Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.Thi...Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.This work discussed the c_(d)parameterization at Aiyetoro,Ilaje Local Government Area,Ondo State,Southwestern Nigeria,to quantify the exchange of momentum in this region,The dependence of cd on some one hourly averaged variables sourced from ERA5 Reanalysis over a 71 year period(1950-2020)was clearly analysed.Results of the monthly mean and variability of cd and u10 over the study area showed that November had the lowest monthly mean cd and u10,with values of 0.000825 and 3.38 m/s,respectively,and August had the highest values of 0.001031 and 5.66 m/s,respectively.Furthermore,the cd variability is lowest(63.24%)in November and highest(106.35%)in August.The variability for u10 is lowest in March(198.18%)and greatest in October(304.37%).For the study location,five parameterizations,were statistically evaluated for the predictive power of c_(d) on an annual,seasonal and monthly basis.Furthermore,the cd showed improved performance when using monthly values than when using annual and seasonal values.The equations yielded better performance in the wet season than in the dry season.展开更多
Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Thre...Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Three major dynamic and thermodynamic processes governing the TC-induced SST cooling（SSTC）, vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables：sea surface height（SSH）, wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.展开更多
Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this...Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this solution, the overall port pre-design process can be conducted in a virtual pattern. Moreover, to evaluate the advantages of the new design pattern, an application of port system has been involved in this paper; and in the process of application a computational fluid dynamic analysis is concerned. An ideal effect of cleanness, high efficiency and high precision has been achieved.展开更多
Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear d...Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear dependencies on soil temperature and moisture,while its response to these factors demonstrates pronounced spatiotemporal heterogeneity.Most land carbon cycle models use fixed temperature and moisture sensitivities to project R_(S)changes,which may induce substantial uncertainty in R_(S)estimations and projections.This paper reviews recent progress in understanding spatiotemporal variations of R_(S)sensitivities,their responses to global warming,and advances in parameterizing these sensitivities.The exponential temperature response and parabolic moisture response of R_(S)are summarized,alongside their spatiotemporal sensitivities.Although some models have made progress in parameterizing spatiotemporally heterogeneous temperature and moisture sensitivities of R_(S),critical challenges persist,including insufficient mechanistic explanations,suboptimal validation performance,and poor cross-model consistency.Additionally,limitations in parameterizing the interactive effects of soil temperature and moisture on R_(S)may lead to notable biases in R_(S)estimations.This paper advocates expanding in situ measurements of R_(S)across climatic zones and land cover types,and further deepening the analysis of these data with advanced techniques(e.g.,artificial intelligence)to establish more comprehensive relationships between R_(S)and soil temperature and moisture.Such improvements would optimize land carbon cycle model parameterization,reduce estimation biases,enhance simulation precision,and ultimately provide robust scientific foundations for global carbon budgeting and climate policy formulation to support carbon neutrality goals.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft ...Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Since the view that the localized rail third-order bending mode can cause high-order polygonization(mainly 18-23)of high-speed train wheels was put forward in 2017,many scholars have attempted to link a connection bet...Since the view that the localized rail third-order bending mode can cause high-order polygonization(mainly 18-23)of high-speed train wheels was put forward in 2017,many scholars have attempted to link a connection between the localized rail bending modes and wheel polygonization phenomenon and polygonal wheel passing frequency.This paper first establishes a flexible track model considering the structural and parametric characteristics of fasteners,verifies the model by using vehicle tracking test data,then investigates the influence of fastener parameter matching on the localized rail bending modes,and obtains the following conclusions:(1)There is nearly a 1:1 mapping relationship between the localized rail bending modal frequency and polygonal wheel passing(PWP)frequency,which supports that the localized rail bending mode is one of the causes of wheel polygonization.(2)The iron plate of the fastener system plays a role of dynamic vibration absorber in the vehicle-rail coupled system,and the fastener parameters significantly influence the localized rail bending modal vibration.Finally,this paper proposes a design principle of a high-frequency vibration-absorbing fastener,which provides a feasible solution to mitigate the localized rail bending modal vibration and high-order wheel polygonization.Meanwhile,it points out that this measure may induce other high-frequency vibration problems,e.g.,aggravating modal vibration above 800 Hz.Further,this paper proposes a concept of differentiated arrangement of fasteners,suggesting that different high-frequency vibration-absorbing fasteners be installed in different sections of the whole line to make the localized rail bending modal frequency of the whole line disordered,thus disrupting and further mitigating the development of the wheel polygonization.展开更多
In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory mode...In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
Laser welding is a highly promising joining method for Al alloys.However,certain limitations such as elevated thermal input and keyhole instability are associated with its application in medium-thickness aluminium all...Laser welding is a highly promising joining method for Al alloys.However,certain limitations such as elevated thermal input and keyhole instability are associated with its application in medium-thickness aluminium alloy plates.To address these issues,circular oscillating laser welding combined with post-weld heat treatment was employed to improve the formation quality and mechanical properties of the welds.The effects of the frequency of circular oscillating laser on the forming quality,microstructure,and properties of the welds were analyzed.At an oscillation frequency of 200 Hz,the grain size in the weld zone was reduced compared to single laser welding,and the maximum tensile strength of the weld was observed to reach(264.96±1.33)MPa,representing approximately 61.19%of the base metal.Following the post-weld heat treatment of"solid solution and artificial aging",the grain boundary segregation was diminished.Nanoscale precipitated phases are present in the weld zone.Furthermore,the tensile strength was augmented to(386.35±5.65)MPa,representing approximately 89.23%of the strength of the base metal.The results of this study can provide a theoretical basis and technological reference for the circular oscillating laser welding of medium-thickness 2219-T 6 aluminium alloy plates.展开更多
A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.T...A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.展开更多
The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while ther...The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.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
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
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine gr...This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine grassland stations and North China flux stations, with a total of 34 stations and 77 soil profiles. Measured data indicate that the topsoils of alpine grasslands contain high SOC contents than underlying soil layers, which leads to higher soil porosity values and lower thermal conductivity and bulk density values in the topsoils. However, this stratification is not evident at the lowland stations due to low SOC contents. Evaluations against measured data show that three thermal conductivity schemes used in land surface models severely overestimate the values for soils with high SOC content (i.e. topsoils of alpine grassland), but they are better for soils with low SOC content. A new parameterization is then developed to take the impacts of SOC into account. The new one can well estimate the soil thermal conductivity values in both low and high SOC content cases, and therefore, it is a potential candidate of thermal conductivity scheme to be used in land surface models.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
基金supported by Department of Engineering,University of Campania Luigi Vanvitelli,81031 Aversa,Italy.
文摘Ocean surface waves and upper sea circulation are primarily propelled by wind force and are usually expressed in terms of sea surface drag coefficient(c_(d))that increases with sea surface roughness and wind speed.This work discussed the c_(d)parameterization at Aiyetoro,Ilaje Local Government Area,Ondo State,Southwestern Nigeria,to quantify the exchange of momentum in this region,The dependence of cd on some one hourly averaged variables sourced from ERA5 Reanalysis over a 71 year period(1950-2020)was clearly analysed.Results of the monthly mean and variability of cd and u10 over the study area showed that November had the lowest monthly mean cd and u10,with values of 0.000825 and 3.38 m/s,respectively,and August had the highest values of 0.001031 and 5.66 m/s,respectively.Furthermore,the cd variability is lowest(63.24%)in November and highest(106.35%)in August.The variability for u10 is lowest in March(198.18%)and greatest in October(304.37%).For the study location,five parameterizations,were statistically evaluated for the predictive power of c_(d) on an annual,seasonal and monthly basis.Furthermore,the cd showed improved performance when using monthly values than when using annual and seasonal values.The equations yielded better performance in the wet season than in the dry season.
基金The Major National Basic Research Development Program of China under contract No.2016YFA0202704the National Natural Science Foundation of China under contract Nos 41476008 and 41576018+1 种基金the Basic Fund of Chinese Academy of Meteorological Sciences under contract No.2017Z017the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No.XDA11010303
文摘Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Three major dynamic and thermodynamic processes governing the TC-induced SST cooling（SSTC）, vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables：sea surface height（SSH）, wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.
文摘Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this solution, the overall port pre-design process can be conducted in a virtual pattern. Moreover, to evaluate the advantages of the new design pattern, an application of port system has been involved in this paper; and in the process of application a computational fluid dynamic analysis is concerned. An ideal effect of cleanness, high efficiency and high precision has been achieved.
基金supported by the National Natural Science Foundation of China(Grant No.42175064)。
文摘Soil respiration(R_(S))represents the largest carbon flux from terrestrial ecosystems to the atmosphere,substantially influencing the global carbon budget and climate change.R_(S)exhibits vital yet complex nonlinear dependencies on soil temperature and moisture,while its response to these factors demonstrates pronounced spatiotemporal heterogeneity.Most land carbon cycle models use fixed temperature and moisture sensitivities to project R_(S)changes,which may induce substantial uncertainty in R_(S)estimations and projections.This paper reviews recent progress in understanding spatiotemporal variations of R_(S)sensitivities,their responses to global warming,and advances in parameterizing these sensitivities.The exponential temperature response and parabolic moisture response of R_(S)are summarized,alongside their spatiotemporal sensitivities.Although some models have made progress in parameterizing spatiotemporally heterogeneous temperature and moisture sensitivities of R_(S),critical challenges persist,including insufficient mechanistic explanations,suboptimal validation performance,and poor cross-model consistency.Additionally,limitations in parameterizing the interactive effects of soil temperature and moisture on R_(S)may lead to notable biases in R_(S)estimations.This paper advocates expanding in situ measurements of R_(S)across climatic zones and land cover types,and further deepening the analysis of these data with advanced techniques(e.g.,artificial intelligence)to establish more comprehensive relationships between R_(S)and soil temperature and moisture.Such improvements would optimize land carbon cycle model parameterization,reduce estimation biases,enhance simulation precision,and ultimately provide robust scientific foundations for global carbon budgeting and climate policy formulation to support carbon neutrality goals.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金supported by the National Key R&D Program of China(No.2024YFC3015804)the Basic Science Center Program for“Space Robot Intelligent Manipulation”,China(No.T2388101)。
文摘Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金supported by the National Natural Science Foundation of China(Grant Nos.:52202423,U2268211,and 52475136)the China Postdoctoral Science Foundation(Grant Nos.:2022M712636 and 2023T160546)+1 种基金the Natural Science Foundation of Sichuan Province(Grant No.:2025ZNSFSC0398)the Independent R&D Project of the State Key Laboratory of Traction Power(Grant No.:2023TPL-T14).
文摘Since the view that the localized rail third-order bending mode can cause high-order polygonization(mainly 18-23)of high-speed train wheels was put forward in 2017,many scholars have attempted to link a connection between the localized rail bending modes and wheel polygonization phenomenon and polygonal wheel passing frequency.This paper first establishes a flexible track model considering the structural and parametric characteristics of fasteners,verifies the model by using vehicle tracking test data,then investigates the influence of fastener parameter matching on the localized rail bending modes,and obtains the following conclusions:(1)There is nearly a 1:1 mapping relationship between the localized rail bending modal frequency and polygonal wheel passing(PWP)frequency,which supports that the localized rail bending mode is one of the causes of wheel polygonization.(2)The iron plate of the fastener system plays a role of dynamic vibration absorber in the vehicle-rail coupled system,and the fastener parameters significantly influence the localized rail bending modal vibration.Finally,this paper proposes a design principle of a high-frequency vibration-absorbing fastener,which provides a feasible solution to mitigate the localized rail bending modal vibration and high-order wheel polygonization.Meanwhile,it points out that this measure may induce other high-frequency vibration problems,e.g.,aggravating modal vibration above 800 Hz.Further,this paper proposes a concept of differentiated arrangement of fasteners,suggesting that different high-frequency vibration-absorbing fasteners be installed in different sections of the whole line to make the localized rail bending modal frequency of the whole line disordered,thus disrupting and further mitigating the development of the wheel polygonization.
基金funded by the National Key R&D Program of China,China(No.2024YFF0507903)the National Key Research and Development Program of China(Grant No.2024YFF0507904)the National Natural Science Foundation of China,China(Grant No.52379114).These supports are gratefully acknowledged.
文摘In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金Projects(E2024203001,E2024203049,E2024203071)supported by the Hebei Natural Science Foundation of ChinaProject(236Z1805G)supported by the S&T Program of Hebei,China。
文摘Laser welding is a highly promising joining method for Al alloys.However,certain limitations such as elevated thermal input and keyhole instability are associated with its application in medium-thickness aluminium alloy plates.To address these issues,circular oscillating laser welding combined with post-weld heat treatment was employed to improve the formation quality and mechanical properties of the welds.The effects of the frequency of circular oscillating laser on the forming quality,microstructure,and properties of the welds were analyzed.At an oscillation frequency of 200 Hz,the grain size in the weld zone was reduced compared to single laser welding,and the maximum tensile strength of the weld was observed to reach(264.96±1.33)MPa,representing approximately 61.19%of the base metal.Following the post-weld heat treatment of"solid solution and artificial aging",the grain boundary segregation was diminished.Nanoscale precipitated phases are present in the weld zone.Furthermore,the tensile strength was augmented to(386.35±5.65)MPa,representing approximately 89.23%of the strength of the base metal.The results of this study can provide a theoretical basis and technological reference for the circular oscillating laser welding of medium-thickness 2219-T 6 aluminium alloy plates.
基金supported by the National Science and Technology Major Project,China(No.2017-II-0006-0019)the National Natural Science Foundation of China(No.52375266)the Shaanxi Science Foundation for Distinguished Young Scholars,China(No.2022JC-36)。
文摘A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.
基金the UGC,New Delhi,India for financial assistance via the UGC-Junior Research Fellowship(CSIR-UGC NET JULY 2024)(Student ID:241610090610)。
文摘The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.
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.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金supported by National Natural Science Foundation of China (Grant No. 41105003)Key Project of Chinese Academy of Sciences (Grant No.KZCX2-YW-Q10-2)+1 种基金National Natural Science Foundation of China (Grant No. 91025004)Open Fund from the State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201108) that is cosponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University
文摘This study investigates the stratification of soil thermal properties induced by soil organic carbon (SOC) and its impacts on the parameterization of the thermal properties. Soil parameters were measured for alpine grassland stations and North China flux stations, with a total of 34 stations and 77 soil profiles. Measured data indicate that the topsoils of alpine grasslands contain high SOC contents than underlying soil layers, which leads to higher soil porosity values and lower thermal conductivity and bulk density values in the topsoils. However, this stratification is not evident at the lowland stations due to low SOC contents. Evaluations against measured data show that three thermal conductivity schemes used in land surface models severely overestimate the values for soils with high SOC content (i.e. topsoils of alpine grassland), but they are better for soils with low SOC content. A new parameterization is then developed to take the impacts of SOC into account. The new one can well estimate the soil thermal conductivity values in both low and high SOC content cases, and therefore, it is a potential candidate of thermal conductivity scheme to be used in land surface models.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
