This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The im...This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The impact of the VGs is assessed through the improved delayed detached eddy simulations(IDDES)after validating predictions against previous experimental measurements and other numerical predictions for the base case.The simulations indicate that strategically installed VGs can reduce the average slipstream velocity(U slipstream)and the upper limit of slipstream velocity(U_(slipstream,max))by~17%and~15%,respectively,as well as moving the peaks downstream by approximately train height,thus reducing the danger posed by slipstream to waiting passengers and trackside workers.Analysis shows that the wake turbulent kinetic energy diminishes as the vortex generators decelerate the downwash flow and reduce shear production in the wake.It is also found that the presence of VGs significantly impacts the flow on the upper surface near the tail by modifying the unsteady trailing longitudinal vortices through the formation of additional counter-rotating longitudinal vortices from the VGs.These latter vortices prevent the merging of vortical airflow around the trailing nose tip,which is otherwise induced by the longitudinal vortex of the train.They also reduce vortex intensity through cross-annihilation and cross diffusion as the wake advects downstream,limiting outwards advection through interaction with the image pair,and contributing to a decrease in the peak slipstream value.The method proposed offers a simple approach to wake control leading to significant slipstream benefits.展开更多
To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-posi...To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.展开更多
With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbin...With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.展开更多
Regional turbofan aircraft,which are used for medium-short distances,have a heightened risk of high-altitude Wake Vortices(VV)because of their tail-mounted engines and high horizontal tail configurations.For some regi...Regional turbofan aircraft,which are used for medium-short distances,have a heightened risk of high-altitude Wake Vortices(VV)because of their tail-mounted engines and high horizontal tail configurations.For some regional medium-short-range turbofan aircraft,this threat is higher than that for conventionally designed aircraft.To analyze the flight safety of turbofan aircraft during cruise,this study developed a model to assess wake vortex encounters based on evolutionary high-altitude wake flow patterns.First,the high-altitude wake vortex aircraft dissipation patterns were analyzed by combining Quick Access Recorder(QAR)flight data with the wake vortex evolution model.Then,to consider the uniqueness of the medium-short-range turbofan aircraft,the severity of the wake vortex encounters was simulated using an induced roll moment coefficient.The proposed high-altitude wake vortex encounter model was able to identify and assess the highaltitude wake vortex changes,the bearing moments at different altitudes,and the atmospheric pressure conditions.Using the latest wake separation standards from the International Civil Aviation Organization(ICAO),acceptable safety wake intervals for follower aircraft in different scenarios were determined for the safety assessment.The results indicate that compared to mid and low altitudes,the high-altitude aircraft wake vortex dissipation rate is faster,the ultimate bearing moment is weaker,and the roll moment coefficient is higher,which confirm that there is elevated wake vortex encounter severity for regional turbofan aircraft.As safety is found to deteriorate when encountering wake vortices at altitudes higher than 8 km,new medium-short-range turbofan regional aircraft require higher safety margins than the latest wake separation standards.展开更多
In the Northern Hemisphere,cold wakes induced by tropical cyclones(TCs)are generally biased to the right of the storm track.However,a recent study found that a non-negligible proportion of cold wakes is actually leftw...In the Northern Hemisphere,cold wakes induced by tropical cyclones(TCs)are generally biased to the right of the storm track.However,a recent study found that a non-negligible proportion of cold wakes is actually leftward-biased.To further reveal the underlying physical mechanisms,the three-dimensional dynamic processes for the typical leftward cold wake of Hurricane Jova(2005)are investigated through a sequence of numerical simulations.Results reveal that the vertical advection in response to Jova(2005)is biased to the left of its track in the upper layer.In cooperation with the heterogenous ambient oceanic temperature stratification,the rightward vertical mixing is suppressed while the leftward feature of vertical advection is further intensified,which effectively promotes the formation of leftward cold wake.Additionally,the currents induced by Jova(2005)drive colder(warmer)water to the left(right)when coupled with background horizontal temperature gradients and then strengthen the leftward distribution of the temperature anomaly.These conclusions are substantiated by the control simulation,as the upper-layer temperature anomaly is restored to rightward disposition with homogeneous initial thermal structures.Based on three groups of sensitivity experiments,the leftward pattern of upwelling is found to be inextricably accompanied by the curl of wind stress caused by the movement of TCs.With the increase in translation speed from the stationary state,the symmetric structure of vertical velocity is gradually distorted to be leftward.Furthermore,the leftward bias distance of the upwelling center in the upper layer positively correlates with the radius of maximum wind,indicating that the wind structure can significantly influences the oceanic responses to TCs.展开更多
The increase in aerodynamic drag brings high energy consumption,which is a critical issue in the development of high-speed trains.Inspired by the excellent hydrodynamic characteristics of fish movement in nature,a two...The increase in aerodynamic drag brings high energy consumption,which is a critical issue in the development of high-speed trains.Inspired by the excellent hydrodynamic characteristics of fish movement in nature,a two-dimensional numerical simulation method based on spring-smoothing model and adaptive mesh technology was utilized to explore the effects of different fishtail structures and two flexible motion modes(Eel mode and Lunate-tail mode)on the wake of high-speed trains,and to assess their potential for aerodynamic drag reduction.Results indicate that the biomimetic fishtail successfully suppresses the alternating shedding of vortices in the wake,and induces the aerodynamic drag fluctuation period to align with the fishtail oscillation period.The fishtail length,oscillation mode,and frequency have a significant impact on the wake flow and aerodynamic drag of the train.Among these,a 1850 mm Eel fishtail with parameters ofλ=1 and T=8 s achieves the optimal drag reduction effect,with drag reduction rates of 39.12%and 26.00%for the tail car and the entire train,respectively.These findings provide a theoretical basis for the design of new low-resistance railway trains,promoting the sustainable development of rail transit towards goals of high-speed and energy-efficient.展开更多
The Light Detection and Ranging(LiDAR)data analysis method has emerged as a powerful and versatile tool for characterizing atmospheric conditions and modeling light propagation through various media.In the context of ...The Light Detection and Ranging(LiDAR)data analysis method has emerged as a powerful and versatile tool for characterizing atmospheric conditions and modeling light propagation through various media.In the context of renewable energy,particularly wind energy,LiDAR is increasingly utilized to analyze wind flow,turbine wake effects,and turbulence in complex terrains.This study focuses on advancing LiDAR data interpretation through the development and application of the LiDAR Statistical Barnes Objective Analysis(LiSBOA)method.LiSBOA enhances the capacity of scanning LiDAR systems by enabling more precise optimization of scan configurations and improving the retrieval of wind statistics across Cartesian grids.Unlike conventional approaches,LiSBOA offers fine-grained control over azimuthal resolution and spatial filtering,which allows for the detailed reconstruction of wind fields and turbulence structures.These capabilities are crucial for accurately simulating wind turbine wakes and power capture,particularly in environments with variable atmospheric stability and complex topography.Field deployments and comparative assessments against traditional meteorological mast data demonstrate the effectiveness of LiSBOA.The method reduces wind velocity estimation errors to within 3%and increases the accuracy of turbulence intensity measurements by over 4%.Such improvements are significant for enhancing wind resource assessment,optimizing turbine placement,and refining control strategies for operational turbines.LiSBOA represents a robust advancement in LiDAR data processing for wind energy applications.By addressing limitations in spatial resolution and measurement uncertainty,it supports more reliable modeling of wake interactions and flow variability.This work contributes to improving the efficiency and reliability of wind energy systems through advanced remote sensing and statistical analysis techniques.展开更多
When a ship moves in an oblique flow,its hydrodynamic loads and wake characteristics vary substantially from those in straight-ahead motion.This dissimilarity can be even more complex when the ship operates in a seawa...When a ship moves in an oblique flow,its hydrodynamic loads and wake characteristics vary substantially from those in straight-ahead motion.This dissimilarity can be even more complex when the ship operates in a seaway of shallow water.In this paper,a numerical analysis of the shallow-water effect on the hydrodynamic forces and wake characteristics of an international ship model,KVLCC2,in oblique flows is conducted.Numerical simulations are performed based on the Reynolds Averaged NavierStokes equation in conjunction with the shear stress transport(SST)k-ωturbulence model.Four relative water depths(h=1.2T,1.5T,3.0T,and 24T;T is the ship draft)and five different drift angles(β=0°,5°,10°,15°,and 20°)are considered.Results reveal the following:i)The shallow-water effect is strong and leads to nonlinear increases in the longitudinal force regardless of drift angles and on the transverse force and yaw moment whenever the drift angle increases.ii)In shallow water,the mean wake fraction is sensitive to the drift angle,and the strength of the aft-body vortex on the leeward side increases.展开更多
The sedimentary bed morphology modulated by the wake flow of a wall-mounted flexible aquatic vegetation blade across various structural aspect ratios(A_(R)=l/b,where l and b are the length and width of the blade,respe...The sedimentary bed morphology modulated by the wake flow of a wall-mounted flexible aquatic vegetation blade across various structural aspect ratios(A_(R)=l/b,where l and b are the length and width of the blade,respectively)and incoming flow velocities was experimentally investigated in a water channel.A surface scanner was implemented to quantify bed topography,and a tomographic particle image velocimetry system was used to characterize the three-dimensional wake flows.The results showed that due to the deflection of incoming flow,the velocity magnitude increased at the lateral sides of the blade,thereby producing distinctive symmetric scour holes in these regions.The normalized morphology profiles of the sedimentary bed,which were extracted along the streamwise direction at the location of the maximum erosion depth,exhibited a self-similar pattern that closely followed a sinusoidal wave profile.The level of velocity magnitude enhancement was highly correlated to the postures of the flexible blade.At a given flow velocity,the blade with lower aspect ratios exhibited less significant deformation,causing more significant near-bed velocity enhancement in the wake deflection zone and therefore leading to higher erosion volumes.Further investigation indicated that when the blade underwent slight deformation,the larger velocity enhancement close to the bed can be attributed to more significant flow deflection effects at the lateral sides of the blade and stronger flow mixing with high momentum flows away from the bed.Supported with measurements,a basic formula was established to quantify the shear stress acting on the sedimentary bed as a function of incoming flow velocity and blade aspect ratio.展开更多
Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to...Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.展开更多
Dynamic wake field information is vital for the optimized design and control of wind farms.Combined with sparse measurement data from light detection and ranging(LiDAR),the physics-informed neural network(PINN)framewo...Dynamic wake field information is vital for the optimized design and control of wind farms.Combined with sparse measurement data from light detection and ranging(LiDAR),the physics-informed neural network(PINN)frameworks have recently been employed for forecasting freestream wind and wake fields.However,these PINN frameworks face challenges of low prediction accuracy and long training times.Therefore,this paper constructed a PINN framework for dynamic wake field prediction by integrating two accuracy improvement strategies and a step-by-step training time saving strategy.The results showed that the different performance improvement routes significantly improved the overall performance of the PINN.The accuracy and efficiency of the PINN with spatiotemporal improvement strategies were validated via LiDAR-measured data from a wind farm in Shandong province,China.This paper sheds light on load reduction,efficiency improvement,intelligent operation and maintenance of wind farms.展开更多
基金Project(52372370)supported by the National Natural Science Foundation of ChinaProject(2023ZZTS0379)supported by the Graduate Student Independent Innovation Project of Central South University,ChinaProject(202206370058)supported by the China Scholarship Council。
文摘This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The impact of the VGs is assessed through the improved delayed detached eddy simulations(IDDES)after validating predictions against previous experimental measurements and other numerical predictions for the base case.The simulations indicate that strategically installed VGs can reduce the average slipstream velocity(U slipstream)and the upper limit of slipstream velocity(U_(slipstream,max))by~17%and~15%,respectively,as well as moving the peaks downstream by approximately train height,thus reducing the danger posed by slipstream to waiting passengers and trackside workers.Analysis shows that the wake turbulent kinetic energy diminishes as the vortex generators decelerate the downwash flow and reduce shear production in the wake.It is also found that the presence of VGs significantly impacts the flow on the upper surface near the tail by modifying the unsteady trailing longitudinal vortices through the formation of additional counter-rotating longitudinal vortices from the VGs.These latter vortices prevent the merging of vortical airflow around the trailing nose tip,which is otherwise induced by the longitudinal vortex of the train.They also reduce vortex intensity through cross-annihilation and cross diffusion as the wake advects downstream,limiting outwards advection through interaction with the image pair,and contributing to a decrease in the peak slipstream value.The method proposed offers a simple approach to wake control leading to significant slipstream benefits.
基金The National Natural Science Foundation of China (No. 51908107)。
文摘To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.
基金Supported by the National Natural Science Foundation of China under Grant No.52131102.
文摘With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.
基金supported by the National Natural Science Foundation of China(Nos.U2333209,U1733203)the National Key R&D Program of China(No.2021YFF0603904)the Civil Aviation Administration of China(No.AQ20200019)。
文摘Regional turbofan aircraft,which are used for medium-short distances,have a heightened risk of high-altitude Wake Vortices(VV)because of their tail-mounted engines and high horizontal tail configurations.For some regional medium-short-range turbofan aircraft,this threat is higher than that for conventionally designed aircraft.To analyze the flight safety of turbofan aircraft during cruise,this study developed a model to assess wake vortex encounters based on evolutionary high-altitude wake flow patterns.First,the high-altitude wake vortex aircraft dissipation patterns were analyzed by combining Quick Access Recorder(QAR)flight data with the wake vortex evolution model.Then,to consider the uniqueness of the medium-short-range turbofan aircraft,the severity of the wake vortex encounters was simulated using an induced roll moment coefficient.The proposed high-altitude wake vortex encounter model was able to identify and assess the highaltitude wake vortex changes,the bearing moments at different altitudes,and the atmospheric pressure conditions.Using the latest wake separation standards from the International Civil Aviation Organization(ICAO),acceptable safety wake intervals for follower aircraft in different scenarios were determined for the safety assessment.The results indicate that compared to mid and low altitudes,the high-altitude aircraft wake vortex dissipation rate is faster,the ultimate bearing moment is weaker,and the roll moment coefficient is higher,which confirm that there is elevated wake vortex encounter severity for regional turbofan aircraft.As safety is found to deteriorate when encountering wake vortices at altitudes higher than 8 km,new medium-short-range turbofan regional aircraft require higher safety margins than the latest wake separation standards.
基金supported by the National Natural Science Foundation of China(Grant No.42192552)。
文摘In the Northern Hemisphere,cold wakes induced by tropical cyclones(TCs)are generally biased to the right of the storm track.However,a recent study found that a non-negligible proportion of cold wakes is actually leftward-biased.To further reveal the underlying physical mechanisms,the three-dimensional dynamic processes for the typical leftward cold wake of Hurricane Jova(2005)are investigated through a sequence of numerical simulations.Results reveal that the vertical advection in response to Jova(2005)is biased to the left of its track in the upper layer.In cooperation with the heterogenous ambient oceanic temperature stratification,the rightward vertical mixing is suppressed while the leftward feature of vertical advection is further intensified,which effectively promotes the formation of leftward cold wake.Additionally,the currents induced by Jova(2005)drive colder(warmer)water to the left(right)when coupled with background horizontal temperature gradients and then strengthen the leftward distribution of the temperature anomaly.These conclusions are substantiated by the control simulation,as the upper-layer temperature anomaly is restored to rightward disposition with homogeneous initial thermal structures.Based on three groups of sensitivity experiments,the leftward pattern of upwelling is found to be inextricably accompanied by the curl of wind stress caused by the movement of TCs.With the increase in translation speed from the stationary state,the symmetric structure of vertical velocity is gradually distorted to be leftward.Furthermore,the leftward bias distance of the upwelling center in the upper layer positively correlates with the radius of maximum wind,indicating that the wind structure can significantly influences the oceanic responses to TCs.
基金Project(2025A1515011803)supported by the Guangdong Basic and Applied Basic Research Foundation,ChinaProject(2023JC01020)supported by the Jiangmen Basic and Theoretical Science Research Plan,China。
文摘The increase in aerodynamic drag brings high energy consumption,which is a critical issue in the development of high-speed trains.Inspired by the excellent hydrodynamic characteristics of fish movement in nature,a two-dimensional numerical simulation method based on spring-smoothing model and adaptive mesh technology was utilized to explore the effects of different fishtail structures and two flexible motion modes(Eel mode and Lunate-tail mode)on the wake of high-speed trains,and to assess their potential for aerodynamic drag reduction.Results indicate that the biomimetic fishtail successfully suppresses the alternating shedding of vortices in the wake,and induces the aerodynamic drag fluctuation period to align with the fishtail oscillation period.The fishtail length,oscillation mode,and frequency have a significant impact on the wake flow and aerodynamic drag of the train.Among these,a 1850 mm Eel fishtail with parameters ofλ=1 and T=8 s achieves the optimal drag reduction effect,with drag reduction rates of 39.12%and 26.00%for the tail car and the entire train,respectively.These findings provide a theoretical basis for the design of new low-resistance railway trains,promoting the sustainable development of rail transit towards goals of high-speed and energy-efficient.
文摘The Light Detection and Ranging(LiDAR)data analysis method has emerged as a powerful and versatile tool for characterizing atmospheric conditions and modeling light propagation through various media.In the context of renewable energy,particularly wind energy,LiDAR is increasingly utilized to analyze wind flow,turbine wake effects,and turbulence in complex terrains.This study focuses on advancing LiDAR data interpretation through the development and application of the LiDAR Statistical Barnes Objective Analysis(LiSBOA)method.LiSBOA enhances the capacity of scanning LiDAR systems by enabling more precise optimization of scan configurations and improving the retrieval of wind statistics across Cartesian grids.Unlike conventional approaches,LiSBOA offers fine-grained control over azimuthal resolution and spatial filtering,which allows for the detailed reconstruction of wind fields and turbulence structures.These capabilities are crucial for accurately simulating wind turbine wakes and power capture,particularly in environments with variable atmospheric stability and complex topography.Field deployments and comparative assessments against traditional meteorological mast data demonstrate the effectiveness of LiSBOA.The method reduces wind velocity estimation errors to within 3%and increases the accuracy of turbulence intensity measurements by over 4%.Such improvements are significant for enhancing wind resource assessment,optimizing turbine placement,and refining control strategies for operational turbines.LiSBOA represents a robust advancement in LiDAR data processing for wind energy applications.By addressing limitations in spatial resolution and measurement uncertainty,it supports more reliable modeling of wake interactions and flow variability.This work contributes to improving the efficiency and reliability of wind energy systems through advanced remote sensing and statistical analysis techniques.
基金supported by the National Key R&D Plan Project(No.2019YFD0901003)。
文摘When a ship moves in an oblique flow,its hydrodynamic loads and wake characteristics vary substantially from those in straight-ahead motion.This dissimilarity can be even more complex when the ship operates in a seaway of shallow water.In this paper,a numerical analysis of the shallow-water effect on the hydrodynamic forces and wake characteristics of an international ship model,KVLCC2,in oblique flows is conducted.Numerical simulations are performed based on the Reynolds Averaged NavierStokes equation in conjunction with the shear stress transport(SST)k-ωturbulence model.Four relative water depths(h=1.2T,1.5T,3.0T,and 24T;T is the ship draft)and five different drift angles(β=0°,5°,10°,15°,and 20°)are considered.Results reveal the following:i)The shallow-water effect is strong and leads to nonlinear increases in the longitudinal force regardless of drift angles and on the transverse force and yaw moment whenever the drift angle increases.ii)In shallow water,the mean wake fraction is sensitive to the drift angle,and the strength of the aft-body vortex on the leeward side increases.
基金supported by the National Science Foundation under Grant No.2327916.
文摘The sedimentary bed morphology modulated by the wake flow of a wall-mounted flexible aquatic vegetation blade across various structural aspect ratios(A_(R)=l/b,where l and b are the length and width of the blade,respectively)and incoming flow velocities was experimentally investigated in a water channel.A surface scanner was implemented to quantify bed topography,and a tomographic particle image velocimetry system was used to characterize the three-dimensional wake flows.The results showed that due to the deflection of incoming flow,the velocity magnitude increased at the lateral sides of the blade,thereby producing distinctive symmetric scour holes in these regions.The normalized morphology profiles of the sedimentary bed,which were extracted along the streamwise direction at the location of the maximum erosion depth,exhibited a self-similar pattern that closely followed a sinusoidal wave profile.The level of velocity magnitude enhancement was highly correlated to the postures of the flexible blade.At a given flow velocity,the blade with lower aspect ratios exhibited less significant deformation,causing more significant near-bed velocity enhancement in the wake deflection zone and therefore leading to higher erosion volumes.Further investigation indicated that when the blade underwent slight deformation,the larger velocity enhancement close to the bed can be attributed to more significant flow deflection effects at the lateral sides of the blade and stronger flow mixing with high momentum flows away from the bed.Supported with measurements,a basic formula was established to quantify the shear stress acting on the sedimentary bed as a function of incoming flow velocity and blade aspect ratio.
基金the Science and Technology Project of China South Power Grid Co., Ltd. under Grant No. 036000KK52222044 (GDKJXM20222430).
文摘Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.
基金supported by the National Natural Science Foundation of China(Grant Nos.12072105,11932006,and 52308498)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220976).
文摘Dynamic wake field information is vital for the optimized design and control of wind farms.Combined with sparse measurement data from light detection and ranging(LiDAR),the physics-informed neural network(PINN)frameworks have recently been employed for forecasting freestream wind and wake fields.However,these PINN frameworks face challenges of low prediction accuracy and long training times.Therefore,this paper constructed a PINN framework for dynamic wake field prediction by integrating two accuracy improvement strategies and a step-by-step training time saving strategy.The results showed that the different performance improvement routes significantly improved the overall performance of the PINN.The accuracy and efficiency of the PINN with spatiotemporal improvement strategies were validated via LiDAR-measured data from a wind farm in Shandong province,China.This paper sheds light on load reduction,efficiency improvement,intelligent operation and maintenance of wind farms.
