Development of Helmholtz-Energy based Multi-Parameter Property-Models for New Binary and Multinary Working-Fluid Mixtures

由于所涉及的储热特性，卡诺电池的反向设计很可能会确定新的二元和多元带状混合物为理想的工作流体。而且很可能只有很少的数据可以在文献中获得这些以前没有考虑过的混合物的热力学性质。逆设计方法中的性质计算通常依赖于基于物理的状态方程，但精度有限。然而，在第二步中，需要准确的属性数据来验证反向设计的结果。对于能源技术的应用，热力学性质数据的准确计算通常依赖于经验多参数状态方程，其形式是约化亥姆霍兹能量。由于需要描述新的工作流体，文献中只有有限的数据集可用，这导致在将纯流体的这种经验状态方程拟合到小数据集方面取得了很大进展。对这一进展至关重要的方法学发展是：-减少方程参数之间的相互关系；-考虑理想曲线作为合理外推的标准；-考虑多种特别敏感的性质以检查物理上的合理行为；-在完全非线性的优化和拟合算法中引入复杂的约束；-引入由实验数据和亥姆霍兹能量导数的模拟结果组成的混合数据集。用约化亥姆霍兹能量表示的经验多参数状态方程高精度地描述了与能源技术有关的混合物的热力学性质。然而，将这样的模型与混合物的性质相适应会带来额外的自由度，到目前为止，只有对测量良好的系统才能安全地掌握这些自由度。该项目的目标是将在纯流体方面取得的进展转化为多参数混合模型的开发。然而，由于模型和热力学性质表面的更复杂的结构，纯流体的研究结果不能简单地转移到混合物中。相反，必须开发新的方法，使类似的解决方案成为可能。理想情况下，建议的项目可以与其他项目合作，这些项目可以提供适当的实验数据和分子模拟结果，并允许结果的示范应用，建议的项目可以提供实现SPP目标所需的热力学性质基础。在该项目中制定的方法论办法将允许在相对较短的时间内为多种技术和科学应用开发经改进的财产模型。

Due to characteristics of the involved heat storage, the inverse design of Carnot-Batteries will likely identify new binary and multinary zoetrope mixtures as ideal working fluids. And very likely only few data will be available in literature for thermodynamic properties of these previously not considered mixtures. Property calculations in inverse design approaches usually rely on physically based equations of state with limited accuracy. However, in a second step accurate property data are required to validate the results of the inverse design. For applications in energy technologies, accurate calculations of thermodynamic property data usually rely on empirical multiparameter equations of state in terms of the reduced Helmholtz energy. The need to describe new working fluids, for which only restricted data sets are available in literature, resulted in large progress with regard to fitting such empirical equations of state for pure fluids to small data sets. The methodological developments, which are crucial for this progress, were: - The reduction of intercorrelations between parameters of the equations; - The consideration of ideal curves as criteria for reasonable extrapolation; - The consideration of a multitude of particularly sensitive properties in order to check physically reasonable behavior; - The introduction of complex constraints in completely non-linear optimization and fit algorithms; - The introduction of hybrid data-sets consisting of experimental data and of simulation results for derivatives of the Helmholtz energy. Thermodynamic properties of mixtures relevant in energy technologies are described in high accuracy by empirical multiparameter equations of state formulated in terms of the reduced Helmholtz energy as well. However, fitting such models to mixture properties results in additional degrees of freedom, which are safely mastered only for well measured systems to date. The goal of the project proposed here is to transfer the progress that has been made for pure fluids to the development of multiparameter mixture models. However, due to the more complex structure of both the models and the thermodynamic-property surface, findings for pure fluids cannot be simply transferred to mixtures. Instead, new approaches have to be developed, which enable analogous solutions. Ideally in cooperation with other projects, which can contribute suitable experimental data and results of molecular simulations and which allow for an exemplary application of results, the proposed project can deliver the thermodynamic property basis required to achieve the goals of the SPP. The methodological approaches developed in this project will allow for the development of improved property models for a multitude of technical and scientific applications in relatively short time.