Stochastic Doppler shift and encountered wave period distributions in Gaussian waves


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1 Ocean Engineering 26 (1999) Stochastic Doppler shift and encountered wave period distributions in Gaussian waves G. Lindgren a,*, I. Rychlik a, M. Prevosto b a Department of Mathematical Statistics, Lund University, Box 118, SE Lund, Sweden b IFREMER, Technopole de BrestIroise, BR 70, FR29280, Plouzané, France Received 18 August 1997; accepted 13 November 1997 Abstract We show how to calculate the encountered wave period distribution for a ship traveling with constant speed on a Gaussian random sea with a directionally distributed frequency spectrum Elsevier Science Ltd. All rights reserved. Keywords: Directional spectrum; Encountered spectrum; Random waves; Wave length; Wave period 1. Introduction Encountered wave period is the time elapsed between successive up or downcrossings of a mean level for an object moving with constant speed on a random sea. This is a random quantity. The Doppler effect changes the frequency of each elementary deterministic periodic wave that makes up the random sea. These changes result in a complicated change in the statistical distribution of the encountered wave period. We shall be concerned with the (half) zero crossing wave period, defined as the time between a downcrossing of the mean level and the following upcrossing, measured at a fixed point, and the (half) zero crossing encountered wave period, i.e. the time between mean level down and upcrossings for a moving object that moves with constant horizontal speed on the surface. We shall also have use for the (half) zero crossing wave length along a line, which is the distance between mean level * Corresponding author /99/$ see front matter 1998 Elsevier Science Ltd. All rights reserved. PII: S (98)
2 508 G. Lindgren et al. / Ocean Engineering 26 (1999) down and upcrossings for a section of the surface along a straight line, observed at a fixed time instant. Wave period and wave length can be considered as extreme cases of encountered wave period, which are found when moving with speed across the sea surface. Then, 0 will give wave period, and will give the equivalent of wave length after normalization. With finite, the Doppler shift in frequency for elementary waves will result in a complicated change in the encountered wave period pattern for a random sea. For a genuinely random sea, wave periods, measured at a fixed position, and encountered wave period when moving at constant speed, and wave length, are all random quantities. Their statistical distributions are in general very difficult to obtain, but for Gaussian sea states, where sea elevation is described by a Gaussian random process, one can calculate these distributions numerically with high accuracy, based on the spectral density function of the process. This paper addresses the relation between the statistical distribution of wave period measured at a fixed point, and the speed dependent encountered wave period on a Gaussian random sea. We shall calculate and compare these distributions using the very accurate method for zero crossing distributions in Gaussian processes described in Rychlik and Lindgren (1993, 1995); Lindgren et al. (1997). This method uses as input the spectral density function for the encountered speed dependent Gaussian process. This method to calculate wave period distributions is the only known method that gives correct answers valid for general spectra. The encountered spectrum is found by transformation of variables and numerical integration of the fixed point directional frequency spectrum. The transformation agrees with the one given by St. Denis and Pierson in their pioneering paper (St. Denis and Pierson, 1953) on ship motion in a random sea. We use however, slightly different notation, resulting a more explicit formula for the encountered spectrum. 2. Basic definitions 2.1. Covariance and spectra We consider a time dependent random wave (t,x) with timeparameter t and space parameter x (x 1,x 2 ) 2. Thus, { (t,x 0 ), t } describes the elevation at a fixed location x 0 as a function of time t, while { (t 0,x),x 2 } is the elevation at time t 0 as a function of the coordinate x. The mean level is set to zero, E( (t,x)) 0, and the covariance function is denoted, (with ( 1, 2 )), r(, ) Cov( (t,x), (t,x )). To distinguish between time and space descriptions, we use superscripts and K, respectively. Thus, we denote the wave angular frequency spectral density by R ( ) ( ) and the wave number spectral density by R (K) ( ), with
3 G. Lindgren et al. / Ocean Engineering 26 (1999) r ( ) ( ) Cov( (t,x 0 ), (t,x 0 )) and, for ( 1, 2 ), , r (K) ( ) Cov( (t 0,x), (t 0,x )) exp(i )R ( ) ( )d 2 exp(i )R(K) ( )d. For unidirectional waves, with onedimensional space parameter x and onedimensional wave number, there is a simple relation between the spectral densities R ( ) ( ) and R (K) ( ), ( onedimensional), see e.g. (Price and Bishop, 1974; Sect. 9.2). We express the relation between the onesided forms of the spectra, R ( ) + ( ) and R (K) + ( ) defined for > 0 and > 0, respectively. The relation follows from the dispersion relation, 2 g tanh( h), >0, > 0, (1) where h is the water depth, and g denotes the constant of gravity. With /, the group velocity, the relation becomes R ( ) + ( ) 1 R(K) + ( ), >0, > 0. (2) This follows from linear wave theory and superposition of the simple harmonic waves (t,x) A cos(t x ), whose frequency and period, are connected via Eq. (1). For deep water, where h 1 and tanh( h) 1, the relations simplify to 2 g and R ( ) + ( ) 2 g R(K) + ( 2 /g), 0. (3) The twosided frequency density is R ( ) ( ) R ( ) + ( )/2, Directional spectrum To find the distribution of the encountered wave frequency, we have to take into account the direction of the elementary waves, relative to the direction of the ship movement. We define a directional wave frequency density, G(, ), relative to the direction 0 0, as a function of frequency and direction. For computational simplicity, we define it for, and /2 /2. This means that waves with > 0 are trailing waves which travel in the direction and waves with 0 are meeting and travel in the direction.
4 510 G. Lindgren et al. / Ocean Engineering 26 (1999) With independence between frequency and direction one has G(, ) R ( ) ( )D( ), where /2 D( )d 1. /2 In the examples we shall use a truncated Pierson Moskowitz frequency spectrum, with R ( ) + ( ) g2 5 e ( p / ) 4 for 0 max, (4) where max is a truncation limit, in the examples taken as a multiple of the peak frequency p Remark 1 In our notation, wave propagation direction is defined by the combination of in ( /2, /2] and in (, ), with the sign of determining the propagation in direction or. This does not agree with conventional notation, where in [0,2 ) determines the direction and is always positive. Our reason for choosing this notation is that it gives more transparent formulas for the calculation of encountered spectrum. The difference between meeting and trailing waves is also more easily illustrated; see for example Fig Wave characteristics For a time dependent wave process (t,x) with time parameter t and onedimensional space parameter x we define the following wave characteristics, describing a half wave: Fig. 1. Speed dependent encountered frequency spectrum for isotropic (left), meeting (middle) and trailing (right) waves with Pierson Moskowitz spectrum. Curves from right to left in each figure correspond to speed c m, when c 0.125(0.125)2.5.
5 G. Lindgren et al. / Ocean Engineering 26 (1999) T zero crossing wave period time from down to up crossing, L zero crossing wave length distance from down to up crossing, For a Gaussian wave the corresponding mean values can be expressed by the spectral moments, m k k R ( )d and M k k R (K) ( )d. The explicit expressions are T z E[T] m 0 /m 2, L z E[L] M 0 /M 2. These expectations are to be taken in the ergodic sense, i.e. they are equal to the averages of observed wave periods or wave lengths, for a random wave observed over infinite time or space horizon Encountered wave frequency spectrum Wave length and wave period are two extremes of encountered wave period, which is what is observed on a ship traveling at a constant speed across the random surface. With speed, the elevation at time t is (t) (t,( t,0)), assuming the movement is in the direction 0 0. Wave length and wave period are the two cases found by letting (with t 0), and 0, respectively. With finite, the Doppler change in frequency for the elementary waves results in a complicated change in the distribution of the observed wave period. The distribution of the encountered wave period can be found via the encountered frequency spectrum, by the numerical algorithms presented in Section 5. These algorithms use the spectral density of the encountered wave process, or alternatively the covariance function, and we therefore need an explicit expression for the encountered, speed dependent, wave frequency spectrum R (K) ( ). (We use the notation for the encountered frequencies to distinguish from the fixed location frequencies.) The derivation is found in Section 4 and it gives the same result as the one given by St. Denis and Pierson (1953), with slightly different notation. 3. Examples 3.1. Specifications We shall now give three examples of encountered wave frequency spectrum, and of the corresponding distributions of encountered wave period, calculated by the tools described in Section 5.
6 512 G. Lindgren et al. / Ocean Engineering 26 (1999) We use as fixed point frequency spectrum a truncated Pierson Moskowitz spectrum, defined in onesided form as R ( ) + ( ) g2 5 e ( p / ) 4, for 0 max, with peak frequency p 1, 1.25, and truncated at max 2 p. (The truncation is necessary in order to avoid too many waves with very small period; for more details on the effect of truncation, see Lindgren et al. (1997)). This gives an average (half) zero crossing period at a fixed location (i.e. 0), of where T z m 0 /m , max m k k R ( ) + ( )d. 0 We use three different types of directional dependence: Example 1: isotropic waves In an isotropic case the average energy of waves is the same in all directions, and the directional spectrum is 1 G isotropic (, ) R ( ) + ( ) 2, (5) for /2 /2 and. Example 2: meeting nonisotropic waves For a nonisotropic spectrum with meeting waves, all waves meet the ship in an acute angle, and we take in this example the directional spectrum G meeting (, ) R ( ) + ( ) 2 cos2 ( ), (6) for /2 /2 and 0. Example 3: trailing nonisotropic waves For a nonisotropic spectrum with trailing waves, all waves travel more or less in the same direction as the ship. We take as the directional spectrum G trailing (, ) R + ( ) ( ) 2 cos2 ( ), (7) for /2 /2 and >0.
7 G. Lindgren et al. / Ocean Engineering 26 (1999) Encountered frequency spectrum The speed dependent encountered frequency spectrum R ( ) ( ) has to be found by numerical integration according to Eq. (14) in Section 4. We shall first study how the encountered wave spectrum depends on the speed. In order to calibrate the models we shall compare with the velocity p of the elementary wave with frequency p 1, equal to the peak frequency in the studied Pierson Moskowitz spectrum. From the deep water dispersion relation, p 2 p/g, it follows that it has velocity p p g/ p, since p 1, (8) p and we therefore normalize speed by dividing by g. We have calculated the encountered spectrum for the three cases, c p, c 0.125(0.125)2. Fig. 1 shows the spectrum for isotropic, meeting, and trailing waves. As it will be noted in Remark 2, the spectrum may be singular for g/(4 ), which is equal to half the frequency of the encountered standing wave, coming from right behind the ship. The singularity is clearly seen for the isotropic and trailing cases. The figures show the spectral densities in twosided, unsymmetric, form, defined for both positive and negative. Frequencies > 0 correspond to encountered trailing waves, 0 to meeting waves. Thus, one can see how trailing and meeting encountered waves contribute to the total encountered wave energy. To get a onesided frequency spectrum, one just has to add the two sides Encountered wave period With the encountered spectra obtained by integration according to Eq. (14) we can investigate the statistical properties of the encountered wave period. The mean encountered period gives the average dependence on the speed. The average T z m 0 /m 2 is shown in Fig. 2 for the three cases. For isotropic and meeting waves the mean encountered period decreases with inceasing speed, while for trailing waves, the mean period first increases to a maximum, when the ship speed is less than the speed of the dominant waves, after which it decreases with further increased speed. (In fact, the maximum T z occurs with slightly larger than p.) If one wishes to obtain more detailed information about the encountered period distribution one can compute the entire probability density function. This can be done by means of the routine wave t in the WAT toolbox as described in Section 5; see Rychlik and Lindgren (1995). The results are shown in Fig. 3 for the three examples. Note in particular the general shift towards shorter periods in the case of meeting and isotropic waves, but note also that, for trailing waves, with speed p /2, equal to half the speed of the peak frequency wave, the encountered wave period is almost doubled. Even for higher speed, 2 p, we can see that although the average T z is small there is a large probability that very long wave periods occur.
8 514 G. Lindgren et al. / Ocean Engineering 26 (1999) Fig. 2. Encountered mean wave period as a function of speed, c p, for c (0.125)2.5. Isotropic (solid), meeting (dashdotted), and trailing (dashed) waves, with Pierson Moskowitz spectrum. The three speed values illustrated in Fig. 3 are marked with circles. Fig. 3. Encountered wave period density functions for Gaussian waves with isotropic (left), meeting (middle) and trailing (right) Pierson Moskowitz waves; 0 (solid), p /2 (dashed), p (dashdotted), 2 p (dotted). 4. Derivation of encountered spectrum We use the symbol to denote frequencies in the fixed location spectrum and the symbol for encountered angular frequencies. If the directional fixed location spectral density is G(, ), the covariance between (0,0) and (t,x) becomes, with x (x 1,x 2 ), /2 Cov( (0,0), (,x)) cos( x 1 cos x 2 sin )G(, )d d, /2 where 2 /g > 0 at infinite depth; cf. Eq. (3). Note that we allow between and and restrict to positive values. For an object moving with speed along the x 1 axis the covariance function of the encountered waves is
9 G. Lindgren et al. / Ocean Engineering 26 (1999) r ( ) ( ) Cov (0,0), (,(,0)) (9) /2 cos 2 g cos G(, )d d. /2 To find the encountered frequency spectrum, we make a change of variables, 2 g cos ( ), where 0 ( ) g 2 cos. The dependent constant 0 ( ) is important in the future. In fact, 2 0 ( ) is equal to the dependent frequency of an encountered standing wave, seen from the ship. Letting 0( ) 0 ( )/2, we further define T( ) ( /2, /2]; 0( ) >, (10) ± ( ) 0 ( ) 0 ( ) ( ), for 0( ). (11) From Eq. (9) we get r ( ) ( ) where cos T( ) G (, + ( )) G (, ( )) d d, (12) G (, ) G(, ) / G(, ) 1 2. (13) g cos Inserting ± ( ) into the denominator of Eq. (13), we get 1 2 g ± cos 1 ± 0 ( ) 1 0( ), (note that 0 ( ) > 0). The sum of integrals within large parentheses in Eq. (12) is the encountered spectrum. We summarize these results in a theorem.
10 516 G. Lindgren et al. / Ocean Engineering 26 (1999) Theorem 1 The spectral density for the encountered waves, when moving with speed along the x 1 axis on a homogeneous random sea, is where R ( ) ( ) T( ) G (, + ( ))d G (, ( ))d, (14) T( ) G (, ± ( )) G(, ± ( )), (15) 1 0( ) with 0( ) g/(4 cos ), and where T( ), ± ( ), are defined by Eq. (10) and Eq. (11), respectively. Here is used as argument for encountered wave frequency, to distinguish it from, the frequency of the individual wave components at a fixed point. In particular, as 0, + ( ), while ( ), and 0 ( ). Hence, lim 0 /2 ( ) /2 R ( ) G(, )d R ( ) 0 ( ). Remark 2 Since the denominator in Eq. (15) is 0 for 0(0) i.e. when cos y + : g/(4 ), the integral in Eq. (14) may be generalized when 0(0). However, a substitution of variables, cos y + (1 u 2 ), removes the singularity for > 0(0). For 0(0) g/(4 ) the singularity can not be removed, and the encountered spectral density R ( ) ( ) can in fact be infinite for that value. 5. Crossing distributions 5.1. Regression approximation The statistical distribution of wave length or wave period in a random wave, is in general impossible to express in closed analytic form. In general, the probability density function for the distribution of the time T from 0 to the first upcrossing zero of a random process { ( ), 0} is given by Durbin s formula, f T (t) f (t) (0) E( (t) + I(t) (t) 0), (16) see Durbin (1985), Rychlik (1987a), Lindgren and Rychlik (1991). Here, x + max(0,x) and I(t) is the indicator function of the event
11 G. Lindgren et al. / Ocean Engineering 26 (1999) (s) 0 for all s (0,t). Thus, the density of the first passage time T involves the expectation of the variable (t) + I(t), which in turn depends on an infinite number of random variables. This expectation is therefore not readily obtainable. For a Gaussian process, one can approximate the crossing distance distribution with high and controllable accuracy by replacing the infinite dimensional expectation of (t) + I(t) in Eq. (16) by a finite dimensional one. This is done by replacing the infinite dimensional indicator I(t) by the finite dimensional indicator, I N (t), of the event (s) 0 for s s k, k 1, N, where s 1,,s N are suitably chosen time points between 0 and t. The theoretical justification for this procedure, called a regression approximation, can be found in Rychlik (1987b), Lindgren and Rychlik (1991). The choice of s 1,, s N can be made in a recursive fashion, and by taking a moderately large N, one obtains high numerical accuracy. In most cases, N 5 suffices Numerical tools To calculate the zero crossing density f T (t) one needs a dedicated algorithm that can calculate finite dimensional integrals like E( (t) + I N (t)), for any Gaussian process (t). The authors have developed a package of such algorithms for use in crossings and extreme value theory. The general technique is described in Rychlik and Lindgren (1993). Special adaptions of the algorithms for use in wave analysis and for calculations of specific wave distributions are collected in WAT, a toolbox of MATLAB algorithms, described in Rychlik and Lindgren (1995). 1 The WAT functions calculate, with desired high accuracy, the probability density for mean level crossing distances, joint density of wave length and amplitude variables of different types, for example the time and height differences between a local minimum and the next maximum, and wave characteristics based on the minimum and maximum between successive mean level crossings. Processes which can be handled are Gaussian processes and memoryless transformations of Gaussian processes. The algorithms need as input the covariance function of the Gaussian process. This can be of any form, analytical or as a tabulated function. The process can also be specified by its spectral density function, in which case the covariances are calculated by means of the Fast Fourier Transform. It should be emphasized that the density functions calculated by WAT give 1 See also
12 518 G. Lindgren et al. / Ocean Engineering 26 (1999) numerically accurate values for the exact theoretical density functions in a Gaussian process. To the authors knowledge, all other methods, suggested in the literature, are approximations based on a fitting of a standard statistical distribution, e.g. by means of moment fitting, or are based on too simplified approaches which do not account for the full spectral content of the process. For typical examples, see Gran (1992). Acknowledgements Part of this work was supported by the Office of Naval Research under Grant N and carried out during a visit by George Lindgren to the Center for Stochastic Processes, Chapel Hill. References Durbin, J., The firstpassage density of continuous Gaussian process to a general boundary. Journal of Applied Probability 22, Gran, S., A Course in Ocean Engineering. Elsevier, Amsterdam. Lindgren, G., Rychlik, I., Slepian models and regression approximations in crossing and extreme value theory. International Statistical Review 59, Lindgren, G., Rychlik, I., Prevosto, M., The relation between wave length and wave period distributions in random Gaussian waves. Proceedings of the Seventh International Offshore and Polar Engineering Conference. The International Society of Offshore and Polar Engineers, Honolulu, pp Price, W.G., Bishop, R.E.D., Probabilistic Theory of Ship Dynamics. Chapman and Hall, London. Rychlik, I., 1987a. A note on Durbin s formula for the first passage density. Statistics and Probability Letters 5, Rychlik, I., 1987b. Regression approximations of wavelength and amplitude distributions. Advances in Applied Probability 3, Rychlik, I., Lindgren, G., CROSSREG, a technique for first passage and wave density analysis. Probability in Engineering and Information Sciences 7, Rychlik, I., Lindgren, G., WAVE Analysis Toolbox a Tutorial. Department of Mathematical Statistics, Lund University, Sweden. St. Denis, M., Pierson, W., On the motions of ships in confused seas. Transactions, The Society of Naval Architects and Marine Engineers 61,
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