Dynamic Characteristics Of System Research Articles

Data-enhanced convolutional network based on air conditioning system start/stop time prediction

Most enterprise workshop operators frequently adjust the start/stop time of air conditioning systems based on indoor and outdoor temperatures and humidity to accommodate changing demand and weather conditions. However, relying on personal subjective experience for these adjustments often leads to operational delays or energy waste due to the lack of precision in determining optimal timing. Predicting air conditioning system start and stop times is crucial for energy consumption and savings in HVAC systems. Traditional data-driven methods have been insufficient in this regard, as they mainly focus on feature mapping and overlook the dynamic coupling relationships of process variables, resulting in subpar predictions. In response to this challenge, the paper introduces a novel approach known as the Periodicity and Long-Term Convolutional Neural Network (PLCNN). This method converts one-dimensional regression prediction data into two-dimensional data containing time series features to capture the dynamic coupling characteristics of the air conditioning system while maintaining the independent variation relationships of features. Experimental results using real factory floor data have demonstrated the superior performance of the PLCNN method. Specifically, this method achieved a 14.96% lower error rate compared to the traditional method and an 8.18% improvement compared to the deep learning method. Moreover, the implementation of the PLCNN method in the optimal control of air conditioning systems led to a significant 19.43% reduction in total monthly energy consumption. In conclusion, the proposed method offers a promising alternative to traditional approaches to forecasting and provides a solution to the common challenges encountered in traditional prediction tasks.

Structural and dynamic characteristics of horseshoe vortex systems in front of rigid vegetation inclined upstream under conditions of overland flow

Structural and dynamic characteristics of horseshoe vortex systems in front of rigid vegetation inclined upstream under conditions of overland flow

Effects of periodic long-wave deviations on the dynamic behaviors of spur gear systems

Noise reduction plays a major role in the drive train of motor vehicles operating at higher speeds. Long-wave deviations of gear systems related to manufacturing and assembly errors, such as tooth pitch deviation and geometric eccentricity, exhibit circumferential variations and have been demonstrated to be significant contributors to noise generation. However, a comprehensive and effective model that considers multiple long-wave deviations simultaneously has seldom been studied. To fill this gap, a new numerical model of spur gear systems with long-wave deviations is established, in which the coupling effects of both tooth pitch deviation and geometric eccentricity are taken into consideration. A time-varying mesh stiffness (TVMS) analytical model for spur gear pairs is established, which considers the relationship of errors between pitch deviation and eccentricity. The time-varying contact state of the gear pair under periodic long-wave deviations is also analyzed. A translation-torsion coupled dynamic model is established to investigate the excitation behavior of gear pairs with periodic long-wave deviations. The accuracy of the TVMS and the dynamic model is verified through comparisons with reference and experimental results. Results show that gear pairs with long-wave deviations exhibit periodic fluctuations in TVMS compared to ideal gear pairs, accompanied by phase differences. The mesh stiffness impact phenomenon becomes less pronounced for higher gear precision under lower-load conditions. Conversely, the periodic vibration characteristics induced by eccentricity become less prominent for the lower gear precision level. The excitation behavior of the gear pair with pitch deviation is masked by eccentricity at lower rotational orders, but it still dominates at higher rotational orders. This method can predict the dynamic characteristics of gear transmission systems with periodic long-wave deviations, providing theoretical guidance for reducing the vibration and noise levels.

Dynamic Modeling of a HeXe-Cooled Mobile Nuclear Reactor with Closed Brayton Cycle

Helium-xenon (HeXe)-cooled mobile nuclear reactors have promising potential in future low-carbon energy systems. However, there is currently a lack of fast and reliable tools for analyzing the complicated dynamic characteristics of such systems. In this study, we developed a comprehensive dynamic modeling approach for a HeXe-cooled nuclear power system coupled with a closed Brayton cycle (CBC). The system’s key components, including the reactor, printed circuit heat exchanger (PCHE), and turbomachinery, are lumped-modeled to capture their time-varying behavior. A step-solving algorithm that incorporates HeXe mass conservation iteration is designed. The verification results demonstrate that the dynamic program is robust and reliable, with each time step converging within 25 iterations and the HeXe mass remaining within the range of 3.755 ± 0.01 kg throughout the simulation meeting the law of mass conservation. Then, a 1500 s frozen start-up simulation for the coupled system is conducted, in which the CBC is started in the first 500 s by increasing the main shaft speed to 40% of the rated value, and then the reactor is started by inserting external reactivity between 500 and 800 s. Both the dynamic process and the steady-state performance after the start-up are analyzed. The results show that the system achieved a stable electrical output of 5.7 MWe with a thermal efficiency of 32.5%. This study lays a solid foundation for future work aimed at improving the overall efficiency and performance of HeXe-cooled nuclear power systems.

Dynamic Characteristics of Rotor-SFD System with Inertial Effect of SFD

Dynamic Characteristics of Rotor-SFD System with Inertial Effect of SFD

Dynamic Response of a Rotor Supported on Hybrid Bearings in Air, Water, and Liquid Nitrogen: Measurements and Comparisons to Predictions

Abstract Modern liquid rocket engine turbopumps implement hybrid bearings, which combine hydrostatic and hydrodynamic fluid film principles, in compact and lightweight units known for their superior reusability, durability, and reliability. This study aims to provide extensive and reliable test data for the purpose of anchoring and benchmarking predictive tools for these bearings. This paper provides comprehensive dynamic response measurements of a rotor weighing 10.40 kg and approximately 60 mm in diameter at the bearing locations. The test rotor is supported on two hybrid journal bearings with a bearing length to bearing diameter ratio of approximately 0.42 and a bearing radial clearance to rotor radius ratio of around 0.0017. The bearings are tested with air, water, and liquid nitrogen, serving as surrogate test fluids for common propellants in liquid rocket engines. Rotor run-up and speed coast-down tests are conducted to measure shaft motion responses. The results clearly demonstrate that the rotordynamic characteristics of the test rotor supported on the hybrid journal bearings largely rely on properties of the test fluids and their feeding conditions. Notably, when air is pressurized into the test bearings, the shaft motion responses differ markedly from those observed with water and liquid nitrogen, primarily due to significantly lower stiffness and damping coefficients. Furthermore, rotor speed coast-down tests for each test fluid exhibit notable differences in coast-down speed over time characteristics, depending on the test fluid properties. The predicted rotor imbalance responses exhibit excellent correlation with the measurement data. The steady increase of demand and growth of the fluid film bearing technology for reusable rocket engines require accurate bearing design tools, the extensive component testing, and the implementation of the technology. The validated and developed bearing predictive models from previous research demonstrate well the characteristics of bearings under various operating fluids conditions. The findings and database presented in this work contribute not only to comprehending the performance of hybrid bearings but also to understanding and enhancing the dynamic characteristics of rotor systems supported on hybrid bearings.

Study on the Effect of Cracks in Diaphragm Couplings on the Dynamic Characteristics of Shaft System

Diaphragm couplings are prone to developing diaphragm cracks under prolonged high-speed operating conditions, which can lead to degradation in the performance of the transmission system and affect the dynamics of the shafting system. To investigate the effects of diaphragm cracks on the dynamics of couplings and the shafting system, a finite element model of a diaphragm coupling with a crack failure is established using ANSYS finite element software to analyze the time-varying characteristics of the diaphragm coupling’s angular and radial stiffness. A shaft dynamics model of the diaphragm coupling with a crack is developed using Timoshenko beam elements to analyze the impact of different crack lengths and locations on the dynamics of the shafting system. The validity of the dynamic model for a diaphragm coupling with a crack is verified through a constant speed experiment conducted on a rotor test bench. The results indicate that diaphragm crack failure causes a change in the periodicity of the time-varying stiffness of the diaphragm coupling, leading to a distinct 2× component appearing in the frequency domain of the transmission shaft system.

Analysis of Dynamic Characteristics of Reconfigurable Modular Reactor Systems for Renewable Methanol Synthesis

Analysis of Dynamic Characteristics of Reconfigurable Modular Reactor Systems for Renewable Methanol Synthesis

Vibration monitoring of masonry bridges to assess damage under changing temperature

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

Chaotic laser time series prediction based on an improved logistic mapping algorithm echo state network

Chaos synchronization plays vital functions in the fields of optical chaos secure communication. The synchronization performance can be significantly degraded by parameter mismatches between the chaotic transmitter and receiver. In this paper, the Deep-Logistical Mapping Echo State Network (D-LMESN) is proposed to enhance the performance of chaos synchronization. The network is upgraded by using an improved logical mapping algorithm and a deep reserve pool structure with phase space reconstruction. Results show that D-LMESN exhibits better performance in the prediction of chaotic time series, thanks to the adaptive parameter adjustment, which increases the ability to capture the dynamic characteristics of complex systems. Compared with ESN, the mean square error of this model is reduced by 55% and 72%, respectively, in chaotic laser simulation and actual data experiments. This provides a new possibility, to our knowledge, for the development of chaotic secure communication.

Simulation method for dynamic characteristics of rotor-support system subjected to environmental loads

An improved finite element method was proposed to investigate the dynamic characteristics of rotor system with environmental loads (temperature and aerodynamic forces) in aeroengines. According to the axial temperature distribution within shaft elements, the polynomials were established for physical properties of material. The derivation for coefficient matrices under thermal loads, and the tensile stiffness matrix considering axial aerodynamic forces were completed, based on the Timoshenko beam theory. A high-pressure rotor was simulated with operating temperature 200∼600∘C, and the magnitude of aerodynamic force is 105N. Furthermore, a squeezed film damper (SFD) was adopted to this rotor-support system, its steady responses were solved by the equivalent coefficient method, and the Newmark-HHT method was utilized to solve the transient responses. The results indicate that the first two critical speeds are reduced by 1.85 % and 2.07 %, which is mainly caused by thermal loads. The rising temperature leads to lower elastic modulus, higher Poisson's ratio and expansion ratio. With the amount of a mass unbalance being 500 g·mm located at the 1st-stage compressor disk (CD1): the 1st resonance peaks at CD1 and turbine disk (TD) increase by 4.56 % and 4.98 %, respectively, and the 2nd resonance peaks at CD1 and TD rise 5.88 % and 5.00 %, separately. Owing to SFD damping, the first two resonance peaks at CD1 and TD decrease by 69 % and 51 %, respectively. Compared to 40 °C, the first two resonance peaks at CD1 increase by 88 % and 55 % at the oil temperature 100 °C. The oil-film damping deteriorates significantly due to the lower oil viscosity in higher temperature. When operating near the first-order critical speed, the transient responses of rotor perform as simple harmonics, circular precession trajectory, dominant rotational frequency, and periodic motions. Overall, the dynamic characteristics of rotor-SFD-support systems exposed to environmental loads can be predicted by the proposed simulation method.

Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning.

Care as usual has failed to stem the tide of mental health challenges in children and young people. Transformed models of care and prevention are required, including targeting the social determinants of mental health. Robust economic evidence is crucial to guide investment towards prioritised interventions that are effective and cost-effective to optimise health outcomes and ensure value for money. Mental healthcare and prevention exhibit the characteristics of complex dynamic systems, yet dynamic simulation modelling has to date only rarely been used to conduct economic evaluation in this area. This article proposes an integrated decision-making and planning framework for mental health that includes system dynamics modelling, cost-effectiveness analysis, and participatory model-building methods, in a circular process that is constantly reviewed and updated in a 'living model' ecosystem. We describe a case study of this approach for mental health system policy and planning that synergises the unique attributes of a system dynamics approach within the context of economic evaluation. This kind of approach can help decision makers make the most of precious, limited resources in healthcare. The application of modelling to organise and enable better responses to the youth mental health crisis offers positive benefits for individuals and their families, as well as for taxpayers.

Fault diagnoses of a nonlinear cracked rotor-bearing system based on vibration energy space and incremental learning approach

Health services of rotor-bearing systems are directly related to safe and stable works of rotating machineries in the industrial production. To address the issues of a low impact on vibration characteristics caused by faults in the higher rotational speed region, narrow and weak excitation signals, high dimensionality related to fault induction mechanisms, and difficulty in diagnosing using artificial intelligence technology due to the small number of fault samples in rotor systems, a novel fault detection method of nonlinear rotor systems through the incremental two-dimensional principal component analysis (I2DPCA) of the machine learning on the vibration energy space is proposed. Unlike mainly analyzing dynamic characteristics of rotor-bearing systems in vibration space, the method applies signals of the vibration energy space such as energy tracks, energy-time histories and energy-frequency spectra to have more advantages in vibration amplitudes caused by cracks in the higher rotational speed region and to overcome narrow and weak signals in the early fault stage. Then, the I2DPCA is applied to extract online low dimensional fault features and to process small vibration features of the rotor system. Simulation and experiment results show that performances of between-class margins and within-class clusters of different health conditions on the vibration energy space are more perfect than that on the vibration space. Based on the I2DPCA algorithm and the energy space method, the recognition rate of crack faults within the high rotational speed region is up to 99.6%, and it has good convergence and online learning ability in the case of small samples. The achieved conclusions of the method could provide a novel reference direction for fault diagnoses of rotor systems.

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy, with its abundant reserves, green and low-carbon characteristic, high energy density, diverse sources, and wide applications, is gradually becoming an important carrier in the global energy transformation and development. In this paper, the off-grid wind power hydrogen production system is considered as the research object, and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell, including underload, overload, variable load, and start- stop are analyzed. On this basis, the characteristic extraction of wind power output data after noise reduction is carried out, and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study, and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems, and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

A novel and effective method for characterizing time series correlations based on martingale difference correlation.

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.

Theoretical Analysis of Viscoelastic Friction System Characteristics of Robotic Arm Brake Based on Fractional Differential Theory

In engineering practice, the nonlinear vibration effect can easily lead to chaos in the system, which will not only reduce the performance of the system but also lead to premature fatigue of components, control failure, and increased safety risks. In view of the core position of the robotic arm in modern industry, this study relies on the robotic arm brake system to explore the theoretical basis of integrated viscoelastic materials as a vibration isolation layer. By analyzing the dynamic characteristics of the friction braking system with fractional differential terms, it aims to provide a new perspective for understanding and controlling the chaotic phenomena of a class of nonlinear friction systems. Firstly, we construct a model of a friction system and analyze its dynamic characteristics in detail. The self-excited vibration of the system under disturbance is studied. The relationship between amplitude and frequency is calculated by a nonlinear approximate analytical algorithm, and the accuracy of this relationship is verified by a numerical algorithm. Then, we compare the differences between non-fractional systems and fractional systems. It is found that with the increase in the fractional order term, the vibration amplitude of the system decreases significantly, which helps to reduce the nonlinear characteristics generated by the friction system and narrow the range of unstable solutions. Secondly, we also study the influence of parameter coefficients on the amplitude–frequency characteristics and analyze the local static bifurcation characteristics through singularity theory. Finally, we study the dynamic bifurcation behavior under different parameter perturbations and find that the change in system parameters will lead to the alternation of periodic motion and chaotic motion.

Influence analysis of position error to the operating characteristics of opposed-piston free-piston engine generator with position observation optimization

Stable operation is a crucial challenge for opposed-piston free-piston engine generator (FPEG). Due to the absence of mechanical constraints, variations position-based operation parameters would have a significantly impact on the operation performance of the system. This paper conducted a comprehensive investigation into the effects of position fluctuations on the system's dynamic characteristics and thermodynamic performance by leveraging a detail system model with stable operation. Additionally, a novel real-time position observation approach for the mover was proposed, leveraging observation coils. The findings indicate that the opposed-piston FPEG system can quickly recover to stable operational state within a few cycles for a one-time position error, if there is no misfire in the power cylinder. The acceptable range for stable operation of the opposed-piston FPEG is between +35 % and −48 % for the one-time position error. With the continuous position error varying from +20 % to −30 %, there is a noticeable decrease in the compression ratio, operating frequency, and overall system performance. A significant linear relationship has been found between the flux linkage value of the observation coil and the position of the mover. The maximum difference between the observed position calculated by the proposed approach and the actual position was below 4 %.

Reliability indicators management of thermoelectric systems for thermal modes under limited power consumption

The temperature dependence of electronic components has a significant impact on the technology and design methods of information systems. Thermal management systems are an essential component of on-board systems, for which the most important requirements are reliability, weight, dimensions and power consumption. The thermoelectric method of heat extraction is one of the most promising for products with localized and unevenly distributed heat-loaded elements. The requirement to improve system reliability while increasing dynamics and reducing weight is a fundamental problem, its solution is relevant, and finding compromise solutions between reliability and dynamics is the goal of research. The paper shows that the main significant indicators of thermoelectric systems for ensuring thermal conditions are reliability, dynamics, power consumption and controllability. A mathematical model of a thermal management system consisting of: a heat-loaded object - a thermoelectric cooler - a device for discharging heat into the environment. The determining relationship between the intensity of failures and the geometry of thermocouples and current modes, dynamics and energy indicators and physical properties of the thermocouple material, and the possibility of finding compromises between reliability indicators and cooler dynamics are revealed. The expediency of optimized management of the executive body by a set of interrelated basic energy, time, reliability indicators and the areas of their preferred use is substantiated. The practical value of the research lies in the intensification of heat exchange of the heat sink with the external environment, reduction of the temperature difference, which makes it possible to reduce the intensity of failures and dynamic characteristics of thermoelectric systems for ensuring thermal conditions.

Coordinated Control of Transient Voltage Support in Doubly Fed Induction Generators

The large-scale integration of wind power significantly alters the voltage dynamic characteristics of power systems. Wind turbines have a weak ability to withstand grid disturbances and have difficulty in providing effective reactive power support during transient periods. The sensitivity of wind turbines to the grid voltage significantly increases the probability of large-scale, cascading off-grid events. This paper proposes a coordinated control strategy to enhance the transient reactive power support capability of doubly fed wind farms. The additional stator current demagnetization control reduces the risk of a crowbar protection action after a fault and ensures that the unit power is controllable. Based on the voltage–reactive power coupling relationship, each unit can produce reactive power according to the voltage–reactive power sensitivity matrix during the transient period. After the reactive power output of the unit reaches the limit, transient active and reactive combined control is further adopted to reduce the active power output of the unit to a certain extent and improve the reactive power support capability. Finally, two cases are built in the PSCAD to verify the effectiveness of the proposed control strategy. The results show that the proposed control strategy can enable the wind farm to output more reactive power to the grid during the transient period, effectively supporting the system voltage during the transient process and avoiding further deterioration of the fault.

Towards the dual heat sources recovery applied by organic Rankine cycle: An experimental assessment

The organic Rankine cycle (ORC) is a potential methodology for the thermal-power conversion of renewable energy (geothermal, solar, etc.). Towards the energy integrated application, the single heat source driven ORC could not satisfy the industrial development. In this work, a R1233zd(E)-based ORC driven by dual heat sources has been experimentally studied, and the dynamic system characteristics were primarily carried out. Considering the higher-temperature heat source with 100 °C–130 °C and the lower-temperature heat source with 55 °C–85 °C, the behaviors of the devices are evaluated, and the overall system performance are compared. After that, the comprehensive analysis on the effect of heat source temperatures is developed from the energy and exergy aspects. The results show that the net output and thermal efficiency mostly depend on the higher-temperature heat source. Both of the increasing temperature of the two heat sources would contribute to the enhancement of exergy efficiency, due to the increase in net output and the decrease in exergy loss, respectively. The contribution of the coordinate of the two heat sources are highlighted. Considering the combinations of heat sources with 130 °C/55 °C, 100 °C/55 °C,100 °C/85 °C, 130 °C/85 °C, the thermal efficiencies are sequentially 9.19 %–8.26 %, 7.24 %–6.98 %, 7.38 %–7.35 %, 8.83 %–8.29 %, and the exergy efficiencies are 13.03 %–11.80 %, 10.59 %–10.27 %, 12.12 %–11.98 %, 13.82 %–13.16 % in sequence.