Modelling Liquid Hydrogen Boil-Off Rate

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I am working on modelling the liquid hydrogen boil-off for a storage tank (30m3) over a period of 2.5 hours using three different insulation materials - polyurethane foam, aerogel, and MLI.
The relevant thermal conductivity, heat transfer coefficients, and thicknesses along with all other required parameters are known. I have attempted to use the online simulation tool boilFAST to simulate each scenario however, the results show a negative spike in boil-off rate which I don't see being possible as this would imply that there is an increase in the volume of liquid hydrogen. Does anyone have any experience in modelling similar scenarios or know another simulation tool that might be useful? Thanks in advance.
 
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Please provide the data on heat transfer coefficients, thicknesses, thermal conductivities, shape. Also, starting mass- or volume fraction of liquid and starting temperature or pressure.
 
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Certainly. Shape = horizontal cylinder with hemispherical end caps, volume = 30.02 m^3, initial liquid volume = 29.44 m^3, inner diameter = 2.93 m, length = 2.5 m (cylinder), relief pressure = 0.25 MPa, liquid temp = 20 K, pressure = 0.092 MPa, ambient temp = 293.15 K, insulation (MLI) thermal conductivity k = 0.00009 W m^-2 K^-1, thickness = 42.7 mm, corresponding heat transfer coefficient (k/thickness) = 0.0021 W m^-2 K^-1. Thank you very much for any help.
 
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What is the tank made of, and what is its wall thickness?
 
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Thermodynamic data on H2 are given in this reference: https://nvlpubs.nist.gov/nistpubs/Legacy/MONO/nbsmonograph168.pdf
The surface area for heat transfer is about 50 M^2, so the rate of heating is on the order of $$\dot{Q}=50(0.0021)(293-22)=28.5 W=102\ kJ/hr$$
The specific volume of liquid H2 at 20 K is 0.01412 m^3/kg, so the mass of liquid H2 originally in the tank is 29.44/0.01412 = 2085 kg.. At these temperatures, the heat of vaporization is about 450 kJ/kg, So, in 2.5 hours, roughly 0.6 kg would boil off.
 
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FAQ: Modelling Liquid Hydrogen Boil-Off Rate

What factors influence the boil-off rate of liquid hydrogen?

The boil-off rate of liquid hydrogen is influenced by several factors, including ambient temperature, pressure, insulation quality of the storage container, heat ingress, and the initial temperature of the liquid hydrogen. These factors determine how much heat is transferred into the liquid hydrogen, causing it to evaporate.

How is the boil-off rate of liquid hydrogen calculated?

The boil-off rate of liquid hydrogen is calculated using thermodynamic principles and heat transfer equations. The fundamental formula involves the rate of heat ingress into the storage container divided by the latent heat of vaporization of liquid hydrogen. Computational models often incorporate additional parameters like specific heat capacities, thermal conductivities, and external environmental conditions.

What are common methods to reduce the boil-off rate of liquid hydrogen?

Common methods to reduce the boil-off rate of liquid hydrogen include improving the insulation of storage containers, using vacuum insulation, minimizing heat leaks through design enhancements, and employing active cooling systems. Additionally, maintaining storage temperatures close to the boiling point of hydrogen can also help reduce boil-off.

Why is it important to model the boil-off rate of liquid hydrogen accurately?

Accurate modeling of the boil-off rate of liquid hydrogen is crucial for the efficient design and operation of storage and transportation systems. It helps in minimizing hydrogen losses, ensuring safety, optimizing insulation materials, and reducing operational costs. Accurate models are essential for applications in space exploration, hydrogen fuel cells, and other industries where liquid hydrogen is used.

What challenges are faced in modeling the boil-off rate of liquid hydrogen?

Challenges in modeling the boil-off rate of liquid hydrogen include accounting for complex heat transfer mechanisms, variations in material properties at cryogenic temperatures, and the dynamic nature of storage conditions. Additionally, external factors such as environmental temperature fluctuations and mechanical vibrations can complicate the modeling process. Accurate experimental data and advanced computational tools are often required to address these challenges.

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