Reduced order models for thermal analysis : final report : LDRD Project No. 137807.

Report, Sandia; Gartling, D. K.; Hogan, Robert

doi:10.2172/991534

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method successfully disambiguates between the inherent dominant modes of the system and those due to external actuation. Several model order reduction studies have been reported for nonlinear heat transfer related problems earlier [24][25][26][27]. Most of such previous studies were using the solver intrusive methods described earlier.…”

Section: Introductionmentioning

confidence: 99%

“…Most of such previous studies were using the solver intrusive methods described earlier. Also, there are not many examples of the development of ROMs for non-linear heat transfer applications with thermal radiation [27]. The novelty of the present work is the use of the data driven DMDc method for system identification and prediction for nonlinear heat transfer systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

System Identification and Prediction in Radiative Heat Transfer Using Dynamic Mode Decomposition

Rao¹

2021

IJHT

View full text Add to dashboard Cite

Transient heat transfer processes that are abundant in industry, can be highly non-linear, and complex. Although numerical models can be developed for the processes, the models can be slow, and computationally expensive. Most often, such models are developed in commercial software, rendering the final assembled equations inaccessible to the user. These models may need to be simplified and reduced in order to be utilized in real time applications such as model based controls. Here, a non-solver intrusive data-driven system identification and model order reduction method for practical radiative heat transfer problems is presented for real-time predictions. The method uses a novel modification to the technique of dynamic mode decomposition with controls. The underlying data for system identification could be from either experiments or numerical simulations. In this demonstrative study, the data was generated from a numerical simulation, which represented heat transfer in a lumped thermal mass network. The typical dynamic mode decomposition formulation was augmented with polynomial terms to better identify the non-linear form of the equations governing radiative heat transfer. The extracted system was then used to predict results with different initial and boundary conditions. The predicted results were compared with data generated from the numerical simulation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%