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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2401.08676 |
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| _version_ | 1866913807082717184 |
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| author | Cheng, Qiyun Yang, Huihua Shi, Shanbin Ji, Wei |
| author_facet | Cheng, Qiyun Yang, Huihua Shi, Shanbin Ji, Wei |
| contents | The design and optimization of cryogenic propellant storage tanks for NASA's future space missions require fast and accurate predictions of long-term fluid behaviors. Computational fluid dynamics (CFD) techniques are high-fidelity but computationally prohibitive. Coarse mesh nodal techniques are fast but heavily rely on empirical correlations to capture prominent three-dimensional phenomena. A data-driven based concurrent coupling (DCC) approach has been developed to integrate CFD with nodal techniques for efficient and accurate analysis. This concurrent coupling scheme generates case-specific correlations on the fly through a short period of co-solving CFD and nodal simulations, followed by a long-period nodal simulation with CFD-corrected solutions. This paper presents the approach development, stability analysis, and efficiency demonstration, specifically for modeling two-phase cryogenic propellant tank self-pressurization and periodic mixing phenomena. Linear regression is employed to derive the data-driven correlations. The self-pressurization experiments of Multipurpose Hydrogen Test Bed experiments and K-Site tank are used to validate the approach. The DCC approach accurately predicts temperature stratifications while reducing computational time by as much as 70% compared to pure CFD simulations. Additionally, the DCC approach mitigates the risks of numerical instability and correlation loss inherent in current domain decomposition or overlapping-based coupling methods, making it a flexible and user-friendly approach for integrated CFD and nodal analysis of cryogenic tank behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Data-Driven Based Concurrent Coupling Approach for Cryogenic Propellant Tank Long-term Pressure Control Predictions Cheng, Qiyun Yang, Huihua Shi, Shanbin Ji, Wei Fluid Dynamics Computational Physics The design and optimization of cryogenic propellant storage tanks for NASA's future space missions require fast and accurate predictions of long-term fluid behaviors. Computational fluid dynamics (CFD) techniques are high-fidelity but computationally prohibitive. Coarse mesh nodal techniques are fast but heavily rely on empirical correlations to capture prominent three-dimensional phenomena. A data-driven based concurrent coupling (DCC) approach has been developed to integrate CFD with nodal techniques for efficient and accurate analysis. This concurrent coupling scheme generates case-specific correlations on the fly through a short period of co-solving CFD and nodal simulations, followed by a long-period nodal simulation with CFD-corrected solutions. This paper presents the approach development, stability analysis, and efficiency demonstration, specifically for modeling two-phase cryogenic propellant tank self-pressurization and periodic mixing phenomena. Linear regression is employed to derive the data-driven correlations. The self-pressurization experiments of Multipurpose Hydrogen Test Bed experiments and K-Site tank are used to validate the approach. The DCC approach accurately predicts temperature stratifications while reducing computational time by as much as 70% compared to pure CFD simulations. Additionally, the DCC approach mitigates the risks of numerical instability and correlation loss inherent in current domain decomposition or overlapping-based coupling methods, making it a flexible and user-friendly approach for integrated CFD and nodal analysis of cryogenic tank behaviors. |
| title | A Data-Driven Based Concurrent Coupling Approach for Cryogenic Propellant Tank Long-term Pressure Control Predictions |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2401.08676 |