scholarly journals A reduced-order model of mould heat transfer in the continuous casting of steel

2016 ◽  
Vol 40 (19-20) ◽  
pp. 8530-8551 ◽  
Author(s):  
Lance C. Hibbeler ◽  
Melody M. Chin See ◽  
Junya Iwasaki ◽  
Kenneth E. Swartz ◽  
Ronald J. O’Malley ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Continuous Casting ◽  
Reduced Order Model ◽  
Continuous Casting Of Steel ◽  
Order Model ◽  
Reduced Order

Numerical Investigation of Heat Transfer in the Wake of a Single Highly Confined Bubble in a Horizontal Minichannel

2018 ◽  
Author(s):  
John R. Willard ◽  
D. Keith Hollingsworth
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Enhancement ◽  
Numerical Investigation ◽  
Channel Flow ◽  
Heat Generation ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Transfer Enhancement ◽  
Order Model ◽  
Reduced Order

Confined bubbly flows in millimeter-scale channels produce significant heat transfer enhancement when compared to single-phase flows. Experimental studies support the hypothesis that the enhancement is driven by a convective phenomenon in the liquid phase as opposed to sourcing from microlayer evaporation or active nucleation. A numerical investigation of flow structure and heat transfer produced by a single bubble moving through a millimeter-scale channel was performed in order to document the details of this convective mechanism. The simulation includes thermal boundary conditions emulating those of the experiments, and phase change was omitted in order to focus only on the convective mechanism. The channel is horizontal with a uniform-heat-generation upper wall and an adiabatic lower surface. A Lagrangian framework was adopted such that the computational domain surrounds the bubble and moves at the nominal bubble speed. The liquid around the bubble moves as a low-Reynolds-number unsteady laminar flow. The volume-of-fluid method was used to track the liquid/gas interface. This paper reviews the central results of this simulation regarding wake heat transfer. It then compares the findings regarding Nusselt number enhancement to a reduced-order model on a two-dimensional domain in the wake of the bubble. The model solves the advective-diffusion equation assuming a velocity field consistent with fully developed channel flow in the absence of the bubble. The response of the uniform-heat-generation upper wall is included. The model assumes a temperature profile directly behind the bubble which represents a well-mixed region produced by the passage of the bubble. The significant wake heat transfer enhancement and its decay with distance from the bubble documented by the simulation were captured by the reduced-order model. However, the channel surface temperature recovered in a much shorter distance in the simulation compared to the reduced-order model. This difference is attributed to the omission of transverse conduction within the heated surface in the two-dimensional model. Beyond approximately one bubble diameter into the bubble wake, the complex flow structures are replaced by the momentum field of the precursor channel flow. However, the properties and thickness of the heated upper channel wall govern the heat transfer for many bubble diameters behind the bubble.

scholarly journals Proper Orthogonal Decomposition-Based Method for Predicting Flow and Heat Transfer of Oil and Water in Reservoir

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Xianhang Sun ◽  
Bingfan Li ◽  
Xu Ma ◽  
Yi Pan ◽  
Shuangchun Yang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Basis Functions ◽  
Hot Water ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Flow And Heat Transfer ◽  
Proper Orthogonal

Calculation process of some reservoir engineering problems involves several passes of full-order numerical reservoir simulations, and this makes it a time-consuming process. In this study, a fast method based on proper orthogonal decomposition (POD) was developed to predict flow and heat transfer of oil and water in a reservoir. The reduced order model for flow and heat transfer of oil and water in the hot water-drive reservoir was generated. Then, POD was used to extract a reduced set of POD basis functions from a series of “snapshots” obtained by a finite difference method (FDM), and these POD basis functions most efficiently represent the dynamic characteristics of the original physical system. After injection and production parameters are changed constantly, the POD basis functions combined with the reduced order model were used to predict the new physical fields. The POD-based method was approved on a two-dimensional hot water-drive reservoir model. For the example of this paper, compared with FDM, the prediction error of water saturation and temperature fields were less than 1.3% and 1.5%, respectively; what is more, it was quite fast, where the increase in calculation speed was more than 70 times.

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