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-电池的反设计将有可能确定新的二元和多元共异物混合物作为理想的工作流体。很可能在文献中只有很少的数据可以得到这些以前没有考虑过的混合物的热力学性质。反设计方法中的性能计算通常依赖于基于物理的状态方程，精度有限。然而，在第二步中，需要准确的属性数据来验证反设计的结果。对于能源技术的应用，热力学性质数据的精确计算通常依赖于经验多参数状态方程的约化亥姆霍兹能量。由于文献中只有有限的数据集，需要描述新的工作流体，因此在将纯流体的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；-考虑将理想曲线作为合理外推的标准；-考虑大量特别敏感的特性，以检查物理上合理的行为；-在完全非线性优化和拟合算法中引入复杂约束；-引入混合数据集，包括实验数据和亥姆霍兹能量导数的模拟结果。用经验多参数状态方程以约化亥姆霍兹能量的形式描述了与能源技术相关的混合物的热力学性质。然而，将这样的模型拟合到混合特性会导致额外的自由度，这些自由度只有在迄今为止测量良好的系统中才能安全地掌握。这里提出的该项目的目标是将纯流体方面取得的进展转移到多参数混合模型的发展上。然而，由于模型和热力学性质表面的结构更为复杂，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，与其他项目合作，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，拟议的项目可以提供实现SPP目标所需的热力学性质基础。该项目中开发的方法方法将允许在相对较短的时间内为众多技术和科学应用开发改进的性质模型。

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.