Numerical investigation on subcooled pool film boiling of liquid hydrogen in different gravities

Wang, Jiaojiao; Li, Yanzhong; Wang, Lei; Xia, Siqi; Ren, Jianhua; Mao, Hongwei; Xu, Yuanyuan

doi:10.1016/j.ijhydene.2020.10.079

Cited by 24 publications

(4 citation statements)

References 40 publications

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“…To overcome this, hydrogen can be liquefied by cooling to approximately 20 K (−253 • C) at atmospheric pressure [5][6][7]. The resulting temperature difference between the volume of stored liquid and the ambient environment ensures that heat ingress into the LH 2 is inevitable, which can cause it to evaporate [8]. The evaporated vapor is termed boil-off gas (BOG) [9][10][11][12][13] and its generation leads to a pressure increase in the relevant storage tank in a process known as self-pressurization, necessitating venting of the tank into the atmosphere and loss of valuable hydrogen.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Liquid Hydrogen Boil-Off

et al. 2022

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A model has been developed and implemented in the software package BoilFAST that allows for reliable calculations of the self-pressurization and boil-off losses for liquid hydrogen in different tank geometries and thermal insulation systems. The model accounts for the heat transfer from the vapor to the liquid phase, incorporates realistic heat transfer mechanisms, and uses reference equations of state to calculate thermodynamic properties. The model is validated by testing against a variety of scenarios using multiple sets of industrially relevant data for liquid hydrogen (LH2), including self-pressurization and densification data obtained from an LH2 storage tank at NASA’s Kennedy Space Centre. The model exhibits excellent agreement with experimental and industrial data across a range of simulated conditions, including zero boil-off in microgravity environments, self-pressurization of a stored mass of LH2, and boil-off from a previously pressurized tank as it is being relieved of vapor.

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Section: Introductionmentioning

confidence: 99%

Modelling of Liquid Hydrogen Boil-Off

et al. 2022

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“…By raising the input heat flux to the fluid, the creation of the bubble increases till a situation where a continuous creation of bubbles makes a layer of vapor that covers the heating surface [9]. This condition is known as film boiling and the amount of heat flux that causes this transition between the boiling conditions is known as Critical Heat Flux (CHF) [10]. In other words, CHF is the maximum heat flux that one surface can handle before experiencing a transition from nucleate boiling to film boiling [11].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids' superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modelling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN's superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithms models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable to consider many effective parameters into account with very higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF, but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

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“…LH2 which burns cleanly and can serve as a storage of energy produced from carbon-free or low-carbon sources, will be crucial to this transition [9,10]. But because of the difference in temperature between the volume the surrounding air and LH2, heat will inevitably enter the LH2, which could cause the LH2 to evaporate [11,12]. The evaporated vapor is known as boil-off gas (BOG) [13] and in a process known as self-pressurization, its creation causes the pressure in the appropriate storage tank to rise, forcing the venting of the tank into the atmosphere and the loss of valuable hydrogen.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Effect of Various Tank Geometries and Relief Pressure on Liquid Hydrogen (LH2) Boil-Off Losses

Kumar,

Sleiti

2024

Preprint

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This research investigates the effect of various storage tank geometries and relief pressure on boil-off losses for liquid hydrogen (LH2). The effect of storage volume of LH2 in the tanks for the various geometries is analyzed as well. The model takes into consideration the heat transfer between the liquid and vapor phases, integrates actual heat transfer mechanisms, and employs variety equations of state to estimate evaporative losses. For this, a software package BoilFAST has been used to investigate the boil-off losses. The model was validated against experimental data available in open literature from NASA. Results showed that the effect of relief pressure on the evaporation of LH2 is very high. The LH2 volume is reduced by 34% in the case of cuboid shape and 22% the for spherical shape of the tank from relief pressure of 111 to 10 kPa. However, the effect of the filled volume of LH2 in the storage tank on the evaporation of LH2 is minimal as the volume is reduced only by less than 5% for all tank shapes. Overall, for various tank geometries, spherical shape showed minimum evaporation losses compared to other tank geometries.

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Numerical investigation on subcooled pool film boiling of liquid hydrogen in different gravities

Cited by 24 publications

References 40 publications

Modelling of Liquid Hydrogen Boil-Off

Modelling of Liquid Hydrogen Boil-Off

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Modeling and Analysis of Effect of Various Tank Geometries and Relief Pressure on Liquid Hydrogen (LH2) Boil-Off Losses

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