Efficient uncertainty quantification of turbine blade leading edge film cooling using bi-fidelity combination of compressed sensing and Kriging

Mohammadi-Ahmar, Akbar; Mohammadi, Arash; Raisee, Mehrdad

doi:10.1016/j.ijheatmasstransfer.2020.120360

Cited by 13 publications

(11 citation statements)

References 66 publications

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“…34 The real relationship between the variable and the response value can be expressed as Equation ( 11). 35,36…”

Section: Kriging Modelmentioning

confidence: 99%

“…To know more about Kriging, please refer to the corresponding references for specific introduction. [35][36][37][38][39]…”

Section: Kriging Modelmentioning

confidence: 99%

“…Kriging method is suitable for the prediction of nonlinear problems 34 . The real relationship between the variable and the response value can be expressed as Equation ) 35,36

y = f ((), x) = u ((),, γ, x) + σ^{2} z ((), x),

where u ( γ , x )represents the deterministic part of the model, also known as the deterministic drift.…”

Section: Surrogate Model Buildingmentioning

confidence: 99%

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Surrogate model‐based heat dissipation optimization of air‐cooling battery packs involving herringbone fins

Gao

et al. 2021

Int J Energy Res

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For lithium-ion batteries (LIBs) used in electric vehicles (EVs), their performance is greatly affected by temperature. To ensure the safety and reliability of EVs, a reliable and efficient battery thermal management system (BTMS) is essential. This paper mainly analyzes air cooling BTMS. First, we propose an air-cooling heat dissipation method for battery modules with herringbone fins and long sleeves, and prove the effectiveness of the program by comparing it with the finless and sleeveless schemes. Second, parametric modeling of the scheme is carried out, the response surface methodology (RSM), radial basis function (RBF), and Kriging surrogate model of the scheme are constructed through design experiments. Then, the Kriging model is determined by the model evaluation index to be well adapted to the problem. We build a multiobjective optimization model (MOOM) and optimize it by multi-objective genetic algorithm (MOGA). Finally, it is found that the maximum temperature (T max ) and the maximum temperature difference (ΔT max ) decrease by 1.5 K and 36.79% without considering the cooling cost. When considering the cooling cost, T max and ΔT max dropped by 0.69 K and 17.92%, respectively. The optimization result improves the heat dissipation effect of the battery module and controls the cooling cost within the required range. Besides, optimization analysis can be carried out according to different actual cost requirements, which provide corresponding guidance for large-scale air-cooling heat dissipation analysis of LIBs. K E Y W O R D S battery thermal management system (BTMS), electric vehicles (EVs), Kriging surrogate model, multi-objective optimization model (MOOM)

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“…34 The real relationship between the variable and the response value can be expressed as Equation ( 11). 35,36…”

Section: Kriging Modelmentioning

confidence: 99%

“…To know more about Kriging, please refer to the corresponding references for specific introduction. [35][36][37][38][39]…”

Section: Kriging Modelmentioning

confidence: 99%

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Surrogate model‐based heat dissipation optimization of air‐cooling battery packs involving herringbone fins

Gao

et al. 2021

Int J Energy Res

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“…Film cooling is a more systematic, structured and efficient technique which helps in the protection of the turbine from the hot gas passing by removing the internal cooling air through a hole or by inserting it into the outer parts of the blade. Since the removal of the cooling film [35] from the engine computer, the cooling film suspension must be well designed and properly designed to reduce the loss of thermal efficiency, Figure 19 [36].…”

Section: External Surface Film Coolingmentioning

confidence: 99%

“…Temperature distribution on different sections [36] In the upcoming years the newer and easier techniques of cooling may become more accessible as the resources, technology and science develops [35]. The numerical study of flow field and heat transfer performance due to rotational effects on turbine blade cooling is studied using k-ω SST model (3-D) under constant heat flux supplied to leading edge and internal cooling takes place using 7 jets.…”

Section: Figure 19mentioning

confidence: 99%

Comprehensive Review on Leading Edge Turbine Blade Cooling Technologies

Sharma¹,

Kumar²,

Singh³

et al. 2021

IJHT

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Developments in the gas turbine technology have caused widespread usage of the Turbomachines for power generation. With increase in the power demand and a drop in the availability of fuel, usage of turbines with higher efficiencies has become imperative. This is only possible with an increase in the turbine inlet temperature (TIT) of the gas. However, the higher limit of TIT is governed by the metallurgical boundary conditions set by the material used to manufacture the turbine blades. Hence, turbine blade cooling helps in drastically controlling the blade temperature of the turbine and allows a higher turbine inlet temperature. The blade could be cooled from the leading edge, from the entire surface of the blade or from the trailing edge. The various methods of blade cooling from leading edge and its comparative study were reviewed and summarized along with their advantages and disadvantages.

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Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Efficient uncertainty quantification of turbine blade leading edge film cooling using bi-fidelity combination of compressed sensing and Kriging

Cited by 13 publications

References 66 publications

Surrogate model‐based heat dissipation optimization of air‐cooling battery packs involving herringbone fins

Surrogate model‐based heat dissipation optimization of air‐cooling battery packs involving herringbone fins

Comprehensive Review on Leading Edge Turbine Blade Cooling Technologies

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

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