Modelling

15 pages, 1677 KB

Open AccessArticle

An Analytical Thermal Model for Coaxial Magnetic Gears Considering Eddy Current Losses

by Panteleimon Tzouganakis, Vasilios Gakos, Christos Papalexis, Christos Kalligeros, Antonios Tsolakis and Vasilios Spitas

Modelling 2025, 6(4), 114; https://doi.org/10.3390/modelling6040114 (registering DOI) - 25 Sep 2025

Abstract

This work presents an analytical 2D model for estimating eddy current losses in the permanent magnets (PMs) of a coaxial magnetic gear (CMG), with a focus on loss minimization through magnet segmentation. The model is applied under various operating conditions, including different rotational speeds, load levels, and segmentation configurations, to derive empirical expressions for eddy current losses in both the inner and outer rotors. A 1D lumped-parameter thermal model is then used to predict the steady-state temperature of the PMs, incorporating empirical correlations for the thermal convection coefficient. Both models are validated against finite element analysis (FEA) simulations. The analytical eddy current loss model exhibits excellent agreement, with a maximum error of 2%, while the thermal model shows good consistency, with a maximum temperature deviation of 5%. The results confirm that eddy current losses increase with rotational speed but can be significantly reduced through magnet segmentation. However, achieving an acceptable thermal performance at high speeds may require a large number of segments, particularly in the outer rotor, which could influence the manufacturing cost and complexity. The proposed models offer a fast and accurate tool for the design and thermal analysis of CMGs, enabling early-stage optimization with minimal computational effort. Full article

16 pages, 6023 KB

Open AccessArticle

Investigation of Aerodynamic Pressure Characteristics Inside and Outside a Metro Train Traversing a Tunnel in High-Altitude Regions

by Fei Wang, Haisheng Chen, Tianji Liu, Xingsen He, Chunjie Cheng, Lin Xu and Shengzhong Zhao

Modelling 2025, 6(4), 113; https://doi.org/10.3390/modelling6040113 - 24 Sep 2025

Abstract

The numerical method was employed to analyze the transient pressure characteristics of a metro train passing through a tunnel in high-altitude regions. The transient pressure evolution inside and outside the train under varying ambient pressures is analyzed and compared. The findings indicate that while ambient pressure minimally impacts the waveform of the exterior transient pressure, it significantly influences the peak value. Specifically, as ambient pressure rises, the maximum transient pressure (P-max) and the peak-to-peak transient pressure (ΔP) on the train’s exterior surface increase linearly, whereas the minimum transient pressure (P-min) decreases linearly. Moreover, this study analyzed pressure changes within the metro train under varying ambient pressures to assess their impact on passengers’ ear comfort. The trend of pressure peak reduction and delay inside the metro train with a certain degree of airtightness remains well aligned for different ambient pressures. In areas of high altitude with low atmospheric pressure, the requirements for the tightness performance of the train are lower. Full article

(This article belongs to the Special Issue Recent Advances in Computational Fluid Mechanics)

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17 pages, 3604 KB

Open AccessArticle

Cloud-Edge Collaborative Inference-Based Smart Detection Method for Small Objects

by Cong Ye, Shengkun Li, Jianlei Wang, Hongru Li, Xiao Li and Sujie Shao

Modelling 2025, 6(4), 112; https://doi.org/10.3390/modelling6040112 - 24 Sep 2025

Abstract

Emerging technologies are revolutionizing power system operation and maintenance. Intelligent state perception is pivotal for stable grid operation, with small object detection technology being vital for identifying minor hazards in power facilities. However, challenges like small object size, low resolution, occlusion, and low confidence arise in small object detection for power operation and maintenance. This paper proposes PyraFAN, a feature fusion method designed for small object detection, and introduces a cloud-edge collaborative inference based smart detection method. This method boosts detection accuracy while ensuring real-time performance. Additionally, a graph-guided distillation method is developed for edge models. By quantifying model performance and task similarity, multi-model collaborative training is realized to improve detection accuracy. Experimental results show that compared with standalone edge models, the proposed method improves detection accuracy by 6.98% and reduces the false negative rate by 19.56%. The PyraFAN module can enhance edge model detection accuracy by approximately 12.2%. Updating edge models via cloud model distillation increases the mAP@0.5 of edge models by 2.7%. Compared to cloud models, the cloud-edge collaboration method reduces average inference latency by 0.8%. This research offers an effective solution for improving the accuracy of deep learning based small object detection in power operation and maintenance within cloud-edge computing environments. Full article

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Modelling, Volume 6, Issue 4 (December 2025) – 3 articles

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