Prediction and surrogate modelling of thermodynamics properties of mixtures with application to the inverse design under uncertainty

混合物热力学性质的预测和替代建模及其在不确定性下逆设计中的应用

• 批准号: 526254705
• 负责人: Professorin Dr.-Ing. Gabriele Raabe
• 金额: --
• 依托单位: Institut für Thermodynamik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/526254705?language=en
• 关键词: Prediction; surrogate; modelling; thermodynamics; properties

The selection of a suitable working fluid represents one of the most important factors in the design of a thermodynamic cycle. As the fluid has to meet manifold criteria, mixtures are gaining increasingly importance in order to obtain the most appropriate solution, i.e. the required combination of properties unattainable by pure compounds. Two different strategies are followed in the literature for the fluid design: 1. A computer-aided model (mixture) design approach in combination with the formulation of a mixed-inter-nonlinear-programming (MINLP) problem - which though usually employs property models with limited accuracy or models, which do not include widely used refrigerants. 2. A screening approach, i.e. performing system simulations of the defined cycle for a large number of fluids described by highly accurate multiparameter Helmholtz equations of state (HEOS) that are considered state of the art for the calculation of thermophysical properties. HEOS though are too computationally demanding to allow for their use in the MINLP. Furthermore, potential working fluid mixtures whose mixing parameters or models for the HEOS are missing need to be excluded from screenings. The aim of the proposed project is to overcome these two main restrictions when using HEOS is the working fluid selection. This will be achieved by the development of dedicated surrogate models based on Gaussian processes (GP) for HEOS of (binary) refrigerant mixtures to enable the efficient calculation of their thermodynamic properties in a MINLP based design approach. Additionally, molecular simulations on mixtures not yet described by optimized HEOS will allow to derive their mixing parameters so that they can also be included in the optimization process. To account for the mismatch between the HEOS and molecular simulations, we will introduce stochastic HEOS models, for which stochastic GP surrogates will be generated. These stochastic surrogate models will be employed in a MINLP to identify a suitable mixture for a specific application. The stochastic approach followed in this proposal will additionally allow for optimization considering uncertainties of the underlying property models.

选择合适的工质是热力循环设计中最重要的因素之一。由于流体必须满足多种标准，为了获得最合适的溶液，混合物变得越来越重要，即纯化合物无法达到的所需性能组合。在文献中，流体设计遵循两种不同的策略：1.计算机辅助模型(混合物)设计方法与混合交互非线性规划(MINLP)问题的公式相结合-尽管通常使用精度有限的属性模型或不包括广泛使用的制冷剂的模型。2.筛选方法，即对由高精度多参数Helmholtz状态方程(HEOS)描述的大量流体执行所定义的循环的系统模拟，该多参数Helmholtz状态方程被认为是计算热物理性质的最新技术。然而，HEO在计算上要求太高，不允许在MINLP中使用它们。此外，需要将其混合参数或HEOS模型缺失的潜在工作流体混合物排除在筛查之外。建议项目的目的是克服当使用HEOS作为工质选择时的这两个主要限制。这将通过为(二元)制冷剂混合物的HEOS开发基于高斯过程(GP)的专用替代模型来实现，以便能够在基于MINLP的设计方法中有效地计算其热力学性质。此外，对尚未被优化的HEOS描述的混合物的分子模拟将允许导出它们的混合参数，以便它们也可以被包括在优化过程中。为了解释HEOS和分子模拟之间的不匹配，我们将引入随机HEOS模型，该模型将产生随机GP替代。这些随机替代模型将在MINLP中使用，以确定适合特定应用的混合物。本提案中采用的随机方法还将考虑到基础属性模型的不确定性进行优化。