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Research Article Characteristics of the differential quadrature method and its improvement Wang Fangzong, Liao Xiaobing, Xie Xiong College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, Hubei Province, China Correspondence should be addressed to Wang Fangzong; [email protected] Abstract The differential quadrature method has been widely used in scientific and engineering computation. However, for the basic characteristics of time domain differential quadrature method, such as numerical stability and calculation accuracy or order, it is still lack of systematic analysis conclusions. In this paper, according to the principle of differential quadrature method, it has been derived and proved that the weighting coefficients matrix of differential quadrature method meets the important V-transformation feature. Through the equivalence of the differential quadrature method and the implic it Runge-Kutta method, it has been proved that the differential quadrature method is A-stable and s-stage s-order method. On this basis, in order to further improve the accuracy of the time domain differential quadrature method, a class of improved differential quadrature method of s-stage 2s-order have been proposed by using undetermined coefficients method and Padé approximations. The numerical results show that the improved differential quadrature method is more precise than the traditional differential quadrature method. method has the capability of producing highly accurate solutions with minimal computational effort [3, 4] when the method is applied to problems with globally smooth solutions. So far, the differential 1. Introduction The differential quadrature method (DQM) was first proposed by Bellman and his associates in the early 1970s [1, 2], which is usually used for solving ordinary and partial differential equations. As an quadrature method has been widely applied to boundary-value problems in many areas of engineering and science, such as structural mechanics [5-8], transport process [9], dynamic systems [10-12], calculation of transmission line transient response [13, 14], etc. A comprehensive review of the chronological development of the differential quadrature method can be found in Reference [4]. Although the differential quadrature method has been successfully applied in so many fields, for the basic characteristics of the method, analogous extension of the quadrature for integrals, it can be essentially expressed as the values of the derivatives at each grid point as weighted linear sums approximately of the function values at all grid points within the domain under consideration. The differential quadrature method is conceptually simple and the implementation is straightforward. It has been recognized that the differential quadrature 1 such as numerical stability and calculation accuracy or order, not much work about them has been done in this area for the differential quadrature method. According to Fung [15], using Lagrange interpolation functions as test functions, the differential quadrature in time domain was shown to be equivalent to the recast implicit Runge-Kutta method [16-18], besides, some low-order algorithms were discussed in detail. accuracy with the defined three grid Conclusions are then given in Section 7. points. 2. Traditional differential quadrature method Suppose function f (x) is sufficiently smooth in the whole interval, there are (s+1) grid points with coordinates as ci , i (0, s) . The first order derivative f 1 (ci ) at each grid point ci , i (1, s) , is However, the method used by Fung is not the traditional sense of differential quadrature method, but involved post-processing (i.e. numerical solution at the end of grid points adopts polynomial extrapolation). In this paper, using general polynomial as test functions [19], the weighting coefficients matrix of differential quadrature method is proved to satisfy V- approximated by a linear sum of all the function values in the whole domain, that is s f 1 (ci ) g ij f (c j ) , i (1, s) (1) j 0 Where f (ci ) represent function values at a grid point ci , g ij is the weighting coefficients. In order to compute the weighting coefficients g ij transformation [17, 20]. The equivalent implicit Runge-Kutta method is constructed through the differential quadrature method. Hence, making use of Butcher fundamental order theorem and the method of linear stability analysis [17, 18], the basic characteristics of the differential quadrature method can be systematically analysed. Unfortunately, the differential quadrature method is only a method of sstage s-order and A-stable. Consequently, the differential quadrature method can’t yield higher accurate solutions to the boundary-value problems with fewer computational efforts. Based on above deduction, the method of undetermined coefficients is used to make the stability function of the equivalent Runge-Kutta method become the diagonal Padé approximations to the exponential function [17, 18]. Therefore, a class of improved differential quadrature method of s-stage 2s-order is derived. The manuscript is arranged as follows. In Section 2, the weighting coefficients matrix of traditional differential quadrature method using general polynomial as test functions is briefly discussed. In Section 3, the equivalent relationship between the differential quadrature method and the Runge-Kutta methods is deduced. In Section 4, the stability and in Equation (1), the test functions can be chosen as rk ( x) x k , (k 0 ,1 , s) (2) Substituting Equation (2) into Equation (1) gives s k 0 , 0 g ij , i (1, s) (3) j 0 s g ij c kj j 0 k cik 1 , i , k (1, s) (4) Equation (3) can be expanded into matrix form as g 11 g 21 g s1 g 12 g 1s 1 g 10 0 g 22 g 2 s 1 g 20 0 g s 2 g ss 1 g s 0 0 (5) Let g10 g11 g 21 g 20 G0 , G g g s1 s0 g1s 1 g2s 1 , e (6) g ss 1 g12 g 22 gs2 Using Equation (6), Equation (5) can be rewritten as (7) G0 Ge From k 1, 2 , s , Equation (4) can be expanded as g i 0 c0 g i1c1 g i 2 c2 g is cs 1 2 2 2 2 g i 0 c0 g i1c1 g i 2 c2 g is cs 2ci g c s g c s g c s g c s sc s 1 i1 1 i2 2 is s i i0 0 accuracy characteristics of the differential quadrature method are studied. A class of improved differential quadrature method of s-stage 2s-order and A-stable is proposed in Section 5. In Section 6, the transient response of a double-degree-of-freedom system is computed, which is given to verify the computational (8) Since initial grid point c 0 is usually defined as 0, Equation (8) reduces to 2 g i1 c1 g i 2 c 2 g is c s 1 2 2 2 g i1 c1 g i 2 c 2 g is c s 2c i s s g c g c g c s sc s 1 i2 2 is s i i1 1 1 α s [1 , 2 , , s ]T V 1c s , s (9) c s [c1s ,c2s , ,c ss ]T Equations (13), i.e. G VA s1 V 1 , is called the From i 1, 2,s , Equation (9) can be also expanded implicit expression of the weighting coefficients matrix of the differential quadrature method, and is also called V-transformation. into matrix form as c1 c G 2 c s c12 c1s 1 2c1 c 22 c 2s 1 2c 2 c s2 c ss 1 2c s 1 c1 1 c 2 1 c s sc1s 1 sc2s 1 scss 1 1 2 s c ss 1 s 1 1 s 1 2 c c When the grid points have been selected, the weighting coefficients matrices G and G 0 are easy to calculate with the above formula. Obviously, the weighting coefficients of the differential quadrature method depend on the test functions and distribution of grid points, but are independent of some specific problems. There are four typical grid points’ distributions: Legendre grid points, Chebyshev grid points, Chebyshev-Gauss-Lobatto grid points and Uniform grid points (also called Equally spaced grid points) [18]. This paper will focus on the latter three kinds of commonly used grid points, which are defined as follows: 1) Chebyshev grid points (10) Vandermonde matrix V is defined as follows: 1 c1 1 c 2 V 1 c s c1s 1 c2s 1 c ss 1 (11) Making use of Equations (11), Equations (10) can be expressed as c1 c 1 G V 2 c s 1 c1 1 c 2 1 c s c12 c 22 c s2 1 c1s 1 c 2s 2 c ss 0 2 s c1 c1 1 c 22 c 2s c s2 c ss 1 s 0 0 1 2 1 s ck 1 V G V As 2) Chebyshev-Gauss-Lobatto grid points ck (12) 1 2 1 0 s s 1 0 0 1 2 0 0 1 k 1 cos , k (0, s) . 2 s 3) Uniform grid points k ck , k (0, s) . s 3. The equivalence of differe ntial quadrature method and Runge-Kutta method In order to analyse the numerical stability and order of the differential quadrature method, the differential quadrature method in time domain can be transformed into equivalent implicit Runge-Kutta method. Consider the following ordinary differential equation: (13) Where As is defined as 0 1 As 0 0 1 2k 1 1 cos , k (1, s 1) , 2 2s 2 c 0 0 , cs 1 . Finally, it can be inferred that 1 (15) dx f (t, x ) , 0 t T dt x (t 0) x 0 (14) (16) In the following, t n ,t n 1 represent respectively the beginning and the end points at each step. h t n1 t n with will be used to denote the step size. The time interval 3 [t n ,t n 1 ] will be normalized. i.e, c (t t n ) h , c1 c A b T cs t [t n ,t n1 ] , c [0,1] . At the same time, Equation (16) can be made in the standard normalized form d (17) x x(t n ch) x hf (t n ch , ~ x) , ~ dc then, using s-stage differential quadrature method to solve Equation (17) yeilds x x1 ) ~ f (t n c1 h , ~ 1 G G 0 x n h (18) ~ f (t c h , ~ x x ) s s s n 4. Analysis of the basic characteristics of the differential quadrature method The stability and accuracy characteristics of the equivalent Runge Kutta method will be investigated next. From Equation (13) and (20), it can be inferred that (19) 1 c12 c1s 1 2 s c2 c2 2 1 2 s cs cs s Equation (25) reduces to (21) Therefore, the weigthing coefficients matrix A also satisfies V-transformation. It can be inferred from Equation (19) that s aij c kj 1 s ~ x i x n h a ij f (t n c j h,~ x j ) , i (1, s) j 1 (22) Sinc cs 1 , t n cs h t n h t n1 , therefore, ~ x i (i s) is the approximate solution at the end of the step. Then, ~ x can be rewritten as the following form: cik , i (1, s ) , k (1, s ) k (26) from Equation (25), it can be inferred that 1 1 bTV [1, ,, ] 2 s s (27) Similarly, Equation (27) reduces to s ~ x s x n 1 x n h a sj f (t n c j h,~ xj) s (25) On the other hand, since b j a sj , j (1, s ) and cs 1 , j 1 j 1 c1s 1 c 2s 1 c ss 1 c1 c2 c s (20) Clearly, making use of Equation (13) and (20) leads to A VA sV 1 a12 a1s 1 c1 a 22 a 2 s 1 c 2 a s 2 a ss 1 c s a11 a 21 a s1 Let G 1 A [ a ij ] , i , j (1 , s) (24) where b T (b1 ,b2 , ,bs ) (a s1 , a s 2 , , a ss ) . where ~ xi x (t n ci h), i (1, s) . Since G0 Ge , Equation (18) reduces to x x1 ) ~ 1 f (t n c1 h , ~ 1 1 x n hG ~ 1 f (t c h , ~ x x ) n s s s a11 a1s a s1 a ss b1 bs s 1 bi cik 1 k (23) , k (1, s) (28) i 1 x n h b j f (t n c j h,~ xj) Obviously, it has been shown that the equivalent Runge-Kutta method at least satisfies simplifying j 1 where b j a sj , j (1, s ) . It can be seen that Equation assumptions C(s) and B(s) from Equation (26) and (28). Furthermore, it can be verified that the equivalent Runge-Kutta method only satisfies simplifying assumptions D(0). From Theorem 5.1 on p.71 in Reference [17], it can be concluded that the implicit Runge Kutta method or the corresponding differential quadrature method is s-stage s-order. The stability function R(z ) of the equivalent Runge- (22) and (23) are the standard forms for an s-stage Runge-Kutta method. Since ~ x s x n1 , the equivalent Runge-Kutta method is a reducible method. In fact, the traditional differential quadrature method generally doesn’t involve post-processing, so the Runge-Kutta method converted from traditional differential quadrature method will naturally become a reducible method. The Runge-Kutta method can be conveniently summarized in the Butcher tableau [18] Kutta method or the corresponding differential quadrature method is given by the formula[16-18] as 4 R( z ) det(I z (eb T A)) det(I zA) Because As and As are a class of special matrices, (29) Equation(35) can be evaluated as (k 1)! ( z ) s k 1 det(I zA ) ( s 1)! k s (36) R( z ) 1 (k 1)! s k 1 det(I zAs ) 1 k z ( s 1)! k s Where, as usual, I is the identity matrix of dimension s. Due to grid points’s asymmetric distribution, the equivalent implicit Runge-Kutta methods is not a symmetric method. As a result, there is an unique adjoint method (also called reflected method) [16, 18], which defines as c A (b ) T 1 s Since cs 1 , resulting in c1 0 , and it implies 1 0 . It can be verified that the stability function of equivalent Runge-Kutta method is A-acceptability of p-order ( p s 1 ) rational approximation to , satisfying c e Pc T PA P eb A b Pb (30) 1 1 s s P R 1 (31) exponential function [21]. Therefore, the corresponding differential quadrature method is Astable. In the following, the three-stage differential quadrature method using Uniform grid points will be given as an example. When s=3, c1 , c 2 and c 3 are where P is given by 1 3 , 2 3 ,1. It can be worked out that matrices G 0 and G are given by Futhermore, from Theorem 343B on p.221 in Reference [18], if the original method satisfies the simplifying assumptions C(s) and B(s), then the adjont method also satisfies the same simplifying assumptions. Hence, the adjont method enjoys V- 3 1 2 1 G0 ,G 3 2 9 1 2 transformation: 1 c1 1 c2 * 1 A V As (V ) , V 1 c s (c1 ) s 1 (c2 ) s 1 (32) (c s ) s 1 and the Butcher tableau method would be 1 3 2 c A 3 bT 1 0 0 0 1 2 0 1 2 1 0 s s 1 (33) 1 A 1 1 with 1 β s [1 , 2 , , s ]T (V ) 1 (c ) s s (c ) s [(c1 ) s ,(c2 ) s , ,(c s ) s ]T (34) det(I z ( PA P -1 )) det(I zA ) det(I z (VA sV 1 )) det(I zAs ) det(I z (V As (V ) 1 )) det(I zAs ) det(I zAs ) det(I zAs ) 1 1 0 0 9 4 1 0 9 1 1 0 2 (37) 4 9 2 9 0 0 5 36 1 9 1 4 1 4 (38) Α is 2 1 27 11 1 27 2 1 3 1 3 2 3 1 1 9 4 9 1 1 (39) the stability function of the equivalent Runge-Kutta method is 1 1 2 1 z z 3 27 R(z) (40) 2 11 2 1 3 1 z z z 3 54 27 Equation (29) can be reduced to R( z ) 1 3 2 3 1 2 3 1 2 11 9 2 3 of equivalent Runge-Kutta 23 36 7 9 3 4 3 4 the V-transformation of matrix where As is also defined as 0 1 A*s 0 0 1 k (35) 5 and the Butcher tableau of the adjoint method would be c 0 1 3 A 2 T (b ) 3 0 1 36 1 9 1 4 0 2 9 4 9 0 5 36 1 4 3 4 0 Then, the stability function of this new Runge-Kutta method becomes 1 (k 1)! 1 k ( z ) s k 1 ~ det(I zAs ) ( s 1)! k s R( z ) ~ 1 (k 1)! s k 1 det(I zAs ) 1 k z ( s 1)! k s (41) (44) From Equation (43) and (44), it can be inferred that ~ the last column elements in As determine the stability the V-transformation of matrix Α* is 1 * A 1 1 0 1 3 2 3 0 0 1 1 9 4 0 9 0 0 1 2 0 1 2 1 27 1 1 3 0 1 3 2 3 0 1 9 4 9 1 function. To improve the order of new Runge-Kutta method, undetermined coefficients γ can be selected (42) so that the stability function of new Runge-Kutta method becomes the diagonal Padé approximations to s the exponential function (defined by e z ): s 5. The improved differential quadrature method (k 1)! ( z ) s k 1 ( s 1 )! k s R( z ) ez 1 (k 1)! s k 1 1 k z ( s 1)! k s 1 1 k Based on the above deduction, the traditional differential quadrature method is a method of s-stage s-order. Compared with the multi-stage high-order Runge-Kutta method, for example, Gauss method (sstage 2s-order method), it has the disadvantage of lower precision. As is well known that: if the stability function of a numerical method is diagonal Padé approximations to the exponential function, then this method is the method of A-stable and s-stage 2s-order [22]. Inspired by this idea, the stability function of new Runge-Kutta method or new differential quadrature method have been converted into diagonal Padé approximation to the exponential function by using undetermined coefficients method. From conveniently obtained as s 2 , γ 2 1 12 ,1 2 ; s 3 , γ 3 1 60 , 1 5 ,1 2 ; s 4 , γ 4 1 280 ,1 14 , 9 28 , 1 2 ; …… , s , so that coefficients After getting γ s 1, T ~ ~ matrix A or G can also be easily computed through using Equation (43). Therefore, a class of new RungeKutta ~ ~ A (G ) 1 0 0 0 method of s-stage 2s-order has been successfully constructed. In other words, a class of improved differential quadrature method of s-stage 2s- without changing b and V, a new Runge-Kutta ~ c A method is redefined as bT 0 0 1 2 (45) s By comparing the coefficients on both sides of Equation (45), undetermined coefficients γ can be Equation (35), it can be seen that the stability function of the equivalent Runge-Kutta method will be ~ determined by As and As . Suppose As As As , 0 1 V 0 0 s order has been derived. Besides, the adjoint method of new Runge–Kutta method is also s-stage 2s-order. Take the same as above, the improved differential 1 2 quadrature method using Uniform grid points will be given as an example. It can be worked out that the new matrices G 0 and G are given by ~ 1 V 1 VA sV 1 s s 1 (43) 6 u1 (0) 0 v1 (0) 0 , u 2 (0) 0 v 2 (0) 0 17 17 4 10 6 2 3 ~ 4 ~ 11 1 G0 G 4 (46) 2 3 , 6 75 33 42 12 2 2 and the Butcher tableau of new Runge-Kutta method would be 1 73 23 13 3 120 60 120 17 17 ~ 2 97 c A 3 120 60 120 (47) 3 7 b T 1 27 40 20 40 3 1 0 4 4 ~ The V-transformation of matrix Α is 1 1 3 ~ 2 A 1 3 1 1 Then, the Butcher be c the exact solution of this problem is 5 2 x1 1 3 cos( 2t ) 3 cos( 5t ) x 3 5 cos( 2t ) 4 cos( 5t ) 2 3 3 The following three Figures show the displacement error trajectories comparison of traditional differential quadrature method and improved differential quadrature method with the same step size h=0.5s. In these Figures, the exact solution at each step is used for comparison. From Figure1-3, it can be evident that improved differential quadrature method is two orders of magnitude higher than traditional differential quadrature method. The error of improved differential -5 -4 quadrature method range between 10 and 10 , even with a large step size h=0.5s. In order to compare the calculation precision of the traditional differential quadrature method and the improved differential quadrature method better, dynamic Equations (51) were computed for different time steps. Table 1 shows the computational results at t=60s. Comparing with the exact solution, the numerical results using traditional differential quadrature method are the same until the first decimal place when s=5, h=1s; while the numerical results using improved differential quadrature method are the same until the third decimal place. When s=10, h=2s, the numerical results using traditional differential quadrature method are the same until the fifth decimal place; while the numerical results using improved differential quadrature method are the same until the seventh decimal place (especially using Chebyshev grid points and Chebysev-Gauss-Lobatto grid points, the numerical results are almost the exact solution). 1 1 3 T 2 b 3 ~ A* 3 40 13 120 17 120 1 4 3 20 17 60 23 60 0 3 40 7 120 17 120 3 4 (49) ~ The V-transformation of matrix Α* is 1 ~* A 1 1 0 1 3 2 3 0 0 1 1 9 4 0 9 0 0 1 2 1 1 60 1 1 5 1 1 2 0 1 3 2 3 0 1 9 4 9 1 (50) 6. Numerical examples Consider a two-degree-of-freedom system governed by 2 0 x1 6 2 x1 0 0 1 x2 2 4 x2 10 (53) The differential quadrature method can be used to find the numerical solutions to transient response directly. It can be seen the detailed calculation steps of solving second-order differential equations in Reference [11]. 1 1 1 1 0 0 1 9 60 3 9 4 1 2 4 (48) 1 0 1 9 5 3 9 1 1 1 0 1 1 1 2 2 tableau of the adjoint method would 0 (52) (51) With initial condition 7 10 x1 (t ) 10 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 -5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10 7.5 8.5 9.5 10 t/s (a) Error trajectories of x1 (t ) 10 x 2 (t ) 10 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 -5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 t/s (b) Error trajectories of x2 (t ) Fig 1. Error trajectories comparison of different DQM using Chebyshev grid (s=3, h=0.5s) 10 x1 (t ) 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 0.5 1.5 2.5 3.5 4.5 5.5 6.5 t/s (a) Error trajectories of x1 (t ) 8 7.5 8.5 9.5 10 10 10 x 2 (t ) 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 -5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10 t/s (b) Error trajectories of x2 (t ) Fig 2. Error trajectories comparison of different DQM using Chebyshev-Gauss-Lobatto grid (s=3, h=0.5s) 10 10 x1 (t ) 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 -5 0.5 1.5 3 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10 7.5 8.5 9.5 10 t/s (a) Error trajectories of x1 (t ) 10 10 x 2 (t ) 10 10 10 10 0 -1 traditional DQM improved DQM -2 -3 -4 -5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 t/s (b) Error trajectories of x2 (t ) Fig 3. Error trajectories comparison of different DQM using Uniform grid (s=3, h=0.5s) 9 Table 1 Computational results of the displacement (t=60s) s=5 h=1s s=10 h=2s t=60s x1 (t ) x2 (t ) Exact solution 2.264386552 5.469005157 Traditional DQM-Chebyshev 2.253975383 5.491424542 Improved DQM-Chebyshev 2.264406270 5.468965760 2.276613635 5.439926064 2.264406270 5.468965760 Traditional DQM-Uniform 2.342513616 5.278336836 Improved DQM-Uniform 2.264406270 5.468965760 Traditional DQM-Chebyshev 2.264383332 5.469011768 Improved DQM-Chebyshev 2.264386552 5.469005157 2.264399479 5.468978613 2.264386552 5.469005157 Traditional DQM-Uniform 2.265087814 5.467565205 Improved DQM-Uniform 2.264386561 5.469005173 Traditional DQM-ChebyshevGauss-Lobatto Improved DQM-ChebyshevGauss-Lobatto Traditional DQM-ChebyshevGauss-Lobatto Improved DQM-ChebyshevGauss-Lobatto 7. Conclusion (3) differential quadrature method can also gives more accurate solutions. This is the main mechanism that differential quadrature method has been successfully applied in many fields. In this paper, the linear stability and the order of differential quadrature method in time domain are systematically studied in detail and a class of new differential quadrature method of s-stage 2s-order is proposed. From the above analysis and derivation, the following conclusions can be made: (4) Finally, by making the stability function of equivalent Runge-Kutta method become the diagonal Padé approximations to the exponential function, a class of improved differential quadrature method of s-stage 2s-order and Astable is proposed. Therrfore, the improved differential quadrature method can be extended to multi-degree-of-freedom time domain dynamic systems, which can produce higher accurate at lower computational cost. (1) Based on general polynomial as test functions, the weighting coefficients matrix of the differential quadrature method satisfies the Vtransformation. It plays an extremely important role in the analysis of basic characteristics of differential quadrature method and its improvement. (2) The traditional differential quadrature method can be converted into equivalent Runge-Kutta method of A-stable and s-stage s-order. Therefore, compared with the commonly used single-stage low-order numerical integral methods, even with Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgements a small number of grid points, the traditional The authors gratefully acknowledge the support from the National Natural Science Foundation of China 10 IEEE M icrowave and Guided Wave Letters, vol.9, no.4, pp.145-147, 1999. (NSFC) through its grant 51377098. References [14] Xu Qinwei. “Equivalent-circuit interconnect modeling based on the fifth-order differential quadrature methods,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.11, no.3, pp.1068-1079, 2003. 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