Advancing the Cercignani–Lampis–Lord model and its application in direct simulation Monte Carlo calculations
Accurately predicting the aerodynamics of hypersonic vehicles in rarefied gas regime requires a gas–surface interaction (GSI) model with high fidelity. The Cercignani–Lampis–Lord (CLL) model was regarded as one of the most accurate GSI models and commonly employed in rarefied gas flow studies. However, systematic molecular dynamics simulations reveal that the normal scattering kernel of the CLL model fails under certain hypersonic conditions, since the normal energy accommodation coefficient exceeds the permissible range of [0, 1]. This issue frequently arises when the incident gas molecules have substantial energy difference along the tangential and normal directions. To overcome this limitation, a modified CLL model (mCLL) is proposed in this work by introducing two independent coefficients σn and γn in the normal scattering kernel. This modification extends the applicable range and enhances the accuracy of the GSI model. Validation against the wind tunnel experimental data in slip regime confirms that the mCLL model possesses better accuracy in predicting both the wall pressure and heat flux distributions. Moreover, in direct simulation Monte Carlo calculations of highly rarefied gas flow, the mCLL model exhibits pronounced discrepancies in aerodynamic force and heat compared to the Maxwell and CLL models. Consequently, the mCLL model provides an advanced boundary condition, which is particularly important for hypersonic rarefied gas flow study.
Authors
- Tengfei Liang (ORCID: https://orcid.org/0000-0001-6924-4490)
- Junchao Deng
- Jun Zhang (ORCID: https://orcid.org/0000-0002-3731-4594)
Institutions
- Northwestern Polytechnical University (CN)
- Beihang University (CN)
Publication Details
- Journal
- Physics of Fluids
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1063/5.0284108
- Citations
- 2
- Primary Topic
- Gas Dynamics and Kinetic Theory
- Type
- article
- Field-Weighted Citation Impact
- 7.45
Funders
- National Natural Science Foundation of China