pith. sign in

arxiv: 2508.21802 · v2 · pith:5HAECRZLnew · submitted 2025-08-29 · ⚛️ physics.flu-dyn

An Adaptive Real-Time Forecasting Framework for Cryogenic Fluid Management in Space Systems

Qiyun Cheng , Huihua Yang , Wei Ji This is my paper

Pith reviewed 2026-05-18 19:46 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn
keywords cryogenic fluid managementreal-time forecastingadaptive correctiondata-driven methodsspace systemspropellant tanksNASA experiments
0
0 comments X

The pith

ARCTIC adds a data-driven correction layer to precomputed simulations so cryogenic tank forecasts stay accurate in real time despite model flaws and noise.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces ARCTIC, a method that takes existing nodal simulations of cryogenic tanks and refines their outputs on the fly with live sensor readings. Conventional approaches suffer from high compute costs, imperfect models, and sudden changes in boundary conditions during space missions. The framework applies two updating steps—auto-calibration and observation-correction—to adapt forecasts without touching the original physics equations. Tests on synthetic cases of self-pressurization, sloshing, and periodic operation, plus real data from NASA hydrogen test beds, show clear gains in accuracy under noise and disturbances. The low overhead and compatibility with existing tools point toward onboard use for autonomous propellant management.

Core claim

ARCTIC integrates real-time sensor data with precomputed nodal simulations through a data-driven correction layer that uses auto-calibration and observation-correction mechanisms to refine forecasts dynamically without altering the base physics model, delivering improved accuracy across synthetic scenarios and NASA experimental data under model imperfections, noise, and boundary fluctuations.

What carries the argument

The data-driven correction layer that refines outputs from precomputed nodal simulations by applying real-time sensor data through auto-calibration and observation-correction mechanisms.

If this is right

  • Enables reliable real-time predictions that support autonomous cryogenic fluid management during deep-space operations.
  • Preserves accuracy when the base model contains imperfections or when sensor data contains noise.
  • Keeps computational cost low enough for direct onboard deployment in space systems.
  • Works alongside existing simulation software without requiring model rewrites.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The non-intrusive correction approach could transfer to other engineering domains where precomputed physics models exist but real-time adaptation is required.
  • Hybrid simulation-plus-correction structures may reduce the need for ever-higher-fidelity real-time solvers in resource-limited environments.
  • The same pattern suggests a route for predictive control in additional cryogenic or multiphase fluid systems beyond propulsion tanks.

Load-bearing premise

The data-driven correction layer can keep improving forecasts from precomputed simulations without any change to the underlying physics model, and the two updating mechanisms are enough to track evolving states and sudden disturbances.

What would settle it

Running ARCTIC on the NASA K-Site or Multipurpose Hydrogen Test Bed datasets and finding no measurable drop in forecast error relative to the uncorrected nodal simulations under added noise or boundary changes would falsify the claim.

read the original abstract

Accurate real-time forecasting of cryogenic tank behavior is essential for the safe and efficient operation of propulsion and storage systems in future deep-space missions. While cryogenic fluid management (CFM) systems increasingly require autonomous capabilities, conventional simulation methods remain hindered by high computational cost, model imperfections, and sensitivity to unanticipated boundary condition changes. To address these limitations, this study proposes an Adaptive Real-Time Forecasting Framework for Cryogenic Propellant Management in Space Systems, featuring a lightweight, non-intrusive method named ARCTIC (Adaptive Real-time Cryogenic Tank Inference and Correction). ARCTIC integrates real-time sensor data with precomputed nodal simulations through a data-driven correction layer that dynamically refines forecast accuracy without modifying the underlying model. Two updating mechanisms, auto-calibration and observation and correction, enable continuous adaptation to evolving system states and transient disturbances. The method is first assessed through synthetic scenarios representing self-pressurization, sloshing, and periodic operations, then validated using experimental data from NASA's Multipurpose Hydrogen Test Bed and K-Site facilities. Results demonstrate that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations, offering a robust real-time forecasting capability to support autonomous CFM operations. The framework's compatibility with existing simulation tools and its low computational overhead make it especially suited for onboard implementation in space systems requiring predictive autonomy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes an Adaptive Real-Time Forecasting Framework for Cryogenic Propellant Management in Space Systems. It introduces the ARCTIC method, which integrates real-time sensor data with precomputed nodal simulations via a data-driven correction layer that refines forecasts without modifying the underlying physics model. Two updating mechanisms—auto-calibration and observation and correction—enable adaptation to evolving states and transient disturbances. The approach is assessed on synthetic scenarios for self-pressurization, sloshing, and periodic operations, then validated with experimental data from NASA's Multipurpose Hydrogen Test Bed and K-Site facilities. The abstract claims that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations, while offering low computational overhead and compatibility with existing simulation tools for onboard use in autonomous CFM operations.

Significance. If the claimed accuracy gains are substantiated through detailed quantitative validation, the framework could provide a practical, lightweight solution for real-time forecasting in cryogenic systems aboard future deep-space missions. The non-intrusive correction approach addresses computational cost and model sensitivity issues that currently limit autonomous CFM capabilities, potentially enabling more reliable predictive autonomy without requiring full physics-model updates.

major comments (2)
  1. [Abstract] Abstract: The assertion that 'Results demonstrate that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations' is unsupported by any quantitative metrics, error bars, baseline comparisons to the precomputed simulations alone, convergence behavior, or failure-mode analysis. This absence prevents evaluation of whether the data-driven correction layer and the two updating mechanisms suffice for the claimed robustness across the tested regimes.
  2. [Abstract] Abstract: The validation statement referencing 'synthetic self-pressurization/sloshing cases and NASA MHTB/K-Site data' supplies no specifics on test conditions, imposed noise levels, boundary fluctuation magnitudes, or how forecast errors were quantified and compared, leaving the central empirical claim unevaluable.
minor comments (1)
  1. [Abstract] Abstract: The expansion of the ARCTIC acronym as 'Adaptive Real-time Cryogenic Tank Inference and Correction' is internally consistent, but the manuscript title refers to 'Cryogenic Fluid Management'; minor alignment of terminology would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments on the abstract. We agree that the current wording does not supply sufficient quantitative support or validation specifics, which limits evaluability of the central claims. We will revise the abstract to incorporate key metrics and details drawn from the manuscript body.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that 'Results demonstrate that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations' is unsupported by any quantitative metrics, error bars, baseline comparisons to the precomputed simulations alone, convergence behavior, or failure-mode analysis. This absence prevents evaluation of whether the data-driven correction layer and the two updating mechanisms suffice for the claimed robustness across the tested regimes.

    Authors: We concur that the abstract, as a concise summary, lacks the quantitative metrics, error bars, baseline comparisons, and robustness analysis needed for direct evaluation. The manuscript body presents these elements through direct comparisons of ARCTIC-corrected forecasts against precomputed nodal simulations, including error reductions under the stated conditions. We will revise the abstract to briefly report representative accuracy gains, mention of error quantification, and confirmation that the correction layer and updating mechanisms were tested for robustness, thereby addressing the concern without altering the manuscript's technical content. revision: yes

  2. Referee: [Abstract] Abstract: The validation statement referencing 'synthetic self-pressurization/sloshing cases and NASA MHTB/K-Site data' supplies no specifics on test conditions, imposed noise levels, boundary fluctuation magnitudes, or how forecast errors were quantified and compared, leaving the central empirical claim unevaluable.

    Authors: We agree that the abstract provides insufficient detail on the validation setup. The manuscript describes the synthetic scenarios (self-pressurization, sloshing, periodic operations) and the NASA experimental datasets, along with the error metrics employed. We will revise the abstract to include concise references to the range of imposed conditions, noise characteristics, and the comparison methodology used, enabling readers to assess the empirical support while preserving the abstract's length. revision: yes

Circularity Check

0 steps flagged

No circularity; abstract describes external-data correction of independent simulations

full rationale

The abstract presents ARCTIC as a non-intrusive method that takes precomputed nodal simulations as given inputs and applies a separate data-driven correction layer plus two updating mechanisms (auto-calibration and observation-and-correction) driven by real-time sensor data. No equations, parameter-fitting steps, or derivation chain appear in the provided text. The claimed accuracy gains are asserted from external synthetic cases and NASA MHTB/K-Site experiments rather than being redefined or forced by the method's own construction. This matches the default expectation of a self-contained methodological proposal with no detectable reduction of outputs to inputs by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities. The approach implicitly assumes that precomputed simulations plus a correction layer can capture real behavior, but no details allow identification of fitted quantities or unstated assumptions.

pith-pipeline@v0.9.0 · 5743 in / 1201 out tokens · 64691 ms · 2026-05-18T19:46:31.653620+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    Liquid hydrogen (LH₂), for example, is widely used as a high - performance propellant due to its high specific impulse and favorable thrust-to-weight ratio [1]

    Introduction Cryogenic fluids are essential in space missions, serving as both propellants and life-support resources for launch vehicles and deep -space systems. Liquid hydrogen (LH₂), for example, is widely used as a high - performance propellant due to its high specific impulse and favorable thrust-to-weight ratio [1]. However, the cryogenic nature of ...

    work page

  2. [2]

    We develop ARCTIC, an adaptive, non- intrusive forecasting framework that integrates offline nodal simulations with real -time measurement data to enable fast and accurate thermodynamic state predictions in cryogenic propellant tanks

    work page

  3. [3]

    The framework incorporates a dual updating mechanism that combines periodic auto- calibration with event-triggered observation and correction, allowing the model to adapt dynamically to both gradual system changes and fast transients such as maneuver-induced sloshing

    work page

  4. [4]

    A lightweight, correlation -based mapping strategy is implemented to compensate for model imperfections, uncertain boundary conditions, and unmodeled cryogenic behaviors without parameter tuning or numerical model modification

    work page

  5. [5]

    The remainder of this paper is structured as follows: Section 2 details the proposed ARCTIC framework

    ARCTIC is thoroughly evaluated using synthetic test scenarios and validated with experimental data from NASA’s Multipurpose Hydrogen Test Bed (MHTB) and K-Site facilities, demonstrating robust performance and real-time applicability for supporting autonomous CFM systems. The remainder of this paper is structured as follows: Section 2 details the proposed ...

    work page

  6. [6]

    offline predictions

    The ARCTIC Framework: Adaptive Real-Time Forecasting for Cryogenic Tanks In a typical space mission, the geometry of the cryogenic propellant tank is prede fined, and the mission profile is well-planned prior to launch. Consequently, a nodal model of the cryogenic storage tank can be constructed using initial and boundary conditions based on the mission p...

    work page

  7. [7]

    inertial tubes

    Model and Numerical Implementations of Cryogenic Tanks This section presents the numerical models and empirical correlations used for nodal code simulations of cryogenic tanks. 3.1 Mass, momentum, and energy conservations In system modeling, a fluid system is first divided into several distinct zero-dimensional control volumes, referred to as “lumps”. Eac...

    work page

  8. [8]

    measurement data

    Synthetic Test in Virtual Environment 13 To evaluate the capability of the ARCTIC framework in capturing the combined effects of multiple unmodeled phenomena, synthetic tests are conducted using virtual environment first. The cryogenic tank model, shown in Figure 3, is cylindrical with a height of 1 meter and an inner diameter of 1 meter. Initially, the t...

    work page

  9. [9]

    The nodal models used in this section incorporate empirical correlations as the 20 Virtual Environment Model to enhance the accuracy of offline predictions

    Experiment Validations In this section, cryogenic tank experiments conducted at several NASA research centers are used to validate the ARCTIC framework. The nodal models used in this section incorporate empirical correlations as the 20 Virtual Environment Model to enhance the accuracy of offline predictions. The experimental conditions are summarized in T...

    work page 1990

  10. [10]

    The total injected hydrogen mass matches the experimental report

    As shown in Figure 11(b), the injection mass flow rate is assumed to increase linearly until reaching its maximum, then remains constant throughout the pressurization phase. The total injected hydrogen mass matches the experimental report

    work page

  11. [11]

    During the holding period, instead of dynamically adjusting the mass flow rate to maintain constant pressure, the offline simulation assumes a constant mass flow rate, ensuring the total injection amount matches the experimental data

    work page

  12. [12]

    For sloshing conditions, the offline simulation neglects the gradual amplitude increase, assuming zero sloshing until t = 58 s, at which point the maximum amplitude is instantly reached and held constant. The discrepancies between the experimental conditions and offline simulations highlight the necessity of ARCTIC framework to refine predictions and corr...

    work page

  13. [13]

    Demonstration of Adaptive Forecasting and Control Under Offline Model Deviation The previous sections demonstrated that the proposed ARCTIC framework can generate accurate forecasts based on offline simulations, which accounts for pre-scheduled control actions, such as increasing the venting mass flow rate. However, a more practical scenario could involve...

    work page

  14. [14]

    Conclusion This work presents and validates the ARCTIC (Adaptive Real -time Cryogenic Tank Inference and Correction) framework for accurate forecasting of thermodynamic behavior in cryogenic storage tanks. By integrating a nodal model with a real-time monitoring agent, ARCTIC derives data-driven correlations from real-time sensor data, effectively compens...

    work page

  15. [15]

    W. H. Knuth, and J. H. Beveridge, High/variable mixture ratio oxygen/hydrogen engines, Advanced Earth-to-Orbit Propulsion Technology 1988, 1, NASA, Marshall Space Flight Center (1988). https://ntrs.nasa.gov/api/citations/19900019321/downloads/19900019321.pdf

    work page arXiv 1988

  16. [16]

    C. B. Muratov, V . V . Osipov, and V . N. Smelyanskiy, Issues of Long-Term Cryogenic Propellant Storage in Microgravity, NASA/TM–2011–215988. https://ntrs.nasa.gov/api/citations/20120011680/downloads/20120011680.pdf

    work page arXiv 2011

  17. [17]

    Simonini, M

    A. Simonini, M. Dreyer, A. Urbano, and et al., Cryogenic propellant management in space: open challenges and perspectives, npj Microgravity, 10, 34 (2024). https://doi.org/10.1038/s41526-024-00377-5

    work page doi:10.1038/s41526-024-00377-5 2024

  18. [18]

    Seo, and S

    M. Seo, and S. Jeong, Analysis of self-pressurization phenomenon of cryogenic fluid storage tank with thermal diffusion model, Cryogenics, 50, pp. 549–555 (2010). https://doi.org/10.1016/j.cryogenics.2010.02.021

    work page doi:10.1016/j.cryogenics.2010.02.021 2010

  19. [19]

    Z. Liu, K. Yuan, Y . Liu, Y . Qiu, and G. Lei, Fluid sloshing thermo -mechanical characteristic in a cryogenic fuel storage tank under different gravity acceleration levels, International Journal of Hydrogen Energy, 47, pp. 25007-25021 (2022). https://doi.org/10.1016/j.ijhydene.2022.05.243

    work page doi:10.1016/j.ijhydene.2022.05.243 2022

  20. [20]

    Z. Duan, Y . Zhu, C. Wang, Y . Yuan, H. Xue, and W. Tang, Numerical and theoretical prediction of the thermodynamic response in marine LNG fuel tanks under sloshing conditions, Energy, 270, 126935 (2023). https://doi.org/10.1016/j.energy.2023.126935 27

    work page doi:10.1016/j.energy.2023.126935 2023

  21. [21]

    Z. Liu, K. Yuan, Y . Liu, M. Andersson, and Y . Li, Fluid sloshing hydrodynamics in a cryogenic fuel storage tank under different order natural frequencies, Journal of Energy Storage, 52, 104830 (2022). https://doi.org/10.1016/j.est.2022.104830

    work page doi:10.1016/j.est.2022.104830 2022

  22. [22]

    W. L. Johnson, and J. R. Stephens, NASA’S Cryogenic Fluid Management Technology Development Roadmap, NASA GRC-E-DAA-TN60252. https://ntrs.nasa.gov/api/citations/20190000305/downloads/20190000305.pdf

    work page arXiv

  23. [23]

    Dezfuli, A

    H. Dezfuli, A. Benjamin, C. Everett, and et al., NASA System Safety Handbook. V olume 2: System Safety Concepts, Guidelines, and Implementation Examples, NASA/SP-2014-612

    work page 2014

  24. [24]

    Roychoudhury, L

    I. Roychoudhury, L. Spirkovska, M. Daigle, and et al., Real -Time Monitoring and Prediction of Airspace Safety, NASA/TM–2015–218928. https://ntrs.nasa.gov/api/citations/20180006637/downloads/20180006637.pdf

    work page arXiv 2015

  25. [25]

    Majumdar, A

    A. Majumdar, A. LeClair, J. Hartwig, and S. M. Ghiaasiaan, Numerical modeling of no vent filling of a cryogenic tank with thermodynamic vent system augmented injector, Cryogenics, 131, 103651 (2023). https://doi.org/10.1016/j.cryogenics.2023.103651

    work page doi:10.1016/j.cryogenics.2023.103651 2023

  26. [26]

    Parisi, T

    M. Parisi, T. Panontin, S. Wu, K. McTigue, and A. Vera, Effects of Communication Delay on Human Spaceflight Missions, Human-Centered Aerospace Systems and Sustainability Applications, 98, pp. 64–73 (2023). https://doi.org/10.54941/ahfe1003920

    work page doi:10.54941/ahfe1003920 2023

  27. [27]

    M. S. Feather, B. Kennedy, R. Mackey, and et al., Demonstrations of System -Level Autonomy for Spacecraft, 2021 IEEE Aerospace Conference (50100), Big Sky, MT, USA, March 06-13 (2021). https://doi.org/10.1109/AERO50100.2021.9438427

    work page doi:10.1109/aero50100.2021.9438427 2021

  28. [28]

    Cullimore, S.G

    B.A. Cullimore, S.G. Ring, and D.A. Johnson, SINDA/FLUINT User’s Manual, C&R Technologies, Version 6, (2020)

    work page 2020

  29. [29]

    Daigle, V .N

    M.J. Daigle, V .N. Smelyanskiy, J. Boschee, and M. Foygel, Temperature Stratification in a Cryogenic Fuel Tank, Journal of Thermophysics and Heat Transfer, 27, pp. 116–126 (2013). https://doi.org/10.2514/1.T3933

    work page doi:10.2514/1.t3933 2013

  30. [30]

    Kartuzova, and M

    O.V . Kartuzova, and M. Kassemi, CFD jet mixing model validation against zero-boil-off tank (ZBOT) microgravity experiment, Proceedings of AIAA Propulsion and Energy 2019 Forum, Indianapolis, IN, Aug 19-22 (2019). https://doi.org/10.2514/6.2019-4282

    work page doi:10.2514/6.2019-4282 2019

  31. [31]

    Majumdar, J.Valenzuela, A

    A. Majumdar, J.Valenzuela, A. LeClair and J. Moder, Numerical modeling of self-pressurization and pressure control by a thermodynamic vent system in a cryogenic tank, Cryogenics, 74, pp. 113-122 (2016). https://doi.org/10.1016/j.cryogenics.2015.12.001 28

    work page doi:10.1016/j.cryogenics.2015.12.001 2016

  32. [32]

    Schallhorn, D

    P. Schallhorn, D. Campbell, S. Chase, J. Piquero, C. Fortenberry, X. Li and L. Grob, Upper Stage Tank Thermodynamic Modeling Using SINDA/FLUINT, 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Sacramento, California, 9 July - 12 July (2006). https://doi.org/10.2514/6.2006-5051

    work page doi:10.2514/6.2006-5051 2006

  33. [33]

    E. Lan, S. Shi, W. Ji, and M. Ishii, Modeling and Simulation of Cryogenic Propellant Tank Pressurization in Normal Gravity, Applied Thermal Engineering, 236, Part D, 121628 (2024). https://doi.org/10.1016/j.applthermaleng.2023.121628

    work page doi:10.1016/j.applthermaleng.2023.121628 2024

  34. [34]

    Panzarella, and M

    C.H. Panzarella, and M. Kassemi, On the validity of purely thermodynamic descriptions of two-phase cryogenic fluid storage, Journal of Fluid Mechanics, 484, pp. 41–68 (2003). https://doi.org/10.1017/S0022112003004002

    work page doi:10.1017/s0022112003004002 2003

  35. [35]

    Cheng, H

    Q. Cheng, H. Yang, S. Shi, and W. Ji, A Data -Driven Based Coupling Approach for Cryogenic Propellant Tank Long-Term Pressure Control Predictions, Cryogenics, 149, 104098 (2025). https://doi.org/10.1016/j.cryogenics.2025.104098

    work page doi:10.1016/j.cryogenics.2025.104098 2025

  36. [36]

    https://doi.org/10.1016/j.ijheatmasstransfer.2024.125885

    Eymon Lan, Shanbin Shi, Coupled modeling and simulation of tank self -pressurization and thermal stratification, International Journal of Heat and Mass Transfer, 232, 125885 (2024). https://doi.org/10.1016/j.ijheatmasstransfer.2024.125885

    work page doi:10.1016/j.ijheatmasstransfer.2024.125885 2024

  37. [37]

    P. A. Marques, S. Ahizi, and M. A. Mendez, Real -time data assimilation for the thermodynamic modeling of cryogenic storage tanks, Energy, 302, 31739 (2024). https://doi.org/10.1016/j.energy.2024.131739

    work page doi:10.1016/j.energy.2024.131739 2024

  38. [38]

    Hastings, R.H

    L.J. Hastings, R.H. Flachbart, J.J. Martin, and et al., Spray Bar Zero-Gravity V ent System for On-Orbit Liquid Hydrogen Storage, NASA/TM—2003 –212926. https://ntrs.nasa.gov/api/citations/20040000092/downloads/20040000092.pdf

    work page arXiv 2003

  39. [39]

    Kassemi, O

    M. Kassemi, O. Kartuzova, and S. Hylton, V alidation of two-phase CFD models for propellant tank self-pressurization: Crossing fluid types, scales, and gravity levels, Cryogenics, 89, pp. 1–15 (2018). https://doi.org/10.1016/J.CRYOGENICS.2017.10.019

    work page doi:10.1016/j.cryogenics.2017.10.019 2018

  40. [40]

    Van Dresar, C

    N. Van Dresar, C. Lin, and M. Hasan, Self-pressurization of a flightweight liquid hydrogen tank: effects of fill level at low wall heat flux, NASA-TM-105411, Lewis Research Center, Cleveland, Ohio (1992). https://ntrs.nasa.gov/api/citations/19920009200/downloads/19920009200.pdf

    work page arXiv 1992

  41. [41]

    Kartuzova, and M

    O. Kartuzova, and M. Kassemi, Validation of a Two- Phase CFD Model for Predicting Tank Self - Pressurization in the Ground- Based K -Site Experiment, AIAA SCITECH 2025 Forum, 6- 10 January, Orlando, FL (2025). https://doi.org/10.2514/6.2025-0353

    work page doi:10.2514/6.2025-0353 2025

  42. [42]

    Moran, N

    M. Moran, N. McNelis, M. Kudlac, M. Haberbusch, and G. Satornino, Experimental Results of Hydrogen Slosh in a 62 Cubic Foot (1750 Liter) Tank, NASA Technical Memorandum, AIAA -94-3259 (1994). 29 https://ntrs.nasa.gov/api/citations/19940030744/downloads/19940030744.pdf

    work page arXiv 1994

  43. [43]

    R. J. Goldstein, and K. Lau, Laminar natural convection from a horizontal plate and the influence of plate-edge extensions, Journal of Fluid Mechanics, 129, 55–75 (1983). https://doi.org/10.1017/S0022112083000646

    work page doi:10.1017/s0022112083000646 1983

  44. [44]

    H. Q. Yang, J. West, CFD Extraction of Heat Transfer Coefficient in Cryogenic Propellant Tanks, Proceedings of 51st AIAA/SAE/ASEE Joint Propulsion Conference, Orlando, FL, USA (2015). https://doi.org/10.2514/6.2015-3856

    work page doi:10.2514/6.2015-3856 2015

  45. [45]

    R. K. Rajput, Heat and Mass Transfer. 3rd Edition, S. Chand and Company Limited (2006)

    work page 2006

  46. [46]

    G. Jeon, S. Jeong, and J. Park, Experimental and numerical investigation of the influences of sloshing motion on the change in boil -off gas/boil-off rate in a cryogenic liquid tank, Ocean Engineering, 298, 117173 (2024). https://doi.org/10.1016/j.oceaneng.2024.117173

    work page doi:10.1016/j.oceaneng.2024.117173 2024

  47. [47]

    Ludwig, M.E

    C. Ludwig, M.E. Dreyer, and E.J. Hopfinger, Pressure variations in a cryogenic liquid storage tank subjected to periodic excitations, International Journal of Heat and Mass Transfer, 66, pp. 223–234 (2013). http://dx.doi.org/10.1016/j.ijheatmasstransfer.2013.06.072

    work page doi:10.1016/j.ijheatmasstransfer.2013.06.072 2013

  48. [48]

    Kassemi, S

    M. Kassemi, S. Hylton, and O.V . Kartuzova, Zero- boil-off tank (ZBOT) experiment – CFD self - pressurization model validation with ground-based & microgravity results, Proceedings of AIAA 2018 Joint Propulsion Conference, Cincinnati, Ohio, July 9-11 (2018). https://doi.org/10.2514/6.2018-4940

    work page doi:10.2514/6.2018-4940 2018

  49. [49]

    Kartuzova, M

    O. Kartuzova, M. Kassemi, J. Agui, and J. Moder, Self -pressurization and spray cooling simulations of the multipurpose hydrogen test bed (MHTB) ground- based experiment, Proceedings of 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Cleveland, OH, July 28-30 (2014). https://doi.org/10.2514/6.2014-3578

    work page doi:10.2514/6.2014-3578 2014

  50. [50]

    Kartuzova, M

    O. Kartuzova, M. Kassemi, and D. Hauser, Validation of a Two Phase CFD Model for Predicting Propellant Tank Pressurization and Pressure Collapse in The Ground Based K Site Hydrogen Slosh Experiment, AIAA SciTech Forum, Orlando, FL, Jan 8-12 (2024). https://ntrs.nasa.gov/api/citations/20230018454/downloads/K-Site-H2-sloshing-presentation-AIAA- SciTech2024.pdf

    work page arXiv 2024

  51. [51]

    Deep Operator Network Based Surrogate Model Development for Perturbed Pipe Flow Heat Transfer,

    Q. Cheng, M. H. Sahadath, H. Yang, and W. Ji, "Deep Operator Network Based Surrogate Model Development for Perturbed Pipe Flow Heat Transfer," Trans. Am. Nucl. Soc., 131, 524-527 (2024)

    work page 2024