2013
DOI: 10.1016/j.ijheatmasstransfer.2012.09.009
|View full text |Cite
|
Sign up to set email alerts
|

Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
3
0
1

Year Published

2015
2015
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 15 publications
(5 citation statements)
references
References 22 publications
0
3
0
1
Order By: Relevance
“…Their flexibility and the possibility of multi-objective search have made them a popular choice for building design optimisation [7,22,9,21,26] and inverse heat transfer problems [27,10]. Their computational cost, higher than that of gradient descent techniques, can be reduced by the use of surrogate models [33] or model reduction. Some examples of genetic algorithms applied to the fitting of resistances and capacitances in building nodal models are also available [20,36,38].…”
Section: Introductionmentioning
confidence: 99%
“…Their flexibility and the possibility of multi-objective search have made them a popular choice for building design optimisation [7,22,9,21,26] and inverse heat transfer problems [27,10]. Their computational cost, higher than that of gradient descent techniques, can be reduced by the use of surrogate models [33] or model reduction. Some examples of genetic algorithms applied to the fitting of resistances and capacitances in building nodal models are also available [20,36,38].…”
Section: Introductionmentioning
confidence: 99%
“…Method of inverse problem finds a wide application in technical issues. In [24], a substitute calculation model for the inverse heat conduction problem was discussed. Boundary condition for a heated beam was sought for using this model and the finite element method.…”
Section: Plusmentioning
confidence: 99%
“…W pierwszej z nich efektywność algorytmu genetycznego porównano z efektywnością sieci neuronowej w rozwiązaniu tego samego problemu, natomiast w drugiej przystosowano GA do potrzeb optymalizacji wielokryterialnej. Algorytmy genetyczne zastosowano do rozwiązywania zagadnień odwrotnych wymiany ciepła mających na celu identyfikację warunków brzegowych w pracach [20][21][22]. Optymalizacja rojem cząsteczek posłużyła do wyznaczenia zależnej od temperatury pojemności cieplnej autorom pracy [23] natomiast w [24] została wykorzystana do identyfikacji zmiennej w czasie gęstości strumienia ciepła.…”
Section: Wstępunclassified