Quenching characteristics of a continuously-heated rod in natural sea water

Fu, Ben-Ran; Ho, Yuan-Hong; Ho, Ming-Xi; Pan, Ching‐Tsai

doi:10.1016/j.ijheatmasstransfer.2015.11.093

Cited by 38 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A consolidated database of MFB temperature has been compiled, which contains 516 experimental data points from 15 sources ,,− ,− , with the following ranges: Δ T sub = 0–40 K, P s = 0.1–2 MPa, β f /β w = 0.002–0.531, D = 1.2–24 mm, L = 50–300 mm, and R a / R ref = 0.05–1.94. The experimental conditions of the database are summarized in Table S1.…”

Section: Methodsmentioning

confidence: 99%

“…Then, a large number of quenching experiments were conducted. The effects of system pressure, − liquid subcooling, , substrate material, − initial temperature, , surface conditions, and oxidation − on MFB temperature were studied in detail to develop more accurate models. Table summarizes the models of the MFB temperature of a rod.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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

show abstract

“…As the existance of film boiling regime determines the total duration of quenching process of the fuel bundle, further delay might bring cladding to the overheating state or even a failure of the emergency cooling system, the worst case [1,2]. Fu et al [4] reported their study of using brass rod to simulate the quenching process of the nuclear fuel rod after the power plant shutdown. Information on Leidenfrost temperature and maximum heat flux were highlighted.…”

Section: Introduction mentioning

confidence: 99%

Characterization of The Heat Transfer on Spray Quenching for Different Material Properties

Sabariman

Specht²

2021

Atom Indo.

View full text Add to dashboard Cite

A broad range of water spray applications as a means of two-phase cooling scheme has encouraged researches in the thermal management system to support safety and process efficiency in industries. In the application of above saturation temperature, the cooling process follows the boiling curve where the dissipated heat flux is figured out as a function of the wall temperature. Knowledge on constructing the boiling curve is an essential part in order to define the moving boundary, and boundary value problems occur in metal cooling process analysis involving heat transfer and phase change. The objective of the research was to characterize the boiling parameters on different materials in the regime of film boiling, transition boiling, and nucleate boiling as the basis for its boiling curve construction. To explain the influence of material properties, this work is featuring, firstly, the calculated vapor film thickness in film boiling regime by promoting self-developed analytical model of single droplet and, secondly, the calculated boiling width which indicates a strong combination of surface temperature and heat flux observed as the boiling phenomena. This is obtained by calculating the propagation of wetting front and 100 o C points. This experimental work employed a volumetric spray flux of 4.2, 10 and 13.7 kg/m 2 s to cool a hot metal samples of aluminum alloy AA6082 and nickel heated up to 560 °C. An infrared camera was used to record the temperature drop over time. Heat flux calculation follows the numerical procedure according to 1D energy balance model. Calculated vapor film thickness explains why the HTC tends to increase with the decrease of the surface temperature. Leidenfrost and Departure from Nucleate Boiling (DNB) temperatures are found to be inversely proportional to the heat penetration coefficient of the metal while maximum heat flux and boiling width increase with it.

show abstract

“…The quenching process is the rapid cooling of high temperature objects by exposure to a much cooler liquid, and is frequently used in several engineering applications, such as metallurgy and nuclear industries. The behavior of quenching process is generally influenced by many parameters, such as the surface properties of the substance, thermal-hydraulic properties of the coolant, and temperatures of the substance and coolant [6] . In this process the heat transfer from a solid wall to adjacent liquid is limited by the occurrence of film boiling in which a stable vapor film layer covers the hot solid surface.…”

Section: Introductionmentioning

confidence: 99%

An experimental analysis of quenching of continuously heated vertical rod with aqueous Al 2 O 3 nanofluid

Patra¹,

Nayak²,

Gosh³

et al. 2017

REFFIT

View full text Add to dashboard Cite

Quenching characteristics of metallic vertical rod with an internal heater has been investigated in pure water and water-based nanofluid with alumina nanoparticles of 0.001% by volume. Initial temperature of the testing rod (12 mm diameter and 10 0 0 mm length) ranges from 200 °C to 250 °C. The internal heater may be used to simulate the decay heat generated by the nuclear fuel rod after the power plant shut- down. Tests have been performed with varied range of experimental conditions of decay heat, constant water and nanofluid flow rates. The cooling curves follow a general trend of a rapid temperature drop up to almost 100 °C of the rod surface temperature irrespective of the operating parameters and the location of the thermocouple. The obtained results during quenching process indicated that HTC (heat transfer coefficient) for nanofluids are more than de-ionized water. It was also observed that in identical circum- stances, the quenching time of the specimen in Al 2 O 3 nanofluid considerably decreased as compared to water. The results exhibit that nanofluids can enhance the reflood heat transfer performance in terms of quenching rate for a long vertical rod causing liquid-droplets-induced depositions of nanoparticles result- ing in making a pre-coating effect characterized by higher wettability.

show abstract

Quenching characteristics of a continuously-heated rod in natural sea water

Cited by 38 publications

References 23 publications

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

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

Characterization of The Heat Transfer on Spray Quenching for Different Material Properties

An experimental analysis of quenching of continuously heated vertical rod with aqueous Al 2 O 3 nanofluid

Contact Info

Product

Resources

About