Research article Special Issues

Turing patterns in a 3D morpho-chemical bulk-surface reaction-diffusion system for battery modeling

  • Received: 02 July 2023 Revised: 12 February 2024 Accepted: 22 March 2024 Published: 07 April 2024
  • In this paper we introduce a bulk-surface reaction-diffusion (BS-RD) model in three space dimensions (3D) that extends the so-called DIB morphochemical model to account for the electrolyte contribution in the application, in order to study structure formation during discharge-charge processes in batteries. Here we propose to approximate the model by the bulk-surface virtual element method (BS-VEM) on a tailor-made mesh that proves to be competitive with fast bespoke methods for PDEs on Cartesian grids. We present a selection of numerical simulations that accurately match the classical morphologies found in experiments. Finally, we compare the Turing patterns obtained by the coupled 3D BS-RD model with those obtained with the original 2D version.

    Citation: Massimo Frittelli, Ivonne Sgura, Benedetto Bozzini. Turing patterns in a 3D morpho-chemical bulk-surface reaction-diffusion system for battery modeling[J]. Mathematics in Engineering, 2024, 6(2): 363-393. doi: 10.3934/mine.2024015

    Related Papers:

    [1] Patarawadee Prasertsang, Thongchai Botmart . Improvement of finite-time stability for delayed neural networks via a new Lyapunov-Krasovskii functional. AIMS Mathematics, 2021, 6(1): 998-1023. doi: 10.3934/math.2021060
    [2] Jenjira Thipcha, Presarin Tangsiridamrong, Thongchai Botmart, Boonyachat Meesuptong, M. Syed Ali, Pantiwa Srisilp, Kanit Mukdasai . Robust stability and passivity analysis for discrete-time neural networks with mixed time-varying delays via a new summation inequality. AIMS Mathematics, 2023, 8(2): 4973-5006. doi: 10.3934/math.2023249
    [3] Boonyachat Meesuptong, Peerapongpat Singkibud, Pantiwa Srisilp, Kanit Mukdasai . New delay-range-dependent exponential stability criterion and H performance for neutral-type nonlinear system with mixed time-varying delays. AIMS Mathematics, 2023, 8(1): 691-712. doi: 10.3934/math.2023033
    [4] Yonggwon Lee, Yeongjae Kim, Seunghoon Lee, Junmin Park, Ohmin Kwon . An improved reachable set estimation for time-delay linear systems with peak-bounded inputs and polytopic uncertainties via augmented zero equality approach. AIMS Mathematics, 2023, 8(3): 5816-5837. doi: 10.3934/math.2023293
    [5] Rupak Datta, Ramasamy Saravanakumar, Rajeeb Dey, Baby Bhattacharya . Further results on stability analysis of Takagi–Sugeno fuzzy time-delay systems via improved Lyapunov–Krasovskii functional. AIMS Mathematics, 2022, 7(9): 16464-16481. doi: 10.3934/math.2022901
    [6] Yude Ji, Xitong Ma, Luyao Wang, Yanqing Xing . Novel stability criterion for linear system with two additive time-varying delays using general integral inequalities. AIMS Mathematics, 2021, 6(8): 8667-8680. doi: 10.3934/math.2021504
    [7] Xingyue Liu, Kaibo Shi . Further results on stability analysis of time-varying delay systems via novel integral inequalities and improved Lyapunov-Krasovskii functionals. AIMS Mathematics, 2022, 7(2): 1873-1895. doi: 10.3934/math.2022108
    [8] Xiao Ge, Xinzuo Ma, Yuanyuan Zhang, Han Xue, Seakweng Vong . Stability analysis of systems with additive time-varying delays via new bivariate quadratic reciprocally convex inequality. AIMS Mathematics, 2024, 9(12): 36273-36292. doi: 10.3934/math.20241721
    [9] Huahai Qiu, Li Wan, Zhigang Zhou, Qunjiao Zhang, Qinghua Zhou . Global exponential periodicity of nonlinear neural networks with multiple time-varying delays. AIMS Mathematics, 2023, 8(5): 12472-12485. doi: 10.3934/math.2023626
    [10] Wentao Le, Yucai Ding, Wenqing Wu, Hui Liu . New stability criteria for semi-Markov jump linear systems with time-varying delays. AIMS Mathematics, 2021, 6(5): 4447-4462. doi: 10.3934/math.2021263
  • In this paper we introduce a bulk-surface reaction-diffusion (BS-RD) model in three space dimensions (3D) that extends the so-called DIB morphochemical model to account for the electrolyte contribution in the application, in order to study structure formation during discharge-charge processes in batteries. Here we propose to approximate the model by the bulk-surface virtual element method (BS-VEM) on a tailor-made mesh that proves to be competitive with fast bespoke methods for PDEs on Cartesian grids. We present a selection of numerical simulations that accurately match the classical morphologies found in experiments. Finally, we compare the Turing patterns obtained by the coupled 3D BS-RD model with those obtained with the original 2D version.



    Over the last two decades, many researches used LKF method to get stability results for time-delay systems [1,2]. The LKF method has two important technical steps to reduce the conservatism of the stability conditions. The one is how to construct an appropriate LKF, and the other is how to estimate the derivative of the given LKF. For the first one, several types of LKF are introduced, such as integral delay partitioning method based on LKF [3], the simple LKF [4,5], delay partitioning based LKF [6], polynomial-type LKF [7], the augmented LKF [8,9,10]. The augmented LKF provides more freedom than the simple LKF in the stability criteria because of introducing several extra matrices. The delay partitioning based LKF method can obtain less conservative results due to introduce several extra matrices and state vectors. For the second step, several integral inequalities have been widely used, such as Jensen inequality [11,12,13,14], Wirtinger inequality [15,16], free-matrix-based integral inequality [17], Bessel-Legendre inequalities [18] and the further improvement of Jensen inequality [19,20,21,22,23,24,25]. The further improvement of Jensen inequality [22] is less conservative than other inequalities. However, The interaction between the delay partitioning method and the further improvement of Jensen inequality [23] was not considered fully, which may increase conservatism. Thus, there exists room for further improvement.

    This paper further researches the stability of distributed time-delay systems and aims to obtain upper bounds of time-delay. A new LKF is introduced via the delay partitioning method. Then, a less conservative stability criterion is obtained by using the further improvement of Jensen inequality [22]. Finally, an example is provided to show the advantage of our stability criterion. The contributions of our paper are as follows:

    The integral inequality in [23] is more general than previous integral inequality. For r=0,1,2,3, the integral inequality in [23] includes those in [12,15,21,22] as special cases, respectively.

    An augmented LKF which contains general multiple integral terms is introduced to reduce the conservatism via a generalized delay partitioning approach. For example, the tt1mhx(s)ds, tt1mhtu1x(s)dsdu1, , tt1mhtu1tuN1x(s)dsduN1du1 are added as state vectors in the LKF, which may reduce the conservatism.

    In this paper, a new LKF is introduced based on the delay interval [0,h] is divided into m segments equally. From the LKF, we can conclude that the relationship among x(s), x(s1mh) and x(sm1mh) is considered fully, which may yield less conservative results.

    Notation: Throughout this paper, Rm denotes m-dimensional Euclidean space, A denotes the transpose of the A matrix, 0 denotes a zero matrix with appropriate dimensions.

    Consider the following time-delay system:

    ˙x(t)=Ax(t)+B1x(th)+B2tthx(s)ds, (2.1)
    x(t)=Φ(t),t[h,0], (2.2)

    where x(t)Rn is the state vector, A,B1,B2Rn×n are constant matrices. h>0 is a constant time-delay and Φ(t) is initial condition.

    Lemma2.1. [23] For any matrix R>0 and a differentiable function x(s),s[a,b], the following inequality holds:

    ba˙xT(s)R˙x(s)dsrn=0ρnbaΦn(a,b)TRΦn(a,b), (2.3)

    where

    ρn=(nk=0cn,kn+k+1)1,
    cn,k={1,k=n,n0,n1t=kf(n,t)ct,k,k=0,1,n1,n1,
    Φl(a,b)={x(b)x(a),l=0,lk=0cl,kx(b)cl,0x(a)lk=1cl,kk!(ba)kφk(a,b)x(t),l1,
    f(l,t)=tj=0ct,jl+j+1/tj=0ct,jt+j+1,
    φk(a,b)x(t)={bax(s)ds,k=1,babs1bsk1x(sk)dskds2dss1,k>1.

    Remark2.1. The integral inequality in Lemma 2.1 is more general than previous integral inequality. For r=0,1,2,3, the integral inequality (2.3) includes those in [12,15,21,22] as special cases, respectively.

    Theorem3.1. For given integers m>0,N>0, scalar h>0, system (2.1) is asymptotically stable, if there exist matrices P>0, Q>0, Ri>0,i=1,2,,m, such that

    Ψ=ξT1Pξ2+ξT2Pξ1+ξT3Qξ3ξT4Qξ4+mi=1(hm)2ATdRiAdmi=1rn=0ρnωn(timh,ti1mh)Ri×ωn(timh,ti1mh)<0, (3.1)

    where

    ξ1=[eT1ˉET0ˉET1ˉET2ˉETN]T,
    ξ2=[ATdET0ET1ET2ETN]T,
    ξ3=[eT1eT2eTm]T,
    ξ4=[eT2eT3eTm+1]T,
    ˉE0=hm[eT2eT3eTm+1]T,
    ˉEi=hm[eTim+2eTim+3eTim+m+1]T,i=1,2,,N,
    Ei=hm[eT1eTim+2eT2eTim+3eTmeTm(i+1)+1]T,i=0,1,2,,N,
    Ad=Ae1+B1em+1+B2mi=0em+1+i,
    ωn(timh,ti1mh)={eiei+i,n=0,nk=0cn,keicn,0ei+1nk=1cn,kk!e(k1)m+k+1,n1,
    ei=[0n×(i1)nIn×n0n×(Nm+1i)]T,i=1,2,,Nm+1.

    Proof. Let an integer m>0, [0,h] can be decomposed into m segments equally, i.e., [0,h]=mi=1[i1mh,imh]. The system (2.1) is transformed into

    ˙x(t)=Ax(t)+B1x(th)+B2mi=1ti1mhtimhx(s)ds. (3.2)

    Then, a new LKF is introduced as follows:

    V(xt)=ηT(t)Pη(t)+tthmγT(s)Qγ(s)ds+mi=1hmi1mhimhtt+v˙xT(s)Ri˙x(s)dsdv, (3.3)

    where

    η(t)=[xT(t)γT1(t)γT2(t)γTN(t)]T,
    γ1(t)=[tt1mhx(s)dst1mht2mhx(s)dstm1mhthx(s)ds],γ2(t)=mh[tt1mhtu1x(s)dsdu1t1mht2mht1mhu1x(s)dsdu1tm1mhthtm1mhu1x(s)dsdu1],,
    γN(t)=(mh)N1×[tt1mhtu1tuN1x(s)dsduN1du1t1mht2mht1mhu1t1mhuN1x(s)dsduN1du1tm1mhthtm1mhu1tm1mhuN1x(s)dsduN1du1],
    γ(s)=[x(s)x(s1mh)x(sm1mh)].

    The derivative of V(xt) is given by

    ˙V(xt)=2ηT(t)P˙η(t)+γT(t)Qγ(t)γT(thm)Qx(thm)+mi=1(hm)2˙xT(t)Ri˙x(t)mi=1hmti1mhtimh˙xT(s)Ri˙x(s)ds.

    Then, one can obtain

    ˙V(xt)=ϕT(t){ξT1Pξ2+ξT2Pξ1+ξT3Qξ3ξT4Qξ4+mi=1(hm)2ATdRiAd}ϕ(t)mi=1hmti1mhtimh˙xT(s)Ri˙x(s)ds, (3.4)
    ϕ(t)=[xT(t)γT0(t)γT1(t)γTN(t)]T,
    γ0(t)=[xT(t1mh)xT(t2mh)xT(th)]T.

    By Lemma 2.1, one can obtain

    hmti1mhtimh˙xT(s)Ri˙x(s)dsrl=0ρlωl(timh,ti1mh)Ri×ωl(timh,ti1mh). (3.5)

    Thus, we have ˙V(xt)ϕT(t)Ψϕ(t) by (3.4) and (3.5). We complete the proof.

    Remark3.1. An augmented LKF which contains general multiple integral terms is introduced to reduce the conservatism via a generalized delay partitioning approach. For example, the tt1mhx(s)ds, tt1mhtu1x(s)dsdu1, , tt1mhtu1tuN1x(s)dsduN1du1 are added as state vectors in the LKF, which may reduce the conservatism.

    Remark3.2. For r=0,1,2,3, the integral inequality (3.5) includes those in [12,15,21,22] as special cases, respectively. This may yield less conservative results. It is worth noting that the number of variables in our result is less than that in [23].

    Remark3.3. Let B2=0, the system (2.1) can reduces to system (1) with N=1 in [23]. For m=1, the LKF in this paper can reduces to LKF in [23]. So the LKF in our paper is more general than that in [23].

    This section gives a numerical example to test merits of our criterion.

    Example 4.1. Consider system (2.1) with m=2,N=3 and

    A=[011001],B1=[0.00.10.10.2],B2=[0000].

    Table 1 lists upper bounds of h by our methods and other methods in [15,20,21,22,23]. Table 1 shows that our method is more effective than those in [15,20,21,22,23]. It is worth noting that the number of variables in our result is less than that in [23]. Furthermore, let h=1.141 and x(0)=[0.2,0.2]T, the state responses of system (1) are given in Figure 1. Figure 1 shows the system (2.1) is stable.

    Table 1.  hmax for different methods.
    Methods hmax NoDv
    [15] 0.126 16
    [20] 0.577 75
    [21] 0.675 45
    [22] 0.728 45
    [23] 0.752 84
    Theorem 3.1 1.141 71
    Theoretical maximal value 1.463

     | Show Table
    DownLoad: CSV
    Figure 1.  The state trajectories of the system (2.1) of Example 4.1.

    In this paper, a new LKF is introduced via the delay partitioning method. Then, a less conservative stability criterion is obtained by using the further improvement of Jensen inequality. Finally, an example is provided to show the advantage of our stability criterion.

    This work was supported by Basic Research Program of Guizhou Province (Qian Ke He JiChu[2021]YiBan 005); New Academic Talents and Innovation Program of Guizhou Province (Qian Ke He Pingtai Rencai[2017]5727-19); Project of Youth Science and Technology Talents of Guizhou Province (Qian Jiao He KY Zi[2020]095).

    The authors declare that there are no conflicts of interest.



    [1] B. Bozzini, D. Lacitignola, I. Sgura, Spatio-temporal organization in alloy electrode-position: a morphochemical mathematical model and its experimental validation, J. Solid State Electrochem., 17 (2013), 467–469. https://doi.org/10.1007/s10008-012-1945-7 doi: 10.1007/s10008-012-1945-7
    [2] D. Lacitignola, B. Bozzini, I. Sgura, Spatio-temporal organization in a morphochemical electrodeposition model: Hopf and Turing instabilities and their interplay, Eur. J. Appl. Math., 26 (2015), 143–173. https://doi.org/10.1017/S0956792514000370 doi: 10.1017/S0956792514000370
    [3] D. Lacitignola, B. Bozzini, R. Peipmann, I. Sgura, Cross-diffusion effects on a morphochemical model for electrodeposition, Appl. Math. Model., 57 (2018), 492–513. https://doi.org/10.1016/j.apm.2018.01.005 doi: 10.1016/j.apm.2018.01.005
    [4] D. Lacitignola, B. Bozzini, M. Frittelli, I. Sgura, Turing pattern formation on the sphere for a morphochemical reaction-diffusion model for electrodeposition, Commun. Nonlinear Sci. Numer. Simulat., 48 (2017), 484–508. https://doi.org/10.1016/j.cnsns.2017.01.008 doi: 10.1016/j.cnsns.2017.01.008
    [5] D. Lacitignola, I. Sgura, B. Bozzini, T. Dobrovolska, I. Krastev, Spiral waves on the sphere for an alloy electrodeposition model, Commun. Nonlinear Sci. Numer. Simulat., 79 (2019), 104930. https://doi.org/10.1016/j.cnsns.2019.104930 doi: 10.1016/j.cnsns.2019.104930
    [6] A. Madzvamuse, A. W. Chung, C. Venkataraman, Stability analysis and simulations of coupled bulk-surface reaction-diffusion systems, Proc. R. Soc. A, 471 (2015), 20140546. https://doi.org/10.1098/rspa.2014.0546 doi: 10.1098/rspa.2014.0546
    [7] A. Madzvamuse, A. W. Chung, The bulk-surface finite element method for reaction-diffusion systems on stationary volumes, Finite Elem. Anal. Des., 108 (2016), 9–21. https://doi.org/10.1016/j.finel.2015.09.002 doi: 10.1016/j.finel.2015.09.002
    [8] M. Frittelli, A. Madzvamuse, I. Sgura, The bulk-surface virtual element method for reaction-diffusion PDEs: analysis and applications, Commun. Comput. Phys., 33 (2023), 733–763. https://doi.org/10.4208/cicp.OA-2022-0204 doi: 10.4208/cicp.OA-2022-0204
    [9] P. Hansbo, M. G. Larson, S. Zahedi, A cut finite element method for coupled bulk-surface problems on time-dependent domains, Comput. Meth. Appl. Math., 307 (2016), 96–116. https://doi.org/10.1016/j.cma.2016.04.012 doi: 10.1016/j.cma.2016.04.012
    [10] K. Deckelnick, C. M. Elliott, T. Ranner, Unfitted finite element methods using bulk meshes for surface partial differential equations, SIAM J. Numer. Anal., 52 (2014), 2137–2162. https://doi.org/10.1137/130948641 doi: 10.1137/130948641
    [11] M. Cheng, L. Ling, Kernel-based meshless collocation methods for solving coupled bulk-surface partial differential equations, J. Sci. Comput., 81 (2019), 375–391. https://doi.org/10.1007/s10915-019-01020-2 doi: 10.1007/s10915-019-01020-2
    [12] M. Frittelli, I. Sgura, Matrix-oriented FEM formulation for reaction-diffusion PDEs on a large class of 2D domains, Appl. Numer. Math., 2023. https://doi.org/10.1016/j.apnum.2023.07.010 doi: 10.1016/j.apnum.2023.07.010
    [13] V. Simoncini, Computational methods for linear matrix equations, SIAM Rev., 58 (2016), 377–441. https://doi.org/10.1137/130912839 doi: 10.1137/130912839
    [14] M. Frittelli, A. Madzvamuse, I. Sgura, Virtual element method for elliptic bulk-surface PDEs in three space dimensions, Numer. Methods Partial Differ. Equations, 39 (2023), 4221–4247. https://doi.org/10.1002/num.23040 doi: 10.1002/num.23040
    [15] I. Sgura, A. S. Lawless, B. Bozzini, Parameter estimation for a morphochemical reaction- diffusion model of electrochemical pattern formation, Inverse Probl. Sci. Eng., 27 (2019), 618–647. https://doi.org/10.1080/17415977.2018.1490278 doi: 10.1080/17415977.2018.1490278
    [16] I. Sgura, L. Mainetti, F. Negro, M. G. Quarta, B. Bozzini, Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems, J. Comput. Sci., 66 (2023), 101900. https://doi.org/10.1016/j.jocs.2022.101900 doi: 10.1016/j.jocs.2022.101900
    [17] A. Quarteroni, Numerical models for differential problems, Milano: Springer, 2009. https://doi.org/10.1007/978-88-470-1071-0
    [18] J. Smoller, Shock waves and reaction-diffusion equations, New York: Springer, 1994. https://doi.org/10.1007/978-1-4612-0873-0
    [19] B. Ahmad, A. Alsaedi, F. Brezzi, L. D. Marini, A. Russo, Equivalent projectors for virtual element methods, Comput. Math. Appl., 66 (2013), 376–391. https://doi.org/10.1016/j.camwa.2013.05.015 doi: 10.1016/j.camwa.2013.05.015
    [20] L. Mascotto, The role of stabilization in the virtual element method: a survey, Comput. Math. Appl., 151 (2023), 244–251. https://doi.org/10.1016/j.camwa.2023.09.045 doi: 10.1016/j.camwa.2023.09.045
    [21] L. Beirão da Veiga, F. Brezzi, L. D. Marini, A. Russo, The Hitchhiker's guide to the virtual element method, Math. Mod. Meth. Appl. Sci., 24 (2014), 1541–1573. https://doi.org/10.1142/S021820251440003X doi: 10.1142/S021820251440003X
    [22] M. Frittelli, A. Madzvamuse, I. Sgura, C. Venkataraman, Preserving invariance properties of reaction-diffusion systems on stationary surfaces, IMA J. Numer. Anal., 39 (2019), 235–270. https://doi.org/10.1093/imanum/drx058 doi: 10.1093/imanum/drx058
    [23] A. Kazarnikov, H. Haario, Statistical approach for parameter identification by Turing patterns, J. Theor. Biol., 501 (2020), 110319. https://doi.org/10.1016/j.jtbi.2020.110319 doi: 10.1016/j.jtbi.2020.110319
    [24] M. C. D'Autilia, I. Sgura, V. Simoncini, Matrix-oriented discretization methods for reaction-diffusion PDEs: comparisons and applications, Comput. Math. Appl., 79 (2020), 2067–2085. https://doi.org/10.1016/j.camwa.2019.10.020 doi: 10.1016/j.camwa.2019.10.020
    [25] I. Sgura, B. Bozzini, D. Lacitignola, Numerical approximation of Turing patterns in electrodeposition by ADI methods, J. Comput. Appl. Math., 236 (2012), 4132–4147. https://doi.org/10.1016/j.cam.2012.03.013 doi: 10.1016/j.cam.2012.03.013
  • This article has been cited by:

    1. Yanyan Sun, Xiaoting Bo, Wenyong Duan, Qun Lu, Stability analysis of load frequency control for power systems with interval time-varying delays, 2023, 10, 2296-598X, 10.3389/fenrg.2022.1008860
    2. Xiao Ge, Xinzuo Ma, Yuanyuan Zhang, Han Xue, Seakweng Vong, Stability analysis of systems with additive time-varying delays via new bivariate quadratic reciprocally convex inequality, 2024, 9, 2473-6988, 36273, 10.3934/math.20241721
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1508) PDF downloads(198) Cited by(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog