With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used...With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third-generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data are analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects are compared during a period where wind has a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase prediction errors by 38%. Wave frequency and coherence analysis show that both models can predict the wave variation time scale with the same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.展开更多
Real-time predicting of stochastic waves is crucial in marine engineering.In this paper,a deep learning wave prediction(Deep-WP)model based on the‘probabilistic’strategy is designed for the short-term prediction of ...Real-time predicting of stochastic waves is crucial in marine engineering.In this paper,a deep learning wave prediction(Deep-WP)model based on the‘probabilistic’strategy is designed for the short-term prediction of stochastic waves.The Deep-WP model employs the long short-term memory(LSTM)unit to collect pertinent information from the wave elevation time series.Five irregular long-crested waves generated in the deepwater offshore basin at Shanghai Jiao Tong University are used to validate and optimize the Deep-WP model.When the prediction duration is 1.92s,2.56s,and,3.84s,respectively,the predicted results are almost identical with the ground truth.As the prediction duration is increased to 7.68s or 15.36s,the Deep-WP model’s error increases,but it still maintains a high level of accuracy during the first few seconds.The introduction of covariates will improve the Deep-WP model’s performance,with the absolute position and timestamp being particularly advantageous for wave prediction.Furthermore,the Deep-WP model is applicable to predict waves with different energy components.The proposed Deep-WP model shows a feasible ability to predict nonlinear stochastic waves in real-time.展开更多
An adjoint variational method for wave data assimilation in the LAGFD-WAM wave model is proposed. The adjoint equation of the wavenumber energy spectrum balance equation is derived. And fortunately, its characteristic...An adjoint variational method for wave data assimilation in the LAGFD-WAM wave model is proposed. The adjoint equation of the wavenumber energy spectrum balance equation is derived. And fortunately, its characteristic equations are the same as those in the LAGFD-WAM wave model. Simple experiments on the functional optimization and assimilation effectiveness during the prediction period indicated that the adjoint variational method is effective for wave assimilation and that the initial optimization of the wave model is important for the short-range wave prediction. All of this is under the assumption that the wind field is accurate, the method is the important first step for combined wind and wave data assimilation systems. Key words variational data assimilation - adjoint equation - short-range wave prediction Project 97701 and Q99E05 of the Youth Science Fund supported by State Oceanic Administration and Shandong Province, and also funded by Project 863-818-06-02 and Project 921-2.展开更多
Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-pa...Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-parameter AVO/AVA inversion using only PP-wave angle gathers is often highly ill-posed,leading to instability and inaccuracy in the inverted elastic parameters(e.g.,P-and Swave velocities and bulk density).Seismic AVO/AVA analysis simultaneously using both PP-wave(pressure wave down,pressure wave up)and PS-wave(pressure wave down,converted shear wave up)angle gathers has proven to be an effective method for reducing reservoir interpretation ambiguity associated with using the single wave mode of PP-waves.To avoid the complex PS-wave processing,and the risks associated with PP and PS waveform alignment,we developed a method that predicts PS-wave angle gathers from PP-wave angle gathers using a deep learning algorithm—specifically,the cGAN deep learning algorithm.Our deep learning model is trained with synthetic data,demonstrating a strong fit between the predicted PS-waves and real PS-waves in a test datasets.Subsequently,the trained deep learning model is applied to actual field PP-waves,maintaining robust performance.In the field data test,the predicted PS-wave angle gather at the well location closely aligns with the synthetic PS-wave angle gather generated using reference well logs.Finally,the P-and S-wave velocities estimated from the joint PP and PS AVA inversion,based on field PP-waves and the predicted PS-waves,display a superior model fit compared to those obtained solely from the PP-wave AVA inversion using field PPwaves.Our contribution lies in firstly carrying out the joint PP and PS inversion using predicted PS waves rather than the field PS waves,which break the limit of acquiring PS-wave angle gathers.展开更多
This paper proposes a novel method for incorporating wave domain prediction in a three-channel(3CH)architecture,which is the optimal architecture from a transparency point of view,to overcome the poor transparency pro...This paper proposes a novel method for incorporating wave domain prediction in a three-channel(3CH)architecture,which is the optimal architecture from a transparency point of view,to overcome the poor transparency problem of using the wave variable method in a time-delay teleoperation system.A 3CH teleoperation control architecture is established by selecting parameters of the 4CH architecture sensibly for the system without force sensor in the master side.The communication channel is divided into a two-port model by combining force and velocity information reasonably to extend the wave variable method to a 3CH architecture.Then the I/O signal of the two-port model is transformed into wave variable.A predictor is added to the wave domain of the master side to further improve the transparency of the system,and a regulator is designed to ensure the passivity of the predictor.Experimental results show that the proposed method can guarantee stability and improve the transparency of the teleoperation system with time-delay.展开更多
Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale a...Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.展开更多
The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking th...The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.展开更多
In this study the effect of the surface waves over sea surface roughness (z0) and drag coefficient (CD) is investigated by combining an ocean wave model and a simplified algorithm, which estimates z0 and CD with and w...In this study the effect of the surface waves over sea surface roughness (z0) and drag coefficient (CD) is investigated by combining an ocean wave model and a simplified algorithm, which estimates z0 and CD with and without dependence on the sea state. This investigation was possible from several numerical simulations with the Wave-Watch-III (WW3) model for complex wind conditions. The numerical experiments were performed for idealized like-hurricanes with different translation speed (0, 5 and 10 m/s) and maximum wind speed (MWS) at the centre (35, 45 and 55 m/s). It is observed that z0 and CD are strongly dependent on the sea state, via substantial modification in Charnock parameterization (zch). As the hurricane translation speed increases more discrepancies in z0 and CD are observed in opposite quadrants around the region of MWS. As for instance, higher, longer and older (or more developed) waves, located in the front-right quadrant, produce lower values of z0 and CD. In the rear-left quadrant, where the waves are lower, shorter and younger (or less developed), higher values of z0 and CD are observed. In addition the difference between values on opposite quadrants increases as the hurricane intensity increases, showing the hurricane intensification dependence. Interesting aspects are observed in scatter plotting wave age versus Charnock coefficient. It is also observed that zch, which has a constant value of 0.0185, is modified by the sea state, where young waves produce higher values of zch, while old waves are related to lower values of zch when compared with zch without dependence on sea state.展开更多
In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get...In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.展开更多
Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales rangin...Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent <span style="font-family:Verdana;">rotating and rectilinear particle motions. A mathematical statistics-based</span><span style="font-family:Verdana;"> wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This </span><span style="font-family:Verdana;">affects</span><span style="font-family:Verdana;"> a different approach to the conventional deterministic model forecasting of waves.</span>展开更多
The impact of ERS-1 altimeter significant wave height on analysis of wave field and wave pre- dictions is tested through analysis of selected cases. Application of the altimeter data may modifg initial tield and thus ...The impact of ERS-1 altimeter significant wave height on analysis of wave field and wave pre- dictions is tested through analysis of selected cases. Application of the altimeter data may modifg initial tield and thus 24-hour prediction of significant wave height. However the variations in initial wave field almost make no effect on 48-hour predictions.展开更多
In this paper, flow reconstruction accuracy and flow prediction capability of discontinuous transonic flow field by means of proper orthogonal decomposition （POD） method is studied. Although linear superposition of ...In this paper, flow reconstruction accuracy and flow prediction capability of discontinuous transonic flow field by means of proper orthogonal decomposition （POD） method is studied. Although linear superposition of ＂high frequency waves＂ in different POD modes can achieve the reconstruction of the shock wave, the smoothness of the solution near the shock wave cannot be guaranteed. The modal coefficients are interpolated or extrapolated and different modal components are superposed to realize the prediction of the flow field beyond the snapshot sets. Results show that compared with the subsonic flow, the transonic flow with shock wave requires more POD modes to reach a comparative reconstruction accuracy. When a shock wave exists, the interpolation prediction ability is acceptable. However, large errors exist in extrapolation, and increasing the number of POD modes cannot effectively improve the prediction accuracy of the flow field.展开更多
Under the influence of a one-dimensional stationary outfield with the equilibrium between kinetic and potential energy produced by it,a modified Sch(?)rdinger equation in the form i((?)ψ/(?)t)t=a (?)~2ψ/ax^2-ib (?),...Under the influence of a one-dimensional stationary outfield with the equilibrium between kinetic and potential energy produced by it,a modified Sch(?)rdinger equation in the form i((?)ψ/(?)t)t=a (?)~2ψ/ax^2-ib (?),where b=b_o(?)T/(?)x,is used to describe the behavior of the probability wave on the six-month departure charts at the 500 hPa level.It is found that C=2πa/L-b_o(?)T/ax and when L→∞,then C= -b_o(?)T/(?)x,where C is wave velocity,a and b are constants,and L is wavelength.The motion direction of probability waves is against the outfield temperature gradient,and their velocity is related to the absolute value of temperature gradient.The motion of waves shrinks in heat sinks and expands in heat sources,which have been verified in practice.Finally the six-month departure probability wave and the modified Sch(?)rdinger equation are used in the MOS predictions of temperature and rainfall in spring-summer 1981-1985 in Jilin Province and the accuracy for trend predictions is equal to 80%.展开更多
An improved model for numerically predicting nonlinear wave forces exerted on an offshore structure is pro- posed.In a previous work[9],the authors presented a model for the same purpose with an open boundary condi- t...An improved model for numerically predicting nonlinear wave forces exerted on an offshore structure is pro- posed.In a previous work[9],the authors presented a model for the same purpose with an open boundary condi- tion imposed,where the wave celerity has been defined constant.Generally,the value of wave celerity is time-de- pendent and varying with spatial location.With the present model the wave celerity is evaluated by an upwind dif- ference scheme,which enables the method to be extended to conditions of variable finite water depth,where the value of wave celerity varies with time as the wave approaches the offshore structure.The finite difference method incorporated with the time-stepping technique in time domain developed here makes the numerical evolution effec- tive and stable.Computational examples on interactions between a surface-piercing vertical cylinder and a solitary wave or a cnoidal wave train demonstrates the validity of this program.展开更多
The significant wave height prediction holds critical value for marine energy development,coastal infrastructure planning,and ensuring safety in maritime operations.The precision of such predictions carries substantia...The significant wave height prediction holds critical value for marine energy development,coastal infrastructure planning,and ensuring safety in maritime operations.The precision of such predictions carries substantial the oretical and practical weight.This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction,serving as a methodical guidepost for academicians.The study introduces an all-encompassing predictive framework for significan wave height,which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs.The framework is structured into four principal layers i...feature selection,basic prediction,data decomposition,and parameter optimization.The ensuing sections meticulously dissect the methodologies within these strata,elucidating their core concepts,distinctive features merits,and constraints,and their applicability to significant wave height prediction.To wrap up,the study delves into fresh research inguiries and avenues pertinent to the discipline,thereby broadening the comprehension of significant wave height prediction.In essence,this scholarly article imparts critical knowledge beneficial to the realm of marine technology.展开更多
In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model select...In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model selection of fuzzy IF-THEN rules structure and numbers is not an automatic process.In addition,in the ANFIS model extraction of fuzzy antecedent and consequent parameters is a gradient-based method which makes the answer susceptible to entrap in local optima.To cope with the ANFIS deficiencies,herein the PSO algorithm is coupled with the wave predictor FIS models in three viewpoints to optimize fuzzy subtractive clustering parameters,i.e.radii of clustering and quash factor,and the antecedent and consequent parameter of fuzzy IF-THEN rules.At first viewpoint,two PSO algorithms are used to optimize fuzzy subtractive clustering parameters and fuzzy IF-THEN rule parameters.In the second viewpoint,a PSO algorithm is used to optimize subtractive clustering parameters while the ANFIS model is used to tune the fuzzy IF-THEN rule parameters.In the third viewpoint,only a PSO algorithm is used to optimize the subtractive clustering parameters along with fuzzy IF-THEN rule parameters.Gathered data sets by National Data Buoy Center(NDBC)at Lake Michigan are used to evaluate the developed models for prediction of wave parameters including significant wave height and peak spectral period.Results indicate the efficiency of PSO algorithm to improve the ANFIS model accuracy.展开更多
文摘With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third-generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data are analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects are compared during a period where wind has a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase prediction errors by 38%. Wave frequency and coherence analysis show that both models can predict the wave variation time scale with the same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.
基金supported by Hainan Provincial Natural Science Foundation of China(Grant no.520QN290)the 2020 Research Pro-gram of Sanya Yazhou Bay Science and Technology City(Grant No.SKJC-2020-01-006)the Hainan Provincial Science and Technol-ogy Plan-Sanya Yazhou Bay Science and Technology City Natural Science Foundation Joint Project(2021JJLH0062).
文摘Real-time predicting of stochastic waves is crucial in marine engineering.In this paper,a deep learning wave prediction(Deep-WP)model based on the‘probabilistic’strategy is designed for the short-term prediction of stochastic waves.The Deep-WP model employs the long short-term memory(LSTM)unit to collect pertinent information from the wave elevation time series.Five irregular long-crested waves generated in the deepwater offshore basin at Shanghai Jiao Tong University are used to validate and optimize the Deep-WP model.When the prediction duration is 1.92s,2.56s,and,3.84s,respectively,the predicted results are almost identical with the ground truth.As the prediction duration is increased to 7.68s or 15.36s,the Deep-WP model’s error increases,but it still maintains a high level of accuracy during the first few seconds.The introduction of covariates will improve the Deep-WP model’s performance,with the absolute position and timestamp being particularly advantageous for wave prediction.Furthermore,the Deep-WP model is applicable to predict waves with different energy components.The proposed Deep-WP model shows a feasible ability to predict nonlinear stochastic waves in real-time.
文摘An adjoint variational method for wave data assimilation in the LAGFD-WAM wave model is proposed. The adjoint equation of the wavenumber energy spectrum balance equation is derived. And fortunately, its characteristic equations are the same as those in the LAGFD-WAM wave model. Simple experiments on the functional optimization and assimilation effectiveness during the prediction period indicated that the adjoint variational method is effective for wave assimilation and that the initial optimization of the wave model is important for the short-range wave prediction. All of this is under the assumption that the wind field is accurate, the method is the important first step for combined wind and wave data assimilation systems. Key words variational data assimilation - adjoint equation - short-range wave prediction Project 97701 and Q99E05 of the Youth Science Fund supported by State Oceanic Administration and Shandong Province, and also funded by Project 863-818-06-02 and Project 921-2.
基金funded by the National Natural Science Foundation of China(No.42325403 and No.42122029)the Deep Earth Probe and Mineral Resources Exploration—National Science and Technology Major Project(No.2024ZD1004201)+3 种基金the Young Expert of Taishan Scholars Project(No.tsqn202408095)the Independent Innovation Research Project(Science and Engineering)—Youth Fund(No.24CX06012A)of China University of Petroleumthe Qingdao Postdoctoral Funding Program(No.QDBSH20240202023)the CNPC Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications(2022DQ0604-02)。
文摘Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-parameter AVO/AVA inversion using only PP-wave angle gathers is often highly ill-posed,leading to instability and inaccuracy in the inverted elastic parameters(e.g.,P-and Swave velocities and bulk density).Seismic AVO/AVA analysis simultaneously using both PP-wave(pressure wave down,pressure wave up)and PS-wave(pressure wave down,converted shear wave up)angle gathers has proven to be an effective method for reducing reservoir interpretation ambiguity associated with using the single wave mode of PP-waves.To avoid the complex PS-wave processing,and the risks associated with PP and PS waveform alignment,we developed a method that predicts PS-wave angle gathers from PP-wave angle gathers using a deep learning algorithm—specifically,the cGAN deep learning algorithm.Our deep learning model is trained with synthetic data,demonstrating a strong fit between the predicted PS-waves and real PS-waves in a test datasets.Subsequently,the trained deep learning model is applied to actual field PP-waves,maintaining robust performance.In the field data test,the predicted PS-wave angle gather at the well location closely aligns with the synthetic PS-wave angle gather generated using reference well logs.Finally,the P-and S-wave velocities estimated from the joint PP and PS AVA inversion,based on field PP-waves and the predicted PS-waves,display a superior model fit compared to those obtained solely from the PP-wave AVA inversion using field PPwaves.Our contribution lies in firstly carrying out the joint PP and PS inversion using predicted PS waves rather than the field PS waves,which break the limit of acquiring PS-wave angle gathers.
基金Supported by the National High Technology Research and Development Programme of China(No.2006AA04Z245)the Basic Research Universities Special Fund Operations(No.JUSRP11127)
文摘This paper proposes a novel method for incorporating wave domain prediction in a three-channel(3CH)architecture,which is the optimal architecture from a transparency point of view,to overcome the poor transparency problem of using the wave variable method in a time-delay teleoperation system.A 3CH teleoperation control architecture is established by selecting parameters of the 4CH architecture sensibly for the system without force sensor in the master side.The communication channel is divided into a two-port model by combining force and velocity information reasonably to extend the wave variable method to a 3CH architecture.Then the I/O signal of the two-port model is transformed into wave variable.A predictor is added to the wave domain of the master side to further improve the transparency of the system,and a regulator is designed to ensure the passivity of the predictor.Experimental results show that the proposed method can guarantee stability and improve the transparency of the teleoperation system with time-delay.
基金National Natural Science Foundation of China(No.41874125,No.41430322).
文摘Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.
基金The authors would like thank LI Renjiang and HU Bin from the China Three Gorges Corporation for providing many valuable suggestions for the establishment of the physical models.This work was supported by the National Natural Science Foundation of China(No.U23A2045)the China Three Gorges Corporation(YM(BHT)/(22)022)the Scientific Research Project of Chongqing Municipal Bureau of Planning and Natural Resources(Evaluation and Reinforcement Technology of Surge Disaster Caused by High and Steep Dangerous Rocks in Chongqing Reservoir Area of the Three Gorges Project,KJ-2023046).
文摘The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.
文摘In this study the effect of the surface waves over sea surface roughness (z0) and drag coefficient (CD) is investigated by combining an ocean wave model and a simplified algorithm, which estimates z0 and CD with and without dependence on the sea state. This investigation was possible from several numerical simulations with the Wave-Watch-III (WW3) model for complex wind conditions. The numerical experiments were performed for idealized like-hurricanes with different translation speed (0, 5 and 10 m/s) and maximum wind speed (MWS) at the centre (35, 45 and 55 m/s). It is observed that z0 and CD are strongly dependent on the sea state, via substantial modification in Charnock parameterization (zch). As the hurricane translation speed increases more discrepancies in z0 and CD are observed in opposite quadrants around the region of MWS. As for instance, higher, longer and older (or more developed) waves, located in the front-right quadrant, produce lower values of z0 and CD. In the rear-left quadrant, where the waves are lower, shorter and younger (or less developed), higher values of z0 and CD are observed. In addition the difference between values on opposite quadrants increases as the hurricane intensity increases, showing the hurricane intensification dependence. Interesting aspects are observed in scatter plotting wave age versus Charnock coefficient. It is also observed that zch, which has a constant value of 0.0185, is modified by the sea state, where young waves produce higher values of zch, while old waves are related to lower values of zch when compared with zch without dependence on sea state.
基金supported by the National Natural Science Foundation of China(No.92167202,61925102,U21B2014,62101069)the National Key R&D Program of China(No.2020YFB1805002)。
文摘In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.
文摘Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent <span style="font-family:Verdana;">rotating and rectilinear particle motions. A mathematical statistics-based</span><span style="font-family:Verdana;"> wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This </span><span style="font-family:Verdana;">affects</span><span style="font-family:Verdana;"> a different approach to the conventional deterministic model forecasting of waves.</span>
文摘The impact of ERS-1 altimeter significant wave height on analysis of wave field and wave pre- dictions is tested through analysis of selected cases. Application of the altimeter data may modifg initial tield and thus 24-hour prediction of significant wave height. However the variations in initial wave field almost make no effect on 48-hour predictions.
基金supported by the ‘‘National Natural Science Foundation–Outstanding Youth Foundation’’
文摘In this paper, flow reconstruction accuracy and flow prediction capability of discontinuous transonic flow field by means of proper orthogonal decomposition （POD） method is studied. Although linear superposition of ＂high frequency waves＂ in different POD modes can achieve the reconstruction of the shock wave, the smoothness of the solution near the shock wave cannot be guaranteed. The modal coefficients are interpolated or extrapolated and different modal components are superposed to realize the prediction of the flow field beyond the snapshot sets. Results show that compared with the subsonic flow, the transonic flow with shock wave requires more POD modes to reach a comparative reconstruction accuracy. When a shock wave exists, the interpolation prediction ability is acceptable. However, large errors exist in extrapolation, and increasing the number of POD modes cannot effectively improve the prediction accuracy of the flow field.
文摘Under the influence of a one-dimensional stationary outfield with the equilibrium between kinetic and potential energy produced by it,a modified Sch(?)rdinger equation in the form i((?)ψ/(?)t)t=a (?)~2ψ/ax^2-ib (?),where b=b_o(?)T/(?)x,is used to describe the behavior of the probability wave on the six-month departure charts at the 500 hPa level.It is found that C=2πa/L-b_o(?)T/ax and when L→∞,then C= -b_o(?)T/(?)x,where C is wave velocity,a and b are constants,and L is wavelength.The motion direction of probability waves is against the outfield temperature gradient,and their velocity is related to the absolute value of temperature gradient.The motion of waves shrinks in heat sinks and expands in heat sources,which have been verified in practice.Finally the six-month departure probability wave and the modified Sch(?)rdinger equation are used in the MOS predictions of temperature and rainfall in spring-summer 1981-1985 in Jilin Province and the accuracy for trend predictions is equal to 80%.
基金China National Sicence Foundation with Grant No.91870003
文摘An improved model for numerically predicting nonlinear wave forces exerted on an offshore structure is pro- posed.In a previous work[9],the authors presented a model for the same purpose with an open boundary condi- tion imposed,where the wave celerity has been defined constant.Generally,the value of wave celerity is time-de- pendent and varying with spatial location.With the present model the wave celerity is evaluated by an upwind dif- ference scheme,which enables the method to be extended to conditions of variable finite water depth,where the value of wave celerity varies with time as the wave approaches the offshore structure.The finite difference method incorporated with the time-stepping technique in time domain developed here makes the numerical evolution effec- tive and stable.Computational examples on interactions between a surface-piercing vertical cylinder and a solitary wave or a cnoidal wave train demonstrates the validity of this program.
基金supported by the Open Project of Xiangjiang Laboratory(No.22XJ02003)National Science Fund for Outstanding Young Scholars(No.62122093)+1 种基金Science&Technology Project for Young and Middle-aged Talents of Hunan(No.2023TJ-Z03)University Fundamental Research Fund(Nos.23-ZZCX-JDZ-28 and ZK21-07).
文摘The significant wave height prediction holds critical value for marine energy development,coastal infrastructure planning,and ensuring safety in maritime operations.The precision of such predictions carries substantial the oretical and practical weight.This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction,serving as a methodical guidepost for academicians.The study introduces an all-encompassing predictive framework for significan wave height,which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs.The framework is structured into four principal layers i...feature selection,basic prediction,data decomposition,and parameter optimization.The ensuing sections meticulously dissect the methodologies within these strata,elucidating their core concepts,distinctive features merits,and constraints,and their applicability to significant wave height prediction.To wrap up,the study delves into fresh research inguiries and avenues pertinent to the discipline,thereby broadening the comprehension of significant wave height prediction.In essence,this scholarly article imparts critical knowledge beneficial to the realm of marine technology.
文摘In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model selection of fuzzy IF-THEN rules structure and numbers is not an automatic process.In addition,in the ANFIS model extraction of fuzzy antecedent and consequent parameters is a gradient-based method which makes the answer susceptible to entrap in local optima.To cope with the ANFIS deficiencies,herein the PSO algorithm is coupled with the wave predictor FIS models in three viewpoints to optimize fuzzy subtractive clustering parameters,i.e.radii of clustering and quash factor,and the antecedent and consequent parameter of fuzzy IF-THEN rules.At first viewpoint,two PSO algorithms are used to optimize fuzzy subtractive clustering parameters and fuzzy IF-THEN rule parameters.In the second viewpoint,a PSO algorithm is used to optimize subtractive clustering parameters while the ANFIS model is used to tune the fuzzy IF-THEN rule parameters.In the third viewpoint,only a PSO algorithm is used to optimize the subtractive clustering parameters along with fuzzy IF-THEN rule parameters.Gathered data sets by National Data Buoy Center(NDBC)at Lake Michigan are used to evaluate the developed models for prediction of wave parameters including significant wave height and peak spectral period.Results indicate the efficiency of PSO algorithm to improve the ANFIS model accuracy.
