Extension of the multi-scale-network-model for the virtual commissioning of complex material flow systems

用于复杂物流系统虚拟调试的多尺度网络模型的扩展

• 批准号: 508338261
• 负责人: Professorin Dr. Simone Göttlich
• 金额: --
• 依托单位: Lehrstuhl für wissenschaftliches Rechnen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/508338261?language=en
• 关键词: Extension; multi; scale; network; model

In this project, a macroscopic flow model for material flow simulation is being further developed and linked with a physics-based model to form a multi-scale network model for use in virtual commissioning in material intensive production plants.Modern production plants are complex and extensive systems with multiple requirements, which can be developed faster and with higher quality using virtual commissioning. For virtual commissioning of control systems, time-deterministic hardware-in-the-loop simulations are needed, which are connected to the control system instead of the real machines. Here, the material flow defined as the whole movement of piece goods in a factory plant, is a particularly computationally intensive element. In preliminary work, a macroscopic flow model based on a hyperbolic partial differential equation was developed, whose computation time is independent of the number of simulated goods, so that material flow-intensive plants such as those in beverage technology can be simulated efficiently. In the previous project "OptiPlant", this model was accelerated by parallelization and the numerical solution was validated. In addition, the fast model was used for automated throughput optimization of the material flow layout. In this project, the applications of the flow model will be extended so that different piece goods, geometries of the plant layout, sources and sinks can be modeled. The flow model is a non-local equation in a bounded domain in two spatial dimensions that incorporates influences from the local environment. The non-locality in combination with the bounded domain leads to special challenges for the numerical and theoretical treatment of the flow model, which have not been explored yet and will now be implemented. Further, combining the modeling with real data will allow parameter estimation to define the previously unknown parameters of the flow model. The application of the flow model to more complex material flow systems (with inflow and outflow of goods, variation of material and geometry and dynamic layout) and the modeling with parameters specifically optimized for the use case, enables the use of the flow model in the virtual commissioning of material flow-rich plants in industry. For the extension as well as for the parameter estimation new real data with more variations are needed, for which a real demonstrator will be developed together with industry contacts and built. Since specific unit loads cannot be represented in the flow model, it is to be combined with a physics-based model such that it is possible to switch between the two types of models in a multi-scale network model. For this purpose, a prediction method has already been developed in the previous project, which predicts results of the physics-based model for the control cycle. In order to avoid having to model the scenarios more than once, a common initial model is to be developed from which the scenarios in both models can be generated.

在本项目中，将进一步开发用于物料流模拟的宏观流模型，并将其与基于物理的模型相连接，以形成用于物料密集型生产工厂虚拟调试的多尺度网络模型。现代生产工厂是具有多种需求的复杂和广泛的系统，使用虚拟调试可以更快地开发和提高质量。对于控制系统的虚拟调试，需要时间确定性硬件在环仿真，其连接到控制系统而不是真实的机器。在这里，物料流被定义为工厂中单件货物的整个运动，是一个特别计算密集的元素。在前期工作中，开发了基于双曲型偏微分方程的宏观流动模型，其计算时间与模拟货物的数量无关，因此可以有效地模拟饮料技术等物流密集型工厂。在以前的项目“OptiPlant”中，通过并行化加速了该模型，并验证了数值解。此外，快速模型被用于自动化的物流布局的吞吐量优化。在这个项目中，流动模型的应用将得到扩展，以便可以模拟不同的单件货物、工厂布局的几何形状、源和汇。该流动模型是一个非局部方程在一个有界域中的两个空间维度，包括当地环境的影响。结合有界域的非局部性导致特殊的挑战的数值和理论处理的流动模型，尚未探索，现在将实施。此外，将建模与真实的数据相结合将允许参数估计以定义流动模型的先前未知的参数。将流动模型应用于更复杂的物料流系统（具有货物的流入和流出、材料和几何形状的变化以及动态布局）以及使用针对用例专门优化的参数进行建模，使得能够在工业中的物料流丰富的工厂的虚拟调试中使用流动模型。 对于扩展以及参数估计，需要具有更多变化的新的真实的数据，为此，将与行业联系人一起开发并建造一个真实的演示器。由于在流量模型中无法表示特定的单位负荷，因此将其与基于物理的模型相结合，从而可以在多尺度网络模型中在两种类型的模型之间切换。为此，在先前的项目中已经开发了一种预测方法，该方法预测控制循环的基于物理的模型的结果。为了避免不得不多次模拟各种设想情况，将制定一个共同的初始模型，从这个模型中可以生成两个模型中的设想情况。