Liebert, Artur

Automating the structuring of Solution Principles within conceptual design is crucial for efficiently covering Function Structures while reducing time-intensive manual processes. Solution Principles are central in bridging functional requirements and technical implementations, yet traditional methods depend heavily on human expertise. To address this, a novel approach leveraging a search algorithm is proposed to automatically identify an optimal set of Solution Principles for a given Function Structure. The approach formalizes the problem and provides rules for the selection and application of Solution Principles. Key components include a function for applying Solution Principles to functions and a heuristic that minimizes principle selection, guiding the search toward optimal solutions. An evaluation shows the potential of this method to reduce time and effort in early product design.

The chaotic and dynamic nature of heat transfer results is either time-consuming or inaccurate predictions of the temperature field in simulations. In particular, the simulation of burning buildings is complex and at the same time the key enabler in saving lives and keeping property damage to a minimum. Deep Learning Neural Networks are a possibility to speed up the simulation process and make real-time predictions of temperature behavior or even replace the simulation process as a whole in the long run. This paper proposes a novel procedure to create a convolutional neural network-based method CNN1D3D, that is capable of approximating the behavior of temperature in burning rooms. The data used for training is created with time intensive, numerical simulations. CNN1D3D's architecture consists of an convolution-based temporal and spatial encoding as well of a transposed convolution based decoder, that creates temperature predictions in real-time. The work shows a possibility for a distinct feature extraction for temporal and spatial features. It shows how solutions generated by simulations based on differential equations hold implicitly the necessary information needed for the method to recreate the context of the data set. This forms a basis for the abstraction onto further fluid dynamics applications. This work has several real-world applications and forms the basis for future rescue route calculations. The solution can be generalized on other applications with similar data structures. It provides the opportunity to capture the complexity of interdependencies and correlations in the field of fluid dynamics.

One possibility to extend the service life of engi-neering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. The current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. The final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.