Experimental investigation on transient boiling heat transfer during quenching of fuel cladding surfaces

Xiong, Juntao; Wang, Zefeng; Xiong, Ping; Lü, Tao; Yang, Yanhua

doi:10.1016/j.ijheatmasstransfer.2019.119131

Cited by 42 publications

(24 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because FeCrAl has a thermal effusivity of 9127.2 J-m −2 K −1 s −0.5 , included in thermal effusivity range of training dataset of this study (from 8473.4 to 10298.8 J-m −2 K −1 s −0.5 ), the trained MLP model enables the prediction of the quenching behavior of FeCrAl by its interpolation capacity. To evaluate the performance of the trained machine learning model, predicted quenching curves by the MLP model were compared with FeCrAl quenching experiments conducted by Kang et al [42] and Xiong et al [52] .…”

Section: Accident Tolerant Fuel (Fecral)mentioning

confidence: 99%

“…[52] were compared. In their experiments, FeCrAl rodlet having 20 mm outer diameter and 120 mm length was quenched under 5 and 10 °C subcooling conditions.…”

mentioning

confidence: 99%

“…The FeCrAl quenching curves predicted by the MLP model corresponds to TC locations of experiment. Because there is a difference in temperature change rate between prediction and experimental data due to the different rod diameters of training dataset of MLP model (9.5 mm) and Xiong et al[52] (20 mm), only predicted quenching curves were plotted in Fig.20. The average minimum film boiling temperatures measured by thermocouples at three axial locations (25, 50, 75 mm) were similar with those predicted by the MLP model.…”

mentioning

confidence: 99%

See 2 more Smart Citations

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

View full text Add to dashboard Cite

Quenching heat transfer is a representative complex phenomenon in thermal-hydraulic engineering. Despite the tremendous effort s to precisely predict the quenching behavior, conventional analysis methodology and correlations have exhibited limited prediction capacity on quenching heat transfer according to axial locations of heater rods. The deviations of existing models result from the uncertainty of axial heat conduction with low-resolution temperature measurement and limited regression performance. In this study, machine learning models were trained with optical fiber temperature measurement data having high spatial resolution about quenching to overcome the intrinsic limitation of conventional prediction methods. Support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) models were trained, and the MLP model showed best prediction performance (R 2 = 0.9999 and test RMSE = 1.41 °C) due to its strong analysis of the underlying relationship between input and output with interpolation capacity. The optimal MLP model (5-30-30-30-1 architecture) showed good agreement with experimental data for bottom quenching behaviors of Inconel-600 and Monel K500, in aspects of minimum film boiling temperature, quench front propagation velocity, and transient boiling curve by providing spatially resolutive quenching behavior that cannot be obtained from conventional correlations and having high uncertainty in existing computational analysis methods. The developed MLP model has the capability to predict the quenching behavior of FeCrAl accident tolerant fuel cladding surface, and better predictability on minimum film boiling temperature (average error of 3.7%) than conventional correlation having 7.1% average error. The suggested methodology using machine learning technique will contribute to innovatively improve the predictability on quenching phenomena with reducing uncertainty.

show abstract

Section: Accident Tolerant Fuel (Fecral)mentioning

confidence: 99%

“…[52] were compared. In their experiments, FeCrAl rodlet having 20 mm outer diameter and 120 mm length was quenched under 5 and 10 °C subcooling conditions.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

View full text Add to dashboard Cite

show abstract

“…A consolidated database of MFB temperature has been compiled, which contains 516 experimental data points from 15 sources 3,5,[13][14][15][17][18][19][20][21][22][23][24][25]27 with the following ranges: S1. A previous study 34 showed that embedding physical knowledge into the inputs can enhance the generalization of the ML model.…”

Section: Data Collectionmentioning

confidence: 99%

“…To quantify this effect, the Jacob number, Ja = c p,f ΔT sub /h fg , is used to show the effect of subcooling on MFB temperature. 26,27 Furthermore, based on the study of Cai et al, 37 the density ratio of liquid and vapor, ρ l /ρ v , is introduced to account for the pressure effect on MHF temperature. The effects of sample geometry and roughness are characterized using the length-to-diameter ratio and relative surface roughness.…”

Section: Data Collectionmentioning

confidence: 99%

Machine Learning Approach for Predicting Minimum Film Boiling Temperature Considering the Surface Roughness Effect

Tang,

Yu,

Gong

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The minimum film boiling (MFB) temperature, a crucial industrial design parameter for nuclear reactors, cryogenic milling, and grinding, refers to the lowest sustainable temperature at which stable film boiling occurs. Despite the development of many thermodynamic and hydrodynamic models, a universal model for predicting the MFB temperature is still required. In recent years, machine learning (ML) methods have been shown to outperform previous correlations in predicting the MFB temperature. However, these ML methods still have limitations in interpretability and the inclusion of key parameters, such as surface roughness, in the database. To address these issues, this study applies six ML models and a data interpretation method (SHapley Additive exPlanations, SHAP) to investigate MFB temperature. The extreme gradient boosting (XGBoost) model performs the best among these ML models, with an MAE of 2.6% for predicting MFB temperature without considering surface roughness, significantly outperforming the existing correlations. To account for surface roughness, a reduced database excluding data points without roughness values was used to train new XGBoost models. To fully use the removed data, a strategy is developed where normalized roughness and the MFB temperature predicted by the XGBoost model trained with the complete database excluding roughness are used as input variables. This approach improves the performance and generalization capabilities under insufficient surface roughness data sources by transferring the knowledge acquired from the complete database to the ML model developed by using the reduced database. The results show that the contributions of input variables and the physical variation trend of the XGBoost models are consistent with previous experimental results and theories.

show abstract

Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Sun,

Liu,

Hao

et al. 2023

Small

View full text Add to dashboard Cite

In order to reveal the dynamic response characteristic of thin film thermocouples (TFTCs), the nichrome/nisil (NiCr/NiSi) TFTCs are prepared onto the glass substrate. With short pulse infrared laser system, NiCr/NiSi TFTCs are dynamically calibrated. The thermoelectric electromotive force (TEF) curves of NiCr/NiSi TFTCs are recorded by the memory hicorder system, which could reflect TEF signals with resolution ratio in nanosecond and microvolt, simultaneously. With increasing laser energy from 15.49 to 29.59 mJ, TEF curves display more and more violent oscillation, even negative value. The results show that the bounce of thermal energy happens between two interfaces of TFTCs because the thermal conductivity of glass and air is significantly lower than that of NiSi/NiCr TFTCs. The bounce of thermal energy results in the obvious decrease of nNiCr and nNiSi, as well as oscillation of TEF. For laser energy in 29.59 mJ, the bounce of thermal energy in NiCr film could result in nNiCr < nNiSi. Then, TEF value appears abnormal negative value. Based on the results, the complex thermal energy transport process in TFTCs dynamic calibration is revealed, which results in the oscillation of thermal energy and TEF signal.

show abstract

Experimental investigation on transient boiling heat transfer during quenching of fuel cladding surfaces

Cited by 42 publications

References 31 publications

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Machine Learning Approach for Predicting Minimum Film Boiling Temperature Considering the Surface Roughness Effect

Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Contact Info

Product

Resources

About