Dynamic Method Research Articles

Facile and efficient fabrication of carbon nanotube reinforced epoxy vitrimers through dynamic crosslinking

AbstractCarbon nanotubes (CNT) is an ideal candidate for reinforced epoxy vitrimer composites, however, achieving a desirable improvement of the mechanical properties is a great challenge owing to CNT is extremely easy to aggregate. In this study, well‐dispersed CNT reinforced epoxy vitrimers, namely EVD/CNT, were prepared by combining ultrasonic dispersion and dynamic crosslinking techniques, using multiple wall carbon nanotube as reinforcement filler, sebacic acid as curing agent, diglycidyl ether of bisphenol A (DGEBA) as epoxy monomer, zinc acetylacetonate as transesterification catalyst, torque rheometer (internal mixer) as dynamic crosslinking equipment. The CNT reinforced epoxy vitrimers, namely EVS/CNT, were prepared using traditional static curing method. The structure and morphology, mechanical properties and stress relaxation behavior of EVS/CNT and EVD/CNT were comparatively investigated, and the influence of CNT content on the structure and properties of EVD/CNT prepared by dynamic crosslinking method were also studied. The results indicated that dynamic crosslinking technology can stably disperse CNT into epoxy vitrimer resin matrix under continuous shear force. However, CNT experienced severe secondary agglomeration during the static curing process. When the loading amount of CNT was 2 wt%, the tensile strength and elongation at break of EVD/CNT‐2 were 3.2 times and 2.2 times higher than those of EVS/CNT‐2, respectively. The Young's modulus and glass transition temperature (Tg) of EVD/CNT‐2 (6.43 ± 1.10 MPa, 29.6°C) were also higher than those of EVS/CNT‐2 (5.46 ± 0.31 MPa, 25.4°C). Moreover, dynamic crosslinking technology greatly shortened the reaction time of CNT reinforced epoxy vitrimers, improved production efficiency and reduced reaction energy consumption, and was expected to be used for large‐scale and industrial production.

Decentralized Output-Feedback Adaptive Event-Triggered Control for Interconnected Nonlinear Delay Systems with Actuator Failures

This paper investigates decentralized adaptive event-triggered fault-tolerant control for interconnected nonlinear delay systems with actuator failures. The actuator failures suffered include loss of effectiveness and bias faults. A control scheme based on the K-filter is proposed, which effectively compensates for the effects of unknown actuator failures. A hyperbolic tangent function and neural network are introduced to approximate the unknown interconnection function and nonlinear delay function. By introducing the dynamic surface control method, the “explosion of complexity” issue is addressed. Furthermore, our proposed controller can ensure that all states of the corresponding closed-loop system are semi-globally uniformly ultimately bounded and that the tracking error can converge to a small neighborhood of zero. Meanwhile, Zeno behavior can be effectively avoided. Finally, the validity of the proposed control scheme is verified using a simulation example.

Two-minute standing endurance test for axial postural abnormalities in patients with Parkinson’s disease

BackgroundPhoto-based measurement methods are used to assess axial postural abnormalities (PA) in Parkinson’s disease (PD). However, they capture only moments in time. We developed the 2-minute standing endurance test (2 M-SET), which specifically captures temporal changes in posture, as a novel dynamic method for measuring axial PA in patients with PD. Research questionThis study aimed to verify the effectiveness and validity of the 2 M-SET for capturing temporal changes in axial PA in patients with PD. MethodsTwenty-eight patients with PD participated. The participants attempted to maintain an upright posture for 2 minutes during three tasks: standing, stepping in place, and walking. The rate of change in postural angle was recorded at 10-second intervals. Based on the results, the 2 M-SET was developed. Therapists evaluated the 2 M-SET using the NeuroPostureApp© to measure anterior trunk flexion (ATF) angles and lateral trunk flexion (LTF) angles at 0, 10, 30, 60, and 120 seconds. To assess reliability, the congruence between the measurements obtained by the therapists and those obtained using a three-dimensional motion-analysis system was examined. For validity, we assessed whether the ATF and LTF angles measured by the therapists could accurately capture postural changes at regular intervals over time. ResultsThe average postural changes over 2 minutes for the standing, stepping in place, and gait tasks were 59.2±83.5%, 37.6±30.7%, and 45.4±50.6%, respectively. The intraclass correlation coefficients showed high reliability, with values of 0.985 and 0.970 for the ATF and LTF angles, respectively. SignificanceThe results of our proposed 2 M-SET method, which uses temporal photo-based measurements to assess the patient’s ability to maintain an upright standing position for 2 minutes, demonstrate the potential to capture temporal changes in axial PA. Data Availability StatementThe data supporting the findings of this study are available upon reasonable request and approval from the local ethics committee.

On the reliability of the dynamic terrain method to generate ABL winds for environmental applications

On the reliability of the dynamic terrain method to generate ABL winds for environmental applications

Dynamic grouping maintenance optimization by considering the probabilistic remaining useful life prediction of multiple equipment

For multi-equipment maintenance of modern production equipment, the economic correlation and degradation uncertainty may lead to insufficient or excessive maintenance, increasing maintenance costs. This paper proposes a dynamic grouping maintenance method based on probabilistic remaining useful life (RUL) prediction for multiple equipment. Long short term memory (LSTM) is developed to predict the equipment probability RUL by the Variational Auto-Encoder (VAE) resampling. Then, the dynamic grouping maintenance model is constructed to minimize the maintenance cost rate under the known probabilistic RUL information. The gazelle optimization algorithm (GOA) is used to determine the optimal maintenance time for each equipment. To better verify the effectiveness of the proposed method, a numerical case with six wind turbines is introduced to analyse the performance of GOA. Moreover, the advantages of dynamic grouping maintenance is verified by comparing with independent maintenance, whose maintenance cost rate is reduced by 10.01%.

Numerical Investigations on Wake Effects of Fish Cages

Abstract A typical fish farm in Norway usually consists of 6 to 12 fish cages, and these fish cages are moored together in a grid shape. Due to wake effects, individual fish cages may experience different flow velocities depending on their positions in the fish farm. Hereby, the drag forces on individual fish cages can be different. The drag forces on the downstream fish cages are difficult to be estimated without knowing the exact flow velocity, which will lead to challenges with the mooring system design. In this study, the computational fluid dynamic (CFD) method combined with a porous media model is employed to investigate the wake effects on the fish farm. The flow field through the fish cages and the drag forces on the downstream fish cages are investigated and discussed.

A FEM/DEM adaptive remeshing strategy for brittle elastic failure initiation and propagation

AbstractThis article presents an adaptive remeshing strategy between the finite element method (FEM) and the discrete element method (DEM). To achieve this strategy, an edge‐to‐edge coupling method based on Lagrange multipliers has been set‐up to ensure the continuity of velocities at the interface. To switch from a computation initially purely FEM to a FEM‐DEM one, a field transfer method was required. In particular, a displacement field transfer method has been set‐up. The switching from a FEM subdomain to a DEM one is activated by a transition criterion. Each time a FEM subdomain is substituted by a DEM one, the DEM subdomain microscopic properties are set‐up with respect to the subdomain geometry and desired particle refinement. This is performed thanks to the linking to the so‐called “Cooker,” a tool distributed along with the GranOO Workbench. Two subdomain remeshing cases were dealt with: that of an initially FEM subdomain that is converted to DEM, and that of DEM subdomains which coalesce. A numerical test case shows that the dynamic remeshing method behaves as expected: FEM subdomains are substituted by DEM ones when the transition criterion is met, and DEM subdomains coalesce when required. The final numerical test case shows a good agreement with a crack propagation experiment of the literature, while a speedup of about 20 was observed when compared to pure DEM computation.

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems

The preconditioned Crank-Nicolson Markov Chain Monte Carlo (pCN-MCMC) method is an efficient approach for large-scale Bayesian inference problems. It is constructed by discretizing a stochastic partial differential equation (SPDE). When the gradient information is coupled in this SPDE, the corresponding SPDE is also referred to as the Langevin diffusion. In this work, we present the replica exchange preconditioned Crank-Nicolson Langevin dynamic (repCNLD) Monte Carlo method to speed up the convergence of the pCN-MCMC and tackle the challenge of multimodal distribution. We use the Crank-Nicolson scheme to discretize the repLD and set the preconditioned matrix as the covariance matrix of the Gaussian prior. The discretization error is analyzed. Moreover, we use the multi-variance strategy (m-repCNLD) to improve computational efficiency. Perturbations are introduced into the scheme due to the likelihood estimation error, leading to the biased swapping rate. To correct the bias, we provide an unbiased estimator derived from some assumptions on the forward solvers. The accuracy and efficiency of the proposed method are demonstrated by sampling Gaussian mixture distribution and solving PDE-based nonlinear inverse problems numerically.

Dynamic laser ablation loading of a linear Paul trap

We present a detailed method for accumulating Ca+ ions controllably in a linear Paul trap. The ions are generated by pulsed laser ablation and dynamically loaded into the ion trap by switching the trapping potential on and off. The loaded ions are precooled by buffer gas and then laser-cooled to form Coulomb crystals for verifying quantity. The number of ions is controlled by manipulating the trapping potential of the ion trap, partial pressure of buffer gas and turn-on time of the entrance end cap voltage. With single-pulse laser ablation, the number of trapped ions ranges from tens to ten thousand. The kinetic energy of loaded ions can be selected via the optimal turn-on time of the entrance end cap. Using multiple-pulse laser ablation, the number is further increased and reaches about 4×104 . The dynamic loading method has wide application for accumulating low-yielding ions via laser ablation in the ion trap.

Research on Strategy Optimization of Green Agricultural Production Trusteeship to Promote Black Land Protection

The whole trusteeship of green agricultural production plays an important role in promoting the protection of black land, and it is particularly crucial to clarify the behavioral characteristics and game relationships of agents involved in the whole trusteeship. This study uses the dynamic evolutionary game method to construct a tripartite evolutionary game model of governments, service organizations and farmers participating in the whole trusteeship of green agricultural production, aiming to come up with effective strategies to promote the widespread application of green agricultural production trusteeship and achieve agricultural green development. The results are as follows: (1) At present, the agricultural production model in Northeast China is dominated by traditional agricultural production and supplemented by green agricultural production. (2) Incentive and punitive measures will encourage agents to adopt positive strategies. (3) In areas with a poor awareness of green agricultural production trusteeship, the government’s incentive and punishment measures for farmers and service organizations are imbalanced. (4) The relatively high cost of trusteeship leads to a lack of market competitiveness, which has a negative impact on service organizations promoting green agricultural production trusteeship. This study provides an effective reference for improving the overall implementation effect of black land protection in Northeast China.

Hierarchical dynamic estimation of fire service accessibility based on POI big data

Failure to obtain timely fire services will lead to thermal runaway phenomenon, which will seriously threaten the lives of people and property safety. In this study, a dynamic method was proposed to estimate fire service accessibility based on real-time traffic. To validate the method, a total of 110,055 POls extracted from online maps and existing 12 fire stations in Yiyang were used as fire service demand points and supply points. Travel times of fire trucks from supply points to demand points were calculated based on real-time traffic, and a total of 550,275 valid samples were obtained. The results showed that the ≥10 min fire service coverage rate in Yiyang is only 71.60 %. Based on the distribution area of demand points ≥10 min, the locations of three optimization solutions of new fire stations are proposed. By calculating the total average time (TAT) of the three optimization solutions of proposed new fire stations, it is concluded that TAT decreases by 743.99 s, 99.72 s, 92.59 s, respectively. From perspectives of safety and economy, the construction of the 10 fire stations of optimized solution I will effectively improve the regional fire service level.

Dissolution behavior and thermodynamic study of N-ethyl-2,2-diisopropylbutylamide in fourteen mono solvents by experiments and molecular simulation

N-ethyl-2,2-diisopropylbutylamide (WS-27) is a new cooling agent with a soothing, long-lasting cooling effect, widely used in personal care and cosmetic products. The dissolution behavior of WS-27 in fourteen mono solvents (methanol, ethanol, n-propanol, isopropanol, n-butanol, n-pentanol, methyl acetate, ethyl acetate, isopropyl acetate, propyl acetate, butyl acetate, dichloromethane, cyclohexanone, and acetone) was measured using a dynamic laser monitoring method from 263.15 K to 298.15 K under 101.6 ± 1.2 kPa. In these selected solvents, the solubility of WS-27 increased with increasing temperature. The van't Hoff equation, λh equation, modified Apelblat equation, Wilson model, and NRTL (non-random two-liquid) model were applied to correlate the experimental solubility data of WS-27, and the modified Apelblat equation showed the best-fitting results. In addition, the intermolecular interactions and solvent effect were analyzed using the Hirshfeld surface analysis, molecular electrostatic potential surface analysis, and KAT-LSER model analysis to understand the dissolution behavior of WS-27. The results indicate that the solubility of WS-27 is influenced by multiple factors, and there can be other factors as well. These fundamental data can provide essential information for the crystallization and purification process of WS-27.

Application of deep reinforcement learning in electricity demand response market: Demand response decision-making of load aggregator

With the large-scale integration of renewable energy generation, developing the potential of demand response is becoming more and more significant. However, the number of consumers is huge, the electricity consumption behaviors are various and the information of consumers is incomplete, thus there are great difficulties in modeling and processing demand response by the conventional mathematical methods. Therefore, how to accurately predict the response behaviors of consumers and select the appropriate consumers for demand response is worthy of in-depth discussion. In this paper, the decision-making method of the load aggregators who participant in demand response market on behalf of the small and medium-sized consumers based on Deep Q-network algorithm is proposed, supporting the aggregators to properly guide the potential demand response consumers. At the same time, a dynamic consumer classification method based on self-organizing maps algorithm is also proposed, supporting the aggregators to accurately predict the response behaviors of the consumers. Simulation results show that the proposed method can effectively realize the classification of the consumers and result in a more beneficial demand response.•The proposed method does not require complete information, such as demand response behavior parameters of the consumers.•Self-organizing maps can realize a dynamic classification of demand response consumers as the case may be.•DQN algorithm can effectively realize the demand response decision-making of aggregators under the condition of incomplete information.

Investigating the effects of temperature on thermal and mechanical properties of polyurethane/polycaprolactone/graphene oxide nanocomposites: focusing on creating a smart polymer nanocomposite via molecular dynamics method

Polycaprolactone, a biodegradable polyester renowned for its biocompatibility, controlled degradation capability, and processability, finds application across diverse industrial sectors. Graphene oxide has been utilised to strengthen polymers and rectify any existing issues. This research aims to investigate the effects of temperature on the thermal and mechanical properties of polyurethane/polycaprolactone/graphene oxide nanocomposites, focusing on creating a smart polymer nanocomposite via the molecular dynamic method. The research is innovative as it investigates the nanocomposite's thermal behaviour and strength in detail. Additionally, the use of molecular dynamics methods to achieve the goal of creating a smart polymer nanocomposite represents an innovative approach that contributes to the advancement of nanocomposite materials. The results show that raising the initial temperature to 350 K decreased heat flux and thermal conductivity to 681.85 W/m2 and 0.82 W/m.K, respectively, while increasing the structure volume to 27364 nm3. Additionally, as the Temp increased from 300 to 350 K, the ultimate strength and Young's modulus values decreased from 72.59–68.22 MPa and 6.63–6.36 MPa, respectively. In general, this research provided valuable insights into understanding and evaluating the attributes of polyurethane/polycaprolactone/graphene oxide polymer nanocomposite through molecular dynamic simulations.

Threshold capillary pressure of caprocks for CO2 storage: Numerical insight on the dynamic and residual method

The dynamic threshold pressure method and the residual capillary pressure method provide an estimate of the threshold capillary pressure for CO2 storage. In the dynamic method, the threshold capillary pressure is determined by comparing the discharge measured when the CO2 starts flowing into the sample with the one measured when only water is flowing. In the residual method, a high CO2 pressure is applied on the vessel at the inlet of a water-saturated sample while water cannot flow from the vessel at its end. The threshold capillary pressure is estimated as the difference between the CO2 and the water pressures at the sample boundaries at equilibrium conditions.Sensitive analyses showed the impact of material properties and experimental conditions on results. Both methods allow estimating the threshold capillary pressure within a few days and the estimates are influenced by the applied pressures. The dynamic method estimate is not precautionary, and it tends to the exact value as the applied CO2 overpressure decreases. The residual method estimate is influenced by the drainage conditions and the volume of the vessels: it is generally precautionary and tends to increase with the volume of the vessels and the applied pressure. Suggestions for test interpretation and planning are also provided.

Dynamic error prediction and link strain feedback control for a novel heavy load multi-DOF envelope forming machine

Thin walled and high rib components (TWHRC) are widely used for rocket cabins, aircraft wings, panels and frames in aerospace equipment as critical load bearing structures. This paper is aimed to develop a novel multi-DOF envelope forming machine (MEFM) with parallel kinematic mechanism (PKM) to realize efficient manufacturing of TWHRC with high performance. Since the heavy load MEFM with PKM has many total differences with the light load PKM, such as dynamic error and precision control, this paper conducts research on dynamic error prediction modelling and precision control method for this novel heavy load MEFM.Firstly, the configuration of MEFM is developed and the kinematic model of MEFM is established. Then, a RFC (rigid-flexible coupling) and RFE (rigid finite element) coupling dynamic model of MEFM is established considering time-variation of kinematic configuration caused by large deformation of all the kinematic elements. Based on the dynamic model, the dynamic deformation and error of MEFM are predicted and their generation mechanisms are revealed. It is found that the linear compression deformation of two-force links is the largest among all the kinematic elements, and it is the most sensitive to the heavy forming load and the most insensitive to the installation error of strain gauges, thus suiting for the feedback of large deformation of MEFM. Based on the above findings, a novel link strain feedback and dynamic error compensation control method is proposed. Finally, experiments under different control methods are conducted, showing that the motion precision of envelope tool and forming precision of formed components under the proposed control method are remarkably improved compared with that under the traditional control method (The geometric deviation of formed components under the proposed control method is remarkably reduced to −18 ∼ 32um from −64 ∼ 54um under the traditional control method). The experimental results verify that the proposed dynamic error prediction model and link strain feedback control method for heavy load MEFM are reasonable.

Dynamic accuracy measurement method for star trackers using a time-synchronized high-accuracy turntable

Star trackers are typically used in a spacecraft to provide absolute attitude information to the on-board attitude control system so as to promote high accuracy. The performance of the star tracker is rather important. Attitude incorrectness provided by star trackers may lead to bad navigation with big deviations, even failure of satellites. Therefore, how to realize and verify the accuracy is crucial. As a matter of fact, it is difficult to validate accuracy of star trackers on the ground, especially for star trackers under highly dynamic conditions. In this paper, an accuracy measurement method for star trackers under dynamic conditions is proposed, utilizing a high-accuracy swing table to provide reference to compare. To this end, a swing table, star tracker, and the test equipment are synchronized, in order to reduce systematic errors. As the motion trajectory of the swing table can be set beforehand, the initial attitude of the star tracker can be predicted through a set of coordinate transformations. As a result, the star tracker is able to keep tracking, regardless of the angular velocity of the swing table. This makes the statistical sample points more sufficient and the results more reliable. Moreover, it can evaluate the angular velocity of star trackers up to 20°/s. In comparison with the conventional method with simulated stars, this method utilizes real navigation stars as observation targets making the measurement results much closer to the on-orbit performance. Lastly, but much more importantly, it can also verify the performance of a star tracker in one experiment, such as sensitivity, static performance, capture probability, and so on. Experimental results demonstrate that the proposed method is effective, especially for highly dynamic star trackers. Such a measurement environment is close to the in-orbit conditions, and it can satisfy the stringent requirement for star trackers under high dynamics.

Dynamic consumption and portfolio choice considering information learning and stochastic interest rate

This paper investigates the consumption–portfolio choice of investors with information learning ability when faced with historical performance, within a stochastic interest rate framework. We simultaneously analyze investor behaviors, specifically momentum trading and contrarian trading. Using the dynamic programming method, we derive the closed-form solutions. The numerical results demonstrate how investors with heterogeneous characteristics make consumption–investment allocations in response to different historical performances. We find that momentum investors tend to overreact to short-term market fluctuations, while contrarian investors focus on long-term investment potential and market reversals. In addition, our findings suggest that information utility is time-sensitive and decays over time.

Assessment of Reliability of Power Systems Under Adverse Weather Condition Using Markov System Dynamic Method

Abstract We aim to extend the Markov System Dynamic method (MSD) to evaluate the reliability of repairable systems in two-state weather (2SW) models. Increasing the number of components in real practices has limited the use of the Markov method, because of increasing the number of equations and the complexity of the problem. There is no such restriction in the MSD method. Thus, it can be applied to complex systems. Theoretically, we indicate that the 2SW model is a type of linear dynamical system. Therefore, we can use MSD to solve it. Through an example, the validity of the proposed method is confirmed in comparison to the Markov method for the 2SW model. Since the MSD method does not use equations, it can be a preferred alternative for calculating the reliability of 2SW models. Accordingly, we apply the MSD method for assessing the reliability of a four-component system.

A Novel Explicit Canonical Dynamic Modeling Method for Multi-Rigid-Body Mechanisms Considering Joint Friction

Friction is an inevitable phenomenon in mechanical systems that affects the dynamic characteristics of systems. To reduce the modeling complexity of complex multi-rigid-body mechanisms, a novel explicit canonical dynamic modeling method considering joint friction is proposed. Based on the explicit dynamic modeling theory that we have proposed, the solution of the constraint force required by the joint friction modeling of multi-rigid-body mechanisms is derived and improved, which greatly simplifies the solution of the constraint force. According to the obtained explicit expression of the constraint force equations, two joint friction models of the Coulomb–viscous effect and Stribeck effect are derived in analytical form. Moreover, the Stribeck effect of the joint is experimentally analyzed. A five-axis tree-chain mechanism and a three-loop closed-chain mechanism are chosen to demonstrate the method and compared with ADAMS software. Moreover, the proposed model is analyzed and compared with other methods.