CDS&E: Rigorous formulas for industrial supercritical-fluid mixture properties via systematic evaluation of molecular virial coefficients, and methods to expand their applicati

点击翻译按钮获取中文摘要

Understanding material properties is vital to technological development: to design, control, or optimize a device or manufacturing process, how that physical object or material will behave when heat or mechanical forces are applied to it must be understood. This behavior is governed by the thermophysical properties of the material. While these properties generally can be measured experimentally, such an approach is typically found to be impractical due to the large number of variables involved. To remedy this, scientists and engineers rely on mathematical models to predict material properties by fitting physically based models to available data so that the thermophysical properties at conditions outside the values of the measured data can be computed. While useful, this approach can be limited in its predictive capabilities by a lack of validation data. A better approach would be based on computing material properties from molecular considerations using models describing how the molecules that make up the material interact. Developing accurate descriptions of molecular dynamics, however, is challenging, as is the interpretation of these model predictions at the macroscopic scale. In response to these limitations, this research program will expand upon a rigorous but neglected approach to the computation of thermophysical constants (virial coefficients) that model thermodynamic behavior deviations from idealized representations, allowing for the prediction of properties under realistic situations. This will be accomplished by using state-of-the-art molecular-level simulations to identify virial coefficient values for 31 representative chemical compounds that span a range of important chemical processing and energy applications. This research has the capability to transform the thinking of molecular-model developers on how they formulate and test their methods and models as well as property-model developers on how they formulate and parameterize models to match thermodynamic data. The research team is developing and disseminating computational tools that can assist other developers to perform their own calculations of virial coefficients, thereby broadening the scope of mixture data well beyond what is targeted in this project. Positive impact also will be made through training of students, with a focus on underrepresented groups, community outreach, and dissemination of educational materials.In this project, several avenues will be pursued to bring recent advances in computational cluster-integral methods toward practical applications. First, the research team will engage in a systematic effort to evaluate temperature-dependent virial coefficients from empirical and first-principles intermolecular potentials for a collection of 31 species, and all possible mixtures formed from them. These results will form a database that will be a publicly accessible resource of hundreds of thousands of coefficients that can be used to compute all thermodynamic properties of these mixtures in the vapor and supercritical-fluid phases. Second, the coefficients will be evaluated through a comprehensive comparison to available experimental data from the literature. Such data encompass reported virial coefficients, volumetric properties, and thermal properties of supercritical mixtures. This comparison is valuable in assessing the quality of the molecular models and to pinpoint their weaknesses. The database then will be used to further study the behavior of mixtures including: (a) how knowledge of critical-point singularities in mixtures can be used to accelerate convergence of the virial series; and (b) studying the Joule-Thomson effect in mixtures. Finally, the feasibility of applying these coefficients toward parameterization of thermodynamic models used widely in engineering practice will be investigated to broaden the range of application of these data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.