Computational Analysis of Nonlinear Models Arising from Van der Waals Fluids and Planck’s Radiation Law

Authors

  • Fatima Siddiqui Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Sindh, Pakistan
  • Zubair Ahmed Kalhoro Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Sindh, Pakistan
  • Sanaullah Jamali University of Sindh, Laar Campus, Badin, Sindh, Pakistan
  • Imran Qasim Memon Department of Basic Sciences and Related Studies, Mehran University of Engineering & Technology, Jamshoro, Pakistan

DOI:

https://doi.org/10.53560/PPASA(63-3)723

Keywords:

Derivative-free Iterative Methods, Hermite Interpolation, Systems of Nonlinear Equations, Van der Waals Equation, Planck’s Radiation Law, Computational Efficiency, Basins of Attraction

Abstract

Nonlinear equations and systems of nonlinear equations arise in many physical and engineering models, such as equations of state for real gases and thermal radiation laws. Newton-type methods for these problems require derivatives or Jacobian matrices, which may be unavailable or expensive to evaluate. This study presents a derivative-free three-step iterative scheme, denoted PS6 (proposed sixth-order scheme), in which the first two steps use first-order divided differences and the derivative in the third step is approximated by a cubic Hermite interpolating polynomial. For scalar equations, PS6 converges with order six and uses four function evaluations per iteration, giving an efficiency index of . The scheme is extended to systems of nonlinear equations without evaluating Jacobian matrices, and its sixth-order convergence is proved for both cases by Taylor expansion. The computational efficiency index of PS6 exceeds those of four existing fourth-, fifth- and sixth-order schemes for every system size. In numerical experiments on models arising from the Van der Waals equation of state, Planck’s radiation law and particle interaction, and on two coupled nonlinear systems, PS6 required the same number of iterations as or fewer iterations than the compared methods, attained the smallest errors and residuals, and had the shortest central processing unit (CPU) time on both systems; the computational order of convergence agreed with the theoretical order. Basins of attraction for three test polynomials show that 71–92% of the initial points in the square [−1, 1] × [−1, 1] converge, mostly within three iterations. PS6 is therefore an accurate and efficient alternative for nonlinear problems in which derivatives are unavailable or costly.

References

1. J.M. Ortega and W.C. Rheinboldt (Eds.). Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York, USA (1970).

2. J.F. Traub (Ed.). Iterative Methods for the Solution of Equations. Prentice-Hall, Englewood Cliffs, NJ, USA (1964).

3. O.S. Solaiman and I. Hashim. Efficacy of optimal methods for nonlinear equations with chemical engineering applications. Mathematical Problems in Engineering 2019: 1728965 (2019). https://doi.org/10.1155/2019/1728965

4. O.S. Solaiman and I. Hashim. Optimal eighth-order solver for nonlinear equations with applications in chemical engineering. Intelligent Automation and Soft Computing 27(2): 379-390 (2021). https://doi.org/10.32604/iasc.2021.015285

5. A. Gilat and V. Subramaniam. Numerical Methods for Engineers and Scientists: An Introduction with Applications Using MATLAB (3rd ed.). John Wiley & Sons, Hoboken, NJ, USA (2014).

6. A.M. Ostrowski. Solution of Equations and Systems of Equations. Academic Press, New York, USA (1960).

7. J.F. Steffensen. Remarks on iteration. Scandinavian Actuarial Journal 1933(1): 64-72 (1933). https://doi.org/10.1080/03461238.1933.10419209

8. X. Wang and T. Zhang. A family of Steffensen type methods with seventh-order convergence. Numerical Algorithms 62(3): 429-444 (2013). https://doi.org/10.1007/s11075-012-9597-3

9. I.K. Argyros, M. Kansal, V. Kanwar, and S. Bajaj. Higher-order derivative-free families of Chebyshev–Halley type methods with or without memory for solving nonlinear equations. Applied Mathematics and Computation 315: 224-245 (2017). https://doi.org/10.1016/j.amc.2017.07.051

10. P. Sivakumar and J. Jayaraman. Some new higher order weighted Newton methods for solving nonlinear equation with applications. Mathematical and Computational Applications 24(2): 59 (2019). https://doi.org/10.3390/mca24020059

11. S. Jamali, Z.A. Kalhoro, A.W. Shaikh, M.S. Chandio, and S. Dehraj. A new three step derivative free method using weight function for numerical solution of non-linear equations arises in application problems. VFAST Transactions on Mathematics 10(2): 164-174 (2022). https://doi.org/10.21015/vtm.v10i2.1289

12. M. Senol, L. Akinyemi, H. Nkansah and W. Adel. New solutions for four novel generalized nonlinear fractional fifth-order equations. Journal of Ocean Engineering and Science 9: 59-65 (2024). https://doi.org/10.1016/j.joes.2022.03.013

13. X. Wang and M. Sun. A new family of fourth-order Ostrowski-type iterative methods for solving nonlinear systems. AIMS Mathematics 9(4): 10255-10266 (2024). https://doi.org/10.3934/math.2024501

14. A. Zein. A new family of optimal fourth-order iterative methods for solving nonlinear equations with applications. Journal of Applied Mathematics 2024: 9955247 (2024). https://doi.org/10.1155/2024/9955247

15. S. Jamali, Z.A. Kalhoro, A.W. Shaikh, and M.S. Chandio. Solution of chemical engineering models and their dynamics using a new three-step derivative free optimal method. Journal of Hunan University Natural Sciences 50(1): 236-245 (2023). https://doi.org/10.55463/issn.1674-2974.50.1.24

16. Syaharuddin, H. Hidayah, Mahsup, S. Mehmood and W. Raza. Accuracy comparison of multivariate Newton-Raphson and Newton-Kantorovich methods through numerical simulation in nonlinear systems. Jurnal ELTIKOM 10(1): 37-46 (2026). https://doi.org/10.31961/eltikom.v10i1.1971

17. R. Meghwar, Z.A. Kalhoro, and S. Jamali. Computationally efficient three-step derivative-free iterative scheme for nonlinear algebraic and transcendental equations. QUEST Research Journal 23(1): 38-45 (2025). https://publications.quest.edu.pk/ojs/index.php/qrj/article/view/108

18. Z. Abbasi, Z.A. Kalhoro, S. Jamali, A.W. Shaikh, and O.A. Rajput. A novel approach for real-world problems based on the Hermite interpolation technique and analysis using basins of attraction. The Sciencetech 5(3): 112-126 (2024). https://journals.qurtuba.edu.pk/ojs/index.php/tst/article/view/85960

19. P. Jarratt. Some fourth order multipoint iterative methods for solving equations. Mathematics of Computation 20(95): 434-437 (1966). https://doi.org/10.1090/S0025-5718-66-99924-8

20. S. Weerakoon and T.G.I. Fernando. A variant of Newton’s method with accelerated third-order convergence. Applied Mathematics Letters 13(8): 87-93 (2000). https://doi.org/10.1016/S0893-9659(00)00100-2

21. D.K.R. Babajee, M.Z. Dauhoo, M.T. Darvishi, and A. Barati. A note on the local convergence of iterative methods based on Adomian decomposition method and 3-node quadrature rule. Applied Mathematics and Computation 200(1): 452-458 (2008). https://doi.org/10.1016/j.amc.2007.11.009

22. F. Soleymani, S.K. Khattri and S.K. Vanani. Two new classes of optimal Jarratt-type fourth-order methods. Applied Mathematics Letters 25: 847-853 (2012). https://doi.org/10.1016/j.aml.2011.10.030

23. J.R. Sharma, R.K. Guha, and R. Sharma. An efficient fourth order weighted-Newton method for systems of nonlinear equations. Numerical Algorithms 62(2): 307-323 (2013). https://doi.org/10.1007/s11075-012-9585-7

24. X.Y. Xiao and H.W. Yin. Increasing the order of convergence for iterative methods to solve nonlinear systems. Calcolo 53(3): 285-300 (2016). https://doi.org/10.1007/s10092-015-0149-9

25. A. Cordero, J.L. Hueso, E. Martínez, and J.R. Torregrosa. Increasing the convergence order of an iterative method for nonlinear systems. Applied Mathematics Letters 25(12): 2369-2374 (2012). https://doi.org/10.1016/j.aml.2012.07.005

26. A. Cordero, J.L. Hueso, E. Martínez, and J.R. Torregrosa. A modified Newton-Jarratt’s composition. Numerical Algorithms 55(1): 87-99 (2010). https://doi.org/10.1007/s11075-009-9359-z

27. A.R. Amiri, A. Cordero, M.T. Darvishi, and J.R. Torregrosa. Preserving the order of convergence: low-complexity Jacobian-free iterative schemes for solving nonlinear systems. Journal of Computational and Applied Mathematics 337: 87-97 (2018). https://doi.org/10.1016/j.cam.2018.01.004

28. A. Cordero, C. Jordán, E. Sanabria-Codesal, and J.R. Torregrosa. Solving nonlinear vectorial problems with a stable class of Jacobian-free iterative processes. Journal of Applied Mathematics and Computing 70(5): 5023-5048 (2024). https://doi.org/10.1007/s12190-024-02166-5

29. M. Abad, A. Cordero, and J.R. Torregrosa. A family of seventh-order schemes for solving nonlinear systems. Bulletin Mathématique de la Société des Sciences Mathématiques de Roumanie 57(105)(2): 133-145 (2014). https://rms.unibuc.ro/bulletin/pdf/57-2/articol_1.pdf

30. A. Cordero, J.G. Maimó, J.R. Torregrosa, and M.P. Vassileva. Solving nonlinear problems by Ostrowski–Chun type parametric families. Journal of Mathematical Chemistry 53(1): 430-449 (2015). https://doi.org/10.1007/s10910-014-0432-z

31. M. Grau-Sánchez, M. Noguera, and S. Amat. On the approximation of derivatives using divided difference operators preserving the local convergence order of iterative methods. Journal of Computational and Applied Mathematics 237(1): 363-372 (2013). https://doi.org/10.1016/j.cam.2012.06.005

32. R. Behl. A derivative free fourth-order optimal scheme for applied science problems. Mathematics 10(9): 1372 (2022). https://doi.org/10.3390/math10091372

33. A. Cordero, A. Ledesma, J.G. Maimó and J.R. Torregrosa. Design and dynamical behavior of a fourth order family of iterative methods for solving nonlinear equations. AIMS Mathematics 9(4): 8564-8593 (2024). https://doi.org/10.3934/math.2024415

34. A. Cordero, J.G. Maimó, A. Rodríguez-Cabral and J.R. Torregrosa. Two-step fifth-order efficient Jacobian-free iterative method for solving nonlinear systems. Mathematics 12(21): 3341 (2024). https://doi.org/10.3390/math12213341

35. B. Bradie. A Friendly Introduction to Numerical Analysis. Pearson Prentice Hall, Upper Saddle River, NJ, USA (2006).

36. K. Madhu and J. Jayaraman. Higher order methods for nonlinear equations and their basins of attraction. Mathematics 4(2): 22 (2016). https://doi.org/10.3390/math4020022

37. H.T. Kung and J.F. Traub. Optimal order of one-point and multipoint iteration. Journal of the ACM 21(4): 643-651 (1974). https://doi.org/10.1145/321850.321860

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Published

2026-09-20

How to Cite

Siddiqui, F., Kalhoro, Z. A., Jamali, S., & Imran Qasim Memon. (2026). Computational Analysis of Nonlinear Models Arising from Van der Waals Fluids and Planck’s Radiation Law. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 63(3). https://doi.org/10.53560/PPASA(63-3)723

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Research Articles