Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,m...Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.展开更多
Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the opt...Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.展开更多
Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness i...Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.展开更多
Steam assisted gravity drainage （SAGD） technology has been industrialized popularization and application in our country, according to the characteristics of Xing group I SAGD experimental zone in liaohe oilfield, SA...Steam assisted gravity drainage （SAGD） technology has been industrialized popularization and application in our country, according to the characteristics of Xing group I SAGD experimental zone in liaohe oilfield, SAGD production stage injection-production parameters such as the operating pressure, Sub - Cool control, steam injection rate, steam dryness, production factor are studied and selected.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of mul...In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.展开更多
The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches...The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.展开更多
Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization me...Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.展开更多
Tunnel boring machine(TBM)rock breaking parameter optimization is a technical challenge in underground engineering.Traditional numerical simulation methods have limitations in computational efficiency and accuracy whe...Tunnel boring machine(TBM)rock breaking parameter optimization is a technical challenge in underground engineering.Traditional numerical simulation methods have limitations in computational efficiency and accuracy when dealing with multi-parameter coupling optimization under complex geological conditions.This study proposes a deep learning method based on the Ada-Attention mechanism for predicting and optimizing parameters such as confining pressure,penetration depth,and cutting tool spacing in TBM rock breaking processes.The method employs a hybrid attention architecture that combines global window mechanisms with local sliding windows,reducing the computational complexity of traditional self-attention mechanisms from O(n^(2))to O(n(w+α)).Additionally,the Newton-Gauss optimization algorithm is introduced to improve the softmax normalization process,enhancing numerical stability and convergence performance.The research constructs a prediction framework covering a temperature range from 25℃to 500℃,using 800 experimental samples for model training and validation.Experimental results show that the Ada-Attention model achieves R^(2)values of 0.92,0.93,and 0.94 for torque,rolling force,and specific energy predictions respectively,obtaining 2-10 times computational speedup compared to traditional Transformer architectures.Generalization capability validation demonstrates that the model exhibits high prediction accuracy in soft sedimentary rock and medium sandstone(R^(2)>0.95),maintains moderate performance levels in hard limestone and crystalline rock(R^(2)=0.80-0.90),while prediction accuracy decreases in complex geological environments such as composite formations and fractured rock masses.This method provides a feasible technical solution for TBM parameter optimization under complex geological conditions.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr...This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.展开更多
To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engi...To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.展开更多
The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sic...The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).展开更多
For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter inte...For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application.A self-governing database capable of automatic capture,storage,calls and analysis is established.With this database,22 geological and engineering variables are selected for correlation analysis.A separated fracturing effect prediction model is proposed,with the fracturing learning curve decomposed into two parts:(1)overall trend,which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance;and(2)local fluctuation,which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight.A policy gradient-genetic-particle swarm algorithm is designed,which can adaptively adjust the inertia weights and learning factors in the iterative process,significantly improving the optimization ability of the optimization strategy.The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters.The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells,and it has good practicability.展开更多
Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To addres...Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To address this limitation,we proposed a split-core sensor structure comprising four magnetic core strips,which achieved non-intrusive current measurement while maintaining detection accuracy.An analytical model of the induced electromotive force was established based on the probe’s geometric configuration,followed by finite element simulations to optimize key parameters including core radius,core width,excitation coil turns,and sensing coil configuration.A complete prototype integrating the measurement probe,excitation circuit,and signal processing circuitry was developed and experimentally validated.The experimental results show a sensitivity of 0.1099 V/A,a hysteresis error of 0.559%,and a repeatability error of 1.574%over a measurement range of±10 A.After polynomial fitting-based error compensation,the nonlinearity error was reduced to 0.208%,achieving performance comparable to closed-core sensors.This work provided a practical solution for applications demanding both high measurement accuracy and installation flexibility.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金The Natural Science Research Project of Anhui Educational Committee(No.2022AH050814)Open Fund of State Key Laboratory of Nuclear Resources and Environment(East China Universityof Technology)(No.2022NRE07)+1 种基金the National Natural Science Foundation of China(No.51964002,52174104)Open Fund of Engineering Research Center of Underground Mine Construction of Ministry of Education(No.JYBGCZX2022105).
文摘Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.
基金funded by the National Natural Science Foundation of China(Grand No.52325904)National Key Research and Development Program of China(Grant No.2023YFB2390200)the National Natural Science Foundation of China(Grant No.52309134).
文摘Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.
基金supported by the Leading Talent of the Qinghai Province Kunlun Talents Programme・High-Level Innovative and Entrepreneurial Talents(QHKLYC-GDCXCY-2024-359).
文摘Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.
文摘Steam assisted gravity drainage （SAGD） technology has been industrialized popularization and application in our country, according to the characteristics of Xing group I SAGD experimental zone in liaohe oilfield, SAGD production stage injection-production parameters such as the operating pressure, Sub - Cool control, steam injection rate, steam dryness, production factor are studied and selected.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the Changsha Major Science and Technology Plan Project,China(No.kq2207002)the Natural Science Foundation of Hunan Province(No.2023JJ40720)the Postgraduate Innovative Project of Central South University,China(No.2022XQLH058)。
文摘In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.
基金Project supported by the National Natural Science Foundation of China (Grant No. U22B2095)the Civil Aerospace Technology Research Project (Grant No. D010103)。
文摘The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.
基金financially supported by Sichuan Science and Technology Program(No.2025ZNSFSC0373)National Natural Science foundation of China(Grant No.52104006)Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance(Grant No.2020CX040202)。
文摘Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.
基金funded by the Natural Science Foundation of Shanghai of China(23ZR1443600).
文摘Tunnel boring machine(TBM)rock breaking parameter optimization is a technical challenge in underground engineering.Traditional numerical simulation methods have limitations in computational efficiency and accuracy when dealing with multi-parameter coupling optimization under complex geological conditions.This study proposes a deep learning method based on the Ada-Attention mechanism for predicting and optimizing parameters such as confining pressure,penetration depth,and cutting tool spacing in TBM rock breaking processes.The method employs a hybrid attention architecture that combines global window mechanisms with local sliding windows,reducing the computational complexity of traditional self-attention mechanisms from O(n^(2))to O(n(w+α)).Additionally,the Newton-Gauss optimization algorithm is introduced to improve the softmax normalization process,enhancing numerical stability and convergence performance.The research constructs a prediction framework covering a temperature range from 25℃to 500℃,using 800 experimental samples for model training and validation.Experimental results show that the Ada-Attention model achieves R^(2)values of 0.92,0.93,and 0.94 for torque,rolling force,and specific energy predictions respectively,obtaining 2-10 times computational speedup compared to traditional Transformer architectures.Generalization capability validation demonstrates that the model exhibits high prediction accuracy in soft sedimentary rock and medium sandstone(R^(2)>0.95),maintains moderate performance levels in hard limestone and crystalline rock(R^(2)=0.80-0.90),while prediction accuracy decreases in complex geological environments such as composite formations and fractured rock masses.This method provides a feasible technical solution for TBM parameter optimization under complex geological conditions.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401501,KJZD-M202401501).
文摘This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
基金supported by the National Key Research and Development Program of China(No.2022YFB3706704)the Academician Special Science Research Project of CCCC(No.YSZX-03-2022-01-B).
文摘To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.
基金Supported by the General Program of the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA(52374004)National Key Research and Development Program(2023YFF06141022023YFE0110900)。
文摘The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).
基金Supported by the National Science and Technology Major Project(2017ZX05009-005-003)National Natural Science Grant Fund for Surface Project(52174045)+1 种基金Chinese Academy of Engineering Strategic Consulting Project(2018-XZ-09)China National Petroleum Corporation(CNPC)-China University of Petroleum(Beijing)Special Project for Strategic Cooperation in Science and Technology(ZLZX2020-01)。
文摘For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application.A self-governing database capable of automatic capture,storage,calls and analysis is established.With this database,22 geological and engineering variables are selected for correlation analysis.A separated fracturing effect prediction model is proposed,with the fracturing learning curve decomposed into two parts:(1)overall trend,which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance;and(2)local fluctuation,which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight.A policy gradient-genetic-particle swarm algorithm is designed,which can adaptively adjust the inertia weights and learning factors in the iterative process,significantly improving the optimization ability of the optimization strategy.The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters.The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells,and it has good practicability.
基金supported by Yunnan Fundamental Research Projects(No.202301AT070181)Yunnan Fundamental Research Projects(No.202401CF070126)+1 种基金Xingdian Talent Support Program of Yunnan Province(No.KKRD202203070)Yunnan High level Science and Technology Talents and Innovation Team Selection Special Project(No.202405AS350001).
文摘Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To address this limitation,we proposed a split-core sensor structure comprising four magnetic core strips,which achieved non-intrusive current measurement while maintaining detection accuracy.An analytical model of the induced electromotive force was established based on the probe’s geometric configuration,followed by finite element simulations to optimize key parameters including core radius,core width,excitation coil turns,and sensing coil configuration.A complete prototype integrating the measurement probe,excitation circuit,and signal processing circuitry was developed and experimentally validated.The experimental results show a sensitivity of 0.1099 V/A,a hysteresis error of 0.559%,and a repeatability error of 1.574%over a measurement range of±10 A.After polynomial fitting-based error compensation,the nonlinearity error was reduced to 0.208%,achieving performance comparable to closed-core sensors.This work provided a practical solution for applications demanding both high measurement accuracy and installation flexibility.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
