Skip to main content
Log in

Modeling and Computing of Fractional Convection Equation

  • Original Paper
  • Published:
Communications on Applied Mathematics and Computation Aims and scope Submit manuscript

Abstract

In this paper, we derive the fractional convection (or advection) equations (FCEs) (or FAEs) to model anomalous convection processes. Through using a continuous time random walk (CTRW) with power-law jump length distributions, we formulate the FCEs depicted by Riesz derivatives with order in (0, 1). The numerical methods for fractional convection operators characterized by Riesz derivatives with order lying in (0, 1) are constructed too. Then the numerical approximations to FCEs are studied in detail. By adopting the implicit Crank–Nicolson method and the explicit Lax–Wendroff method in time, and the second-order numerical method to the Riesz derivative in space, we, respectively, obtain the unconditionally stable numerical scheme and the conditionally stable numerical one for the FCE with second-order convergence both in time and in space. The accuracy and efficiency of the derived methods are verified by numerical tests. The transport performance characterized by the derived fractional convection equation is also displayed through numerical simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Alikhanov, A.A.: A new difference scheme for the time fractional diffusion equation. J. Comput. Phys. 280, 424–438 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  2. Angstmann, C.N., Henry, B.I., Jacobs, B.A., McGann, A.V.: A time-fractional generalised advection equation from a stochastic process. Chaos Solitons Fractals 102, 175–183 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  3. Benson, D.A., Wheatcraft, S.W., Meerschaert, M.M.: Application of a fractional advection-dispersion equation. Water Resour. Res. 36(6), 1403–1412 (2000)

    Article  Google Scholar 

  4. Bouchaud, J.P., Georges, A.: Anomalous diffusion in disordered media: statistical mechanisms, models and physical applications. Phys. Rep. 195(4), 127–293 (1990)

    Article  MathSciNet  Google Scholar 

  5. Celik, C., Duman, M.: Crank-Nicolson method for the fractional diffusion equation with the Riesz fractional derivative. J. Comput. Phys. 231(4), 1743–1750 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, C.M., Liu, F., Turner, I., Anh, V.: A Fourier method for the fractional diffusion equation describing sub-diffusion. J. Comput. Phys. 227(2), 886–897 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Diethelm, K., Ford, J.M., Ford, N.J., Weilbeer, M.: Pitfalls in fast numerical solvers for fractional differential equations. J. Comput. Appl. Math. 186, 482–503 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ding, H.F., Li, C.P., Chen, Y.Q.: High-order algorithms for Riesz derivative and their applications (I). Abstr. Appl. Anal. 2014, 653797 (2014)

    MathSciNet  MATH  Google Scholar 

  9. Ding, H.F., Li, C.P., Chen, Y.Q.: High-order algorithms for Riesz derivative and their applications (II). J. Comput. Phys. 293, 218–237 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ding, H.F., Li, C.P.: High-order algorithms for Riesz derivative and their applications (III). Fract. Calc. Appl. Anal. 19, 19–55 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Ding, H.F., Li, C.P.: High-order numerical algorithms for Riesz derivatives via constructing new generating functions. J. Sci. Comput. 71, 759–784 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fedotov, S., Iomin, A.: Migration and proliferation dichotomy in tumor-cell invasion. Phys. Rev. Lett. 98(11), 118101 (2007)

    Article  Google Scholar 

  13. Henry, B.I., Langlands, T.A.M., Wearne, S.L.: Fractional cable models for spiny neuronal dendrites. Phys. Rev. Lett. 100(12), 128103 (2007)

    Article  Google Scholar 

  14. Jiang, H., Liu, F., Turner, I., Burrage, K.: Analytical solutions for the multi-term time-space Caputo–Riesz fractional advection–diffusion equations on a finite domain. J. Math. Anal. Appl. 389(2), 1117–1127 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Langlands, T.A.M., Henry, B.I.: The accuracy and stability of an implicit solution method for the fractional diffusion equation. J. Comput. Phys. 205(2), 719–736 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Li, C.P., Zeng, F.H.: Numerical Methods for Fractional Calculus. Chapman and Hall/CRC Press, Boca Raton, USA (2015)

    Book  MATH  Google Scholar 

  17. Li, C.P., Ding, H.F.: Higher order finite difference method for the reaction and anomalous-diffusion equation. Appl. Math. Model. 38(15/16), 3802–3821 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Li, C.P., Yi, Q., Kurths, J.: Fractional convection. J. Comput. Nonlinear Dynam. 13(1), 011004 (2018)

    Article  Google Scholar 

  19. Li, J., Liu, F., Feng, L., Turner, I.: A novel finite volume method for the Riesz space distributed-order advection–diffusion equation. Appl. Math. Model. 46, 536–553 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  20. Lubich, C.: Discretized fractional calculus. SIAM J. Math. Anal. 17(3), 704–719 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  21. Metzler, R., Klafter, J.: The random walks guide to anomalous diffusion: a fractional dynamics approach. Phys. Rep. 339(1), 1–77 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  22. Oldham, K.B., Spanier, J.: The Fractional Calculus. Academic Press, New York (1974). (renewed 2002)

    MATH  Google Scholar 

  23. Ortigueira, M.D.: Riesz potential operators and inverses via fractional centred derivatives. Int. J. Math. Math. Sci. 2006, 48391 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  24. Perdikaris, P., Karniadakis, G.E.: Fractional-order viscoelasticity in one-dimensional blood flow models. Ann. Biomed. Eng. 42(5), 1012–1023 (2014)

    Article  Google Scholar 

  25. Podlubny, I.: Fractional Differential Equations. Academic Press, San Diego (1999)

    MATH  Google Scholar 

  26. Schumer, R., Benson, D.A., Meerschaert, M.M., Wheatcraft, S.W.: Eulerian derivation of the fractional advection–dispersion equation. J. Contam. Hydrol. 48(1), 69–88 (2001)

    Article  Google Scholar 

  27. Shen, S., Liu, F., Anh, V.: Fundamental solution and discrete random walk model for a time-space fractional diffusion equation of distributed order. J. Appl. Math. Comput. 28(1/2), 147–164 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  28. Shen, S., Liu, F., Anh, V.: Numerical approximations and solution techniques for the space-time Riesz–Caputo fractional advection–diffusion equation. Numer. Algorithms 56(3), 383–403 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  29. Shen, S., Liu, F., Anh, V., Turner, I., Chen, J.: A novel numerical approximation for the space fractional advection–dispersion equation. IMA J. Appl. Math. 79(3), 431–444 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Sousa, E.: How to approximate the fractional derivative of order \(1<\alpha \le 2\). Int. J. Bifurc. Chaos 22(4), 1250075 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Yang, Q., Liu, F., Turner, I.: Numerical methods for fractional partial differential equations with Riesz space fractional derivatives. Appl. Math. Model. 34(1), 200–218 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  32. Zaslavsky, G.M.: Hamiltonian Chaos and Fractional Dynamics. Oxford University Press, New York (2005)

    Google Scholar 

  33. Zeng, F.H., Zhang, Z.Q., Karniadakis, G.E.: Second-order numerical methods for multi-term fractional differential equations: smooth and non-smooth solutions. Comput. Methods Appl. Mech. Eng. 327, 478–502 (2017)

    Article  MathSciNet  Google Scholar 

  34. Zhang, H., Liu, F., Jiang, X., Zeng, F., Turner, I.: A Crank–Nicolson ADI Galerkin-Legendre spectral method for the two-dimensional Riesz space distributed-order advection–diffusion equation. Comput. Math. Appl. 76, 2460–2476 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Changpin Li.

Additional information

The work was partially supported by the National Natural Science Foundation of China under Grant nos. 11671251 and 11632008.

Appendices

Appendix A

If \(v^{(i)}(x)\) is continuous on \(\mathbb {R}\) and \(v^{(i)}(x)\in L^1(\mathbb {R})\) for \(i=0,1,\ldots ,p+2\), where \(p=6\) for the sixth-order method and \(p=8\) for the eighth-order method, then the sixth- and eighth-order methods [9] formulated through the fractional centered difference scheme (13) for the Riesz derivative \(\frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }\), \(\gamma \in (0,1)\) are given as

$$\begin{aligned} \frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }=\, & {} \frac{\Lambda _s ^\gamma v(x)}{h^\gamma } + O(h^6),\\ \frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }=\, & {} \frac{\Lambda _e ^\gamma v(x)}{h^\gamma } + O(h^8). \end{aligned}$$

Here \(\Lambda _s ^\gamma\) and \(\Lambda _e ^\gamma\) are given by

$$\begin{aligned} \Lambda _s ^\gamma v(x)=\, & {} a_1\Delta _h^\gamma v(x+2h)+a_2\Delta _h^\gamma v(x+h) +a_3\Delta _h^\gamma v(x) \\&+a_2\Delta _h^\gamma v(x-h)+a_1\Delta _h^\gamma v(x-2h),\\ \Lambda _e ^\gamma v(x)=\, & {} b_1\Delta _h^\gamma v(x+3h)+ b_2\Delta _h^\gamma v(x+2h) +b_3\Delta _h^\gamma v(x+h)\\&+b_4\Delta _h^\gamma v(x) + b_3\Delta _h^\gamma v(x-h) +b_2v(x-2h)+b_1\Delta _h^\gamma v(x-3h), \end{aligned}$$

in which \(\Delta _h^\gamma\) is defined by (14) and

$$\begin{aligned} a_1=\, & {} -\left( \frac{\gamma }{1\,152}+\frac{11}{2\,880}\right) \gamma , \; a_2= \left( \frac{\gamma }{288}+\frac{41}{720}\right) \gamma , \; a_3=-\left( \frac{\gamma ^2}{192}+\frac{17\gamma }{160}+1\right) ;\\ b_1=\, & {} \left( \frac{\gamma ^2}{82\,944}+\frac{11\gamma }{69\,120}+\frac{191}{362\,880}\right) \gamma , \; b_2=-\left( \frac{\gamma ^2}{13\,824}+\frac{7\gamma }{3\,840}+\frac{211}{30\,240}\right) \gamma ,\\ b_3=\, & {} \left( \frac{5\gamma ^2}{27\,648}+\frac{3\gamma }{512}+\frac{7\,843}{120\,960}\right) \gamma , \; b_4=-\left( \frac{5\gamma ^3}{20\,736}+\frac{29\gamma ^2}{3\,456}+\frac{5\,297\gamma }{45\,360}+1\right) . \end{aligned}$$

For v(x) defined on (ab), through the zero extension of v(x), \(\frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }\) at \(x_i\) can be approximated as follows:

$$\begin{aligned} \frac{\partial ^\gamma v(x_i)}{\partial |x|^\gamma }=\, & {} \frac{1}{h^\gamma } \left\{ a_1\sum ^{[\frac{b-a}{h}]-2}_{k=-2}g_{i-k}^{(\gamma )} v(x_{k+2})+\, a_2\sum ^{[\frac{b-a}{h}]-1}_{k=-1}g_{i-k}^{(\gamma )} v(x_{k+1}) +a_3\sum ^{[{\frac{b-a}{h}}]}_{k=0}g_{i-k}^{(\gamma )} v(x_{k})\right. \\&\left. +a_2 \sum ^{[\frac{b-a}{h}]+1}_{k=1}g_{i-k}^{(\gamma )}v(x_{k-1}) +a_1\sum ^{[\frac{b-a}{h}]+2}_{k=2}g_{i-k}^{(\gamma )}v(x_{k-2}) \right\} +O(h^6),\\ \frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }=\, & {} \frac{1}{h^\gamma } \left\{ b_1\sum ^{[\frac{b-a}{h}]-3}_{k=-3}g_{i-k}^{(\gamma )} v(x_{k+3})+ b_2\sum ^{[\frac{b-a}{h}]-2}_{k=-2}g_{i-k}^{(\gamma )} v(x_{k+2}) +b_3\sum ^{[\frac{b-a}{h}]-1}_{k=-1}g_{i-k}^{(\gamma )} v(x_{k+1})\right. \\&\left. +\,b_4 \sum ^{[{\frac{b-a}{h}}]}_{k=0}g_{i-k}^{(\gamma )}v(x_{k}) +b_3\sum ^{[\frac{b-a}{h}]+1}_{k=1}g_{i-k}^{(\gamma )} v(x_{k-1}) +b_2\sum ^{[\frac{b-a}{h}]+2}_{k=2}g_{i-k}^{(\gamma )} v(x_{k-2})\right. \\&\left. +\,b_1\sum ^{[\frac{b-a}{h}]+3}_{k=3}g_{i-k}^{(\gamma )} v(x_{k-3})\right\} + O(h^8). \end{aligned}$$

Appendix B

In this appendix, we prove Lemma 3.6.

Proof

The conclusion presented in (i) can be readily obtained by direct calculations. Next we show (ii).

Denote

$$\begin{aligned} c_{1,k}=-3(\gamma -k+1),\; c_{2,k}=\frac{3}{2}(2\gamma -k+2),\; c_{3,k}= -\frac{1}{3}(3\gamma -k+3), \end{aligned}$$

and

$$\begin{aligned} B_k=\frac{q_{3,k-1}^{(\gamma )}}{q_{3,k}^{(\gamma )}},\;m(k)=\frac{k+2}{k-3\gamma -1}. \end{aligned}$$

By (27), one has

$$\begin{aligned} q_{3,k}^{(\gamma )}=\, & {} \frac{6}{11k}\left( c_{1,k}\,q_{3,k-1}^{(\gamma )}+c_{2,k} \,q_{3,k-2}^{(\gamma )} +c_{3,k}\,q_{3,k-3}^{(\gamma )}\right) ,\\ \frac{1}{B_k}=\, & {} \frac{6}{11k}\left( c_{1,k}+c_{2,k}\, B_{k-1}+c_{3,k} \,B_{k-1}B_{k-2}\right) . \end{aligned}$$

We aim to prove that for \(k\ge 5\), \(1\le B_k\le m(k)\) by mathematical induction.

For \(k=5,6,\ldots ,17\), we display the figures in Fig. 3. One can find from this figure that \(B_k\) satisfies \(1\le B_k\le m(k)\), and \(q_{3,k}^{(\gamma )}<0\) and \(q_{3,k}^{(\gamma )}\le q_{3,k+1}^{(\gamma )}\) when \(k=5,6,\ldots ,17.\)

Fig. 3
figure 3

Comparison of \(B_k\) and m(k) for \(k=5,6,\ldots ,17.\)

Suppose that \(1\le B_{k}\le m(k)\) for \(k=l-2,l-1,\; l\ge 18\). When \(k=l\), \(B_l\) is computed as

$$\begin{aligned} \frac{1}{B_l}=\, & {} \frac{6}{11l}\left( c_{1,l}+c_{2,l} B_{l-1}+c_{3,l} B_{l-1}B_{l-2}\right) \nonumber \\=\, & {} \frac{6}{11l}\left[ c_{1,l}+ B_{l-1}(c_{2,l}+c_{3,l} B_{l-2})\right] \nonumber \\\le & {} \frac{6}{11l}\left[ c_{1,l}+B_{l-1}(c_{2,l}+c_{3,l} m(l-2))\right] \nonumber \\\le & {} \frac{6}{11l}\left[ c_{1,l}+ c_{2,l}+c_{3,l} m(l-2)\right] \nonumber \\\le & {} 1, \end{aligned}$$
(55)

since \(m(l-2)=\frac{l}{l-3\gamma -3}<2\) for \(l\ge 18\), and \(c_{2,l}+2c_{3,l}= \gamma -\frac{5}{6}l+1 <0\).

On the other hand,

$$\begin{aligned} \frac{1}{B_l}=\, & {} \frac{6}{11l}\left[ c_{1,l}+ B_{l-1}(c_{2,l}+c_{3,l} B_{l-2})\right] \\\ge & {} \frac{6}{11l}\left[ c_{1,l}+B_{l-1}(c_{2,l}+c_{3,l})\right] \\\ge & {} \frac{6}{11l}\left[ c_{1,l}+ m(l-1)(c_{2,l}+c_{3,l})\right] ,\\ \end{aligned}$$

due to \(c_{2,l}+c_{3,l}=2\gamma -\frac{7}{6}l+2<0\) for \(l\ge 18\). Multiplying m(l) on both sides of the above inequality gives

$$\begin{aligned} \frac{m(l)}{B_l}\ge & {} \frac{6}{11l}\left[ c_{1,l}+ m(l-1)(c_{2,l}+c_{3,l})\right] m(l)\nonumber \\=\, & {} \frac{6}{11l} I_1, \end{aligned}$$
(56)

where \(I_1=\left[ c_{1,l}+ m(l-1)(c_{2,l}+c_{3,l})\right] m(l)\).

Substituting the expressions of \(c_{1,l},c_{2,l},c_{3,l}\) and \(m(l),m(l-1)\) in \(I_1\) yields

$$\begin{aligned} I_1=\, & {} \frac{(l+2)(3l-3\gamma -3)}{l-3\gamma -1} + \frac{(l+1)(l+2)(2\gamma -\frac{7}{6}l+2)}{(l-3\gamma -1)(l-3\gamma -2)}\\=\, & {} \frac{ \left( \frac{11}{6}l^3-10\gamma l^2-\frac{9}{2}l^2+9\gamma ^2 l-3\gamma l -\frac{25}{3}l +18\gamma ^2+34\gamma +16\right) }{(l-3\gamma -1)(l-3\gamma -2)}. \end{aligned}$$

Therefore,

$$\begin{aligned} I_1-\frac{11l}{6} = \frac{\left[ (1+\gamma )l^2 -\left( \frac{15}{2}\gamma ^2+\frac{39}{2}\gamma +12\right) l +18\gamma ^2+34\gamma +16\right] }{(l-3\gamma -1)(l-3\gamma -2)}. \end{aligned}$$
(57)

Define f(l) as

$$\begin{aligned} f(l)=(1+\gamma )l^2 -\left( \frac{15}{2}\gamma ^2+\frac{39}{2}\gamma +12\right) l +18\gamma ^2+34\gamma +16. \end{aligned}$$

By simple calculation, we have

$$\begin{aligned} f'(l)=\, & {} 2(1+\gamma )l-\left( \frac{15}{2}\gamma ^2+\frac{39}{2}\gamma +12\right) \\\ge & {} 2(1+\gamma )\times 18-\left( \frac{15}{2}\gamma ^2+\frac{39}{2}\gamma +12\right) \\=\, & {} 2(-15\gamma ^2+33\gamma +48)>0 \end{aligned}$$

for \({\text{all}}\, \gamma \in (0,1)\).

Hence,

$$\begin{aligned} f(l)\ge f(18)=-117\gamma ^2+7\gamma +124 >0, \end{aligned}$$

for \({\text{all}}\, \gamma \in (0,1)\).

In view of (56) and (57), and \(m(l)>0\), it leads to

$$\begin{aligned} \frac{m(l)}{B_l}> 1, \end{aligned}$$

i.e.,

$$\begin{aligned} \frac{1}{B_l}> \frac{1}{m(l)}. \end{aligned}$$

Combining with (55), we obtain

$$\begin{aligned} 1\le B_k\le m(k), \;\mathrm {for}\; k\ge 5,\;\forall \gamma \in (0,1), \end{aligned}$$

which yields \(q_{3,k-1}^{(\gamma )}\le q_{3,k}^{(\gamma )},\;\mathrm {for}\; k\ge 5\).

It also implies that the sign of \(q_{3,k}^{(\gamma )}\) is same as that of \(q_{3,k-1}^{(\gamma )}\). Since \(q_{3,4}^{(\gamma )}<0\), \(q_{3,k}^{(\gamma )}<0\) for \(k\ge 4\).

All this completes the proof.

Appendix C

Here we introduce \((2-\gamma )\) and \((3-\gamma )\) orders approximations to Riesz derivatives \(\frac{\partial ^\gamma v(x)}{\partial |x|^\gamma }\) defined on (ab) with \(\gamma \in (0,1)\).

For \(0<\gamma <1\), the left and right Riemann–Liouville derivatives can be rewritten as

$$\begin{aligned} _{RL}\mathrm {D}^\gamma _{a,x}v(x)=\, & {} \frac{(x-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )} +\frac{1}{\Gamma (1-\gamma )}\int ^x_a(x-s)^{-\gamma }v'(s)\mathrm {d}s \\=\, & {} \frac{(x-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )} +\,_C\mathrm {D}^\gamma _{a,x}v(x),\\ _{RL}\mathrm {D}^\gamma _{x,b}v(x)=\, & {} \frac{(b-x)^{-\gamma }v(b)}{\Gamma (1-\gamma )} -\frac{1}{\Gamma (1-\gamma )}\int ^b_x(s-x)^{-\gamma }v'(s)\mathrm {d}s\\=\, & {} \frac{(b-x)^{-\gamma }v(b)}{\Gamma (1-\gamma )} +\,_{C}\mathrm {D}^\gamma _{x,b}v(x), \end{aligned}$$

where \(_C\mathrm {D}^\gamma _{a,x}v(x)\) and \(_{C}\mathrm {D}^\gamma _{x,b}v(x)\) represent the left and right Caputo derivatives, respectively.

(i) Choosing \(v'(s)\approx \frac{v(x_{k+1})-v(x_k)}{h}\), \(s\in [x_k,x_{k+1}]\), then the L1 method for the left Riemann–Liouville derivative is presented as [15, 22]

$$\begin{aligned} _{RL}\mathrm {D}^\gamma _{a,x_i}v(x_i)=\, & {} \frac{(x_i-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )} +\frac{1}{\Gamma (1-\gamma )}\sum _{k=0}^{i-1}\int ^{x_{k+1}}_{x_k}(x_i-s)^{-\gamma }v'(s)\mathrm {d}s\\=\, & {} \frac{(x_i-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )}+\sum _{k=0}^{i-1}b^{(\gamma )}_{i-k-1}[v(x_{k+1})-v(x_k)] +O(h^{2-\gamma }), \end{aligned}$$

where \(x_0=a\), \(b_k^{(\gamma )}=\frac{h^{-\gamma }}{\Gamma (2-\gamma )}\left[ (k+1)^{1-\gamma }-k^{1-\gamma }\right]\).

We can naturally extend this method to the right Riemann–Liouville derivative as follows:

$$\begin{aligned} _{RL}\mathrm {D}^\gamma _{x_i,b}v(x_i)=\, & {} \frac{(b-x_i)^{-\gamma }v(b)}{\Gamma (1-\gamma )} -\frac{1}{\Gamma (1-\gamma )}\sum _{k=i}^{M-1}\int ^{x_{k+1}}_{x_k}(s-x_i)^{-\gamma }v'(s)\mathrm {d}s\\=\, & {} \frac{(b-x_i)^{-\gamma }v(b)}{\Gamma (1-\gamma )}-\sum _{k=i}^{M-1}b^{(\gamma )}_{k-i}[v(x_{k+1})-v(x_k)] +O(h^{2-\gamma }), \end{aligned}$$

where \(x_M=b\), \(b_k^{(\gamma )}=\frac{h^{-\gamma }}{\Gamma (2-\gamma )}\left[ (k+1)^{1-\gamma }-k^{1-\gamma }\right]\).

Thus, the Riesz derivative can be approximated by

$$\begin{aligned} \begin{aligned} \frac{\partial ^\gamma v(x_i)}{\partial |x|^\gamma }=&- \frac{1}{2\cos (\frac{\pi }{2}\gamma )} \left( \frac{(x_i-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )} +\frac{(b-x_i)^{-\gamma }v(b)}{\Gamma (1-\gamma )}\right. \\&\left. +\sum _{k=0}^{i-1}b^{(\gamma )}_{i-k-1}[v(x_{k+1})-v(x_k)] -\sum _{k=i}^{M-1}b^{(\gamma )}_{k-i}[v(x_{k+1})-v(x_k)]\right) \\&+O(h^{2-\gamma }). \end{aligned} \end{aligned}$$

(ii) In [1], the author derived the L2-\(1_\sigma\) method for the left Caputo derivatives with \((3-\gamma )\)th order for \(\gamma \in (0,1)\), we can also apply this method to the left and right Riemann–Liouville derivatives.

Considering this integral at node \(x_{i+\sigma }\), \(i\in \{0,1,\dots ,M-1\}\), choosing \(v'(s)\approx \frac{v(x_{k+1})-v(x_k)}{h} +\frac{v(x_{k+1})-2v(x_{k})+v(x_{k-1})}{h^2}(s-x_{k+1/2})\) for \(s\in [x_{k-1},x_{k}]\) and \(v'(s)\approx \frac{v(x_{i+1})-v(x_i)}{h}\) for \(s\in [x_{i},x_{i+\sigma }]\), we can obtain from [1] that

$$\begin{aligned} _C\mathrm {D}^\gamma _{a,x_{i+\sigma }} v(x_{i+\sigma })=\, & {} \frac{1}{\Gamma (1-\gamma )}\left( \sum _{k=1}^{i}\int ^{x_{k}}_{x_{k-1}}(x_{i+\sigma }-s)^{-\gamma }v'(s)\mathrm {d}s + \int ^{x_{i+\sigma }}_{x_i}(x_{i+\sigma }-s)^{-\gamma }v'(s)\mathrm {d}s\right) \nonumber \\=\, & {} \frac{h^{-\gamma }}{\Gamma (2-\gamma )} \sum _{k=0}^i d_{i-k}^{(\gamma ,\sigma )}[v(x_{k+1})-v(x_k)]+O(h^{3-\gamma })\nonumber \\&+\frac{v''(x_{i+1/2})(2\sigma -2+\gamma )}{2(1-\gamma )(2-\gamma )}\sigma ^{1-\gamma }h^{2-\gamma }, \end{aligned}$$
(58)

where

$$\begin{aligned} \left\{ \begin{aligned} c_{0}^{(\gamma ,\sigma )}&=\sigma ^{1-\gamma },\\ c_{k}^{(\gamma ,\sigma )}&=(k+\sigma )^{1-\gamma }-(k-1+\sigma )^{1-\gamma }, \; k\ge 1,\\ {\tilde{c}}_{k}^{(\gamma ,\sigma )}&= \frac{1}{2-\gamma }\left[ (k+\sigma )^{2-\gamma }-(k-1+\sigma )^{2-\gamma }\right] \\&\quad -\frac{1}{2}\left[ (k+\sigma )^{1-\gamma }+(k-1+\sigma )^{1-\gamma }\right] ,\; k\ge 1, \end{aligned}\right. \end{aligned}$$

and for \(i=0\), \(d_{0}^{(\gamma ,\sigma )}=c_{0}^{(\gamma ,\sigma )}\); for \(i\ge 1\),

$$\begin{aligned} d_{k}^{(\gamma ,\sigma )}=\left\{ \begin{aligned}&c_{0}^{(\gamma ,\sigma )} + {\tilde{c}}_{1}^{(\gamma ,\sigma )},&k=0,\\&c_{k}^{(\gamma ,\sigma )} + {\tilde{c}}_{k+1}^{(\gamma ,\sigma )} -{\tilde{c}}_{k}^{(\gamma ,\sigma )},&1\le k\le i-1,\\&c_{i}^{(\gamma ,\sigma )} - {\tilde{c}}_{i}^{(\gamma ,\sigma )},&k=i.\\ \end{aligned}\right. \end{aligned}$$
(59)

If we choose \(\sigma =1-\frac{\gamma }{2}\), then the scheme (58) is just the L2-\(1_\sigma\) method [1] for the left Caputo derivatives.

Similarly, one has

$$\begin{aligned} \begin{aligned} _C\mathrm {D}^\gamma _{x_{i+\sigma '},b} v(x_{i+\sigma '})&=- \frac{1}{\Gamma (1-\gamma )}\left( \int _{x_{i+\sigma '}}^{x_{i+1}}(s-x_{i+\sigma '})^{-\gamma }v'(s)\mathrm {d}s\right. \\&\quad \left. +\sum _{k=i+1}^{M-1}\int ^{x_{k+1}}_{x_k}(s-x_{i+\sigma '})^{-\gamma }v'(s)\mathrm {d}s \right) \\&=-\frac{h^{-\gamma }}{\Gamma (2-\gamma )} \sum _{k=i}^{M-1} {\tilde{d}}_{k-i}^{(\gamma ,\sigma ')}[v(x_{k+1})-v(x_k)] +O(h^{3-\gamma })\\&\quad +\frac{v''(x_{i+1/2})(2\sigma '-\gamma )}{2(1-\gamma )(2-\gamma )}\sigma '^{1-\gamma }h^{2-\gamma }, \end{aligned} \end{aligned}$$
(60)

where

$$\begin{aligned} \left\{ \begin{aligned} c_{0}^{(\gamma ,\sigma ')}&=(1-\sigma ') ^{1-\gamma },\\ c_{k}^{(\gamma ,\sigma ')}&=(k-\sigma ')^{1-\gamma }-(k-1-\sigma ')^{1-\gamma }, \; k\ge 1,\\ {\tilde{c}}_{k}^{(\gamma ,\sigma ')}&= \frac{1}{2-\gamma }\left[ (k-\sigma ')^{2-\gamma }-(k-1-\sigma ')^{2-\gamma }\right] \\&\quad -\frac{1}{2}\left[ (k-\sigma ')^{1-\gamma }+(k-1-\sigma ')^{1-\gamma }\right] ,\; k\ge 1, \end{aligned}\right. \end{aligned}$$

and for \(i=M-1\), \({\tilde{d}}_{0}^{(\gamma ,\sigma )}=c_{0}^{(\gamma ,\sigma ')}\); for \(i\ge 1\),

$$\begin{aligned} {\tilde{d}}_{k}^{(\gamma ,\sigma ')}=\left\{ \begin{aligned}&c_{0}^{(\gamma ,\sigma ')} + {\tilde{c}}_{2}^{(\gamma ,\sigma ')},&k=0,\\&c_{k+1}^{(\gamma ,\sigma ')} - {\tilde{c}}_{k+1}^{(\gamma ,\sigma ')} +{\tilde{c}}_{k+2}^{(\gamma ,\sigma ')},&1\le k\le M-2-i,\\&c_{k+1}^{(\gamma ,\sigma ')} - {\tilde{c}}_{k+1}^{(\gamma ,\sigma ')},&k=M-1-i.\\ \end{aligned}\right. \end{aligned}$$
(61)

If we choose \(\sigma '=\frac{\gamma }{2}\), then the scheme (60) is of \((3-\gamma )\)th order for the right Caputo derivatives.

Combine (58) with (60) and let \(2\sigma -2+\gamma +2\sigma '-\gamma =0\) which follows that \(\sigma +\sigma '=1\). Furthermore, assume that \(\sigma =\sigma '\), i.e., \(\sigma =\sigma '=\frac{1}{2}\), then \(x_{i+\sigma }=x_{i+1/2}=x_{i+\sigma '}\). Thus, one can get the following \((3-\gamma )\)th order scheme for Riesz derivatives:

$$\begin{aligned} \frac{\partial ^\gamma v(x_{i+1/2})}{\partial |x|^\gamma }=\, & {} - \frac{1}{2\cos (\frac{\pi }{2}\gamma )} \left[ \frac{(x_{i+1/2}-a)^{-\gamma }v(a)}{\Gamma (1-\gamma )} +\frac{(b-x_{i+1/2})^{-\gamma }v(b)}{\Gamma (1-\gamma )}\right. \nonumber \\&+\frac{h^{-\gamma }}{\Gamma (2-\gamma )}\left( \sum _{k=0}^i d_{i-k}^{(\gamma ,1/2)}[v(x_{k+1})-v(x_k)]\right. \nonumber \\&\left. \left. -\sum _{k=i}^{M-1} {\tilde{d}}_{k-i}^{(\gamma ,1/2)}[v(x_{k+1})-v(x_k)]\right) \right] +O(h^{3-\gamma }), \end{aligned}$$
(62)

in which \(d_{k}^{(\gamma ,1/2)}\) and \({\tilde{d}}_{k}^{(\gamma ,1/2)}\) are defined by (59) and (61), respectively, where \(\sigma =\sigma '=\frac{1}{2}.\)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Yi, Q. Modeling and Computing of Fractional Convection Equation. Commun. Appl. Math. Comput. 1, 565–595 (2019). https://doi.org/10.1007/s42967-019-00019-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42967-019-00019-8

Keywords

Navigation