The vortex rope in the draft tube is considered as the major contributor to pressure pulsation at partial load (PL) conditions, which causes the hydro unit to operate unstably. Based on the prototype Francis turbine HLA551-LJ-43 in the laboratory, J-grooves are designed on its conical section in this paper. We used numerical simulation to study the effect of the J-grooves on vortex suppression and energy dissipation in the draft tube. Four typical operating conditions were chosen to analyze the vortex suppression; the corresponding flow ratios Q* are 100%, 82%, 69%, and 53%, respectively. Entropy production theory is used to calculate the energy losses and assess the effect of the J-groove on energy dissipation under part-load conditions. By comparing entropy production, circumferential and axial velocity components, swirl intensity, pressure pulsation, and vortex distribution in a draft tube with and without J-grooves at different operating conditions, it can be concluded that the entropy production on the wall containing a conical section with J-grooves is obviously smaller than that without J-grooves, the effects of J-grooves on reducing circumferential velocity component V_u, pressure pulsation, and weakening vortex intensity and vortex rope in the conical section are obvious, especially at part load and deep part-load operating conditions. Using J-grooves shows better performance on vortex control and energy dissipation in the draft tube of a Francis turbine at partial load conditions.

This study focuses on the splitter blade pump–turbine as the research object to analyze the problems of hump characteristics and the hysteresis effect. We simulated the operation of the pump condition with small opening of the guide vane, analyzed the hydraulic loss by using the entropy production theory and entropy wall function, and investigated the study of internal flow transfer characteristics. In this paper, it was first verified that the maximum error of the energy loss calculated by the pressure method and the entropy production method was less than 6% for the working zone. From the quantified energy loss results, a significant instability feature was observed in the 0.65 Q_BEP–0.9 Q_BEP operating interval, accompanied by the phenomenon of the non-overlapping of the characteristic curves. The results show that the hump characteristic with hysteresis effect also exists in the splitter blade pump–turbine. The percentage of energy loss in the hump zone is in descending order of runner, guide vanes, spiral casing, and draft tube, but this changes again at low flow rates. By analyzing the high-entropy production region, it was found that the high-hydraulic-loss region is mainly distributed at the trailing edge of the long blade in the vane-less space, which is different from the traditional runner.

With the increase in wind farms in hilly terrain, it is particularly important to explore the downstream wake expansion of wind turbines in hilly terrains. This study established two complex terrain-applicable super-Gaussian wake models based on the Coanda effect and the wind speed-up phenomenon. Then, by considering the wind shear effect and the law of mass conservation, two three-dimensional (3D) super-Gaussian wake models were obtained. The 3D super-Gaussian models were used to describe the shape of the wake deficit and could reflect the wake changes in the full wake region. The introduction of the Coanda effect could reflect the sinking of the wind turbine wake on the top of a hilly terrain. And considering that the wind speed-up phenomenon could better reflect the incoming velocity distribution of the actual hilly terrain. The validation results demonstrated that the prediction results of the 3D super-Gaussian wake models had negligible relative errors compared to the measured data and could better describe the vertical and horizontal expansion changes of the downstream wake. The models established in this study can assist with the development of complex terrain models and super-Gaussian models, as well as providing guidance for power prediction and wind turbine control strategies in complex terrain.

Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose and model intertwined spatio-temporal dependencies. Current methods typically treat wind power as a unified signal without explicitly separating inherent patterns from external influences, so they have limited prediction accuracy. This paper introduces a novel framework GCN-EIF that decouples external interference factors (EIFs) from inherent wind power patterns to achieve excellent prediction accuracy. Our innovation lies in the physically informed architecture that explicitly models the mathematical relationship: $P\left(t\right)=P_{\mathrm{inherent}}\left(t\right)+\mathrm{EIF}\left(t\right)$. The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. A key methodological contribution is our signal decomposition strategy during the prediction phase, where an EIF is eliminated from historical data to better learn fundamental patterns, and then a predicted EIF is reintroduced for the target period, significantly reducing error propagation. Extensive experiments across diverse wind farm clusters and different weather conditions indicate that GCN-EIF achieves an 18.99% lower RMSE and 5.08% lower MAE than state-of-the-art methods. Meanwhile, real-time performance analysis confirms the model’s operational viability as it maintains excellent prediction accuracy (RMSE < 15) even at high data arrival rates (100 samples/second) while ensuring processing latency below critical thresholds (10 ms) under typical system loads.

The Pelton turbine frequently operates in environments characterized by high head and heavy sedimentation, where the flow containing sediments can cause erosion on the surfaces of flow components, affecting the stability and safety of the unit. To investigate this phenomenon, present study applies the standard k-ε turbulence model and the Discrete Phase Model to conduct a three-phase unsteady numerical simulation of flow with sediment. The straight pipe erosion simulation results align with previous experimental findings, verifying the accuracy of the methods employed herein. The main conclusions are as follows: (1) Severe erosion predominantly occurs at the spray needle and nozzle outlet. The maximum velocity is achieved at the nozzle outlet. There is a “velocity deficit zone” at the needle tip, which can somewhat protect the tip from erosion. (2) Increasing opening (ΔS) reduces internal flow velocity, decreasing sediment transport capacity and erosion severity. The erosion rate at ΔS = 0.62 decreased by about 95% compared to ΔS = 0.07. (3) An increase in sediment concentration CV enhances the interaction between particles and the surface, leading to severe erosion at both the needle and nozzle. The erosion rate at CV = 5% is about 2.5 times that at CV = 1%. Additionally, sediments with smaller diameters and lower densities exhibit better flowability, resulting in more severe erosion on the injector. (4) Analysis results from machine learning (XGBoost) reveal that sediment concentration, diameter, and nozzle opening affect erosion more significantly than sediment density. The results can provide some guidance for the safety maintenance and erosion prediction of Pelton turbine.