CN111597506A - Prediction method for near-shore wave breaking parameters and wave height - Google Patents

Prediction method for near-shore wave breaking parameters and wave height Download PDF

Info

Publication number
CN111597506A
CN111597506A CN202010337588.5A CN202010337588A CN111597506A CN 111597506 A CN111597506 A CN 111597506A CN 202010337588 A CN202010337588 A CN 202010337588A CN 111597506 A CN111597506 A CN 111597506A
Authority
CN
China
Prior art keywords
wave
grid point
height
breaking
shore
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010337588.5A
Other languages
Chinese (zh)
Other versions
CN111597506B (en
Inventor
张弛
李元
戴玮琦
管大为
时健
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hohai University HHU
Original Assignee
Hohai University HHU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hohai University HHU filed Critical Hohai University HHU
Priority to CN202010337588.5A priority Critical patent/CN111597506B/en
Publication of CN111597506A publication Critical patent/CN111597506A/en
Application granted granted Critical
Publication of CN111597506B publication Critical patent/CN111597506B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/17—Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a method for predicting near-shore wave breaking parameters and wave height, in particular to a method for calculating wave breaking parameters by applying a new breaking index formula in a parameterized wave prediction model, simulating and forecasting the wave height based on a wave energy conservation equation. The method has higher prediction precision, and the root mean square percentage error is 7 to 13 percent. Compared with other wave prediction model results of the same type, the prediction precision is improved by 10% -24% (the average improvement is 19%). Especially under the condition of normal waves (the incident wave steepness is less than 0.025), the prediction accuracy is remarkably improved.

Description

Prediction method for near-shore wave breaking parameters and wave height
Technical Field
The invention belongs to the technical field of simulation and prediction of near-shore waves, and particularly relates to a method for predicting near-shore wave breaking parameters and wave height.
Background
The wave is one of main driving forces of the coast physical process, and the accurate prediction of the wave height has very important significance on the simulation of the evolution of the coast morphology, the design of coast wave-resistant buildings and the planning of the development of marine fishery. In shallow water areas, wave breaking is the dominant process of controlling wave height distribution on the seabed, and also influences the nonlinear form, wave currents, sediment movement and seabed landform evolution of waves.
The parameterized wave prediction model based on time averaging is widely used for the simulation and forecast of the near-shore waves due to high calculation efficiency and calculation accuracy. Such models are based on the conservation equation of wave energy, which considers that the attenuation of wave energy during the propagation of waves from deep sea to shallow water is caused by wave breakup. Therefore, the accuracy of the calculation of the wave energy dissipation caused by wave breaking is a key factor affecting the reliability of the wave forecasting model. Existing models tend to refer to a probabilistic distribution of random wave heights and calculate the wave energy loss beyond the critical wave height. The accuracy of this method of calculating wave energy loss depends on a key parameter in the model, the fragmentation index. However, it is difficult to satisfy the high-precision prediction of the near-shore wave height by using the existing fragmentation index formula, and particularly, the prediction precision needs to be improved under the condition of the normal waves.
Reference documents:
[1]Ruessink,B.G.,Walstra,D.J.R.,and Southgate,H.N.Calibration andverification of a parametric wave model on barred beaches.CoastalEngineering,2003,48:139–149.
[2]Apotsos,A.,Raubenheimer,B.,Elgar,S.,and Guza,R.T.Testing andcalibrating parametric wave ransformation models on natural beaches.CoastalEngineering,2008,55:224–235.
[3]Thornton,E.B.,and Guza,R.T.Transformation of wave heightdistribution.Journal of Geophysical research:Oceans,1983,88(C10):5925–5938.
[4]Janssen,T.T.,and Battjes,J.A.A note on wave energy dissipationover steep beaches.Coastal Engineering,2007,54(9):711–716.
[5]Baldock,T.E.,Holmes,P.,Bunker,S.,and van Weert,P.Cross-shorehydrodynamics within an unsaturated surf zone.Coastal Engineering,1998,34:173–196.
[6]Battjes,J.A，and Janssen，J.P.F.M.Energy loss and set-up due tobreaking of random waves.Proceedings of the 16th International Conference onCoastal Engineering,1978,pp.569–587.
[7]Salmon,J.E.,Holthuijsen,L.H.,Zijlema,M.，van Vledder,G.P.,andPietrzak,J.D.Scaling depth-induced wave-breaking in two-dimensional spectralwave models.Ocean Modelling,2015,600 87:30–47.
[8]Lin,S.，and Sheng J.Assessing the performance of wave breakingparameterizations in shallow waters in spectral wave models.Ocean Modelling,2017,120:41–59.
disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention provides a method for predicting the near-shore wave breaking parameters and the wave height, which applies a new breaking index formula in a parameterized wave prediction model to calculate the wave breaking parameters and predict the near-shore wave height, thereby improving the accuracy of wave height prediction.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a method for predicting near-shore wave breaking parameters and wave heights comprises the following steps:
step 1, acquiring forecast water depth data of a coast and wave data collected by a wave buoy;
step 2, taking the water area between the wave buoy and the shore as a calculation range, dividing the calculation range into grids at equal intervals, enabling the starting point of each grid to be located at the wave buoy, and calculating the wave energy flow of a first grid point according to the collected data of the wave buoy;
step 3, sequentially calculating the fragmentation index, the fragmentation wave height and the wave energy dissipation of each grid point from the second grid point, and sequentially calculating the wave energy flow of each grid point according to an energy conservation equation;
step 4, calculating the root mean square wave height of each grid point according to the wave energy flow of each grid point obtained in the step 3;
and 5, acquiring measured wave height data in the calculated water area, carrying out error analysis on the measured wave height data and the wave height data obtained in the step 4, and evaluating the accuracy of wave height prediction according to error evaluation indexes.
Further, in step 2, the wave energy flow expression is as follows:
Figure BDA0002467137560000021
in the formula, FwRepresenting wave energy flow, HrmsIs root mean square wave height, cgThe wave group velocity is shown, theta is a wave direction angle, and rho and g respectively represent the density and the gravity acceleration of the water body; the subscript (n) denotes the nth grid point. The wave group velocity is obtained by linear wave theory calculation, and the root mean square wave height and wave direction angle data of the first grid point are from the wave buoy.
Further, in step 3, the expression of the fragmentation index of the grid point is as follows:
Figure BDA0002467137560000022
wherein γ is a fragmentation index, s0The subscript (n) represents the nth grid point, n is more than or equal to 2.
Further, in step 3, the expression of the fragmentation wave height of the grid point is:
Figure BDA0002467137560000023
the wave energy dissipation expression at the grid points is:
Figure BDA0002467137560000031
according to the energy conservation equation, the wave energy flow expression of the grid point is as follows:
Fw(n)＝Fw(n-1)-Db(n)
in the formula, HbThe breaking wave height is determined, gamma is a breaking index, k is a wave number, and h is water depth; dbIs the wave breaking energy dissipation value, α is the coefficient controlling the breaking strength, fpThe frequency of the spectrum peak is shown as rho and g respectively representing the density and the gravity acceleration of the water body, HrmsIs root mean square wave height；FwRepresenting wave energy flow; the subscripts (n) and (n-1) indicate the nth, (n-1) th grid point, n.gtoreq.2.
Further, in the step 4, from the second grid point, the wave group velocity of each grid point is obtained through the linear wave theory calculation, and the wave direction angle of each grid point is calculated by adopting the law of refraction; and (3) substituting the wave energy flow, the wave group velocity and the wave direction angle of each grid point into the formula (1), and calculating to obtain the root mean square wave height of each grid point.
Further, in the step 5, a Root Mean Square Percentage Error (RMSPE) is used as an error evaluation index, and an expression of the error evaluation index is as follows:
Figure BDA0002467137560000032
in the formula, RMSPE is the root mean square percentage error, N is the total number of grid points, N is the serial number of the grid points, and M and O respectively represent the wave height obtained by the simulation calculation of the grid points and the actually measured wave height. The smaller the RMSPE, the higher the accuracy of the forecast.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention applies a new fragmentation index formula in a parameterized wave prediction model, can obtain high-precision wave height prediction, and has a percentage root mean square error of 7-13%. Compared with the conventional wave prediction model of the same type, the wave height prediction method can improve the overall accuracy of wave height prediction by 10-24%. In addition, the wave height prediction method in the invention can be used for remarkably improving under the condition of normal waves (when the incident wave is steep).
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a graph of the predicted wave height versus the measured wave height for the method of the present invention;
FIG. 3 is a graph comparing the wave height prediction error of the method of the present invention with the error of other wave prediction models;
FIG. 4 is a schematic diagram showing the relationship between the error improvement of the wave height predicted by the method of the present invention and incident wave elements.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The flow of the method for predicting the near-shore wave breaking parameters and the wave height is shown in figure 1, and the method specifically comprises the following steps:
step 1, water depth data of a forecast coast and wave data collected by a wave buoy are obtained. This example includes three prototype coasts, respectively: coast 1, the Duck coast of the United states, with observation times ranging from 10 months 4 days to 10 months 26 days in 1994 for a total of 529 hours; coast 2, Egmond coast, the netherlands, with observation times of 10 months 15 days to 11 months 19 days in 1998 for a total of 841 hours; coast 3, the coast of Terscheling, the Netherlands, with observation times ranging from 5 months 25 days to 6 months 28 days in 1994, for a total of 816 hours.
Step 2, taking the water area between the wave buoy and the shore as a calculation range, dividing the range into grids at equal intervals, wherein the length of each grid is 1 meter, the initial point of each grid is located at the wave buoy, and calculating the wave energy flow of a first grid point according to the data collected by the wave buoy;
the wave energy flow expression is:
Figure BDA0002467137560000041
in the formula, FwRepresenting wave energy flow (unit: kilo-gram per square second), HrmsIs root mean square wave height (unit: meter), cgThe wave group velocity (unit: meter per second), theta is the wave direction angle (radian system), and rho and g respectively represent the density and the gravity acceleration of the water body; the subscript (n) denotes the nth grid point. The wave group velocity is obtained by linear wave theory calculation, and the root mean square wave height and wave direction angle data of the first grid point are from the wave buoy.
Step 3, sequentially calculating the fragmentation index, the fragmentation wave height and the wave energy dissipation of each grid point from the second grid point, and sequentially calculating the wave energy flow of each grid point according to an energy conservation equation;
the crushing index expression of the grid points is as follows:
Figure BDA0002467137560000042
wherein γ is a fragmentation index, s0The incident wave is steep, the incident wave is obtained through a wave buoy, k is wave number, the wave number is obtained by solving a dispersion equation, h is water depth (unit: meter), subscript (n) represents an nth grid point, and n is more than or equal to 2;
the expression of the height of the fragmentation wave at the grid point is as follows:
Figure BDA0002467137560000043
the wave energy dissipation expression at the grid points is:
Figure BDA0002467137560000044
according to the energy conservation equation, the wave energy flow expression of the grid point is as follows:
Fw(n)＝Fw(n-1)-Db(n)
in the formula, HbThe breaking wave height is determined, gamma is a breaking index, k is a wave number, and h is water depth; dbIs the wave breaking energy dissipation value, α is the coefficient for controlling the breaking strength, the value is 1, fpThe frequency of the spectrum peak is shown as rho and g respectively representing the density and the gravity acceleration of the water body, HrmsRoot mean square wave height; fwRepresenting wave energy flow; the subscripts (n) and (n-1) indicate the nth, (n-1) th grid point, n.gtoreq.2.
Step 4, calculating the root mean square wave height of each grid point according to the wave energy flow of each grid point obtained in the step 3; the method specifically comprises the following steps:
calculating the wave group velocity of each grid point from the second grid point through a linear wave theory, and calculating the wave direction angle of each grid point by adopting a refraction law; and (3) substituting the wave energy flow, the wave group velocity and the wave direction angle of each grid point into the formula (1), and calculating to obtain the root mean square wave height of each grid point.
And 5, acquiring measured wave height data in the calculated water area, carrying out error analysis on the measured wave height data and the wave height data obtained in the step 4, and evaluating the accuracy of wave height prediction according to error evaluation indexes. In this embodiment, the measured wave height distribution along the way is calculated by downloading the data of the pressure sensor along the way. The comparison of the wave height predicted using the method of the present invention with the measured wave height is shown in figure 2.
The Root Mean Square Percentage Error (RMSPE) is used as an error evaluation index, and the expression is as follows:
Figure BDA0002467137560000051
in the formula, RMSPE is the root mean square percentage error, N is the total number of grid points, N is the serial number of the grid points, and M and O respectively represent the wave height obtained by the simulation calculation of the grid points and the actually measured wave height. The smaller the RMSPE, the higher the accuracy of the forecast.
In this embodiment, the existing wave prediction models of the same type for comparing the prediction effects include: r2003 model (literature [1 ]); TG1983_ t model (documents [2], [3 ]); JB2007_ t models (documents [2] and [4 ]); b1998_ t model (documents [2], [5 ]); BJ1978_ t model (literature [2], [6 ]); the Sal2015 model (reference [7 ]); l2017 model (document [8 ]).
FIG. 3 shows the root mean square percentage error of predicted wave heights using the method of the present invention compared to the wave prediction model error described above. The errors of the method of the invention on the Duck coast, the Egmond coast and the Terschelling coast are respectively 7%, 13% and 12%, the prediction precision is improved by 22% compared with the R2003 model, the prediction precision is improved by 10% compared with the TG1983_ t model, the prediction precision is improved by 15% compared with the JB2007_ t model, the prediction precision is improved by 17% compared with the B1998_ t model, the prediction precision is improved by 20% compared with the BJ1978_ t model, the prediction precision is improved by 23% compared with the Sal2015 model, and the prediction precision is improved by 24% compared with the L2017 model.
In the embodiment, BSS (Brier Skill score) indexes are introduced to quantitatively evaluate the prediction results of the method and the conventional wave prediction model of the same type, so that the improvement of the accuracy of the wave prediction by using the method is verified.
The BSS index expression is as follows:
Figure BDA0002467137560000052
wherein, RMSPE(a)Indicating the prediction of wave height using the method of the invention, RMSPE(b)Representing the prediction of wave height using the existing same type of wave prediction model. A BSS value greater than 0 indicates that the accuracy of predicting the wave height using the method of the present invention is higher than using the existing same type of wave prediction model. Conversely, a BSS value less than 0 indicates that the accuracy of the wave height predicted by the method is lower than that of the existing wave prediction model of the same type. The closer the BSS value is to 1, the more remarkable the improvement of the prediction precision is.
FIG. 4 shows the error improvement (compared to the R2003 model) versus incident wave element for the Duck coast for predicting wave height using the method of the present invention. The zero point of the abscissa time corresponds to 12 points on 21/1994-9 (eastern time of the united states). It can be seen from the figure that the BSS value is greater than 1 when the incident wave steepness is less than 0.025. The method provided by the invention has the advantages that the error of predicting the wave height is obviously improved under the condition of normal waves.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiments according to the technical spirit of the present invention are within the scope of the present invention.

Claims (6)

1. A method for predicting near-shore wave breaking parameters and wave heights is characterized by comprising the following steps: the method comprises the following steps:
step 1, acquiring forecast water depth data of a coast and wave data collected by a wave buoy;
step 2, taking the water area between the wave buoy and the shore as a calculation range, dividing the calculation range into grids at equal intervals, enabling the starting point of each grid to be located at the wave buoy, and calculating the wave energy flow of a first grid point according to the collected data of the wave buoy;
step 3, sequentially calculating the fragmentation index, the fragmentation wave height and the wave energy dissipation of each grid point from the second grid point, and sequentially calculating the wave energy flow of each grid point according to an energy conservation equation;
step 4, calculating the root mean square wave height of each grid point according to the wave energy flow of each grid point obtained in the step 3;
and 5, acquiring measured wave height data in the calculated water area, carrying out error analysis on the measured wave height data and the wave height data obtained in the step 4, and evaluating the accuracy of wave height prediction according to error evaluation indexes.
2. The method of predicting near-shore wave breaking parameters and wave heights of claim 1, wherein: in the step 2, the wave energy flow expression is as follows:
Figure FDA0002467137550000011
in the formula, FwRepresenting wave energy flow, HrmsIs root mean square wave height, cgThe wave group velocity is shown, theta is a wave direction angle, and rho and g respectively represent the density and the gravity acceleration of the water body; the subscript (n) denotes the nth grid point.
3. The method of predicting near-shore wave breaking parameters and wave heights of claim 2, wherein: in step 3, the expression of the fragmentation index of the grid point is as follows:
Figure FDA0002467137550000012
wherein γ is a fragmentation index, s0The subscript (n) represents the nth grid point, n is more than or equal to 2.
4. The method of predicting near-shore wave breaking parameters and wave heights of claim 3, wherein: in step 3, the expression of the height of the fragmentation wave at the grid point is as follows:
Figure FDA0002467137550000013
the wave energy dissipation expression at the grid points is:
Figure FDA0002467137550000014
according to the energy conservation equation, the wave energy flow expression of the grid point is as follows:
Fw(n)＝Fw(n-1)-Db(n)
in the formula, HbThe breaking wave height is determined, gamma is a breaking index, k is a wave number, and h is water depth; dbIs the wave breaking energy dissipation value, α is the coefficient controlling the breaking strength, fpThe frequency of the spectrum peak is shown as rho and g respectively representing the density and the gravity acceleration of the water body, HrmsRoot mean square wave height; fwRepresenting wave energy flow; the subscripts (n) and (n-1) indicate the nth, (n-1) th grid point, n.gtoreq.2.
5. The method of predicting near-shore wave breaking parameters and wave height of any of claims 2-4, wherein: step 4, from the second grid point, calculating by a linear wave theory to obtain the wave group velocity of each grid point, and calculating the wave direction angle of each grid point by adopting a refraction law; and (3) substituting the wave energy flow, the wave group velocity and the wave direction angle of each grid point into the formula (1), and calculating to obtain the root mean square wave height of each grid point.
6. The method of predicting near-shore wave breaking parameters and wave heights of claim 1, wherein: in the step 5, the root mean square percentage error is used as an error evaluation index, and the expression is as follows:
Figure FDA0002467137550000021
in the formula, RMSPE is the root mean square percentage error, N is the total number of grid points, N is the serial number of the grid points, and M and O respectively represent the wave height obtained by the simulation calculation of the grid points and the actually measured wave height.
CN202010337588.5A 2020-04-26 2020-04-26 Prediction method for near-shore wave breaking parameters and wave height Active CN111597506B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010337588.5A CN111597506B (en) 2020-04-26 2020-04-26 Prediction method for near-shore wave breaking parameters and wave height

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010337588.5A CN111597506B (en) 2020-04-26 2020-04-26 Prediction method for near-shore wave breaking parameters and wave height

Publications (2)

Publication Number Publication Date
CN111597506A true CN111597506A (en) 2020-08-28
CN111597506B CN111597506B (en) 2022-07-15

Family

ID=72183813

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010337588.5A Active CN111597506B (en) 2020-04-26 2020-04-26 Prediction method for near-shore wave breaking parameters and wave height

Country Status (1)

Country Link
CN (1) CN111597506B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408179A (en) * 2021-07-15 2021-09-17 天津大学 Dynamic simulation method for calculating real-time wave breaking-caused mixing
CN116702659A (en) * 2023-08-08 2023-09-05 自然资源部第一海洋研究所 Rapid forecasting method and system for sea wave crushing parameters

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003060241A1 (en) * 2002-01-18 2003-07-24 Artificial Surf Reefs Limited Method of wave modification and reef for wave modification
CN104331599A (en) * 2014-09-30 2015-02-04 江苏省交通规划设计院股份有限公司 Unstructured grid nesting wave numerical simulation method
CN105205200A (en) * 2015-04-27 2015-12-30 河海大学 Verification method for silt coast wave-induced current numerical simulation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003060241A1 (en) * 2002-01-18 2003-07-24 Artificial Surf Reefs Limited Method of wave modification and reef for wave modification
CN104331599A (en) * 2014-09-30 2015-02-04 江苏省交通规划设计院股份有限公司 Unstructured grid nesting wave numerical simulation method
CN105205200A (en) * 2015-04-27 2015-12-30 河海大学 Verification method for silt coast wave-induced current numerical simulation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
B.GRUESSINK 等: "Calibration and verification of a parametric wave model on barred beaches", 《COASTAL ENGINEERING》 *
CHI ZHANG 等: "Parameterization of nearshore wave breaker index", 《COASTAL ENGINEERING》 *
时莹等: "SWAN模型对实验室小尺度浅水波浪模拟研究", 《海洋工程》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408179A (en) * 2021-07-15 2021-09-17 天津大学 Dynamic simulation method for calculating real-time wave breaking-caused mixing
CN116702659A (en) * 2023-08-08 2023-09-05 自然资源部第一海洋研究所 Rapid forecasting method and system for sea wave crushing parameters
CN116702659B (en) * 2023-08-08 2023-11-10 自然资源部第一海洋研究所 Rapid forecasting method and system for sea wave crushing parameters

Also Published As

Publication number Publication date
CN111597506B (en) 2022-07-15

Similar Documents

Publication Publication Date Title
Larson et al. SBEACH: Numerical model for simulating storm-induced beach change
Vickers et al. Fetch limited drag coefficients
Elias et al. Hydrodynamic validation of Delft3D with field measurements at Egmond
CN111597506B (en) Prediction method for near-shore wave breaking parameters and wave height
CN108399279B (en) Method for analyzing and controlling operation loss days of wharf mooring ship under influence of waves
Barthelmie et al. The role of atmospheric stability/turbulence on wakes at the Egmond aan Zee offshore wind farm
Van Rijn Longshore sand transport
CN110543679B (en) Multi-wave-condition large-size fixed marine platform horizontal force calculation method
CN109657409B (en) Sea-crossing bridge structure optimization method based on wind wave joint distribution extreme response
Hlophe Efficient real-time deterministic wave prediction for marine renewable energy
Simons Reliability of circulation models
Bishop et al. Wave prediction models
Lin Study of wave-current-ice interactions over the northwest Atlantic
Khanarmuei et al. Calibration and assimilation in hydrodynamic model of a micro-tidal estuary and comparison with Lagrangian drifter data
Ding et al. Modeling wave attenuation and runup in wetland and vegetated coast
Monbaliu Spectral wave models in coastal areas
Makarynska et al. Hydrodynamic and sediment plume modelling in a macro-tidal estuary
Bahmanzadegan Jahromi et al. Modeling of wind driven waves and estimation of wave energy in Chabahar Bay
Qin Application of the spectral wave model SWAN in Delaware Bay
Fu et al. Committee No I. 1: Environment
Herb et al. Quantifying Wave Energy on Minnesota Lakes
Sun et al. Analysis of wave fields under tropical cyclones in the South China Sea
Spanakis et al. Modelling of seaweed ingress into a nuclear power station cooling water intake
Luca et al. Evaluation of design wave conditions for a floating breakwater to be installed in front of San Marco square (Venice, IT)
Durap et al. Regional Coastal Vulnerability Estimation Using a Hybrid Bayesian Network Model Integrating 3-D Hydrodynamic Transport and Anfis Prediction Models

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant