Energies

Drag reducing agents (DRAs) can reduce the pressure losses in turbulent pipeflow in district heating and cooling networks (DHCNs), which can reduce the operational costs in current networks and allow for reduced investment, installation, and construction costs for future DHCNs. However, their hydraulic effectiveness, performance and stability at DHCN operating conditions remain insufficiently understood. In this work, measurements are performed at relevant conditions for DHCNs in a dedicated flow loop for different DRAs at temperatures of 20–115 °C. The results demonstrate that the DRAs are able to reduce the pressure drop under representative DHCNs operating temperatures by 50–80%. Furthermore, the tested DRAs exhibit stable drag reduction performance during week-long experiments and maintained chemical stability over month-long testing periods. However, the governing mechanisms for drag reduction also result in decreased heat transfer performance in heat exchanger experiments, highlighting a trade-off between hydraulic efficiency and thermal performance. These findings provide experimental evidence supporting the feasibility of DRAs for DHCN applications and form an important step towards future field trials and demonstrations.

The area of the corona discharge region on an insulator is reported as a diagnostic indicator, yet it is almost always obtained by thresholding a segmentation probability map at the conventional 0.5 level and is reported without any uncertainty statement. Using 1600 solar-blind ultraviolet frames, we show that this practice is the dominant error source. Selecting the operating point on a validation split reduces the median relative area error from 20.8% to 3.3% and, unexpectedly, increases Dice from 0.874 to 0.903; a 7.76 M-parameter U-Net then reaches 3.26 ± 0.39% area error, within 0.07 percentage points of an oracle threshold. Dice is not a valid proxy for area: a two-pixel registration shift changes the area by 0.03% but Dice by 0.159, because the annotations are fragmented into about 31 sub-ten-pixel blobs. Split conformal prediction yields intervals whose empirical coverage (93.7–97.8%) meets or exceeds the nominal level even when checkpoint selection, threshold selection and calibration use disjoint data, with a domain of validity of roughly −5% to +10% gain deviation. A GUM budget attributes 15.7% relative standard uncertainty to annotation-boundary ambiguity, against 2.1% for the model and acquisition combined, so the measurement is annotation-limited. The protocol makes the reported image-domain area fraction an interpretable measurand with a stated uncertainty.

Existing deep learning-based fault diagnosis models for wind turbine planetary gearboxes cannot cope with the problem of poor domain generalization ability caused by differences in data distribution under different operating conditions. To address this limitation, a domain generalization fault diagnosis method based on multi-level feature alignment (DGFD-MLFA) for planetary gearbox is proposed. Firstly, the multi-level feature extraction (MLFE) module is designed, which includes a convolutional denoising autoencoder, a multi-scale separable convolution, and a transformer encoder. The MLFE module can extract both local and global features while denoising. Secondly, the multi-level feature alignment (MLFA) module includes the Local Maximum Mean Discrepancy (LMMD) and the Maximum Mean Discrepancy (MMD) is designed to align local and global features respectively, allowing the model to ultimately extract domain-invariant features and enhancing its fault diagnosis capability under unknown operating conditions. Experiment results on nine domain generalization fault diagnosis tasks based on the wind turbine planetary gearbox dataset, including ablation studies and comparisons with different feature alignment methods, show that the proposed model significantly improves fault diagnosis accuracy under both interpolation and extrapolation domain generalization tasks.