To analyze the crack development characteristics of different reinforced concrete beams in bending, explore the acoustic emission characteristics of beam damage and the degradation of flexural performance, a four-point bending test is used in conjunction with acoustic emission technology. This is employed to establish a link between the bending process and the aforementioned characteristics. The objective is to analyze the acoustic emission signals generated by the three different reinforced concrete beams, namely those with suitable reinforcement, less reinforcement, and over reinforcement, and to simulate and validate the bending process of different reinforced concrete beams using the numerical analysis of the finite element method of Abaqus. The RA-AF signals during the damage evolution process of reinforced concrete beams with different reinforcement ratios show significant differences. The proportion of shear crack signals (RA) in beams with appropriate reinforcement, low reinforcement, and excessive reinforcement is 77.6
In the context of electricity and carbon markets, with the in-depth research of virtual power plants and to realize the mutual assistance of electric energy in different regions within the same distribution network, a scheduling strategy of virtual power plant alliance based on dynamic electricity and carbon pricing using the Master–Slave game is proposed. Firstly, an interactive framework of virtual power plant alliance is designed in which the alliance operator formulates the electricity and carbon prices, and each user entity formulates the operation plan according to the prices. Secondly, the information gap decision theory is adopted to handle the uncertainties on the source–load side. Based on the Master–Slave game and source–load interaction, an economic optimal dispatching model for the virtual power plant alliance is established. Finally, the particle swarm optimization algorithm nested with the CPLEX solver is used to solve the model, and the rationality and effectiveness of the proposed strategy are demonstrated through case analysis. The simulation results show that, after considering the electricity energy interaction and dynamic electricity–carbon pricing, the daily operation cost of the virtual power plant alliance was reduced by 47.7%, carbon emissions decreased by 24.6%, and comprehensive benefits increased by 77.2%.
In image inpainting, the identification and inpainting of local detail features and the preservation of global features are crucial. Models based on fractional-order partial differential equations exhibit rich evolutionary behaviors. These behaviors enable them to effectively comprehend image details. Additionally, these models possess a certain sharpening effect in image inpainting. However, they are also prone to issues such as inaccurate identification of large-scale features and over-sharpening. The optimal control model proposed in this paper uses the total variation energy of image global features as the objective function. It also employs the spatial fractional-order vector-valued Cahn–Hilliard equation as the constraint, aiming to achieve a balanced effect between local detail restoration and preservation of global features. The paper aims to optimize the objective function by designing numerical computation schemes for non-convex constraint conditions using L_2 gradient flow, H^-1 gradient flow, and convex splitting. The Split Bregman method is used for further optimization, and a dynamic grayscale adjustment strategy is introduced to maintain grayscale discrimination capability while enhancing computational efficiency. Numerical experiments demonstrate that the new model exhibits certain advantages over other inpainting methods in terms of PSNR values. It also shows strong competitiveness in terms of SSIM values, especially in severely damaged images where it demonstrates greater stability. Compared to traditional fractional-order equation models, the proposed model captures global features and incorporates the dynamic grayscale adjustment strategy, resulting in significantly reduced computation time.
In order to study the factors influencing low temperature radiant floor heating in hot summer and cold winter locales,the impacts of five variables-water supply temperature,inter-tube flow rate,tube diameter,tube spacing,and filler layer thickness-on the average temperature and uniformity of the floor surface were examined using the orthogonal test technique of L25(56).The research demonstrates:water supply temperature＞tube spacing＞tube diameter＞filler layer thickness＞inter-tube flow rate has the most influence on the floor surface's average temperature,while tube spacing＞water supply temperature＞filler layer thickness＞tube diameter＞inter-tube flow rate has the most influence on the floor surface's uniformity.The ideal level combination is water supply temperature is taken as 35 ℃,inter-tube flow rate is taken as 0.4 m/s,tube diameter is taken as 16 mm,tube spacing is taken as the test's findings serve as a guide for designing radiant heating systems for use in hot summer and cold winter climates.They also offer suggestions on how to combine statistics and numerical analytic tools.
Aiming at the power coupling problem caused by the high impedance ratio between the distributed energy(DG)controlled by the virtual synchronous generator(VSG)and the power grid.In this paper,the power coupling mechanism is analyzed,and the coupling relationship between power and impedance ratio and power angle is established by relative static gain coefficient.A virtual power control technology based on virtual negative impedance is proposed.The virtual impedance is used to calculate the virtual voltage,and the obtained virtual voltage is fed back to the power calculation part to form the virtual power to realize power decoupling.Based on the constraint con-dition of coupling degree,the specific solution method of virtual resistance and inductance is given.Finally,the simulation model is built by using MATLAB/Simulink simulation software.By comparing with the traditional virtual impedance technology,it is verified that the proposed virtual power control strategy has a significant effect on weakening the coupling between the active and reactive power loops.
With the continuous improvement of urbanization level and the increasing urban spatial density,the urban heat island effect is intensifying,resulting in the increasing range and intensity of extreme climate,which poses a threat to the physical and mental health of urban residents.Urban heat island effect has become a focus of multidisciplinary research.The technical development of ex-isting methods for studying heat island effect at different scales was reviewed,their advantages and disadvantages,and their applicabili-ty at different scales were analyzed.In addition,the influence factors of heat island effect on street and valley scale were analyzed.Fi-nally,according to the existing problems in the current research process,the research progress and development trend of urban heat is-land at street valley scale were proposed from the aspects of data and evaluation methods.The research results provide a reference for the in-depth study of urban street valley heat island effect under the background of compact city development model,and to provide an effective way to mitigate urban heat island effect.
In order to explore the characteristics of executive function-related brain networks in male college students engaged in long-term aerobic exercise and compare them with a sedentary control group.The brain structural and resting-state functional magnetic resonance imaging(fMRI)data from 15 male college students regularly engaged in aerobic exercise and 15 male college students in the sedentary control group were collected.degree centrality(DC)values were then calculated and extracted within 14 executive function-related brain regions.In addition,four executive function-related brain networks were extracted by means of independent component analysis(ICA).The intergroup comparison revealed that:The aerobic exercise group shows significantly higher DC in the bilateral PCC and HC than the sedentary group.In contrast,the sedentary group shows significantly higher DC in the rIPL than the aerobic exercise group(p＜0.05).The aerobic exercise group shows stronger activation in the DMN,DAN,and VAN within the inferior temporal gyrus,temporal pole,and ventral posterior cingulate gyrus,respectively.While the sedentary group shows stronger activation in VAN within the area of the putamen(AlphaSim corrected,p＜0.001).The research results indicate that:executive function-related brain networks are more active in male college students who engaged in long-term regular aerobic exercises,among which the HC and post-PCC played a crucial role.
Large industrial producers consume a huge amount of energy and are a key area of focus for achieving peak carbon and carbon neutrality targets.Based on the operational monitoring of the traditional energy supply system and the transformed PV(photovoltaic)-biogas-gas-electricity system,and the user-side load characteristics and typical daily cooling,heating and the electricity power load scheduling characteristics were analyzed.The annual energy supply data and important energy supply influencing factors of the PV-biogas system were studied,and an evaluation index system for the comprehensive energy system was built,and five factors:PV-biogas system operation status,economic benefits,energy supply quality,environmental benefits and energy efficiency were collaboratively considered,and the comprehensive performance of traditional energy supply system and PV-biogas-gas-electricity system were evaluated.The results show these as follows.Large industrial production enterprises with mainly thermal requirement,typical daily load peaks occur at 09:00-11:00 or 14:00-17:00,the PV-biogas-gas-electricity multi-energy system can meet the cooling,heating and electricity needs of industrial enterprises.The average penetration rate of PV system is less than 10％during non-force majeure hours,and the proportion of gas storage capacity of the biogas system is higher than 50％during normal hours,and the system supply reliability is improved.The evaluation results of a typical case verify the feasibility and validity of the evaluation index system of PV-biogas-gas-electricity multi-energy system,and provide a reference for the comprehensive utilization and coordinated development of PV-biogas-gas-electricity multi-energy system for large industrial enterprises in the future.
To relieve the impact caused by different arrival times of multiple users in the uplink MIMO-SCMA system, a grouped multiuser transmission model, according to different delays and its corresponding detection scheme, is proposed in this paper. Assuming perfect synchronization in one group, a message-passing algorithm based on serial propagation (SP-MPA) is proposed to reduce the bit error rate (BER), which could transfer the updated information to the next symbol as its initial probability. Furthermore, with a more practical case, the partial Gaussian approximation method (PGA) is designed to decrease the interference resulting from the imperfect synchronization in one group. As the result, the computing complexity of the proposed PGA method could be decreased by at least 20% compared with SP-MPA and the BER could be improved by about 10%.
In order to mitigate the impact of DG(distributed generation)integration into distribution networks on grid safety and economic operation,it is necessary to optimize the planning of DG.Considering the uncertainties and correlations of wind and photovoltaic outputs,a bi-level planning model was established.The upper-level planning model was formulated with the objective function of minimizing the comprehensive costs,which included investment costs of DG,operation and maintenance expenses,active distribution network electricity purchase costs,micro gas turbine fuel costs,pollution control costs,and network loss costs.The lower-level operational model aimed to minimize the annual comprehensive operational costs for each scenario,subjected to constraints such as power balance,node voltage,branch capacity,and DG penetration rate.To address the issues of slow convergence speed and local optima in the whale optimization algorithm,three improvements were introduced:tent mapping,the incorporation of inertia weight,and a nonlinear convergence factor.The proposed improvements were validated through simulation case studies.The results demonstrate that the improved method enhances the performance of the whale optimization algorithm,thereby providing a more effective solution to the proposed model.
Liquid hydrogen storage has the advantages of high storage density per unit volume,low cost for long-distance transporta-tion,etc.However,in the liquid hydrogen storage,the hydrogen gas is liquefied at-253℃ and then is stored in the cryogenic insu-lation vessel,which requires extremely high cryogenic insulation performance.The passive and active thermal insulation technologies for liquid hydrogen storage were focused on,and the current research progress of cryogenic insulation technology for liquid hydrogen storage was reviewed through literature.The principles of different insulation technologies were introduced and the application scopes of different insulation technologies were compared.Meanwhile,the bottleneck problems and shortcomings of the current insulation tech-nologies were analyzed in derail,and the future development trends of each insulation technology were summarized.This study can pro-vide valuable reference for the development of cryogenic insulation technology for liquid hydrogen storage.
A new encod-decoder steganography scheme based on dense residual connection was proposed to solve the problem of poor quality of encod-decoder images and message images generated by image steganography schemes based on encoder-decoder net-works.Different from the existing end-to-end image steganography networks,the proposed scheme does not need to preprocess the im-age,and adopts dense residual connections to transport the features of the shallow network to each layer of the deep network structure,effectively preserving the details of the feature map,and uses channels and spatial attention modules to filter the features,improving the codec's attention to the complex texture region of the image.Experimental results on LFW,PASCAL-VOC12 and ImageNet datasets show that the proposed method can effectively improve image quality under the premise of ensuring the security of the algorithm,inclu-ding the peak signal-to-noise ratio of dense images and carrier images.The mean values of peak signal-to-noise ratio(PSNR)and struc-tural similarity(SSIM)are 36.2 dB and 0.98 respectively.
To address the issues of buffeting and slow response in the sliding mode control system of surface mounted permanent magnet synchronous motors,a control strategy combining a novel sliding mode controller with an improved sliding mode observer was proposed.A new approach law was introduced,utilizing the tanh function as the switching function to ensure smoother transitions.Fur-thermore,the incorporation of a terminal attractor was aimed at enhancing system convergence speed and improving control quality.An improved sliding mode observer was designed to replace the symbol function with a continuous function,compensating for observation errors,reducing buffeting effects,and enhancing observation accuracy.A simulation model of a double closed-loop speed control sys-tem based on the new sliding mode controller and improved sliding mode observer was established,and comparative simulation experi-ments were conducted with a proportional-integral control system and a sliding mode control system based on an exponential reaching law.The simulation results indicate that the proposed control strategy effectively reduces buffeting effects,improves system conver-gence,and enhances the ability to track desired signals.
To study the crack propagation,local buckling and vertical shear strength of partially filled narrow steel box-UHPC composite beam in negative moment zone,reverse forward loadings were taken to five composite beams with different UHPC layer thicknesses and infilled HSC heights,and the failure process,load-crack width curve,load-midspan deflection curve,midspan strain distribution,and web stress-strain curves were obtained and analyzed.The results show that compared to the specimens with NC flange,the shear bearing capacity of the specimens with half-UHPC flange and full-UHPC flange has not been significantly increased,only increased by 8.3％and 11.6％.Compared to the unfilled specimens,the shear bearing capacity of the half-infilled and full-infilled specimens has been increased by 61.7％and 87.2％,respectively.After replacing part NC with UHPC of the flange,the specimens can effectively control crack development,and infilled HSC in the steel box can effectively reduce local buckling of the web.The calculation method for the shear bearing capacity of composite beams was established with the subitem superposition method,considering the contribution of flange,steel girder,and infilled HSC to the shear bearing capacity,and it can accurately predict the shear bearing capacity of count beams.
With the increasing proportion of new energy in the power grid, it is difficult to meet the high-quality frequency modulation requirements of the power system only relying on traditional thermal power units. This paper proposes a capacity allocation method of the wind-photovoltaic-hydropower-thermal multi-source frequency modulation system based on the Stackelberg game theory. Firstly, the thermal power unit is taken as the upper leader and the wind-light-water unit is taken as the lower follower. Secondly, by solving the Stackelberg game model, the optimal frequency regulation capacity of each unit participating in frequency regulation is obtained. Then, the improved Shapley value method is used to optimize the revenue of the wind-light-water unit. Finally, the effectiveness of the proposed method is verified. The Stackelberg game multi-source frequency modulation model can make the frequency modulation capacity allocation more reasonable and give consideration to the revenue.
The temperature and flow rate of coolant in the hot legs of pressurized water reactor(PWR)systems directly reflect the nu-clear power and the heat transfer state of the reactor core,and are key parameters for reactor power control and safety protection.In order to comprehensively understand the distribution and evolution of the coolant flow-thermal coupling field in the upper plenum and hot leg of the Hualong One,and provide references for the measurement and control of core parameters,FEA(finite element analysis)method was employed in this paper to conduct CFD(computational fluid dynamics)numerical simulations of the coolant flow region in the upper ple-num and hot legs.Firstly,a reasonably simplified 3D geometrical model of the upper plenum and hot legs of the Hualong One was estab-lished.Subsequently,the computational domain of the model was discretized into meshes and a mesh sensitivity analysis was performed.Fi-nally,through calculations,a steady-state solution of non-isothermal coolant flow was obtained,with relative errors between flow rate,tem-perature and related design estimates and actual measured values all less than 2％.Analysis of the steady-state characteristics indicates that an uneven coolant temperature distribution at the inlet of the hot legs is caused by insufficient heat exchange between high and low tempera-ture coolants near the vertical inner wall of the upper plenum,with a temperature difference between 14.0 ℃ and 16.3 ℃.As the coolant flows along the axial direction,both the temperature and flow distribution gradually become uniform and stable.Furthermore,the variation of the coolant temperature distribution is dominated by the flow of the low temperature coolant inside the hot legs.
Indoor temperature and relative humidity control in office buildings is crucial, which can affect thermal comfort, work efficiency, and even health of the occupants. In China, fan coil units (FCUs) are widely used as air-conditioning equipment in office buildings. Currently, conventional FCU control methods often ignore the impact of indoor relative humidity on building occupants by focusing only on indoor temperature as a single control object. This study used FCUs with a fresh-air system in an office building in Beijing as the research object and proposed a deep reinforcement learning (RL) control algorithm to adjust the air supply volume for the FCUs. To improve the joint control satisfaction rate of indoor temperature and relative humidity, the proposed RL algorithm adopted the deep Q-network algorithm. To train the RL algorithm, a detailed simulation environment model was established in the Transient System Simulation Tool (TRNSYS), including a building model and FCUs with a fresh-air system model. The simulation environment model can interact with the RL agent in real time through a self-developed TRNSYS–Python co-simulation platform. The RL algorithm was trained, tested, and evaluated based on the simulation environment model. The results indicate that compared with the traditional on/off and rule-based controllers, the RL algorithm proposed in this study can increase the joint control satisfaction rate of indoor temperature and relative humidity by 12.66% and 9.5%, respectively. This study provides preliminary direction for a deep reinforcement learning control strategy for indoor temperature and relative humidity in office building heating, ventilation, and air-conditioning (HVAC) systems.
As the last link of the power system, the distribution network is responsible for ensuring stable power consumption and improving power quality. Therefore, a more reliable and fast fault section location(FSL) method is essential for the stable operation and optimization of distribution networks. In this context, this paper adopts an effective method to apply the quantum annealing algorithm(QA) based on the quantum tunneling mechanism to the distribution network fault section location problem. A quantum Hamiltonian function consisting of potential and kinetic energy terms is constructed based on the theoretical knowledge of QA. Among them, FSL objective function is mapped to the potential energy term, and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect and approximate or even reach the global optimal solution. Based on the quantum Hamiltonian function construction, this paper modifies some parameters in the QA framework to propose an improved quantum annealing algorithm(IQA) to improve the accuracy. In the two test systems of IEEE 33-node distribution network and IEEE 33-node distribution network with distributed generation sources(DGs), QA and IQA are compared and analyzed with other intelligent algorithms using the average number of iterations and localization accuracy as indicators. We find that QA is more likely to obtain the global optimal solution compared with the simulated annealing algorithm(SA). IQA can search for faulty sections with 100% accuracy and the least number of average iterations in both single power distribution networks and distribution networks containing DGs. Under the scenarios of fault signal distortion and increasing fault sections, IQA shows superb competitive advantages by exhibiting good fault tolerance performance, global optimal search capability and stability.
The PH value of slurry in the absorber is an important parameter affecting the efficiency of wet flue gas desulfurization system in coal-fired power plants. The wet flue gas desulfurization system of coal-fired power plant has the characteristics of large lag, nonlinear and strong coupling. It is difficult to accurately control the PH value of the slurry in the absorption tower. In this paper, the advantages of gated recurrent unit (GRU) neural network in processing time series data are used to predict the PH value of slurry in the absorber. Firstly, the data collected in coal-fired power plants were analyzed to screen out the variables with strong correlation with the PH value of grout. Then, the time series data of these variables are used as the input of the model to train the model, and the prediction model of the slurry PH value in the absorption tower in the wet desulfurization system is obtained. Finally, the real data are collected to test the model. The results show that compared with BP neural network model, radial basis function neural network (RBF), recurrent neural network (RNN) and long short-term memory (LSTM) neural network, the proposed model is more accurate and practical.
