Logowanie
Zarejestruj się
Zresetuj hasło
Publikuj i Dystrybuuj
Rozwiązania Wydawnicze
Rozwiązania Dystrybucyjne
Dziedziny
Architektura i projektowanie
Bibliotekoznawstwo i bibliologia
Biznes i ekonomia
Chemia
Chemia przemysłowa
Filozofia
Fizyka
Historia
Informatyka
Inżynieria
Inżynieria materiałowa
Językoznawstwo i semiotyka
Kulturoznawstwo
Literatura
Matematyka
Medycyna
Muzyka
Nauki farmaceutyczne
Nauki klasyczne i starożytne studia bliskowschodnie
Nauki o Ziemi
Nauki o organizmach żywych
Nauki społeczne
Prawo
Sport i rekreacja
Studia judaistyczne
Sztuka
Teologia i religia
Zagadnienia ogólne
Publikacje
Czasopisma
Książki
Materiały konferencyjne
Wydawcy
Blog
Kontakt
Wyszukiwanie
EUR
USD
GBP
Polski
English
Deutsch
Polski
Español
Français
Italiano
Koszyk
Home
Czasopisma
Applied Mathematics and Nonlinear Sciences
Tom 3 (2018): Zeszyt 1 (June 2018)
Otwarty dostęp
On the Method of Inverse Mapping for Solutions of Coupled Systems of Nonlinear Differential Equations Arising in Nanofluid Flow, Heat and Mass Transfer
Mangalagama Dewasurendra
Mangalagama Dewasurendra
oraz
Kuppalapalle Vajravelu
Kuppalapalle Vajravelu
| 03 paź 2018
Applied Mathematics and Nonlinear Sciences
Tom 3 (2018): Zeszyt 1 (June 2018)
O artykule
Poprzedni artykuł
Następny artykuł
Abstrakt
Artykuł
Ilustracje i tabele
Referencje
Autorzy
Artykuły w tym zeszycie
Podgląd
PDF
Zacytuj
Udostępnij
Data publikacji:
03 paź 2018
Zakres stron:
1 - 14
Otrzymano:
12 lis 2017
Przyjęty:
05 lut 2018
DOI:
https://doi.org/10.21042/AMNS.2018.1.00001
Słowa kluczowe
Method of directly defining the inverse mapping
,
Nonlinear systems
,
Nanofluid
,
Brownian motion
,
Stretching surface
,
analytical methods
,
Homotopy analysis method
© 2018 Mangalagama Dewasurendra and Kuppalapalle Vajravelu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Fig. 1
Flow configuration.
Fig. 2
Plot of E(c0,δ), the squared residual error over η ∈ [0,499] as a function of c0 and δ using parameter values Le = 2, Nb = 2,Pr = 1, Nt = 1, n = 0.5, A = 0.1314. The error function has minimum E(c0,δ,A) = 9.71 × 10–5 where c0 = –0.6195 and δ = 0.8462963.
Fig. 3
Plot of E(c0,δ), the squared residual error over η ∈ [0,499] as a function of c0 and δ using parameter values Le = 3, Nb = 1,Pr = 5, Nt = 0, n = 1, A = 7.8902. The error function has minimum E(c0,δ,A) = 9.41 × 10–5 where c0 = –9.30195 and δ = 1.03944.
Fig. 4
Plot of E(c0,δ), the squared residual error over η ∈ [0,499] as a function of c0 and δ using parameter values Le = 2, Nb = 2,Pr = 7, Nt = 0.5, n = 0.8, A = 0.24764. The error function has minimum E(c0,δ,A) = 8.28 × 10–5 where c0 = –0.690605 and δ = 0.8462963.
Fig. 5
Plot of f̂(η), where Curve 1 has Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, Curve 2 has Le = 3, Nb = 1, Pr = 5, Nt = 0, n = 1, and Curve 3 has Le = 2, Nb = 2, Pr = 7, Nt = 0.5, n = 0.8 using their respective error-minimizing convergence control parameter.
Fig. 6
Plot of f̂′(η), where Curve 1 has Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, Curve 2 has Le = 3, Nb = 1, Pr = 5, Nt = 0, n = 1, and Curve 3 has Le = 2, Nb = 2, Pr = 7, Nt = 0.5, n = 0.8 using their respective error-minimizing convergence control parameter.
Fig. 7
Plot of θ̂(η), where Curve 1 has Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, Curve 2 has Le = 3, Nb = 1, Pr = 5, Nt = 0, n = 1, and Curve 3 has Le = 2, Nb = 2, Pr = 7, Nt = 0.5, n = 0.8 using their respective error-minimizing convergence control parameter.
Fig. 8
Plot of ϕ̂(η), where Curve 1 has Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, Curve 2 has Le = 3, Nb = 1, Pr = 5, Nt = 0, n = 1, and Curve 3 has Le = 2, Nb = 2, Pr = 7, Nt = 0.5, n = 0.8 using their respective error-minimizing convergence control parameter.
Fig. 9
Comparison of f(η), θ(η) and ϕ(η) obtained by the MDDiM 3-term approximation and shooting method solutions with Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, where Curve 1 is shooting method results of f(η), Curve 2 is MDDiM results of f(η), Curve 3 is shooting method results of θ(η), Curve 4 is MDDiM results of θ(η), Curve 5 is shooting method results of ϕ(η), Curve 6 is MDDiM results of ϕ(η).
Fig. 10
Plot of Residual Error function verses Terms of approximation, where Curve 1 has Le = 2, Nb = 2, Pr = 1, Nt = 1, n = 0.5, Curve 2 has Le = 3, Nb = 1, Pr = 5, Nt = 0, n = 1, and Curve 3 has Le = 2, Nb = 2, Pr = 7, Nt = 0.5, n = 0.8 using their respective error-minimizing convergence control parameter.
Fig. 11
Plot of |–f̂″(0)| versus n, using Le = 3, Nb = 1, Pr = 5 and Nt = 0.
Fig. 12
Plot of |–θ̂′(0)|, where Curve 1 is |–θ̂′(0)| versus Nt using Le = 3, Nb = 1, Pr = 5, n = 1, Curve 2 is |–θ̂′(0)| versus Nb using Le = 3, Pr = 5, Nt = 0, n = 1.
Fig. 13
Plot of |–ϕ̂′(0)|, where Curve 1 is |–ϕ̂′(0)| versus Nt using Le = 2, Nb = 2, Pr = 1, n = 0.5, Curve 2 is |–ϕ̂′(0)| versus Nb using Le = 2, Pr = 1, Nt = 1, n = 0.5.
Minimum of the squared residual error E(A,c0,δ) for three different sets of parameters.
Le
Nb
Pr
Nt
n
A
c
0
δ
E
(
c
0
,
δ
,
A
)
2
2
1
1
0.5
0.1314
–0.6195
0.673
9.71 × 10
–5
3
1
5
0
1
7.8902
–9.3020
1.0394
9.71 × 10
–5
2
2
7
0.5
0.8
0.2476
–0.6906
0.8463
8.28 × 10
–5
Podgląd