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Optimization of multi-process parameters in secondary cooling solidification process of S30432 continuous casting billet 认领 引用
1
作者 Zhi-qiang Li Ying-xuan Shan +2 位作者 Li Wu Hua Hou Yu-hong Zhao 《China Foundry》 SCIE EI CAS CSCD 2026年第3期407-420,共14页
The synergistic mechanism of multiple process parameters on the solidification structure of niobium containing austenitic stainless steel during continuous casting is complex,which seriously affects the quality of con... The synergistic mechanism of multiple process parameters on the solidification structure of niobium containing austenitic stainless steel during continuous casting is complex,which seriously affects the quality of continuous casting billets and seamless pipes.In order to optimize the quality of continuous casting billet,a finite element model of solidification and heat transfer in continuous casting process was established for the secondary cooling process of continuous casting billet.The control variable method was used to explore the influence of casting speed and superheat on the solidification process.At the same time,an orthogonal scheme was designed to study the coupling effect of multiple process parameters on the heat transfer and solidification state of continuous casting billets,and optimized process parameters were selected.The optimization results of process parameters were verified through production experiments,and it is found that the enrichment of coarse niobium compounds directly causes the initiation and propagation of inner wall cracks during the large deformation hot piercing of S30432 seamless tubes.Process parameter optimization,especially the synergistic effect of the decrease of superheat and increase of specific water flow promotes the grain refinement and expension of equiaxed crystal zone,thereby mitigating the segregation of Nb elements and improving the distribution of niobium compounds. 展开更多
关键词 continuous casting solidification process process parameter optimization niobium compounds crack
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Dimensional control of turbine blades via RSM-based process parameter optimization in investment casting 认领 引用
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作者 Sheng-jie Ren Rui-yuan Zhang +4 位作者 Sheng Meng Hang-yu Li Wen-jing Wang Zhong-min Xiao Kun Bu 《China Foundry》 SCIE EI CAS CSCD 2026年第3期451-461,共11页
To address the dimensional accuracy challenges in investment casting of DD6 nickel-based superalloy hollow turbine blades,a multi-parameter collaborative optimization and deformation response prediction method based o... To address the dimensional accuracy challenges in investment casting of DD6 nickel-based superalloy hollow turbine blades,a multi-parameter collaborative optimization and deformation response prediction method based on response surface methodology was proposed.Using a Box-Behnken design,with pouring temperature,shell temperature,and withdrawal rate as key variables,deformation response data were obtained through numerical simulation,and a second-order model incorporating linear,interaction,and quadratic terms was established to characterize the nonlinear coupling effects of process parameters on dimensional deformation.The results indicate that withdrawal rate is the dominant factor influencing deformation,while shell temperature exhibits a pronounced“U”-shaped nonlinear trend.Significant interactions between process parameters are also observed.The constructed model demonstrates high predictive accuracy,with R2 of 0.978 and an RMSE of 0.0026 mm,and exhibits strong generalization capability,enabling the identification of optimal parameter combinations even beyond the simulated dataset.Compared with conventional orthogonal design methods,the maximum deformation of the optimized process was reduced from 0.2021 mm to 0.1905 mm,achieving an improvement of approximately 5.74%.This work provides a theoretical foundation and practical strategy for dimensional accuracy control and multi-parameter process optimization in the manufacturing of complex thin-walled castings. 展开更多
关键词 response surface method turbine blade process parameter optimization investment casting
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Optimization of smooth blasting parameters and damage mitigation in jointed rock mass roadways 认领 引用
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作者 ZUO Ting LI Xianglong +4 位作者 WANG Jianguo TIAN Xufan HUO Fei CUI Guangjiu ZHANG Xiyuan 《Journal of Mountain Science》 SCIE CSCD 2026年第4期1772-1788,共17页
During mine roadway excavation in jointed and fractured rock masses,drilling and blasting remains a widely adopted method.However,the complex interaction between blasting-induced stress waves and pre-existing structur... During mine roadway excavation in jointed and fractured rock masses,drilling and blasting remains a widely adopted method.However,the complex interaction between blasting-induced stress waves and pre-existing structural planes often leads to overbreak,loosening of the surrounding rock,and an expanded excavation damage zone,posing significant challenges to roadway stability and construction safety.Most existing studies are limited to single-factor analyses or assume homogeneous rock mass behavior,leaving a critical gap in understanding the coupled effects of joint geometric parameters and blasting parameters on damage evolution.This study addresses this gap by developing a numerical model using LSDYNA to investigate blast damage control in jointed rock masses during roadway excavation.A systematic parametric analysis was conducted to evaluate the influence of joint dip angle(α),joint thickness(h),joint position,and blast-hole spacing(d)on blasting performance.The results show that atα=45°,particle vibration velocity at the monitoring points reaches its maximum,and fragmentation is most pronounced along the blast-hole connection line.Reducing the blast-hole spacing to 60 cm increases the peak effective stress at the joint plane to 72.8 MPa,yielding optimal fragmentation while mitigating excessive rock damage commonly associated with larger spacings.As joint thickness increases from 4 cm to 8 cm,the peak effective stress at the joint plane drops from 94.7 MPa to 70.8 MPa.This decrease of approximately 33.7%indicates that thicker joints substantially enhance stress-wave attenuation and energy dissipation.Moreover,increasing the distance between the joint and the blast hole from 5 cm to 15 cm significantly reduces damage in the rock mass between the source and the joint plane.Field validation demonstrates that the optimized smooth blasting scheme,compared to conventional blasting,improves the half-hole rate from 33.3%to 93.3%,increases the average advance per cycle from 2.43 m to 2.92 m,and reduces the depth of blast-induced damage from approximately 2.4 m to 1.5 m.These findings confirm that the proposed blasting parameters markedly enhance excavation quality and effectively limit blast-induced damage in jointed rock masses. 展开更多
关键词 Jointed rock mass Numerical simulation Smooth blasting Parameter optimization Rock mass damage
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Motion Strategy and Control Parameters Optimization for an Underwater Glider Based on 3D Dubins Paths 认领 引用
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作者 HOU Dong-bo CAO Wei +1 位作者 WANG Wen-jun WANG Cong 《China Ocean Engineering》 SCIE EI CSCD 2026年第3期604-618,共15页
This research examines the optimization of motion strategy and control parameters for an Autonomous Underwater Glider(AUG)navigating between two points.For scenarios with specified initial position,target position,and... This research examines the optimization of motion strategy and control parameters for an Autonomous Underwater Glider(AUG)navigating between two points.For scenarios with specified initial position,target position,and heading,this study proposes a three-dimensional multimodal path planning methodology based on the 3D-Dubins path,ensuring both task fulfillment and motion feasibility within AUG dynamics constraints.The path planning approach incorporates ocean current interference and utilizes task objectives and control parameters as inputs.It systematically calculates information including horizontal Dubins type,vertical plane motion modes,and turning point depths to generate the path planning solution.The motion control strategy implements initial control parameter values and utilizes depth measurements as evaluation criteria.Through control parameter adjustments,the strategy facilitates tracking of the designated path.This control approach requires minimal feedback information,with computations executable by shore-based facilities,thereby reducing computational and measurement demands on the AUG and enhancing operational reliability.For specified task objectives,multi-objective optimization of control parameters is conducted using the proposed path planning method and motion control strategy,yielding optimized control parameters and corresponding motion control strategies for various operational requirements. 展开更多
关键词 underwater glider Dubins paths motion strategy parameters optimization energy consumption
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Laser-assisted full-size PDC bit:Drilling performance and parameter optimization 认领 引用
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作者 Bin Liu Bin Xu +3 位作者 Biao Li Bo Zhang Xinjie Huang Tongyuan Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期971-985,共15页
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
关键词 Laser rock breaking Polycrystalline diamond compact(PDC) cutter Combined rock breaking Response surface methodology Parameter optimization
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Geostress Evolution and Construction Parameter Optimization in Shale Gas Infill Well Development 认领 引用
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作者 Yongjun Xiao Yuduo Sun +5 位作者 Jian Zheng Xiaojin Zhou Wang Liu Cheng Shen Qi Deng Hao Zhao 《Energy Engineering》 EI 2026年第3期152-168,共17页
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
关键词 Shale gas horizontal well geostress evolution infill well development numerical simulation construction parameter optimization
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Influence of surface rib parameters of bolts on anchoring performance and optimization 认领 引用
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作者 LIU Xiaohu YANG Yong +3 位作者 YAO Zhishu ZHA Wenhua XI Yanqi WANG Jiaqi 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期100-111,共12页
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
关键词 geotechnical engineering anchorage support anchorage interface resin anchoring agent surface rib parameters anchorage performance optimization
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Optimization Study on Operating Parameters of Rotary Gas-Gas Heat Exchanger in Low-and Medium-Temperature Denitrification System for Cement Kiln Flue Gas 认领 引用
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作者 Kaiwen Cheng Yi Sun +2 位作者 Dong Wang Chen Zhu Fuping Qian 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第3期141-157,共17页
Rotary gas-gas heat exchangers(GGHs)are pivotal for waste heat recovery in low-and mediumtemperature denitrification systems of cement kilns.This study examines the performance of GGHs within such systems by coupling ... Rotary gas-gas heat exchangers(GGHs)are pivotal for waste heat recovery in low-and mediumtemperature denitrification systems of cement kilns.This study examines the performance of GGHs within such systems by coupling computational fluid dynamics(CFD)with the response surface method(RSM),introducing overall system performance(OSP)as the principal optimization criterion.The investigation systematically elucidates the effects of treated flue gas inlet temperature,inlet velocity,and rotor speed on GGH efficiency.Findings reveal that OSP increases with rotor speed but reaches a plateau beyond 1 rpm;it decreases with higher inlet velocity and increases with higher inlet temperature.Response surface analysis identifies treated flue gas inlet temperature as the most influential parameter,highlighting a synergistic effect between rotor speed and inlet temperature,alongside an antagonistic interaction between inlet temperature and inlet velocity.To ensure safe system operation,engineering constraints were incorporated into the optimization framework using a Box-Behnken design.The optimal operational parameters were determined as a treated flue gas inlet temperature of 250℃,inlet velocity of 8 m/s,and rotor speed of 1 rpm,yielding a maximum OSP of 107.74.The integrated CFD-RSM methodology and constraint-aware optimization strategy presented in this study offer a practical reference for enhancing the operational efficiency of industrial waste heat recovery systems,particularly in cement kiln SCR applications. 展开更多
关键词 Waste heat recovery of cement kiln rotary gas-gas heat exchanger response surface method operating parameter optimization
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Parameter optimization and mechanism of magnetic biochar-enhanced microbial electrolysis cell coupling with anaerobic digestion (MEC-AD) for treatment of landfill leachate 认领 引用
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作者 Wenqi Li Jiachen Zhu +4 位作者 Xin Yin Hanbo Chen He Liu Minhua Cui Chongjun Chen 《ENGINEERING Environment》 SCIE EI CAS CSCD 2026年第1期63-80,共18页
Landfill leachate has a highly complex composition containing hazardous substances and refractory organic compounds, which makes its treatment challenging. In this study, a microbial electrolysis cell coupled anaerobi... Landfill leachate has a highly complex composition containing hazardous substances and refractory organic compounds, which makes its treatment challenging. In this study, a microbial electrolysis cell coupled anaerobic digestion (MEC-AD) system was constructed and integrated with magnetic biochar (MBC). The critical parameters (i.e., applied voltage, anode-to-cathode area ratio, and cathode mesh size) were systematically optimized through orthogonal experiments to investigate their impacts on chemical oxygen demand (COD), organic transformation pathways, and microbial community succession in the system. The results demonstrated a maximum COD removal efficiency of 59.7%. The optimal combination of parameters included an applied voltage of 1.2 V, an anode-to-cathode area ratio of 1:0.5, and a cathode mesh size of 200 mesh. Furthermore, spectral analysis revealed significant degradation of aromatic compounds with conjugated double bonds and humic acid-like substances, which indicated that electrochemical stimulation effectively facilitated molecular chain cleavage and enhanced microbial metabolism. Long-chain amides (such as 13-Docosenamide, (Z)-) were hydrolyzed into fatty acids and further transformed into alkanes. On the other hand, aromatic pollutants like 2,4-Di-tert-butylphenol underwent progressive mineralization through hydroxylation and ring-opening reactions. Under applied voltage of 1 V, electroactive bacteria (i.e., Comamonas (22.3%) and Pseudomonas (8.1%)) in anode biofilms formed metabolic networks with fermentative bacteria (Soehngenia) and synergistically enhanced electron transfer and organic reduction with heterotrophic bacteria at the cathode. This research provides theoretical insights into optimized degradation mechanisms of MEC-AD systems and the practical feasibility of its application for landfill leachate treatment. 展开更多
关键词 Landfill leachate MEC-AD Microbial community Parameter optimization
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Photovoltaic Parameter Estimation Using a Parallelized Triangulation Topology Aggregation Optimization with Real-World Dataset Validation 认领 引用
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作者 Jun Zhe Tan Rodney H.G.Tan +4 位作者 Nor Ashidi Mat Isa Sew Sun Tiang Chun Kit Ang Kuo-Ping Lin Wei Hong Lim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期691-736,共46页
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
关键词 Photovoltaic(PV) parameters estimation triangulation topology aggregation optimizer(TTAO) parallel computing optimization
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Advanced Meta-Heuristic Optimization for Accurate Photovoltaic Model Parameterization:A High-Accuracy Estimation Using Spider Wasp Optimization 认领 引用
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作者 Sarah M.Alhammad Diaa Salama AbdElminaam +1 位作者 Asmaa Rizk Ibrahim Ahmed Taha 《Computers, Materials & Continua》 SCIE EI 2026年第3期2269-2303,共35页
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
关键词 modified Spider Wasp Optimizer(mSWO) photovoltaic(PV)modeling meta-heuristic optimization solar energy parameter estimation renewable energy technologies
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A Multi-objective Optimization Method for Resistance Performance of an Icebreaker Bow Based on Fully Parameterized Modeling 认领 引用
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作者 Long Zhu Yanzhuo Xue +3 位作者 Ruinan Guo Yingfei Zan Yang Lu Yicheng Zhang 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期362-376,共15页
The form of an icebreaker bow is numerically optimized using a platform that relies on three methods ship geometry morphing under a fully parameterized modeling approach,a cyclic process of contact compression bending... The form of an icebreaker bow is numerically optimized using a platform that relies on three methods ship geometry morphing under a fully parameterized modeling approach,a cyclic process of contact compression bending failure to calculate the icebreaking loads,and a differential evolution algorithm for optimization.The main objectives of this study are to optimize the total resistance and the average pressure in the ice zone.Surface sensitivity analysis based on an adjoint solver is used to identify the most significant regions of the hull.The hull in these regions is then formed using a cubic nonuniform rational B-spline technique.The differential evolution algorithm is employed to optimize the objectives associated with the hull form and determine the corresponding optimized variables.The optimal values are obtained by comparing the Pareto optimal designs.The optimization results show that the acquired hull form reduces the total resistance by 4.2%and decreases the average pressure in the ice zone by 0.6%.The main modifications introduced by the optimization process are to increase the buttock angle and the waterline angle. 展开更多
关键词 Icebreaker Fully parameterized modeling Optimization Total resistance Average pressure in the ice zone
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Aerodynamic shape optimization of hypersonic aircraft using data-driven generative nonlinear parameterization 认领 引用
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作者 Yan CHEN Jichao LI Jinsheng CAI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期64-76,共13页
Aerodynamic shape optimization of hypersonic vehicles is critically important yet profoundly challenging.The difficulties arise from the need to manage multiple competing objectives,complex three-dimensional geometrie... Aerodynamic shape optimization of hypersonic vehicles is critically important yet profoundly challenging.The difficulties arise from the need to manage multiple competing objectives,complex three-dimensional geometries,and the extreme computational cost of high-fidelity aerodynamic simulations across subsonic,transonic,and hypersonic regimes.Despite recent advances,an effective global optimization strategy for hypersonic aircraft design remains limited,largely hindered by the curse of dimensionality.To remove this barrier,we propose a data-driven generative nonlinear shape parameterization framework for efficient aerodynamic design of hypersonic aircraft.This framework begins by constructing diverse hypersonic aircraft shapes that cover the feasible sub-domains of a high-dimensional design space.A linear dimension reduction method is used to transform the high-dimensional point-cloud database to a low-dimensional modal space.Subsequently,a nonlinear generative model is trained to learn the statistical distribution feature of the linear mode coefficients.The resulting generative latent space provides an efficient,lowdimensional,and expressive parameterization of aerodynamic shapes.The proposed method is validated in both single-point and multi-point optimization of hypersonic aircraft,demonstrating superior efficiency and effectiveness compared with conventional parameterization approaches.This study presents an efficient roadmap for aerodynamic shape parameterization and global optimization of next-generation aircraft. 展开更多
关键词 Aerodynamic shape optimization Deep-learning Dimension reduction Generative parameterization Hypersonic aircraft design
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Model-Based Analysis of Control Parameter Influence and Optimization for Autonomous Underwater Gliders During Spiral Motion 认领 引用
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作者 HOU Dong-bo CAO Wei +1 位作者 WANG Wen-jun WANG Cong 《China Ocean Engineering》 SCIE EI CSCD 2026年第2期382-396,共15页
The Autonomous Underwater Glider(AUG),driven by gravity and buoyancy forces,plays a vital role in ocean observation networks because of its cost efficiency,low noise,and energy-efficient operation.This study investiga... The Autonomous Underwater Glider(AUG),driven by gravity and buoyancy forces,plays a vital role in ocean observation networks because of its cost efficiency,low noise,and energy-efficient operation.This study investigates the motion parameters and energy consumption of AUGs during spiral motion.Dynamic and energy consumption models for three-dimensional movement are established,incorporating variations in seawater density and AUG volume.The performance of AUGs is evaluated across various spiral motion scenarios in terms of spatial,temporal,and energy metrics.For example,during descent,the turning radius ranges from 73.6 m to 457.5 m,the turning angular velocity varies from 1.8°/min to 8.2°/min,and the energy consumption rate spans from 0.86 kJad to 4.29 kJad.Additionally,an optimization boundary surface targeting minimum energy consumption is presented for parameter selection.Considering ocean currents,a multi-objective optimization of control parameters reveals that c1 frequently serves as the critical parameter affecting AUG performance.Both the nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)and nondominated particle swarm optimization(NSPSO)methods are employed,yielding similar Pareto sets.Specific control parameter selections and simulation results for various task requirements demonstrate the achievement of both minimum energy consumption and maximum turning speed.For example,with a turning angle of 0.5π,the optimized maximum angular velocity reaches 8.18°/min,while the minimum energy consumption is 1.708 kJ.These findings offer valuable insights for optimizing control strategies in AUGs’three-dimensional spiral motion,enhancing ocean observation technologies. 展开更多
关键词 autonomous underwater glider spiral motion mathematical models energy consumption multi-objective optimization
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Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking 认领 引用
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作者 Jagriti Chatterjee Subhojyoti Chatterjee +3 位作者 Emil Gillett Nikita Kovalenko Dongyu Fan Christy F.Landes 《Chemical & Biomedical Imaging》 CAS CSCD 2026年第1期79-91,共13页
Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges.Classifying diffusion by traditional methods such as mean square displacement in three-di... Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges.Classifying diffusion by traditional methods such as mean square displacement in three-dimensional single-particle tracking(3D SPT)is difficult,especially when there are mixed motion types.To address this,we employed machine learning(ML),specifically decision tree algorithms with feature selection,to identify the six most relevant features for accurate characterization of trajectories.This work demonstrates the value of ML in advancing our understanding of heterogeneous transport that occurs in charged and crowded environments,with a broad range of applications. 展开更多
关键词 single particle dynamics diffusion machine learning feature selection hyperparameter optimization
Engineering Practice on Selection and Optimization of Operating Parameters for Low-Pollution MBR Membrane Modules 认领 引用
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作者 LI Hao 《外文科技期刊数据库（文摘版）自然科学》 2026年第2期006-010,共5页
Membrane bioreactor technology represents an efficient wastewater treatment method widely employed for treating municipal domestic sewage and industrial wastewater. However, membrane fouling remains the primary bottle... Membrane bioreactor technology represents an efficient wastewater treatment method widely employed for treating municipal domestic sewage and industrial wastewater. However, membrane fouling remains the primary bottleneck hindering long-term stable and cost-effective operation of MBR systems. The development and application of low-fouling membranes offer a novel approach to mitigate fouling and reduce operational energy consumption. This study investigates the impact of membrane materials and structures on fouling behavior across various membrane configurations, elucidates the mechanisms by which factors such as pore size, surface hydrophilicity, and structural design influence fouling trends, and establishes an optimization strategy for membrane configuration based on key parameters including flux rate, aeration intensity, filtration cycles, and chemical cleaning frequency. Results demonstrate that under constraints including influent quality characteristics, treatment scale, and operational costs, selecting appropriate low-fouling membrane components requires adopting a tiered selection strategy emphasizing "material prioritization, configuration alignment, and parameter iteration." By integrating subcritical flow operation, intermittent aeration control, and online maintenance cleaning, this study optimizes process parameters to achieve synergistic improvements in both fouling resistance and operational energy efficiency. The findings provide comprehensive technical references and operational guidance for MBR system design and operational management. 展开更多
关键词 Membrane bioreactor Low-pollution membrane module Parameter optimization Critical flux Membrane fouling control
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Research on Intelligent Optimization Technology of Boiler Combustion Parameters in Panshan Power Plant Under Low-Nitrogen Emission Reduction Conditions 认领 引用
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作者 WANG Xisen 《外文科技期刊数据库（文摘版）工程技术》 2026年第2期053-058,共6页
Against the backdrop of increasingly stringent ultra-low emission standards in the thermal power industry, the traditional manual regulation mode of boiler This study takes the coal‑fired boiler of Panshan Power Plant... Against the backdrop of increasingly stringent ultra-low emission standards in the thermal power industry, the traditional manual regulation mode of boiler This study takes the coal‑fired boiler of Panshan Power Plant as the research object. Centering on the core objective of low‑nitrogen emission reduction and combined with the actual operating conditions of the unit, it analyzes the influencing mechanism of boiler combustion parameters on NOₓ formation, furnace combustion stability and boiler thermal efficiency. By virtue of big‑data acquisition and intelligent algorithms, a multi‑objective intelligent optimization model for boiler combustion parameters is constructed, and dynamic optimization research is conducted on key parameters such as excess air coefficient, primary‑secondary air ratio, burner tilt angle and furnace oxygen content.Comparative analyses of field tests and data simulations verify that the optimized intelligent regulation system can effectively suppress the generation of thermal NOₓ and fuel‑NOₓ in the furnace, drastically reduce flue gas pollutant emissions, improve in‑furnace combustion conditions and lower incomplete combustion losses. Test results demonstrate that after the implementation of the intelligent optimization scheme, NOₓ emission concentrations decrease significantly across all load ranges of the unit, and boiler thermal efficiency is improved effectively. This research resolves industrial pain points of traditional manual adjustment including low precision, prominent hysteresis and poor working‑condition adaptability, and provides technical reference and practical experience for low‑nitrogen and high‑efficiency combustion optimization of similar thermal power generating units. 展开更多
关键词 low-nitrogen emission reduction power plant boiler combustion parameters intelligent optimization thermal efficiency working condition regulation
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Parameter matching and optimization of hybrid excavator swing system 认领 引用 被引量：1
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作者 Chao SHEN Jianxin ZHU +2 位作者 Jian CHEN Saibai LI Lixin YI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第2期138-150,共13页
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
关键词 Hybrid system Energy regeneration Swing braking energy Parameter optimization Improved multi-objective particle swarm optimization(IMOPSO) Adaptive grid
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Bearing Fault Diagnosis with Hybrid CNN-RNN:A Unified-Loop Hyperparameter Optimization Framework via Surrogate-Based Bayesian Optimization 认领 引用
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作者 Jaewan Lee Seonghwan Park Junghwan Kook 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期252-273,共22页
In bearing fault diagnosis for Prognostics and Health Management(PHM),the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing,model configuration,and decision fusio... In bearing fault diagnosis for Prognostics and Health Management(PHM),the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing,model configuration,and decision fusion.However,these components are often optimized independently,resulting in fragmented workflows that limit global optimality,reproducibility,and computational efficiency of the model.This study presents a computationally unified three-stage sequential optimization framework that systematically coordinates the preprocessing selection,model hyperparameter optimization,and decision-level fusion within a consistent surrogate-based optimization architecture.In the first stage,candidate preprocessing schemes reflecting physical faultmechanisms—outer race,inner race,rolling element,and cage faults—are evaluated using a training-free Class Separability Score(CSS),which enables efficient screening without introducing model bias.In the second stage,the hyperparameters of two complementary architectures—an EfficientNetV2-based Convolutional Neural Network(CNN)operating in the time–frequency domain and a Gated Recurrent Unit(GRU)-based Recurrent Neural Network(RNN)operating in the time domain—are optimized independently by maximizing the cross-validated performance.In the third stage,a stacking-based late fusion model is constructed using out-of-fold posterior probabilities to learn the optimal decision rules that exploit cross-model complementarity.All stages are governed by a unified optimization container integrating LatinHypercube Sampling(LHS),Kriging surrogate modeling with expected improvement,and BayesianOptimization withHyperband(BOHB),enabling surrogate-assisted exploration under multi-fidelity computational budgets.Numerical experiments on benchmark datasets,including CaseWestern ReserveUniversity(CWRU)andMAFAULDA,demonstrate the stable convergence behavior of the sequential optimization process and consistent performance gains over single-model baselines.Reproducibility is ensured through fixed data partitions,randomseeds,and normalization boundaries across all stages.The proposed framework provides a structured and reproducible computational modeling approach for bearing fault diagnosis,highlighting how coordinated optimization across multiple stages can improve the robustness and efficiency of complex PHM workflows. 展开更多
关键词 Bearing fault diagnosis hybrid CNN-RNN Bayesian optimization surrogate modeling class separability score
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Multi-Dimensional Collaborative Optimization Strategy for Control Parameters of Thermal-Energy Storage Integrated Systems Considering Frequency Regulation Losses 认领 引用
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作者 Zezhong Liu Jinyu Guo +1 位作者 Xingxu Zhu Junhui Li 《Energy Engineering》 EI 2026年第3期361-390,共30页
With the increasing penetration of renewable energy,the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security.Addressing the challe... With the increasing penetration of renewable energy,the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security.Addressing the challenge of improving the frequency regulation performance of a thermal-storage primary frequency regulation system while reducing its associated losses,this paper proposes a multi-dimensional cooperative optimization strategy for the control parameters of a combined thermal-storage system,considering regulation losses.First,the frequency regulation losses of various components within the thermal power unit are quantified,and a calculation method for energy storage regulation loss is proposed,based on Depth of Discharge(DOD)and C-rate.Second,a thermal-storage cooperative control method based on series compensation is developed to improve the system’s frequency regulation performance.Third,targeting system regulation loss cost and regulation output,and considering constraints on output overshoot and system parameters,an improved Particle Swarm Optimization(PSO)algorithm is employed to tune the parameters of the low-pass filter and the series compensator,thereby reducing regulation losses while enhancing performance.Finally,simulation results demonstrate that the total loss cost of the proposed control strategy is comparable to that of a system with only thermal power participation.However,the thermal power loss cost is reduced by 42.16%compared to the thermal-only case,while simultaneously improving system frequency stability.Thus,the proposed strategy effectively balances system frequency stability and economic efficiency. 展开更多
关键词 Frequency regulation losses of thermal power units energy storage frequency regulation losses series compensation enhanced particle swarm optimization algorithm primary frequency regulation
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