Fuzzy rule-based heterogeneous domain adaptive: A case study of particle motion in flow field from simulation to experiment

In fluid mechanics research, understanding the motion behavior of particles in a flow field is crucial for comprehending particle transport, mixing, and deposition processes. However, due to the complex interactions between particles and fluids, a single method is insufficient to accurately describe the particle motion. To tackle this problem, this study proposes an unsupervised heterogeneous domain adaptation method based on fuzzy principles, called Particle Flow Motion Analysis (PMFA). First, the data originating from both the source and target domains are preprocessed and subjected to Principal Component Analysis (PCA). Then, fuzzy rules are introduced for feature selection. Finally, the Maximum Mean Discrepancy (MMD) and Canonical Correlation Analysis (CCA) algorithms are employed to optimize the distribution disparities and correlations between the heterogeneous domains. By constructing domain adaptation tasks and comparing with five other methods, the performance of the proposed method is evaluated. The results demonstrate that the PFMA achieves an average accuracy of 89.44%, an average recall rate of 85.87%, and an [Formula: see text]1 value of 89.26% across four tasks, outperforming the other five comparative methods. The proposed method holds significant importance in gaining in-depth understanding of particle motion phenomena in fluids and revealing the underlying physical mechanisms and patterns.