Application of POD plus LTI ROM to Battery Thermal Modeling: SISO Case

Asgari, Saeed; Xiao, Hongling; Tsuk, Michael; Kaushik, Shubham

doi:10.4271/2014-01-1843

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Then a machine learning approach is applied to learn the studied system. Offline models coupled with POD have been studied on several physical systems, such as on the modeling of pressure ulcer prevention [8] and thermal behavior of fluid-cooled battery [12].…”

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confidence: 99%

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Ramos

Meglio

Silva

et al. 2020

AIAA Scitech 2020 Forum

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An Inverted Conical Flame, anchored at a central bluff-body, in an unconfined burner configuration, is a flame that may be used to further understand more complex flame configurations, such as in aeronautical engines combustion chambers. This article involves the modeling of an unconfined laminar premixed ICF, anchored at a central cylindrical rod. The computational fluid dynamic modeling of this flame configuration involves the solution of transport equations of species mass, momentum and energy, which is computationally expensive. A skeletal methane/air chemical kinetic mechanism is used to capture a plethora of time and length scales. Reduced Order Models (ROM) have been shown to increase the computational efficiency of dynamical systems modeling. Accordingly, in this work a ROM of the steady ICF is developed, using the volume flow rate of the combustible mixture as input variable of the ROM, and the velocity components and the temperature fields as the output. The aim is to optimize the flame modeling computational time, thus paving the way to the reduced order modeling of acoustically excited, unstable, laminar premixed inverted conical flames. A model of the ICF has been developed with Fluent 2019 R2, and the analysis of a diversity of properties and species included in the combustion process has been made. Since a skeletal chemical kinetic model is used, the characterization of the flame is marked by its different property scales, allowing the flame front recognition over the field of CH 2 . However, this CFD model does not present a static convergence behavior, but oscillates over a pseudo-steady state point. Thus an approximation of the statistical steady state has only been achieved by ensemble averaging the results. Therefore, the ROM of the ICF has been developed over a set of averaged data generated with Fluent, and its results shows agreement with CFD results, presenting an average overall error smaller than 3 %.

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confidence: 99%

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Ramos

Meglio

Silva

et al. 2020

AIAA Scitech 2020 Forum

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“…Reduced order models is a methodology that enables the simple modelling of complex systems, without losing the most important characteristics of those (Luboz et al, 2018). It is a technique that has been used to reduce the burden linked to the parametric study of physical systems in the last decade (Asgari et al, 2017;Xiao et al, 2015). The Static ROM of Twin Builder is used as a model reduction technique.…”

Section: Reduced Order Modelmentioning

confidence: 99%

“…Reduced order model (ROM) is a machine learning methodology which enables the possibility of reducing the burden of parametric exploration for complex systems, without compromising the characteristic behavior of the system (LeCun et al, 2015;Luboz et al, 2018). These models have been applied to different problems, such as thermal, mechanical and fluid dynamic systems (Asgari et al, 2017;Luboz et al, 2018;Xiao et al, 2015). Since the modelling of combustion demands a high cost, the application of reduced order models on these systems is the main motivation of this work.…”

Section: Introductionmentioning

confidence: 99%

Reduced order models applied to laminar diffusion flames

Junqueira¹,

Silva²,

Ramos³

2020

Procceedings of the 18th Brazilian Congress of Thermal Sciences and Engineering

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Computational model of combustion chambers is a major topic of research on the past decades. The modelling of such physical systems enables the analysis of several flame properties, such as velocity, species mass fraction and temperature. However, there is a high computational burden associated with the parametric exploration and the complexity of such systems. To overcome this problem, reduced order models (ROM) have been used to predict the behavior of those systems, decreasing the cost associated to the computational modeling. Accordingly, this work presents modeling of non premixed laminar flames stabilized in the Gülder burner, using a skeletal kinetic mechanism. The computational fluid dynamics flame model has been developed using Fluent 2019 R3 for the isothermal and reactive cases, for different prescribed fuel inlet velocities. The analysis of the flow and flame structures are respectively by means of the axial and radial velocity components and the mass fraction of CH 4 fields in the isothermal case; and the temperature, mass fraction of OH and CO for the reactive case. Then, the ROM is construct with StaticROM from Twin Builder based on the CFD results. The results of the ROM have a maximum absolute error of 0.0025 m/s for the velocity ROM, 0.0737 for the mass fraction of CH 4 , 98.91 K for the temperature, 1.47 10 −3 for the mass fraction of OH and 0.018 for the mass fraction of CO. These errors are acceptable for the isothermal case and for the temperature, however, for the mass fractions of OH and CO it shows a significant difference since the concentration of these species is small.

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“…More recently, a method based on SVD was proposed in Ref. [10,11] to calculate temperature field for battery thermal systems without the need of the system Since the ROM proposed in this paper uses the L TI ROM as a sub-model, a brief introduction of the LTI ROM is provided here. In using the L TI ROM, the thermal problem is treated like a system, for which the inputs are the power dissipation (or loss), and the outputs are the temperature increase at the user specified locations.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [10,11], a uniform heat source distribution is used. For greater accuracy, a non-uniform heat source distribution is calculated from electromagnetic FEA code and such a distribution is used in the ROM.…”

mentioning

confidence: 99%

A computationally efficient transient thermal model for electric machines based on singular value decomposition

Xiao

Liu

Lin

et al. 2014

2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

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A transient reduced order model (ROM) for electric machines based on singular value decomposition (SVD) is proposed. The ROM generation process starts with computation fluid dynamics (CFD) results of an electric machine model, from which SVD is used to calculate the approximate subspace that the temperature solution belongs to. Then a ROM working in the subspace is created. The ROM is shown to provide very similar results as those from CFD under arbitrary transient loss profiles. The ROM runs orders of magnitude faster than the original CFD model.matrices. The current paper uses such a method and extends it to electric machines. In Ref. [10,11], a uniform heat source distribution is used. For greater accuracy, a non-uniform heat source distribution is calculated from electromagnetic FEA code and such a distribution is used in the ROM.The paper is organized as follows. Section II describes the L TI MOR, which is used as a sub-model of the proposed ROM in this paper. Section III presents the SVD ROM along with the Prius motor test case. Finally, section IV is the conclusion.

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Application of POD plus LTI ROM to Battery Thermal Modeling: SISO Case

Cited by 10 publications

References 2 publications

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Reduced order models applied to laminar diffusion flames

A computationally efficient transient thermal model for electric machines based on singular value decomposition

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