Numerical Investigation of the Joule–Thomson Effect in Hydrogen-Enriched Natural Gas Based on Environmental Parameters and Hydrogen Blending Ratios.
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| Title: | Numerical Investigation of the Joule–Thomson Effect in Hydrogen-Enriched Natural Gas Based on Environmental Parameters and Hydrogen Blending Ratios. |
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| Authors: | Jia, Zile1 (AUTHOR), Wang, Zixuan1,2 (AUTHOR), Zhao, Meng2,3 (AUTHOR) pageantry@163.com, Sun, Pan3 (AUTHOR), Wang, Yifei3 (AUTHOR), Tian, Jiayuan1 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2841. 32p. |
| Subject Terms: | *Joule-Thomson effect, *Natural gas, *Pipeline transportation, *Temperature control, *Computational fluid dynamics, *Artificial intelligence, *Thermodynamics, *Hydrogen production |
| Abstract: | Gas blending with hydrogen represents a core research direction for present and future energy transport systems. The throttling of natural gas and hydrogen mixtures through pressure-regulating valves inevitably induces thermodynamic temperature variations. Theoretical analyses and simulated thermal profiles demonstrate that hydrogen blending effectively counteracts the extreme expansion temperature drop post-throttling. This thermodynamic shift alleviates the localized microclimatic thermal conditions favorable to ice-plugging, validating the feasibility of hydrogen injection as a systematic thermal mitigation strategy for high-pressure pipeline networks. This study utilizes computational fluid dynamics software to model the flow field variations in pure hydrogen and gas–hydrogen mixtures under the influence of pressure-regulating valves. Employing a real gas equation of state across varying operational temperatures and pressure conditions, this research calculates and analyzes the flow field variations driven by the Joule–Thomson effect for pure hydrogen and mixtures with varying hydrogen blending ratios. The objective is to inform temperature regulation strategies for long-distance hydrogen–natural gas pipeline networks and to establish an empirical temperature fitting relationship for pure hydrogen. The numerical evaluation indicates a maximum relative error of 6.02% and a maximum absolute error of 0.06877 K. Furthermore, guided by the localized temperature variation patterns, the temperature rise results from 75 pure hydrogen simulation cases were extracted. A Multilayer Perceptron artificial intelligence algorithm was utilized to perform inverse calculation iterations on the thermal data and regulation results. Through the stochastic selection of initial parameters and repeated training iterations referencing the fitting formula, an optimized regulation sequence was obtained. This process drives the fluid temperature to approach the practical regulation target. Following the network training phase, the maximum absolute error between the calculated temperature regulation result and the target regulation temperature is recorded at 0.0556 K, providing a methodological reference for subsequent high-pressure hydrogen applications. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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