Search

Jacket foundations are often used as the main support structure for offshore wind turbines in waters with a depth of 40—60 meters. The jacket foundation is connected to the top of the pile foundation through underwater high-strength grouting to form a top sealing section, which together bears the vertical load. In this paper, centrifugal model tests are conducted for three different grouting effects: normal grouting at the top of a pile foundation, insufficient grouting, and poor grouting with through-holes. The vertical bearing capacity of the foundation and the bearing capacity sharing ratio between the grouting section and the pile body section under the limit state are analyzed, and compared with existing theoretical calculation methods. The results show that the bearing capacity of the grouting sealing cap depends on the sealing effect. For foundations with normal grouting and insufficient grouting of grouting sealing caps, the bearing capacity of grouting sealing caps accounts for approximately 25%—30% of the total bearing capacity of the foundation. When there are through-holes in poor grouting, the bearing capacity of the foundation grouting section develops early and quickly reaches its limit, whose sharing ratio is far lower than the other two bearing capacities. The test results in this paper can provide a reference for the calculation of bearing capacity in foundation design.

The PLAXIS 3D software and the soft soil creep model were used to establish a 3D numerical model of the B2 excavation within an ultra-deep excavation group, allowing for analysis of the effects of time-space factors on ground settlements. Then, the complementary error function and trilinear model were introduced. After that, the numerical results were used to correct the complementary error function. Finally, a rapid approach was proposed for predicting ground settlements while accounting for the temporal and spatial influences. The results indicate that soft soil creep not only causes additional wall deflections resulting in soil settlements but also induces soil settlements independent of these deflections. The impact of ignoring soft soil creep on prediction of ground settlement is not weaker than that of wall deflections. The excavation area determines the construction duration when the excavation depth and rate are identical, impacting ground settlements induced by soft soil creep.

Unbonded flexible pipe has been widely used in the offshore oil and gas industry and its torsional balance plays an important role in safety insurance during the whole service life. However, there are still torsional imbalances due to simplifying assumptions in the current analytical theory. To improve the accuracy of torsion simulation and reduce the impacts on the practical applications of the pipelines, this paper focuses on the wear-resistant layer neglected in previous studies. Based on theoretical derivation, in combination with the finite element method, a correction algorithm for the torsional stiffness and elastic modulus of the tape layer is developed to investigate the effect of tape layer stacking on the overall torsion of the unbonded flexible pipe. The results show that overall torsion results obtained by using the corrected elastic modulus of the tape layer are closer to the test results, which have guiding significance for practical applications. Further sensitivity research shows that the width of the tape layer is positively correlated with the flexible pipe torsion angle, while the tape laying angle is negatively correlated with the overall torsion angle. With an increase in the stacking ratio of the tape, the pipeline torsion angle decreases.

Channel is the navigable area for ships in rivers and canals. The analyses of flow details and experiences of engineers are extensively used for the design of channel nowadays, without thorough consideration of the fluid-structure interaction between flow and ships. In this paper, an inland channel design method based on ship hydrodynamic response is proposed. First of all, the secondary development is undertaken based on Fluent for the problem of ship-flow interaction, in which flow domain is simulated by using the finite volume method, and governing equations of ship are embedded through user defined function (UDF) and solved by using the fourth-order Runge-Kutta method. The dynamic mesh technique is adopted to update the changing computation domains. The hydrodynamic motion response of freely drifting ships in inland waterway is simulated, as well as trajectory. Then, the weighted least square method is used for curve fitting from the drifting trajectories of different ships, by which the channel boundaries are designed. The weight coefficients are obtained through the statistic of sailing frequencies of different ships. Dongliu route with typical navigation obstructions of curve and branch is chosen for the verification of the channel design method. It is found that the channel designed by the method with consideration of ship-flow interaction is consistent with the existing one. Besides, the analyses of flow details and ship responses indicate that ships sailing in this channel can avoid the influences of oblique flow and make use of the energy of flow, which maintains the sailing safety and economy.

In this paper, the characteristics of a pulsating bubble collapse near the nozzle of an underwater tube were investigated both experimentally and numerically. The experiments involved generating pulsating bubbles using the electric spark discharge technology and capturing their transient behavior by high-speed photography. A boundary integral method based on potential flow theory was used to simulate the coupling between the bubble and the tube in the numerical simulation. The results show that the collapsing bubble can produce a jet directed towards the tube, which poses a safety threat to the pipeline equipment. The numerical simulation results and experimental phenomena are in good agreement. In addition, a systematic study was conducted of two dimensionless parameters, i.e., the ratio of bubble distance from the tube nozzle and the tube nozzle radius γ and the ratio of the maximum radius of the bubble and the tube nozzle radius λ. The findings indicate that when γ≤1.5, bubbles will only produce jets pointing into the tube during the collapse phase, and when γ>1.5, the tendency for bubbles to produce jets pointing into the tube increases as λ increases. Moreover, the maximum velocity of the downward jet increases with the increase of λ, and increases and then decreases with the increase of γ. Additionally, the maximum velocity of the downward jet generally increases with the increase of λ and increases and then decreases with the increase of γ. This study provides valuable insights into the dynamic behavior of bubble collapse near the nozzle of a submerged tube, which can provide safety measures for pipeline equipment.

In order to solve the existing problems of relying on manual experience to adjust the algorithm parameters, large difference of weight coefficient, and single result in ship pipe layout, a grid normalized Astar (GNAstar) is proposed. First, the mathematical models are established using bounding box and the grid method. Then, each path node is determined by the normalized weight values of different targets using the branch pipes splitting method, grid marking values, and the parent-child grid search strategy. The cost objective of traditional Astar only considering path length is extended to the comprehensive layout objective of pipes including length, bend consumption, and installation suitability. Finally, the GNAstar proposed is compared with the traditional Astar in a simulation case, and different pipe systems in ship engine room are taken as cases to further compare with the ant colony algorithm and particle swarm-Astar. The results show that the GNAstar proposed can obtain effective engineering solutions, and designers can obtain the corresponding layout result by setting the normalized weight coefficients of different targets.

Doubly-fed pumped storage unit can adjust its power flexibly through variable-speed operation, enabling it to participate in the frequency regulation of the grid in both generating and pumping modes. To explore the frequency regulation capability of doubly-fed pumped storage unit, frequency regulation modules adapted to the characteristics of units under different operating conditions are built, and a frequency regulation strategy with adaptive variable parameters considering the speed and power limit of the unit is proposed. First, based on the dynamic model of the unit considering reversible pump turbine, the frequency regulation modules are built, in which the frequency deviation is converted to additional power command in generating mode but to additional speed command in pumping mode. Then, the impacts of control parameters on the frequency dynamic response are analyzed, and the calculation method of optimal frequency regulation parameters under multi conditions is proposed based on the improved particle swarm optimization algorithm, which aims at minimizing the frequency deviation and takes the speed and power limit of the unit as constraints. Based on the proposed method, an adaptive frequency regulation strategy is further presented in which frequency regulation parameters adjust with the change of operating conditions of the unit. Finally, a four-machine two-zone system model with doubly-fed pumped storage unit is built, and the simulation results show that the frequency regulation strategy proposed can make the unit achieve a great frequency regulation performance under different operating conditions.

The force and deformation of the pile foundation under the combined action of horizontal dynamic load H(t) and torque T are rather complicated. In order to more accurately analyze the internal force and displacement of the H(t)-T combined loaded piles, the shear effect of soil on the pile side is considered based on the Pasternak foundation model, and the H(t)-T coupling factor is introduced to reveal the influence mechanism of multi-directional load on pile response. Then, the virtual work principle is used to derive the comprehensive stiffness matrix of the pile element, and the modified finite beam element method is used to obtain the numerical solution of the internal force and displacement of the H(t)-T combined loaded piles. The results are compared with the results of the existing theoretical solutions and finite element simulation. The parametric analysis shows that soil shear effect can restrain the horizontal displacement of the pile, but has less effect on torsional deformation. The horizontal dynamic load enhances the torsional bearing capacity of the pile, and the torsional angle of the pile top is reduced by 22.6% when the horizontal dynamic load amplitude increases from 0.2Qu to 1.0Qu. Increasing the dimensionless frequency of the dynamic load will reduce the displacement of pile top and the maximum bending moment of pile body. The dynamic effect of simple harmonic load will be weakened with the increase of pile burial depth. The beam element model significantly reduces the number of discrete units and computation time, which can effectively improve the computational efficiency.

In order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal flapping foil propulsion parameters in the environment. The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. Under different propulsion performance requirements, the TD3 algorithm in deep reinforcement learning (DRL) is trained for maximizing rewards and obtaining the optimal combination of multiple parameter actions in continuous intervals. The experimental results demonstrate that the GPR-TD3 method is capable of learning the globally optimal solution for flapping propulsion in the experimental environment, including maximum speed and maximum efficiency. Furthermore, the accuracy of this optimal solution can be intuitively verified through a two-dimensional contour plot in the GPR. Meanwhile, with 290 sets of real samples provided for any given propulsion speed requirement, the agent can recommend a set of action combinations with an error range of 0.23% to 6.68%, which can provide reference for practical applications.

Considering scattering behaviors of strength and fatigue life characteristics of actual materials, a method for evaluating the early fatigue failure of crankshafts has been proposed. The micro-mechanism of the scatter of strength and life characteristics is the nonuniform distribution of microstructures (including micro-defects) inside the material, which can be uniformly characterized by the probability distribution of initial damage. For the same grade of alloy, the median values of strength and fatigue life can be basically the same, but the dispersion can be significantly different, which must be determined by sufficient sampling tests. For a diesel engine crankshaft forging made of the 34CrNi3MoA alloy, the dispersion of fatigue limit was estimated based on the scatter of impact sampling test data, while the mean value of probability distribution of fatigue limit was determined by structural fatigue tests. The safety factor recommended in the conventional design manual only implicitly assumes material dispersion. Considering the actual dispersion of the tested material, the quantitative relationship between safety factor and reliability has been established. Early fatigue failure of the crankshaft can easily occur when the actual dispersion of material fails to meet the specified reliability requirement.

Energy transformation in China is accelerating, with electrified transportation rapidly developing. However, the existing transportation system may struggle to meet the growing demand for renewable energy consumption, and the energy supply from the power grid could face challenges under increased load pressure. As a result, the integrated development of energy and transportation has become a pressing issue, with vehicle-to-grid (V2G) technology emerging as an area of research. This paper first examines the current status of V2G application both domestically and internationally. Then, it focuses on important V2G applications, reviewing the research and practical implementations of V2G in rail transit and new energy electric vehicles. It also introduces a novel V2G mode based on the personal rapid transit (PRT) system and compares the three V2G modes. Finally, it explores the potential of building an energy internet around V2G within the framework of “fusion of transportation and energy” and identifies future research directions.

Mn5Ge2.7Zn0.3 alloy was successfully prepared by using the arc-melting method, and its phase transition, magnetocaloric effect, and critical behavior were studied. The thermomagnetic curve determined that the Curie temperature of the sample was 297.2 K, the isothermal magnetization curve found that the sample had no thermal hysteresis. The sample underwent a second-order phase transition through the M2-H/M plot and Banerjee’s criterion, and the isothermal entropy change of the sample was further calculated according to Maxwell’s equation, and the power-law relationship of field-entropy and the normalization curve also verified the fact of the second-order phase transition. The critical index was solved by the modified Arrott plot(MAP) method, the Kouvel-Fisher(KF) method and the critical isothermal (CI) method, and the accuracy of the critical index was verified by Widom scaling ansatz and scaling laws. Finally, the Arrott-Noaks equation was used to further analyze the critical behavior of the sample, and the normalization slope and magnetic interaction distance were analyzed to confirm the complexity of the magnetic interaction inside the sample.

The ever-increasing proportion of new energy units has led to prominent frequency safety issues in the power system. In order to avoid serious accidents after the system encounters large disturbance, it is necessary to purchase inertia and primary frequency regulation auxiliary services and integrate rapid response resources such as emergency interruptible load service into the system. Therefore, based on a review of the domestic and foreign inertia ancillary service market, the frequency safety constraints considering the emergency interruptible load service are derived, and a joint clearing model of electric energy, inertia, and primary frequency regulation auxiliary services is proposed, considering emergency interruptible load service. Then, the model is transformed into a mixed integer second-order cone programming model for optimization. Finally, the effectiveness of the model is verified based on a numerical example, and the influence of the frequency safety constraints on the system clearing results is discussed.

The anti-penetration performance of a double-arrow honeycomb sandwich structure with negative Poisson’s ratio (NPR) is investigated by using finite element simulation. With the penetration of small-caliber projectiles to nine types of arrowhead NPR honeycomb sandwich structures, the ballistic characteristics are obtained. The change in attitude during projectile penetration is obtained through simulation, and the dynamics simulation model of the projectile penetration double-arrow cell element is established. The simulation results show that when the thickness of the upper and lower layers are kept constant and only the double-arrow angle of the core layer is increased, the ballistic limit of the honeycomb sandwich structure is subsequently reduced. For the same honeycomb sandwich structure, there is a nonlinear relationship between the initial velocity of the bullet and the structural kinetic energy absorption rate. In addition, there is a specific velocity range within which the honeycomb sandwich structure exhibits optimal anti-penetration performance. During the penetration process, the projectile experiences an uneven distribution in the circumferential divection, which generates an asymmetric effect, altering the force environment, and leads to an unstable trajectory in the projectile’s penetration.

Massive air-conditioner resources on the consumer side have a huge potential to be involved in system frequency control, and massive scattered individual air-conditioners can be aggregated into air-conditioner groups to participate in system automatic generation control (AGC). As the operating status of individual air-conditioners is random, it is necessary to accurately evaluate the frequency control reserve capacity of air-conditioner groups. In this paper, a data-driven nonintrusive load disaggregation (NILD) method is proposed to incorporate massive air-conditioner groups into AGC frequency control reserve. First, the proposed method constructs a sequence-to-sequence model to nonintrusively forecast the frequency control reserve capacity of individual air-conditioners. Considering the fact that the participation of air-conditioners in AGC may affect the comfort of users, the feasibility of individual air-conditioners is evaluated to participate in AGC frequency control and a selection mechanism is constructed to determine whether they can be included in the frequency control reserve of air-conditioner groups. In the proposed method, individual air-conditioners within a sub-station that participate in AGC are aggregated to derive the air-conditioner group frequency control reserve of the sub-station, and the air-conditioner group frequency control reserve of all sub-stations are aggregated to derive the entire air-conditioner group frequency control reserve. The experiments simulate different degrees of system frequency drops in which air-conditioner groups work cooperatively with thermal units for AGC frequency control. The simulation results demonstrate the validity of the proposed method.

External factors such as bad weather and external failure seriously affect the reliability of a distribution network. To comprehensively and accurately assess the risk of faults in a distribution network, this paper proposes a fault risk assessment method that considers the difference in entropy value of rare factors. This method uses K-means clustering algorithm to classify failures based on their consequences and an improved association rule mining algorithm to analyze rare environmental factors and evaluate high-risk and low-probability factors, so as to realize the quantitative analysis of the association between rare factors and risk levels. By combining the Pearson correlation coefficient, the correlation of each environmental feature is analyzed and redundant features at different risk levels are eliminated. Then, the component criticality analysis method is used to adjust the risk weight of rare elements, which can quantitatively measure the degree of correlation between the occurrence of individual elements and the fluctuation of the overall risk of the system. According to the fluctuation difference of different factors, the risk weight optimization matrix considering the difference of rare factors is obtained, and the fault risk assessment model of the distribution network is established, in combination with the information theory. Finally, a distribution network fault risk assessment model is established, of which the accuracy and effectiveness is verified by a case analysis of an actual distribution network in a city.

When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors.

To address the issues of transient stability in heterogeneous wind farms with grid-forming energy storage, a transient analysis model of the heterogeneous system is developed, which incorporates both grid-following wind turbine units and grid-forming energy storage sources. The mechanism of frequency interaction among heterogeneous power sources during severe grid short-circuit faults is revealed by the model. It is discovered that grid-forming energy sources with slower rotor angle dynamics exhibits a frequency pinning effect on those with faster rotor angle dynamics, and the latter has a rotor angle co-driving effect on the former under the condition of frequency pinning. By solving the critical d-axis current, the impacts of wind power integration rate, the proportion of energy storage installation, and other parameters on frequency interaction are analyzed. The effects of wind turbine unit reactive power injection and the current limiting strategy of grid-forming energy storage sources are also discussed. Finally, the proposed mechanisms are comprehensively validated by simulations using MATLAB/Simulink platform.

In response to the new power system frequency safety issues caused by the high percentage of renewable energy sources connected to the grid, wind turbines mostly use integrated inertia control for the inertia and primary frequency regulation support provided by the power system. In order to better improve the inertia safety of the system and guarantee the grid frequency stability, dynamic modeling of wind turbines with integrated inertia control is conducted to derive the effective inertia of the wind turbine based on the kinetic energy contained in the wind turbine and the frequency support it provides to the grid. Then, a system frequency response model of the wind turbine in integrated inertia control is established, the analytical formula of the effective inertia time constant in the process of wind turbine frequency regulation is obtained, and the inertia support capability is analyzed. Based on the “equal area principle”, the equivalent inertia evaluation method of the wind turbine in integrated inertia control is derived, which can analyze the inertial support capacity provided by the wind turbine and give quantitative results. Finally, the validity and feasibility of the proposed method is verified in a case study, and the impact of different factors on the equivalent inertia of the wind turbine is analyzed.

The inertial of the system and the maximum frequency deviation caused by the energy storage batteries when participating in grid frequency regulation need to be improved. Virtual synchronous generator control (VSG) for grid side converter is expected to improve the performance of storage battery energy participation in grid primary frequency regulation. Therefore, participation of energy storage batteries in primary frequency control for power grid is proposed considering dynamic frequency inertia characteristics. To utilize the inertial support and primary frequency regulation capability of the energy storage batteries for the grid, an additional active power module is constructed, and the active power generated by the virtual inertial control and droop control strategies of the energy storage batteries are used as additional power for variable rotor inertial control and output feedback model predictive control, respectively. For the mismatch between the inertia characteristics of the virtual synchronous machine and the grid demand, a variable rotor inertia control strategy considering system frequency deviation and system frequency change rate is designed to realize the real-time adjustment of rotor inertia to the system frequency. The output feedback model prediction control of VSG is further proposed to achieve dynamic prediction and compensation of system frequency deviation by establishing a feedback channel between rotor angular frequency increment and torque increment. A comparative analysis with three existing control strategies shows that the control strategy proposed can make full use of the frequency regulation capability of the energy storage battery and VSG to effectively reduce the frequency deviation and frequency variation rate of the grid.

Aimed at the accommodation problem of a high proportion of new energy connected to the power grid, a multi-time scale coordinated scheduling model for power generation systems with a high proportion of new energy including pumped storage power stations is proposed. Based on the characteristics that the prediction accuracy of wind, photovoltaic (PV) and load power improves step by step with the shortening of time scales, a three-phase coordinated scheduling model is constructed with the goal of minimizing the total operating cost of the system, which includes a 24-hour day-ahead plan, a 1-hour intraday plan and a 15-minute real-time plan. Through the coordination of multiple time scales, the power generation plan is revised step by step, which ensures that the pumped storage power, the wind power, the PV power, and the thermal power output track the load well. Based on the CPLEX commercial optimization software, a mixed integer linear programming method is used to simulate and analyze the wind-PV-thermal-pumped storage complementary power generation system with six pumped storage units. The results show that the pumped storage units can take advantage of short load response time and fast power regulation speed to reduce the regulatory burden of thermal power units and relieve their regulatory pressure effectively. The coordination and cooperation on multiple time scales reduces wind and PV power curtailment and improves new energy accommodation level.

It is necessary to consider the carbon emission trading between prosumers when designing a distributed transaction mechanism in a distribution system. In this paper, a peer-to-peer (P2P) electricity-carbon transaction mechanism considering the carbon footprint of electricity trading is established. First, the carbon emission characteristic of energy storage system is analyzed, and a carbon emission flow model considering energy storage system is established. Next, a P2P electricity-carbon transaction model based on the carbon emission flow model is established. Based on the improved Benders decomposition method, the original problem is decomposed into the main problem considering network constraints and the subproblem of optimal scheduling for prosumers. Through distributed solutions, the optimal P2P electricity-carbon trading amount of prosumers is solved to protect the privacy of prosumers. Then, a P2P electricity-carbon trading settlement model based on the Nash bargaining model is established, and the cooperation benefit is distributed according to the contribution of prosumers in the electricity-carbon transaction. Finally, the effectiveness of the proposed model is validated by case studies on the improved IEEE 33-bus distribution system. The proposed model can not only ensure the secure operation of the distribution system, but also promote the reduction of demand-side carbon emission and fairly distribute the benefit among cooperative prosumers.

Line-start permanent magnet synchronous motor (LSPMSM) has a great application potential in the field of water pump due to its self-starting ability, high efficiency, and high power factor. In order to improve the transient performance of the motor with pump load, a new type of asynchronous starting motor rotor topology with non-uniform distribution of rotor bar is proposed. By combining the non-uniform distribution of rotor bar with the built-in permanent magnet into a multi-layer magnetic flux barrier rotor structure, the rotor salient ratio is increased, and the pull-in synchronization ability of the motor is improved. Based on the finite element method, the no-load back EMF, starting ability, pull-in synchronization ability, steady-state electromagnetic torque, demagnetization, stress field, and temperature field distribution of the traditional structure motor and the new structure motor are comprehensively compared and analyzed. The results show that compared with the traditional rotor structure, the new rotor structure effectively improves the pull-in synchronization ability, the efficiency, and the power factor of the motor under rated operating conditions. Finally, a prototype is made and an experimental platform is built to verify the correctness of the simulation results and theoretical analysis.

In the context of achieving the “dual carbon” goal, the task of carbon emission reduction in the power industry urgently needs to be completed. Cross-regional electricity trading can facilitate the remote consumption of surplus renewable resources and contribute to the low-carbon transformation of the power system. Due to the inherent differences in power generation structures across interconnected regions, the impact of carbon costs on the clearing electricity prices within regions varies, leading to dynamic price differences between regions, which, in turn, affects the outcomes of cross-regional electricity trading, and further exacerbates the differences in power generation structures, thereby impacting interval electricity price differences. To address these complexities, this paper proposes an assessment model which considers both carbon costs and interval electricity price differences in evaluating cross-regional electricity trading volumes. The model aims to establish a coherent relationship between interval price differences and cross-regional electricity trading by incorporating carbon costs into the power system production process. It uses the dynamic interval price difference as a signal to determine the trading volume between regions in the evaluation of cross-regional electricity trading volumes. In assessing interval price differences, the model updates the intra-region power generation structure based on unit revenue rates, and contrasts the price differences before and after these structural iterations. Taking the cross-regional electricity trading between two interconnected areas as an example, the results of the computational simulations demonstrate that the proposed model effectively evaluates the dynamic price differences between regions and cross-regional electricity trading volumes. Additionally, it quantifies the impact of power generation structure evolution on interval price differences.

Power side energy storage is a powerful tool for promoting the consumption of new energy and implementing the “dual carbon” strategy. In view of the imperfection of the bidding mechanism of the power side energy storage as an emerging entity participating in the competitive joint electricity market, and starting from the game relationship of the power side energy storage as a price maker participating in the spot electricity market, a bidding game two-level optimization model of the power side energy storage participating in the multi scenario application of the electric energy and reserve ancillary service market is proposed, considering the uncertainty of wind power output and the coupling of the electricity market with the introduction of carbon trading. The energy storage at the upper power supply side, as a leader, makes bidding decisions with the goal of maximizing its own comprehensive income, while the lower power trading center as a follower completes market clearing with the minimum cost of social power purchase and balance regulation. The MATLAB simulation verifies the rationality and effectiveness of the proposed model in optimizing the participation of power side energy storage in multi scenario application bidding in the electricity market and improving the comprehensive operating income of the energy storage.

The grid connection guidelines require wind turbines to have capability to quickly support reactive power in the events of grid faults. However, traditional proportional-integral controllers have the problems of slow transient response and poor adaptability dealing with changes in grid impedance and serious model disturbances. In order to solve this problem, this paper proposes an improved reactive power support control strategy which combines linear active disturbance rejection control(ADRC) with least error squares(LES) filters. The LES filter is used to quickly and accurately detect the amplitude of the fault grid voltage with background harmonics, which can effectively reduce the fault detection delay. The ADRC controller is used to effectively improve the transient reactive power response speed and the control adaptability and robustness of the wind turbines under grid impedance disturbance. The simulation model of direct drive wind turbine based on PSCAD/EMTDC is built considering two scenarios, weak power grid and power grid with background harmonics. The effectiveness of the proposed control strategy is verified by simulation.

Lithium-ion batteries experience capacity decline or even deterioration during the working process. Effective estimation of battery health status is a key challenge in the development of battery management systems. This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries based on the fusion of data-driven models and characteristic parameters. Using differential thermal voltammetry(DTV) to preprocess the experimental data of lithium-ion batteries, this method extracts six useful features, and establishes a SOH estimation model based on two-step Gaussian process regression (GPR) with different kernel functions. The results show that the established model can better approximate the experimental value and shorten the training and prediction time. The average absolute error of SOH estimation is 0.67%—0.97%, which is 20%—30% lower than that of single-step GPR. Therefore, the model has a high robustness and accuracy in estimating the state of health of lithium-ion batteries.

To reduce the losses caused by major power outages in power systems, a two-stage optimization method for black start of power systems is proposed using a multi-energy microgrid (MEMG) as a black-start source. First, the load-side integrated demand response and the operation characteristics of the electric, thermal, and gas physical energy storage devices in the MEMG are modeled as generalized energy storage. Next, a two-stage optimization model for black start of power systems using the multi-energy microgrid as a black-start source is proposed, with the first stage being the optimization sub-model for non-black-start source restoration path and the second stage being the black-start strategy optimization sub-model considering the power system and the MEMG. Then, a Dijkstra’s algorithm-based solution method for the first stage sub-model and a mixed integer quadratic programming-based solution method for the second stage sub-model are respectively proposed. Finally, a case study of a real regional power system in China is conducted to verify the effectiveness and advantages of the proposed black start optimization method.

Academic writing is a time-consuming process that requires authors to possess specific writing skills, as well as a thorough understanding of format standards and publishing ethics of scientific journals. This paper analyzes the submission process for special columns in Journal of Shanghai Jiao Tong University, aiming to address basic considerations that novice authors should keep in mind in the early stages of academic writing. The goal is to help these authors submit manuscripts that are logically structured and meet the academic standards of their target journals. This paper is unique in its focus on common issues encountered in submissions, providing practical advice through a point-by-point analysis. By incorporating easy-to-understand cases, it provides a helpful reference for novice researchers seeking to navigate the requirements and pitfalls of writing for scientific journals.

In this paper, a leader-model-follower matching-based distributed adaptive cooperative control scheme is developed by equivalenting abrupt changing topology to switching topology problem for uncertain heterogeneous multi-agent systems with abrupt changing communication topology to realize leader-follower output consensus. First, local output tracking error is proposed to transform the leader-follower global output consensus problem into the neighboring agents local output consensus problem. Then, the distributed nominal cooperative control design is performed with known system parameters to realize reference model-leader matching and follower-reference model matching, to ensure the leader-follower output consensus with abrupt changing communication topology. Afterwards, the distributed adaptive cooperative controller is studied to realize the asymptotic output tracking of the follower to the leader with unknown parameters under the abrupt change of communication topology. The designed control strategy can ensure the closed loop stabilization of the global agents as well as the leader-follower output consensus with switching topology without relying on global information. Finally, the effectiveness of the designed control scheme is verified by simulation.

In order to investigate the evolution of pore structures in cementitious materials, a statistical model is proposed by taking the cement paste as a random dispersion system of two-phase medium. Simultaneously, the μic platform is employed to simulate the cement hydration. The results obtained from the simulation and the disperse models are compared with each other, and further analysis on calculation conditions and parameters of the disperse models are conducted. The pore size distribution obtained from polydisperse hard sphere model is very close to the simulation in completely dispersion condition. Taking into account the cross and agglomeration effects of hydrated products, the calculation results of the monodisperse concentric-shell model are more consistent to the simulations. Considering the flocculation of cement particles in initial state, the monodisperse hard model is closer to the simulation results. This paper offers a new insight from the viewpoints of mathematics and physics to understand and describe the pore structures of cementitious materials.

The visual assistance driving system for civil aviation aircraft captures information about the surrounding threat scenario using airborne visual sensors, providing pilots with additional information to aid decision-making. However, the threat objects in the airfield obtained by the optical sensors on the airborne differ significantly in scale, and the computing capacity of the onboard platform is limited. Current methods for object detection do not meet the requirements for visual assistance in driving scenarios. To address this issue, a lightweight multi-scale object detection algorithm based on YOLOv5s is proposed. First, the CA-BIFPN feature fusion network is designed by combining the weighted bidirectional feature pyramid network (BIFPN) with the coordinate attention (CA) attention mechanism, which aims to enhance the feature expression of small objects and to improve the capacity of the model to learn multi-scale objects. Then, the GSConv decoupled detection head is designed to improve object detection accuracy by making classification and regression independent. To enhance the detection speed of the network and enable real-time detection of airfield objects, a cross-level partial lightweight neck module is designed to reduce the additional parameters introduced by the decoupled head. A self-built multi-scale airfield object dataset containing real-world and simulated data from airborne visual sensors from a civil aviation aircraft perspective is established to verify the performance of the proposed algorithm. The experiments conducted on this dataset demonstrate that the detection accuracy of the proposed algorithm surpasses that of faster R-CNN, SSD, and other classic multi-scale object detection algorithms like YOLOv6, YOLOv7, and YOLOX. The achieved mean average precision is 71.40%, which is 4.19% higher than that of YOLOv5s. Furthermore, the detection frame rate achieves 71 frame per second on the simulated airborne computing platform, which satisfies the real-time detection requirements.

To solve the problem of cold protection for marine equipment against cold caused by the harsh polar environment, electrical tracing is commonly used in engineering. In this paper, numerical simulation and experimental methods are used to analyze the influencing factors of thermal balance of electric heat tracing tread plate components in typical polar environment with wind speed ranging from 0 to 40 m/s and temperature ranging from -40 ℃ to 0 ℃. Based on numerical simulation and experimental test, the convective heat transfer coefficient of electric heating tread plate components at different wind speeds and temperatures is obtained. The results show that increasing wind speed and decreasing temperature will increase the convective heat transfer coefficient of the tread plate. Wind speed is the main factor affecting the heat transfer of the tread plate, while temperature has little effect. Finally, based on the experimental data and the classical theory of convective heat transfer of plate components, a prediction model of convective heat transfer coefficient of electric heating tread plate components is established, and the correctness of the model is verified by numerical simulation.

This paper addresses the formation control problem for ground mobile robot formations and proposes a formation transition method based on dynamic density guidance. To achieve different formation transitions, a centroidal Voronoi tessellations (CVT) formation control algorithm is utilized to avoid collisions during the transition process. By leveraging the properties of the CVT algorithm, a dynamic density is generated by constructing a transition density function between the initial formation density function and the desired density function. The CVT algorithm then guides the robots in the formation to move and complete the transition and reconstruction of the formation. The simulation results demonstrate that, compared to using the desired density function directly, this method not only successfully resolves certain formation transition failures but also reduces the average positional error of the formation during the transition process.

Considering that the research on resonant three-dimensional swirling waves in vertical cylindrical tanks is relatively rare, this paper has built a set of tank sloshing model test device to study the first-mode and second-mode resonant three-dimensional swirling waves in vertical cylindrical tanks. It is found that when the external excitation frequency is equal to the first mode natural frequency of the free surface movement, three typical waveforms, planar wave, breaking wave, and swirling wave, appear in the cylindrical tank. When the external excitation frequency is close to the first mode natural frequency of the free surface movement, the modulation wave, namely beat phenomenon, appears in the cylindrical tank. When the external excitation frequency is equal to the second mode natural frequency of the free surface movement, three typical waveforms, the second mode wave, the second mode swirling wave, and the irregular wave, appear in the cylindrical tank. The research results in this paper can provide reference for offshore engineering designers to design cylindrical tanks of floating platforms.

A model predictive control (MPC) method based on the vector field guidance law is proposed to improve the effectiveness of path following and collision avoidance for ships. First, the path following and collision-avoidance problems are transformed into heading-control problems by the vector field guidance law. Then, the first-order Nomoto response model is adopted as the ship dynamic model for the model predictive control. Considering the input limitation of the rudder angle, the disturbance observer is introduced to compensate the model error and the environmental disturbances. The stability of the designed path following control system is verified by the Lyapunov theory. Finally, a collision avoidance strategy based on the vector field guidance law is designed to enable the ship to avoid collision autonomously in the process of path following. The simulation results indicate that the proposed methods can make the ship track the target path accurately and realize collision avoidance under the impacts of wave disturbances.

Comprehensive geophysical prospection is an effective technique for karst exploration, but its prediction results usually suffer from significant artificial influence and fuzzy boundaries of karst caves. Based on the machine learning technology, a Gaussian mixture model is used to classify the exploration data of high-density electrical method and data of surface wave method respectively. Then, a Category-boundary algorithm is proposed to further subdivide the classification results, which improves the accuracy of Gaussian mixture model classification. Finally, the classification fusion rules are formulated based on expert experience and geological exploration data. Under the organic combination of survey data-driven and engineering geological knowledge guidance, a new set of high-precision classification and fusion methods is proposed for comprehensive geophysical exploration. By applying this new method to the karst exploration project in southern Zhejiang, a karst cave prediction is made with clearer boundaries. Compared with the actual drilling information, cave prediction and drilling information are highly consistent, which verifies the effectiveness of the method proposed.

There exists a special bidirectionally sheared flow field due to solitons in the South China Sea. An experimental study of the mean drag force of a flexible riser undergoing vortex-induced vibration under bidirectionally sheared flow is conducted. Modal analysis and the beam theory are applied to processing the strain signal measured in the experiment to obtain the mean drag force. The initial displacement, mean drag force, and mean drag coefficient are investigated, and the amplification of the mean drag coefficient is confirmed under bidirectionally sheared flow with comparable amplification to linear shear flow. Meanwhile, a unique phenomenon that the shear force reaches an extreme value in the center of the riser under bidirectionally sheared flow is found in the experiment. The shear force coefficient is proposed and fitted to obtain an empirical formula for the extreme value of the central shear force of the flexible riser under bidirectionally sheared flow, which will provide external load input for the design of the riser in the South China Sea.

In order that the flight vehicle group could form the expected formation, the “leader-follower” formation control law is investigated. First, the distributed extended state observer (DESO) is designed such that the followers could estimate the virtual leader’s position and velocity. Then, the expected positions of the followers are calculated based on the observer outputs and the nominal formation configuration. A dynamic surface control-based position tracking control law is designed for the followers to track the expected positions. Based on the Lyapunov theory, the stability of the proposed method is proved, while numerical simulations validate the effectiveness. The DESO could estimate both the virtual leader’s position and velocity via only the position observations. The method proposed guarantees that the orientation of the formation is consistent with the direction of the virtual leader’s velocity.

In order to achieve high-precision pixel-level detection of multiple surface diseases in metro shield tunnels, a semantic segmentation model SU-ResNet++ based on deep learning is proposed. First, the encoder SE-ResNet50 based on residual unit and attention mechanism is designed and pre-trained, using as the backbone network of U-Net++ to design a new neural network model. Then, through original data collection, data preprocessing, and manual annotation, a shield tunnel surface multiple diseases dataset with 4 500 pictures is constructed. Finally, the proposed method is trained, verified, and tested on a dataset, and applied to practical engineering detection, achieving high-precision pixel-level diseases semantic segmentation. The experimental results indicate that the proposed SU-ResNet++ algorithm is applicable to the detection of shield tunnel disease data, and can automatically and accurately identify the disease category and form. Compared with the traditional semantic segmentation models, its disease identification precision is significantly improved, which meets the practical engineering requirements.

To address the limited endurance of rotorcraft and the restricted flight areas of fixed-wing aircraft, a vertically take-off and landing multi-unit wings tandem configuration is proposed. First, using multi-body kinematics and the lifting-line theory, the aerodynamic characteristics of the centralized deformation are analyzed, and limits of the flight angle of attack during the deformation process are determined. Then, a nonlinear multi-body dynamics model is established using the quasi-Lagrangian equation to comprehensively describe the relative motion characteristics between the unit wings. The flight efficiency of fixed wings and folding wings during the climb process is compared, which verifies the long endurance and good maneuverability of the folding wings. Finally, a modal analysis of the attitude and structural coupling characteristics of the folding wings in steady-level flight is conducted, based on which the cooperative control law of folding and flight is designed to achieve stable control in the process of tilting and folding.

Recoil control for deepwater drilling riser system after emergency disconnection is a necessary technique in deepwater oil exploration. However, some dynamic parameters of the riser system are uncertain and difficult to measure, which poses severe challenges to the riser recoil control. Therefore, an intelligent riser recoil adaptive control method considering system uncertainties is established. Based on the nominal state-space expression of recoil control and closed-loop system stability, the modified control input considering model uncertainties is derived. The radial basis function (RBF) neural network is adopted to approximate model uncertainties, and the weight adaptive law satisfying Lyapunov stability is selected to realize dynamic compensation of uncertainties in control inputs. The results show that the proposed method is applicable to the actual recoil control valve with adjustment speed limit. The uncertainties of tensioner stiffness, damping, mud discharge friction, and riser buoyancy loads have a certain effect on recoil dynamic response and control performance. The RBF adaptive control method can effectively reduce the initial recoil oscillation height and reduce the risk of recoil bottoming. The findings can effectively solve the problem of recoil control without accurate system parameters in the engineering background.

Due to the tip clearance of the pump-jet propulsor (PJP), the flow field characteristics in the PJP are more complicated. To explore the influence of scale effects on the tip clearance flow field of PJP, the unsteady Reynolds-averaged Navier-Stokes (URANS) equation and the SST k-ω turbulence model are used. The computational domain is discretized by structured grid, and the sliding grid is used to deal with the relative motion between the rotor and stationary components. The feasibility of the numerical method is verified by grid uncertainty analysis, and the numerical results are in good agreement with the model-scale PJP experimental data. The open water performance of the three scale PJP models is numerically calculated and analyzed from the perspective of vorticity field and pressure field. The results show that the efficiency of the full-scale PJP model will be improved under all the advance coefficients, the vorticity collapse of the full-scale PJP is earlier, the intensity is lower, the pressure coefficient of the TLV vortex core center is smaller, and the fluctuating pressure amplitude between the tip clearance is lower.

In order to more accurately predict the lateral deformation of retaining structures caused by foundation pit excavation, this paper adopts support the vector machine model, traditional artificial neural network model, and two kinds of recurrent neural network models considering temporal inputs to establish a prediction model for the maximum lateral deformation of retaining structures in different foundation pits, and for the same foundation pit under different working conditions. The results show that the artificial neural network can update and predict the deformation of the retaining structure in real time based on the measured data of the project, which is helpful for timely planning of the next construction process of the project. In the prediction of lateral deformation of retaining structures under different working conditions, the cyclic neural network model considering temporal inputs is better than the traditional artificial neural network model.

This paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study focuses on the design of control strategies for perching maneuvers. First, a training environment for the perching system is established using domain randomization, which incorporates various wind conditions. Then, the SINDYc method is employed to learn sparse models of the perching system offline under different wind conditions, using historical data and a candidate function library, to effectively identify the wind information. Afterwards, the perching control strategy is trained using an IDRL algorithm within the training environment that encompasses multiple wind conditions, resulting in a control strategy for perching in wind-disturbed scenarios. Finally, numerical simulations are conducted to verify the effectiveness of the proposed perching control strategy in wind-disturbed environments.

To address the issue of multi-body folding wings being subjected to aerodynamic load changes, gravity reversal, and external disturbances during coordinated tilting, an improved active disturbance rejection control (ADRC) strategy based on the combination of hyperbolic tangent function and phase compensation is proposed. Using the Lagrange equation, the aerodynamic load on the unit wing is applied to the center of mass, and the offset of the gravity term with the attitude angle of the body is provided, thereby establishing the dynamic model of the multi-body folding wing. An improved ADRC controller is designed to track the desired angular trajectory of the folding wing, and appropriate controller parameters are selected to suppress joint vibration. The simulation results show that the improved ADRC method can effectively achieve trajectory tracking and joint vibration suppression under variable load conditions, and it demonstrates smaller tracking errors and better dynamic performance and robustness when subjected to external disturbances.

Physics-informed neural networks (PINN) are applied to the reconstruction of the ship propeller wake field. First, the principle and basic framework of PINN were introduced. Then, the Burgers equation was selected to verify the feasibility of PINN in solving partial differential equations. After that, the propeller of KVLCC2 in open water is simulated using computational fluid dynamics (CFD) software STAR CCM+, and the flow field information of the KVLCC2 propeller is obtained. Based on the simulated flow field information data, the training sample set was constructed to train PINN. The trained PINN was used to infer the approximate solution of the governing equation at any time and space. Finally, the velocity and pressure distribution obtained by PINN were compared with the velocity and pressure distribution simulated by STAR CCM+. The results validate the reliability of PINN in propeller wake field reconstruction, which can be concluded that PINN can be applied to the reconstruction of the ship propeller wake field.

To solve the problem of inadequate perception of autonomous driving in occlusion and over-the-horizon scenarios, a vehicle-road collaborative perception method based on a dual-stream feature extraction network is proposed to enhance the 3D object detection capabilities of traffic participants. Feature extraction networks for roadside and vehicle-side scenes are tailored based on respective characteristics. Since roadside has rich and sufficient sensing data and computational resources, the Transformer structure is used to extract more sophisticated and advanced feature representations. Due to limited computational capability and high real-time demands of autonomous vehicles, partial convolution (PConv) is employed to enhance computing efficiency, and the Mamba-VSS module is introduced for efficient perception in complex environments. Collaborative perception between vehicle-side and roadside is accomplished through the selective sharing and fusion of critical perceptual information guided by confidence maps. By training and testing on DAIR-V2X dataset, the model size of vehicle-side feature extraction network is obtained to be 8.1 MB, and the IoU thresholds of 0.5 and 0.7 correspond to the average accuracy indexes of 67.67% and 53.74%. The experiment verifies the advantages of this method in detection accuracy and model size, and provides a lower-configuration detection scheme for vehicle-road collaboration.

The application of high-strength steel in extremely cold polar regions can reduce steel consumption and save the cost of fabrication, transportation, and installation of steel structures in the harsh low-temperature environment. In order to study the mechanical properties of HG785 high-strength steel under polar low-temperature conditions, uniaxial tensile tests were conducted on high-strength steel coupons by considering two thicknesses and five low-temperature cases. It was found that the elastic modulus, yield strength, and ultimate tensile strength of HG785 high-strength steel in polar low-temperature environment are higher than those at an ambient temperature of 25 ℃. All tensile coupon specimens failed by traditional necking in a ductile manner without brittle failure tendency. Based on the test results, accurate prediction models for mechanical properties of HG785 high-strength steel in polar low-temperature environment were established by the best subset regression analysis. This will facilitate the application of high-strength steel in the design of structural members, joints, and systems in an efficient manner, and provide theoretical support for the promotion of high-strength steel structures in polar low-temperature regions.

Image restoration under adverse weather conditions is of great significance for the subsequent advanced computer vision tasks. However, most existing image restoration algorithms only remove single weather degradation, and few studies has been conducted on all-in-one weather-degraded image restoration. The denoising diffusion probability model is combined with Vision Transformer to propose a Transformer-based diffusion model for all-in-one weather-degraded image restoration. First, the weather-degraded image is utilized as the condition to guide the reverse sampling of diffusion model and generate corresponding clean background image. Then, the subspace transposed Transformer for noise estimation (NE-STT) is proposed, which utilizes the degraded image and the noisy state to estimate noise distribution, including the subspace transposed self-attention (STSA) mechanism and a dual grouped gated feed-forward network (DGGFFN). The STSA adopts subspace transformation coefficient to effectively capture global long-range dependencies while significantly reducing computational burden. The DGGFFN employs the dual grouped gated mechanism to enhance the nonlinear characterization ability of feed-forward network. The experimental results show that in comparison with the recently developed algorithms, such as All-in-One and TransWeather, the method proposed obtains a performance gain of 3.68 and 3.08 dB in average peak signal-to-noise ratio while 2.93% and 3.13% in average structural similarity on 5 weather-degraded datasets.