| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Thermodynamics of Materials in Extreme Environments
|
| Presentation Title |
Implementing Models for High-Throughput CALPHAD Modeling of Molten Salts with Uncertainty Quantification |
| Author(s) |
Rushi Gong, Shun-Li Shang, Xiaofeng Guo, Zi-Kui Liu |
| On-Site Speaker (Planned) |
Rushi Gong |
| Abstract Scope |
Predictive modeling of molten salts requires advanced thermodynamic models and accurate input data. Recently, the modified quasichemical model with quadruplet approximation (MQMQA) has been implemented into open-source software tools, PyCalphad (pycalphad.org) and ESPEI (espei.org). It facilitates high-throughput CALPHAD modeling with uncertainty quantification (UQ) for molten salts. Additionally, advanced models such as the universal quasichemical model (UNIQUAC) and molecular interaction volume model (MIVM) are commonly used to predict nonideal mixing behaviors in multicomponent mixtures. The Peng-Robinson model, known for its accuracy near the critical point, applies to both liquid and gas properties. These thermodynamic models are being implemented in PyCalphad and ESPEI. Moreover, a template generator is provided to expedite the process, creating templates for PyCalphad model classes and XML database schemas. These advancements provide the community with extensive opportunities to explore thermodynamic modeling with UQ in complex molten salts, thereby making the existing databases continually updatable for the community. |