam, and ultra-heavy oil absorbs heat and reduces its viscosity, which plays a dominant role in the production of MTF. In the compound action zone, hot water, CO2, and N2 exhibit a synergistic effect which enhances overall performance. In the energy-replenishment zone, a small amount of N2 provides pressure maintenance and an additional energy supply. (3) As more cycles of stimulation are conducted, the compound action zone expands, while the energy-replenishment zone contracts. Simultaneously, there is a decrease in contribution rate from the high-temperature viscosity-reduction zone to oil production but an increase from both the compound action zone and energy-replenishment zone up to 30%. Based on the dynamic law of representative wells, this paper proposes a multi-media zonal coupling mechanism, providing a reference for subsequent research on MTF stimulation mechanisms and the adjustment of production measures.

Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data

In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.

Research on Computer Artificial Intelligence Algorithm to Optimize Physical Training Intensity and Recovery Cycle Model

This paper established an association model between high-load training and cardiac damage to minimize sports injuries. It is a key condition to promote the development of modern sports. This project intends to use 8 biochemical parameters related to high-load training to build a mathematical model of high-load training-myocardial damage. A mathematical model for high load training and cardiac damage was established. Firstly, based on sparse feature representation method, the biochemical mechanism model of high-intensity training was constructed. The energy supply law of intramuscular energy supply and the dynamic law of glycolysis energy supply during exercise were extracted. Eight biochemical parameters were obtained after high intensity training. To construct an accurate association between high-intensity training-heart damage by measuring the correlation between eight indicators of performance. The validity of the model is verified by simulation.

Nonlinear control of coaxial double rotor magnetic gear based on high gain observer in wind turbine

AbstractThis paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system.