Special collection of 12 issues of "Journal of Shanghai Jiaotong University" in 2021
To respond to the demand of achieving carbon peaking and carbon neutrality goals, and to construct a complete “source-grid-load-storage” new energy power system, a distributed photovoltaic net load forecasting model based on Hamiltonian Monte Carlo inference for deep Gaussian processes (HMCDGP) is proposed. First, direct and indirect forecasting methods are used to examine the accuracy of the proposed model and to obtain spot forecasting results. Then, the proposed model is used to perform probability forecasting experiments and produce interval prediction results. Finally, the superiority of the proposed model is verified through the comparative experiments based on the net load data of 300 households recorded by Australia Grid. After obtaining the exact net load probabilistic forecasting results, the photovoltaic production can be fully utilized via power dispatch, which can reduce the use of fossil energy and further reduce the carbon emission.
Nowadays, the third energy revolution has taken place. Many developed countries have formulated clean energy development strategies and announced the time for phasing out thermal and nuclear power to reduce carbon emissions. Meanwhile, China has made a commitment to the world that the carbon emissions of China will peak before 2030, and the carbon neutrality will be achieved before 2060. Therefore, it is of great significance to study the development pathway of clean electricity of China. The reserves and characteristics of clean energy such as hydro, wind, and solar in China are analyzed. The medium and long-term power demand of China is projected, and the power system structure in 2030 and 2050 are respectively estimated based on the electric power and energy balance equations. In addition, the trend of carbon emissions is also analyzed. Some suggestions are proposed to guide the development of China’s clean electricity. The results indicate that the “carbon peaking” of China’s power system would arrive in 2027, and the clean electricity of China is projected to exceed 50% of the total energy production in 2030. Thermal and nuclear power can be replaced by clean electricity such as hydro, wind, and solar energy in 2050, the power industry will achieve “zero CO2 emission”, and the transformation of the green power system will be achieved in response to carbon peaking and carbon neutrality goals.
Numerical wave simulation is a significant research topic. In this paper, the open source computational fluid dynamics (CFD) platform, OpenFOAM, is utilized to simulate Stokes fifth-order waves. Since geometrical volume-of-fluid (VOF) could better capture free surface due to its geometrical reconstruction step, the free surface simulations are accomplished by applying OpenFOAM built-in geometrical VOF method-isoAdvector, and the relaxation zone scheme is introduced through secondary development for wave absorption. The mesh density and Courant number convergence analyses with geometrical VOF are conducted. The simulation shows that satisfactory results could be obtained with a large Courant number. The algebraic and geometrical VOF simulated data with respect to wave elevation and phase at varied wave steepnesses and frequencies are recorded and compared with the theoretical value of Stokes fifth-order waves, which demonstrates that geometrical VOF is better than algebraic VOF in the prediction of wave elevation. Finally, the lengths and weights of the wave absorption zone are discussed, and the results imply that the best practice for the wave absorption is assigning the wave absorption zone length at least two times of the wave length along with applying exponential weight distribution.
It is a challenging problem to efficiently calculate and systematically analyze the motion laws and working gait of the inchworm-like soft robot. A simple mechanical model consisting of a rigid slider and a curved beam is established under quasi-static conditions, in order to realize quasi-static modeling and simulation analysis of the inchworm-like soft robot. First, based on the Euler-Bernoulli beam theory, the total potential energy expression of the beam is obtained. Next, combining the boundary conditions and the governing equation derived from the total potential energy based on the variational principle, a set of ordinary differential equations are established. Then, through discretization and dimensionlessness of those equations, a class of nonlinear algebraic equations for numerical solution is proposed. Finally, in the light of the contact situation between curved beam and ground as well as the viscous and slip condition of the system, the motion of the robot is divided into three stages. Through numerical calculations, the different configurations of the curved beam in different stages with the change of the initial curvature amplitude are obtained, which makes it possible to describe the law, the gait, and the net displacement of the soft robot in a motion cycle and solve the problem of movement connection of soft robots at different stages. The quasi-static method is characterized by high computational efficiency, which is more suitable for analyzing the motion configuration of soft robots.
In structural strength topology optimization based on the variable density method, there are gray cells in the optimization result, making it difficult to accurately predict the structural stress which changes greatly before and after post-processing. This paper uses a filter-projection-based structural parameterization method to achieve a continuous decrease in the proportion of structural intermediate density units during the iterative optimization process. By studying the influence of the main optimization parameters of the structural ratio strength problem on the optimization process and structural strength optimization, a novel optimization strategy of structural topology optimization followed by approximate shape optimization is proposed, which realizes the accurate control of the change of structural stress during the optimization process, achieveing structural strength optimization while improving the stability of the optimization process. Typical optimization examples verify the rationality and practicability of the proposed optimization method.
To improve the accuracy of photovoltaic (PV) power prediction, this paper proposes a novel weather classification method. First, it distinguishs the clear days and cloudy days according to the total cloud cover. Then, it further classifies the cloudy days into three subtypes to investigate whether the sun is obscured by clouds. This method can effectively identify the characteristics of key meteorological environmental factors that affect PV output and form a new classification index sky condition factor (SCF) by weighted summation. This method has clear physical meanings, good discrimination, and easy quantification. The reasonable classification of weather types can eliminate the coupling relationship between many meteorological environmental factors and reduce the dimension of input variables, which makes it easy for statistical modeling. Based on the theoretical and the statistical approachs respectively, the modeling and verification are conducted and the results show that the method can effectively improve the accuracy of PV power prediction.
Aimed at the problems of unmanned marine exploration vessels, such as the short voyage time and the limited sensing ability caused by sensor failure under complex marine environments, a long-range unmanned ocean-air stereo exploration vessel driven by wind and solar energy is developed. An elevating ducted wind turbine is designed for high efficiency and low starting wind speed, and a deployable solar photovoltaic power generation system is developed. Therefore, wind power and solar energy can be utilized to realize a hybrid system, which overcomes the instability of single energy power supply, and effectively ensures the endurance of unmanned exploration vessel. Then, a ship-borne tethered ummanned aerial vehicle (UAV) system is developed with an autonomous takeoff and landing control section. Finally, the information fusion technology of ship borne and airborne sensors is adopted to greatly improve the perception ability of the unmanned ship to the surrounding environment and the function of three-dimension detection of sea and air. The unmanned surface vessel (USV) proposed in this paper is permitted to perform the assigned task with different types of loading equipment according to the scenarios.
Automatically extracting key data from annual reports is an important means of business assessments. Aimed at the characteristics of complex entities, strong contextual semantics, and small scale of key entities in the field of corporate annual reports, a BERT-BiGRU-Attention-CRF model was proposed to automatically identify and extract entities in the annual reports of enterprises. Based on the BiGRU-CRF model, the BERT pre-trained language model was used to enhance the generalization ability of the word vector model to capture long-range contextual information. Furthermore, the attention mechanism was used to fully mine the global and local features of the text. The experiment was performed on a self-constructed corporate annual report corpus, and the model was compared with multiple sets of models. The results show that the value of F1 (harmonic mean of precision and recall) of the BERT-BiGRU-Attention-CRF model is 93.69%. The model has a better performance than other traditional models in annual reports, and is expected to provide an automatic means for enterprise assessments.
In order to improve the stress shielding effect caused by excessive elastic modulus of metal plates during fracture healing, a kind of 3D printing oriented lattice structure plate is designed based on topology optimization and the finite element modeling technology. A simplified finite element model of the titanium alloy tibial plate is established by using the finite element method. Combined with the finite element method and the data sampling method, the solid plate system and the lattice plate system are simulated, and the similarities and differences between their performances are compared. Based on the analysis of mechanical properties of lattice plate system, the lightweight design of the plate is realized and the stress shielding effect of the bone is improved. The results show that the weight of the lattice plate can be reduced by about 40% under the condition of guaranteed strength. The lattice plate is sensitive to the thickness. By reducing the thickness of the plate in a small range, the stiffness of the plate can be significantly reduced. The application of the lattice plate can effectively increase the average stress of the skeleton by about 4% and reduce the stress shielding effect of the skeleton. The simulated analysis results can provide references for the optimization design of low stress shielded plates.
A method for online detection and compensation of grinding wheel wear based on machine vision is proposed in this paper. The principle of workpiece-contour-image (WCI) based online wheel wear detection is presented, and the online compensation of wheel-wear-induced contour error is analyzed, based on which, studies are conducted on the developed complex contour grinding platform. The results reveal that the proposed method can effectively detect the wheel wear in real-time and compensate the contour error caused by wheel wear to improve the machining accuracy. The research provides a new method for online detection of wheel wear and prediction of wheel dressing.
Tian Xinliang’s group proposed a novel flow control method called "Flexible coating reduces drag" (FCRD) with a flexible enclosure constructed behind the bluff body to adjust the fluid forces received and the flow pattern around it. Compared with the traditional flow control methods, FCRD does not change the structure of the control object and thus has a positive engineering application prospect. Besides, FCRD brings out a new "fluid-structure-fluid" interaction problem, which needs further investigation.
Aimed at the problem that the conventional absorption heat pumps and compression heat pumps cannot take into account the temperature rise and efficiency, this paper proposes the use of a thermally-coupled hybrid compression-absorption heat pump to achieve high-efficiency and high-temperature output. To meet the demands of different scenarios, a large-temperature-lift cycle and a high-temperature-output cycle are constructed. R245fa and lithium bromide aqueous solution are used as working substance. For output temperature above 100℃, Aspen Plus software is used to establish a mathematical model to predict the cycle performance for calculation. The results show that the optimized coefficient of performance(COP) can be 2.58 or higher when the large-temperature-lift cycle is used to recover the waste heat at 30-40℃. When the high-temperature-output cycle is used to recycle waste heat at 60-70℃, the optimized COP of the cycle can reach 2.83. The cycles proposed are more advantageous than the R245fa compression cycle on temperature lift, output temperature, and efficiency.
The nesting problem is how to nest objects of a specified shape in a two-dimensional space to obtain the maximum space utilization rate, which is of great significance in industrial production. The solution to the nesting problem requires frequent cross-checking of the objects to determine whether the nesting position is legal. The No Fit Polygon algorithm can be used to accelerate the procedure of cross-checking, but the algorithm cannot be used to calculate shapes containing curves, which limits its application. An algorithm based on orbit sliding can calculate No Fit Polygon between shapes which includes arc, but its effectiveness is not satisfactory. Aimed at this problem, and based on trajectory algorithm, the trajectory generation strategy and the profile algorithm are analyzed and improved. The improved algorithm can calculate the No Fit Polygon between shapes containing arc in a shorter time, solving both accuracy and effectiveness problems. Finally, the algorithm is tested in the real punch production process and the results of the test confirms the correctness and effectiveness of the algorithm.
Combined with the current research status of the intelligent vehicle decision-making methods at home and abroad, this paper classifies and summarizes decision-making methods from four aspects: decision input and output, environment interaction, and algorithm types. Besides, it analyzes their advantages and disadvantages, and evaluates applicable scenarios. Moreover, it surveyes the common data sets and current evaluation standards which are used for decision-making researches. Furthermore it discusses the technical difficulties faced by current decision-making methods and future development trends.
The layout design of ship engine room equipment belongs to the multi-objective optimization design problem of confined limited space. As the heart of the ship, the layout of engine room equipment will affect the performance of all aspects of the entire ship. By using CATIA software and knowledge based engineering module, the 3D layout design of the engine room of a multi-purpose cargo ship was studied. Ship engine room classification rules were established to improve the efficiency of knowledge acquisition, equipment virtual area increase rules were established to control equipment spacing, and parameterized assembly were used to complete knowledge reasoning. Finally, the generated layout scheme was verified by experts through three rules of roll torque, interference inspection, and escape time. The results are in line with expectations, and the feasibility and effectiveness of knowledge based engineering in the 3D layout design of ship engine room are verified.
To realize the practical scale application of the spar-type floating offshore wind turbine (FOWT) in the medium depth sea areas, a novel 6 MW spar-type floating offshore wind turbine is analyzed by model test and numerical simulation under extreme conditions. The response of main freedom degrees, the mooring tense and the stress at the danger point are explored by a 1∶65.3 scale model at the State Key Laboratory of Ocean Engineering in Shanghai JiaoTong University. Coupled motion response of the spar-type floating wind turbine is calculated by using numerical simulation software in time domain. The results of the numerical simulation and model test are compared and analyzed in time and frequency domain. The maximum deviation between numerical simulation and model test is less than 12%, which shows that the numerical simulation results are in good agreement with the model test results. The dynamic response energy of the FOWT is mainly concentrated at low frequency and wave frequency. Moreover, the whole FOWT system has an excellent survivability under extreme conditions. Finally, the ultimate load of the wind turbine is predicted, which provides the necessary theoretical basis and calculation parameters for the structural strength calculation.
In view of the shortcomings of the traditional video anomaly detection model, a network structure combining the fully convolutional neural (FCN) network and the long short-term memory (LSTM)network is proposed. The network can perform pixel-level prediction and can accurately locate abnormal areas. The network first uses the convolutional neural network to extract image features of different depths in video frames. Then, different image features are input to memory network to analyze semantic information on time series. Image features and semantic information are fused through residual structure. At the same time, the skip structure is used to integrate the fusion features in multi-mode and upsampling is conducted to obtain a prediction image with the same size as the original video frame. The proposed model is tested on the ped 2 subset of University of California, San Diego (UCSD) anomaly detection dataset and University of Minnesota System(UMN)crowd activity dataset. And both two datasets achieve good results. On the UCSD dataset, the equal error rate is as low as 6.6%, the area under curve reaches 98.2%, and the F1 score reaches 94.96%. On the UMN dataset, the equal error rate is as low as 7.1%, the area under curve reaches 93.7%, and the F1 score reaches 94.46%.
A lattice self-reconfigurable modular soft robot based on the expansion-contraction motion rule is designed, which is composed of several soft modules, each of which is composed of a silica gel main body with positive hexahedron configuration and a master-slave docking surface. The internal bulged design makes it have a good expansion performance. The master-slave docking surface is composed of an iron disk and a suction disk type electromagnet connected with the silica gel main body by thread composition. Based on the relationship between the volume change of the soft module and the internal pressure, the expansion of the soft module is analyzed. The mapping relationship between the inflation pressure and the expansion of soft module is established. Besides, the inflation pressure required for the connection of adjacent two soft modules is obtained. Each soft module can expand 1.5 times under the working pressure of 30 kPa, and the docking and separation of two adjacent soft modules are realized by using the electromagnet connection and the expansion-contraction motion rules of soft modules. The self-reconfiguration of the modular soft robot can be realized by the sequential docking and separation of multiple adjacent modules. The feasibility of self-reconfiguration of soft robot is verified by the self-reconfiguration experiment.
Based on a hybrid excitation generator, a novel electric vehicle range-extender was proposed and the control system structure and the working principle were described. The multi-speed point working area was determined, according to the overall efficiency characteristics of the hybrid excitation range-extender. Based on the flexible adjustable characteristics of the air-gap magnetic field of the hybrid excitation generator, a double-closed-loop generation control algorithm was designed by decoupling the speed-power around the working area of the range-extender. The control strategy model was built by using MATLAB/Simulink and verified based on the prototype of the self-developed hybrid excitation range-extender. The test results show that the hybrid excitation range-extender has fast-dynamic response of output power and small steady-state error of speed and power control. Further, the steady-state and transient operating conditions are both located in the set working area. Therefore the power generation control strategy is feasible.
In the initial design stage of a semi-submersible platform, the main particulars of the platform are the key factor affecting the hydrodynamic performance and construction cost. Therefore, multi-objective optimization of the main particulars of the semi-submersible platform is of great engineering significance. First, the design variables of each platform and sample database are determined by design of experiments. Then, the hydrodynamic performances of the semi-submersible platform are analyzed by using the panel method and Morison’s equation. The distribution of probes for estimating the wave elevations on the calm water surface is arranged, and the airgap can be computed. Based on the database obtained by numerical simulation, the surrogate models based on radial basis function (RBF) are established. Next, the formal parameters in RBF are obtained by using the leave-one-out cross validation method. The surrogate model can greatly improve the optimization efficiency. Finally, by using the multi-objective particle swarm optimization (MOPSO) method, taking safety and economy of offshore platforms as two optimization objectives, and taking platform stability, airgap and horizontal motion performance as constraints, the optimization program for the semi-submersible platform can be obtained. Through the detailed analyses of the optimization program for the semi-submersible platform, the most efficient design strategy for the three-column semi-submersible platform is proposed.
Aimed at the wind-induced response and vibration reduction of an H-rotor type three-bladed vertical axis wind turbine (VAWT), and based on computational fluid dynamics (CFD) method, a numerical simulation is conducted to obtain the blade wind pressure distribution during the rotation period. Then, the wind pressure obtained is applied to the surface of the blades to analyze the wind vibration response of the VAWT. Dampers are arranged at different positions of the VAWT to simulate the vibration reduction capacity. The results show that applying the damper at the connection between the main shaft and the support rod of VAWT could reduce the displacement response of the structure to a certain extent and the maximum drop would reach 44%. Furthermore, the displacement reduction rate of the structure is related to the position of the damper. If a damper is arranged near the top end of the blade, the maximum displacement of the structure would occur at the bottom of the blade. However, if a damper is arranged near the bottom end of the blade, the maximum displacement of the structure would occur at the top of the blade and the maximum drop would reach 40.7%. The results would provide technical reference for research on the vibration reduction of VAWT structures.
To solve the problem that the natural frequency of deck arch bridges would decrease rapidly when the span increases, a novel arch bridge structure named deck V-arch bridge is proposed. The V-shaped members are added between the main girders and the arch ribs to increase the stiffness of the arch bridge, thereby increasing the natural frequency of the structure. Through the timely conversion of the structural system, the first-phase dead load is carried by the arch ribs, while the second-phase dead load and live load are carried by the variable height truss with main girders as upper chords, arch ribs as lower chords, and V-shaped members as webs, with multi-point elastic constraints. The entire structure has the advantages of arch and truss. In order to verify the correctness of the research and calculation of the dynamic characteristics of the deck V-arch bridge, a test bridge with a span of 10 m is built. The first natural frequency of the vertical bending in the plane of the bridge is tested by utilzing the pulsation test. The stiffness and dynamic characteristics are calculated by utilizing the finite element software. The influence of V-shaped member stiffness on the natural frequency and that of the number of V-shaped members on the temperature stress of the structure are analyzed. The necessity of system transformation is studied. The results show that the difference between test value and calculated value of the first natural frequency of vertical bending in the plane is small. The mode shape is in good agreement with the finite element analysis. With little or no additional material, the natural frequency of the structure is significantly increased, especially the in-plane frequency. The stiffness of the V-shaped members has a reasonable setting range. When the inner angles of the triangle formed by the main girders or the arch ribs are between 45 ℃ and 60 ℃,the number of V-shaped web members is suitable. The structural stiffness of deck arch bridge with V-shaped members is greatly improved, and at the position of L/4 (L is the span of the bridge), the upward deflection generated by the static live load of the train is approximately zero. After the transformation of the structural system, the deck V-arch bridge can fully exert the superiority of the arch force.
In order to simulate the impacts of carbon peaking and carbon neutrality goals on power system supply side transformation from, the system dynamics method is used to analyze the main influencing factors for carbon emissions in the process of power structure transformation and their correlations under four different development scenarios. The evolution of power generation structure and power carbon emission in four development paths are studied. The results show that the power system supply side transformation would be affected by many factors. Under the premise of policy support, the development of market absorption mechanism and absorption technology would contribute to the transformation of the power generation structure, which is of great significance to the realization of the dual carbon targets.
In the dynamic and discrete ship block manufacturing process, lack of effective process resource organization and transparency in product processing leads to the problem of high cost and low efficiency for managers to acquire knowledge. A method for dynamic generation and updating of knowledge graph based on processing beat data flow is proposed. The definition of the processing beat data information model is defined by analyzing the processing flow and the station data characteristics of the ship blocks. The graph mapping steps, models, and fusion connection algorithms are proposed for static resources and processing beat data to realize the semantic association of station dynamic time series data and knowledge graphs. Based on the relationship between station process and product structure, the generation of workshop-level dynamic knowledge graph is realized. Taking the production process of a ship block as an example, the knowledge graph visualization prototype system is designed, developed, and verified. The results show that the proposed method is beneficial to the organization, acquisition, and reuse of knowledge in the process of ship block manufacturing.
Aimed at the problem of the compressors of the low temperature air source heat pump system, this paper analyzes the impact of volume ratio on performance and proposes a novel three-cylinder two-stage variable volume ratio rotary compressor. The performance of the proposed compression system is compared with that of the traditional two-stage compression system of the same terminal in the experiments. The results show that the three-cylinder two-stage system operates in a stable manner with a coefficient of performance (COP) of 1.52 at a ambient temperature of -30 ℃,while the traditional two-stage system does not work. The COP of the three-cylinder two-stage system is always 1.25% to 12.41% higher than that of the traditional two-stage systems at any ambient temperature. When the ambient temperature is stable and the water supply temperature increases, the amount of dissipated heat at the terminal increases. At the same time, the maximum heat of external machine decreases, as well as the COP. When the ambient temperature is 7 ℃ and -25 ℃ respectively, and the water supply temperature changes from 40 ℃ to 55 ℃,the COP of the three-cylinder two-stage system is 1.15% to 8.86% and 4.32% to 7.33% higher than that of the traditional two-stage system, respectively. The power consumption of the three-cylinder two-stage system is always 3.78% to 16.67% lower than that of the traditional two-stage system.
Aimed at the uncertainty of equipment quantity and input, the defective products and its rework in the manufacturing process are investigated. Considering the influence of the number or input of the devices on system reliability, a manufacturing system reliability evaluation model is established based on stochastic flow network. The homogeneous Markov process is used to analyze the state of system degradation and maintenance. Considering the constraint of system reliability, a systematic maintenance model is proposed to minimize maintenance cost. The results of the numerical experiment demonstrate that the proposed model is effective and advanced.
In view of feature redundancy in the convolutional neural network, the concept of orthogonal vectors is introduced into features. Then, a method for orthogonal features extraction of convolutional neural network is proposed from the perspective of enhancing the differences between features. By building the structure of parallel branches and designing the orthogonal loss function, the convolution kernels can extract the orthogonal features, enrich the feature diversity, eliminate the feature redundancy, and improve the results of classification. The experiment results on one-dimensional sample dataset show that compared with the traditional convolution neural network, the proposed method can supervise the convolution kernels with different sizes to mine more comprehensive information of orthogonal features, which improves the efficiency of convolutional neural network and lays the foundation for subsequent researches on pattern recognition and compact neural network.
The preparation technology of micro-nano structure on copper surface is studied and optimized. Aqueous solution containing sodium carbonate and sodium molybdate is used as electrolyte, and the copper sample is anodized at a constant voltage to form a layer of oxidation on the copper surface. Then, the copper surface is treated with aqueous solution containing phosphate and sodium dihydrogen phosphate as corrosion solution to obtain a micro-nano structure on the copper. The surface is observed by using a scanning electron microscope. Finally, the analysis software is used to analyze the scanning electron microscope image to calculate the micro-nano structure pores on the copper surface. The results show that when the anodizing voltage is 15 V, the anodizing time is 20 min, the phosphoric acid mass fraction is 20%, and the corrosion time is 30 min, the copper surface is relatively smooth, and the porosity reaches 25.77%. Orthogonal experiments demonstrate that the type, concentration of the corrosive solution, and etching time have a great effect, while the anodizing electrolyte, voltage and electrolysis have no significant effect on the porosity. Using a combination of anodic oxidation and chemical corrosion, micro and nano junctions with uniform and high porosity can be prepared on the copper surface.
In order to overcome the disadvantage of vehicle ride comfort caused by large vibration and torque excitation of vehicle engine in start/stop mode, a flow mode magneto-rheological (MR) mount is designed for low frequency working conditions. Based on the analysis on the influence of exciting current on the viscosity of the MR fluid (MRF) and the relationship between the fluid resistance effect and the flow rate in the damping channel, the magnetic circuit and the damping performance of the MR mount model are analyzed. According to the mathematical model of the MR mount damping force, the multi-objective optimization function of the magnetic circuit is established. The co-simulation optimal platform is developed by using the Isight and ANSYS software. The non-dominated sorting genetic algorithm II (NSGA-II) is used to optimize magnetic circuit design. The dynamic performance test of the MR mount monomer and the vibration isolation performance test of the whole vehicle in start/stop mode are conducted respectively. The results show that the controllable damping force of the optimized MR mount increases by 111.71% and the restoring force increases by 21.99% compared with those before. When the vehicle is in start/stop mode and the excitation current is 1.0A, the peak vibration acceleration of the passive side (the side connected to the body) with the optimized MR mount decreases by 33.3% compared with that before. Besides, the peak vibration acceleration of driver’s seat rail decreases by 21.6%, which significantly improves the ride comfort of the vehicle.
In order to improve the aerodynamic performance and stability of the floating platform of an isolated vertical axis wind turbine, a novel structure design concept of the wind turbine with a coaxial counter-rotating vertical axis was proposed. Based on the computational fluid dynamics theory, a numerical simulation was conducted with the application of the Reynolds-averaged Navier-Stokes (RANS) shear stress transfer (SST) k-ω turbulence model, and combined with the eddy current theory, the aerodynamic performance and stability with different tip speed ratios (TSR) were further compared. The results show that in the same flow field, the floating platform of the counter-rotating wind turbine is more stable. When TSR<1.3, the long-time stall makes the de-vortex of the counter-rotating wind turbine more serious, and the wind energy utilization efficiency is lower. When TSR>1.3, the wind energy in outflow field is more absorbed by the rotor of the counter-rotating wind turbine. In addition, the length of remote vortex is shorter and the intensity is lower. Therefore, the wind energy utilization efficiency is higher. Coaxial counter-rotating has a certain reference value for the performance optimization of the vertical axis wind turbine.
The flow around a cylinder is a common research object of fluid mechanics. As the Reynolds number (Re) increases, the Kelvin-Helmholtz instability of the shear layer will occur in the wake behind the cylinder. Using the large eddy simulation method to investigate the problem numerically in a medium range of Re (Re=2000, 3900, 5000), the refined flow field behind the cylinder can be obtained, and an in-depth study of the instability of the shear layer can be conducted. To get the characteristic frequency of the shear layer instability, two methods, i.e., the traditional analysis of monitoring points and the dynamic mode decomposition method on the local flow field, are used. The results show that the frequencies obtained by the two methods are basically the same. However, compared with the traditional method, the dynamic mode decomposition method can overcome the random error caused by the artificial selection of monitoring points, and can give the characteristic frequency of shear layer instability more conveniently. In addition, it can further analyze the influence of different Re values on the instability characteristics of the shear layer based on different flow field modes.
Aimed at the problems of wide area distribution, resource dispersion, and inefficient aggregation of distributed energy storage, this paper proposes an aggregation model and evaluation method of distributed energy storage based on the adaptive equalization technology. First, this paper establishes an adaptive equalization function model based on dynamic characteristic parameters such as energy storage capacity, power, and state of charge. Then, based on the adaptive equalization function model, it establishes the aggregation model and evaluation method of distributed energy storage, which takes the power regulation rate, adaptive equalization rate, and capacity contribution rate as the dynamic parameters of aggregation degree. The example simulation verifies that the model can realize the fact that each energy storage unit can complete the aggregation from energy storage unit to energy storage aggregate with a smaller internal difference and a higher external aggregation rate. It can be applied to a large number of distributed energy storage aggregation participating in grid auxiliary services, and realize the efficient utilization of energy storage resources.
According to the current situation that the deflection angle of the piezoelectric ceramics direct drive fast steering mirror (FSM) is restricted by small elongation of piezoelectric ceramics and the large deflection angle cannot be realized, a novel piezoelectric FSM is designed. A two-stage lever-type amplification mechanism is adopted to realize the amplification of small displacement of piezoelectric ceramics and a strain gauge attached to the amplification mechanism is considered as the displacement sensor. The experimental results indicate that the deflection angle of the designed FSM is larger than 50 mrad, and the closed-loop linearity is below 0.5%, which satisfies the requirements of large deflection angle and accuracy pointing for light beam.
This paper analyzes the characteristics and influences of new pulse loads of ships. The surge-suppression power supply system for high power and multi-mode pulse loads, the capacitance and inductance calculation methods for the energy buffer unit are proposed, which can realize the power suppression, energy grouping, harmonic control and support backup, so that the safety of the ship power station and high precision power supply for loads can be ensured. The integrated power system simulation model of a new survey ship with high power radar loads is established in MATLAB/Simulink to verify the effectiveness of the surge-suppression power supply system at different modes. The system can not only reduce the impact of impulse load on the system, but also effectively suppress the system voltage harmonics, thus solving the key technical difficulties in the application of high power pulse loads to the independent power system.
Domestic and foreign relevant literatures of granular column collapse movement models are concluded to analyze the effects of initial spatial characteristics, essential physical properties of particles, boundaries, and environment conditions of model on the movement and accumulation characteristics of granular columns. Besides, the related mechanisms of movement and accumulation characteristics of granular columns are also analyzed. Remarkable linear and power relationships exist between the movement distance and the aspect ratios of initial height to initial width. Similarly, remarkable linear and power relationships exist between accumulation height and aspect ratio of initial height to initial width. The movement patterns and energy consumption mechanisms for granular columns with large aspect ratios are significantly different from those with small aspect ratios. A consensus has basically been reached concerning the effect of particle size, particle stiffness, particle breakage, and wet particles on the movement and accumulation characteristics of granular columns. Some preliminary research achievements of the effects of different wall constraints, fluidization phenomenon due to the gas mixing and water condition on the movement and accumulation characteristics of granular column are obtained. However, there still exist disagreements in the conclusions about the influences of initial porosity of granular column, particle friction, and wall friction on the movement and accumulation characteristics of granular column. A review of the current research indicate that the research in the future will be focused on the relationship between forces acted on particles and movement regimes. The mechanisms of the effect of complex particle shape, surface, particle density, and water movement conditions on the movement and accumulation characteristics of granular column collapse will also be focused on in the future.
A carbon dioxide(CO2) ejector expansion air conditioning system for vehicles is developed in a calorimeter laboratory. In experimental tests on a standard mobile air conditioning bench, the effects of different operating parameters on the performance of the CO2 refrigeration system for vehicles are studied, and the performance advantages of the CO2 ejector expansion refrigeration system are comparatively analyzed. The research results show that the cooling capacity of the CO2 ejector expansion refrigeration system for vehicles is almost equal to that of the CO2 conventional cooling system. Both increasing the indoor air flow rate and increasing the compressor speed can effectively increase the cooling capacity of the CO2 ejector expansion refrigeration system, and the ejector can increase the coefficient of performance (COP) of the system by 1.65% to 12.60% under different working conditions. The outdoor temperature has a great impact on the CO2 ejector expansion refrigeration system performance, and the performance of CO2 ejector expansion refrigeration system for vehicles decays obviously at a high ambient temperature.
In order to solve the problems of tool wear measurement in actual production, such as manual operation and shutdown detection, a machining tool wear measurement system based on machine vision is developed in this paper. First, the Otsu segmentation algorithm based on Laplacian edge information is proposed to binarize the images. Then, through rough positioning by morphology-based Canny operator edge detection and image registration, the tool wear area is extracted effectively. Finally, sub-pixel edge detection based on Zernike moment is used to improve the measurement accuracy while the principal curve method is used to fit sub-pixel edge points so as to obtain the smooth edge curve and realize the online measurement of tool wear. In real machining process, the tool wear test results show that the system has a high degree of automation and a rapid running speed. Moreover, its measurement accuracy can reach micron level. This system can be effectively applied to real-time monitoring of tool wear in industry.
Existing decision-making methods for intelligent vehicles do not consider factors such as the right of way information, polite driving of the vehicle, and limited perception range of the vehicle, which may easily lead to safety hazards in merging scenarios. Aimed at these problems, a Stackelberg-game-based decision-making method is proposed. This method constructs a game model combining the right of way and conducts parametric modeling of the merging scenarios. Then, the cooperation factor is introduced to design the corresponding profit function. Finally, the vehicle decision-making solution framework is designed to achieve the maximum value of decision-making benefits in this scenario. The experimental results illustrate that the proposed method can effectively improve the accuracy of vehicle decision-making behavior prediction on the datasets and the decision-making robustness in a high traffic density environment.
Taking the compressor impeller as the research object, and based on the octagonal truss lattice structure, a novel lightweight lattice compressor impeller is designed, and its machinability is verified by using a SLM280 3D printer. In order to understand its 3D printing performance, the 3D printing process of the lattice impeller is simulated based on the finite element method (FEM). Based on the feasibility of using the numerical method to study the 3D printing process, the printing process of the lattice impeller at different power values is analyzed and compared with the solid compressor impeller under the same working condition. The results show that the layer deformation of the lattice impeller and the solid impeller is a process that increases layer by layer. Under the 7 working conditions studied in this paper, the maximum residual deformation and residual stress of the lattice impeller after printing are less than those of the solid impeller. The maximum residual deformation of the lattice impeller can be 20.19% smaller than that of the solid impeller, and the maximum residual stress can be 10.69% smaller than that of the solid impeller. This means that the lattice impeller is not only lighter, but also has a better printing performance than the solid impeller.
Aimed at the problem of instability and deviation of multiple training model in limited samples, this paper proposes a method of distance metric learning based on the Gaussian mixture model, which can solve this problem more reasonably by dividing the dataset. Distance metric learning relies on the excellent feature extraction capabilities of deep neural networks to embed the original data into the new metric space. Then, based on the deep features, the Gaussian mixture model is used to cluster the analyzer and estimate the sample distribution in this new metric space. Finally, according to the characteristics of sample distribution, stratified sampling is used to reasonably divide the data. The research shows that the method proposed can better understand the characteristics of data distribution and obtain a more reasonable data division, thereby improving the accuracy and generalization of the model.
Special collection of 12 issues of "Journal of Shanghai Jiaotong University" in 2021 
