Sabine C. Langer

Jan van Delden, Julius Schultz, Christopher Blech et al.

In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. Deep learning surrogate models present a promising alternative to classical numerical simulations as they can be evaluated magnitudes faster, while trading-off accuracy. To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates. The benchmark features a total of 12,000 plate geometries with varying forms of beadings, material, boundary conditions, load position and sizes with associated numerical solutions. To address the benchmark task, we propose a new network architecture, named Frequency-Query Operator, which predicts vibration patterns of plate geometries given a specific excitation frequency. Applying principles from operator learning and implicit models for shape encoding, our approach effectively addresses the prediction of highly variable frequency response functions occurring in dynamic systems. To quantify the prediction quality, we introduce a set of evaluation metrics and evaluate the method on our vibrating-plates benchmark. Our method outperforms DeepONets, Fourier Neural Operators and more traditional neural network architectures and can be used for design optimization. Code, dataset and visualizations: https://github.com/ecker-lab/Learning_Vibrating_Plates

Sebastian Schmidt, Sabine C. Langer

In the context of building acoustics, sound transmission loss estimations are crucial to quantify the noise pollution in buildings. When developing building prototypes in the sense of an acoustic-oriented design process, it is desirable to have an virtual prototype, especially in early development stages, to estimate, for instance, the influence of different material or geometry configurations on to the sound transmission loss. This contribution aims to present a simple virtual prototype of an building acoustics test facility in accordance with DIN EN ISO 10140 for the measurement of the sound transmission loss of single- and double-leaf walls with and without insulation. Here, the finite element method is used as the numerical modelling method of choice. In the course of this, geometry and mesh creation was done using SALOME 9.14 whereas the institute's in-house research code elPaSo was utilised for the matrix assembly and solving procedure. At first, elPaSo was verified by the commercial software COMSOL 6.3 considering a small-scale test facility. Afterwards, the large-scale test facility finite element model was created using a frequency- and domain-specific discretisation approach. The sound transmission loss of three different test specimens was estimated in one-third-octave bands from 8 Hz to 630 Hz, where the double-leaf wall with insulation exhibited good agreement to the theoretical sound transmission loss profile from literature.

Jan van Delden, Julius Schultz, Sebastian Rothe et al.

Structural vibrations are a source of unwanted noise in engineering systems like cars, trains or airplanes. Minimizing these vibrations is crucial for improving passenger comfort. This work presents a novel design optimization approach based on guided flow matching for reducing vibrations by placing beadings (indentations) in plate-like structures. Our method integrates a generative flow matching model and a surrogate model trained to predict structural vibrations. During the generation process, the flow matching model pushes towards manufacturability while the surrogate model pushes to low-vibration solutions. The flow matching model and its training data implicitly define the design space, enabling a broader exploration of potential solutions as no optimization of manually-defined design parameters is required. We apply our method to a range of differentiable optimization objectives, including direct optimization of specific eigenfrequencies through careful construction of the objective function. Results demonstrate that our method generates diverse and manufacturable plate designs with reduced structural vibrations compared to designs from random search, a criterion-based design heuristic and genetic optimization. The code and data are available from https://github.com/ecker-lab/Optimizing_Vibrating_Plates.

Kristin M. de Payrebrune, Kathrin Flaßkamp, Tom Ströhla et al.

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program \emph{SPP~2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics}, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics.