Search Results (154)

The climatic conditions of a region are a constant object of study, especially now that climate change is clearly affecting quality of life and the way we live. The study of the climatic conditions of a region is conducted through meteorological data. Meteorological installations include a set of sensors to monitor the meteorological and climatic conditions of an area. Meteorological data parameters include measurements of temperature, humidity, precipitation, wind speed, and direction, as well as tools such as an oratometer and a pyranometer, etc. Specifically, the pyranometer is a high-cost instrument, which has the ability to measure the intensity of the sunshine on the surface of the earth, expressing the measurement in Watt/m². Pyranometers have many applications. They can be used to monitor solar energy in a given area, in automated systems such as photovoltaic system management, or in automatic building shading systems. In this research, both the implementation and the evaluation of an integrated low-cost pyranometer system is presented. The proposed pyranometer device consists of affordable modules, both microprocessor and sensor. In addition, a central server, as the information system, was created for data collection and visualization. The data from the measuring system is transmitted via a wireless network (Wi-Fi) over the Internet to an information system (central server), which includes a database for collecting and storing the measurements, and visualization software. The end user can retrieve the information through a web page. The results are encouraging, as they show a satisfactory degree of determination of the measurements of the proposed low-cost device in relation to the reference measurements. Finally, a correction function is presented, aiming at more reliable measurements. Full article

This study investigates the thermal performance of an oil-jet-cooled permanent magnet synchronous motor (PMSM), with a particular focus on end-winding heat dissipation. A high-fidelity numerical model that preserves the full geometric complexity of the end-winding is developed and validated against experimental temperature data, achieving average deviations below 7%. To facilitate efficient parametric analysis, a simplified equivalent model is constructed by replacing the complex geometry with a thermally equivalent annular region characterized by calibrated radial conductivity. Based on this model, the effects of key spray ring parameters—including orifice diameter, number of nozzles, inlet oil temperature, and flow rate—are systematically evaluated. The results indicate that reducing the orifice diameter from 4 mm to 2 mm lowers the maximum winding temperature from 162 °C to 153 °C but increases the pressure drop from 205 Pa to 913 Pa. An optimal nozzle number of 12 decreases the peak winding temperature to 155 °C compared with 162 °C for 8 nozzles, while increasing the oil flow rate from 2 L/min to 6 L/min reduces the peak winding temperature from 162 °C to 142 °C. Furthermore, a non-uniform spray ring configuration decreases maximum stator, winding, spray ring, and shaft temperatures by 5.6–9.2% relative to the baseline, albeit with a pressure drop increase from 907 Pa to 1410 Pa. These findings provide quantitative guidance for optimizing oil-jet cooling designs for PMSMs under engineering constraints. Full article

The synchronous reference frame phase-locked loop (SRF-PLL) is widely employed for grid synchronization in wind farms. However, it may exhibit oscillations under voltage sags or improper parameter settings. These oscillations may compromise the secure integration of large-scale wind power. Therefore, mitigating the oscillations of the SRF-PLL is crucial for ensuring stable and reliable operation. To this end, this paper investigates the underlying oscillation mechanism of the SRF-PLL from local and global perspectives. By taking into account the grid voltage and control parameters, it is revealed that oscillations of the SRF-PLL can be triggered by grid voltage sags and/or the improper control parameters. More specifically, from the local perspective, the SRF-PLL exhibits distinct qualitative behaviors around its stable equilibrium points under different grid voltage amplitudes. As a result, when grid voltage sags occur, the SRF-PLL may exhibit multiple oscillation modes and experience a prolonged transient response. Furthermore, from the global viewpoint, the large-signal analysis reveals that the SRF-PLL has infinitely many asymmetrical convergence regions. However, the sizes of these asymmetrical convergence regions shrink significantly under low grid voltage amplitude and/or small control parameters. In this case, even if the parameters in the small-signal model of the SRF-PLL are well-designed, a small disturbance can shift the operating point into other regions, resulting in undesirable oscillations and a sluggish dynamic response. The validity of the theoretical analysis is further supported by experimental verification. Full article

This study examines a convergence event at the trailing end of a cold front observed in the United States’ Southern Great Plains region on 28 September 1997, using an array of in situ and remote sensing instruments. The event exhibited a structure with elevated divergence near 3 km AGL and moisture transport over both warm and cold sectors. Data from Raman lidar (RL), Atmospheric Emitted Radiance Interferometer (AERI), and Radar Wind Profilers (RWP) were used to characterize vertical profiles of the event, revealing the presence of a narrow moist updraft, horizontal moisture advection, and cloud development ahead of the front. Convection parameters, Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), were derived from collocated AERI and RL. Regions of high CAPE were aligned with areas of high moisture, indicating that convection was more favorable at moist elevated levels than near the surface. RWP observations revealed vorticity structures consistent with existing theories. This study highlights the value of high-resolution, continuous profiling from remote sensors to resolve mesoscale processes and evaluate convection potential. The event underscores the role of elevated moisture and wind shear in modulating convection initiation along a trailing-end cold front boundary where mesoscale and synoptic forces interact. Full article

Corner separation at the junction of blade surfaces and end walls remains a significant challenge in compressor cascade performance. This study proposes a passive flow control strategy inspired by the geometric arrangement of biological fish scales to address this issue. A fish scale-like surface structure was applied to the suction side of a cascade blade to reduce viscous drag and modulate secondary flow behavior. Wind tunnel experiments and numerical simulations were conducted to evaluate its aerodynamic effects. The results show that the fish scale-inspired configuration induced climbing vortices that energized low-momentum fluid near the end wall, effectively suppressing both passage and corner vortices. This led to a reduction in spanwise flow penetration and a decrease in total pressure loss of up to 5.69%. The enhanced control of secondary flows also contributed to improved flow uniformity in the end-wall region. These findings highlight the potential of biologically inspired surface designs for corner vortex suppression and aerodynamic efficiency improvement in turbomachinery systems. Full article

Some of the most intense thunderstorms and extreme weather events on Earth occur in the Hindu Kush Himalaya (HKH) region of Southern Asia. The need to provide end users, stakeholders, and decision makers with accurate forecasts and alerts of extreme weather is critical. To that end, a cutting edge weather modeling framework coined the High Impact Weather Assessment Toolkit (HIWAT) was created through the National Aeronautics and Space Administration (NASA) SERVIR Applied Sciences Team (AST) effort, which consists of a suite of varied numerical weather prediction (NWP) model runs to provide probabilities of straight-line damaging winds, hail, frequent lightning, and intense rainfall as part of a daily 54 h forecast tool. The HIWAT system was first deployed in 2018, and the recently released model archive hosted by the Global Hydrometeorology Resource Center (GHRC) Distributed Active Archive Center (DAAC) provides daily model outputs for the years of 2018–2022. With a nested modeling domain covering Nepal, Bangladesh, Bhutan, and Northeast India, the HIWAT archive spans the critical pre-monsoon and monsoon months of March–October when severe weather and flooding are most frequent. As part of NASA’s Transformation To Open Science (TOPS), this data archive is freely available to practitioners and researchers. Full article

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, focusing specifically on locations with existing solar and wind infrastructure. First, we conduct a historical model comparison of solar and wind energy capacity factors to highlight model uncertainties across the study area. Using future climate projections, we then assess the seasonal patterns of solar and wind capacity factors for three timeframes: historical, mid-century, and end of century. Additionally, we estimate the frequency of solar and wind resource droughts during these periods for the entire WECC and its five operational subregions, finding that certain subregions are more susceptible to energy droughts due to limited renewable resources. Finally, we present day-ahead capacity factor forecasts to support energy storage planning and provide estimates of offshore wind energy capacity within the WECC. Our results indicate that offshore wind capacity factors are nearly twice as high as onshore values, with less seasonal variation, which suggests that offshore wind could offer a more consistent renewable energy supply in the future. Full article

In this paper, the aerodynamic performance of an improved hybrid vertical-axis wind turbine is investigated, and the performance of the hybrid turbine at high tip–speed ratios is significantly enhanced by adding a spoiler at the end of the inner rotor. The improved design increases the average torque coefficient by 7.4% and the peak power coefficient by 32.4%, which effectively solves the problem of power loss due to the negative torque of the inner rotor in the conventional hybrid turbine at high TSR; the spoiler improves the performance of the outer rotor in the wake region by optimizing the airflow distribution, reducing the counter-pressure differential, lowering the inner rotor drag and at the same time attenuating the wake turbulence intensity. The study verifies the validity of the design through 2D CFD simulation, and provides a new idea for the optimization of hybrid wind turbines, which is especially suitable for low wind speed and complex terrain environments, and is of great significance for the promotion of renewable energy technology development. Full article

As the global energy structure transitions towards cleaner sources, large-scale integration of wind power has become a trend for modern power systems. However, the impact of low-inertia power electronic converters and the fault propagation effects at critical nodes pose significant challenges to power system stability. To this end, a Kuramoto-like model analysis method, considering node importance, is proposed in this paper. First, virtual node technology is utilized to optimize the power grid topology model. Then an improved PageRank algorithm embedded by a critical node identification method is proposed, which simultaneously considers transmission efficiency, coupling transmission probability, and voltage influence among nodes. On this basis, the traditional uniform coupling assumption is eliminated, thereby reallocating the coupling strength between critical nodes. In addition, the Kron method is applied to simplify the power grid model, constructing a hybrid Kuramoto-like model that integrates second-order synchronous machine oscillators and first-order wind power oscillators. Based on this model, the transient stability of the wind power integrated power system is analyzed. Finally, through estimating the attraction region range of the stable equilibrium point, a transient stability criterion is proposed for fault limit removal time assessment. The simulation results of the improved IEEE 39-bus system show that coupling strength optimization based on node importance reduces the system’s average critical coupling strength by 17%, significantly improving synchronization robustness. Time-domain simulations validate the accuracy of the method, with the relative error of fault removal time estimation controlled within 10%. This research provides a new analytical tool for transient stability analysis of wind power integration. Full article

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

Air ribs are the critical components of tents. Ten air ribs were designed to study the influence of rise–span ratios on load-bearing performance and explore the failure mechanism. According to the maximum stress that appears at the top and bending regions of the rib, the ribs can be divided into an upright region and an arc-like region. So, a segmentation failure competition mechanism was proposed. In order to enhance the bearing performance, the upright region and arc-like region should be designed to fail at the same time. For the rib named 0.333-S, the stress distributes uniformly and the critical load is 2.62 ${\mathrm{kN}/\mathrm{m}}^2$; the upright region and arc-like region fail at the same time. For the rib named 0.5-S/R, the critical load is 1.465 ${\mathrm{kN}/\mathrm{m}}^2$, and it fails at the upright region, resulting in a reduction of 44%. The tent with ribs named 0.333-S shows better resistance performance against wind load, and the end ribs of this tent deform less. Its maximum displacement is 0.112 m, which is reduced by 65.8% compared with that of the original upright arch tent. Full article

This study addresses the problem of the computational cost of transient CFD simulations, which rely on iterative time-step calculations, by employing deep learning to generate optimized initial conditions for accelerating the Weather Research and Forecasting (WRF) model. To this end, we forecasted wind speed for short time frames over the Houston region using the WRF model data from 2019 to 2022, training the models to predict the X-component (U) wind speed. The so-called global FNO model, trained across all atmospheric heights, was first tested, achieving competitive results. A more refined approach was tested to improve it, training separate models for each altitude level, enhancing accuracy significantly. These ad hoc models outperformed surface and middle atmosphere persistence, achieving 27.64% and 20.46% nRMSE, respectively, while remaining competitive at higher altitudes. Variable selection played a key role, revealing that different physical processes dominate at various altitudes, necessitating distinct input features. The results highlight the potential of deep learning, particularly FNO, in atmospheric modeling, suggesting that tailored models for specific altitudes may enhance forecast accuracy. Thus, this study demonstrates that a deep learning model can be designed to start the iterations of a transient simulation, reducing convergence time and enabling faster, lower-cost predictions. Full article

The vertical profiles of PM_2.5 chemical components are crucial for tracing pollution development, determining causes, and improving air quality. Yet, previous studies only yielded transient and sparse results due to technological limitations. Comprehensive analysis of component vertical distribution across an entire boundary layer remains challenging. Here, we provided a first-ever vertical–temporal continuous dataset of aerosol component concentrations, including sulfate (SO₄²⁻), ammonium (NH₄⁺), nitrate (NO₃⁻), organic matter (OM), and black carbon (BC), using ground-based remote sensing retrieval. The retrieved dataset showed high correlations with in situ chemical observation, with all components exceeding 0.75 and some surpassing 0.90. Using the Beijing 2022 Winter Paralympics as an example, we observed distinct vertical patterns and responses to meteorology and emissions of different components under strictly controlled conditions. During the Paralympics, the emissions contribution (51.12%) surpassed meteorology (48.88%), except SO₄²⁻ and NO₃⁻. Inorganics showed high-altitude transport features, while organics were surface-concentrated, with high-altitude inorganic(organic) concentrations 1.19(0.56) times higher than those near the surface. SO₄²⁻ peaked at 919 m and 1516 m, NH₄⁺ and NO₃⁻ showed an additional peak near 300–500 m, influenced by surface sources and secondary generation. The inorganics exhibited a transport-holding–sinking–resurging process, with NO₃⁻ reaching higher and sinking more. By contrast, organic components massified near 200 m, with a slight increase in high-altitude transport by time. The dispersion of all components driven by a north-westerly wind started 5 h earlier at high altitudes than near the surface, marking the end of the process. The insights gleaned highlight regional inorganic impacts and local organic impacts under the coupling of emission control and meteorology, thus offering helpful guidance for source attribution and targeted control policies. Full article

In desertified regions, monitoring vegetation phenology and elucidating its relationship with climatic factors are of crucial significance for understanding how desertification responds to climate change. This study aimed to extract the spatial-temporal evolution of land surface phenology metrics from 2001 to 2020 using MODIS NDVI products (NASA, Greenbelt, MD, USA) and explore the potential impacts of climate change on land surface phenology through partial least squares regression analysis. The key results are as follows: Firstly, regionally the annual mean start of the growing season (SOS) ranged from day of year (DOY) 130 to 170, the annual mean end of the growing season (EOS) fell within DOY 270 to 310, and the annual mean length of the growing season (LOS) was between 120 and 180 days. Most of the desertified areas demonstrated a tendency towards an earlier SOS, a delayed EOS, and a prolonged LOS, although a small portion exhibited the opposite trends. Secondly, precipitation prior to the SOS period significantly influenced the advancement of SOS, while precipitation during the growing season had a marked impact on EOS delay. Thirdly, high temperatures in both the pre-SOS and growing seasons led to moisture deficits for vegetation growth, which was unfavorable for both SOS advancement and EOS delay. The influence of temperature on SOS and EOS was mainly manifested during the months when SOS and EOS occurred, with the minimum temperature having a more prominent effect than the average and maximum temperatures. Additionally, the wind in the pre-SOS period was found to adversely impact SOS advancement, potentially due to severe wind erosion in desertified areas during spring. The findings of this study reveal that the delayed spring phenology, precipitated by the occurrence of a warm and dry spring in semi-arid desertified areas of northern China, has the potential to heighten the risk of desertification. Full article

The energy transition towards achieving carbon neutrality is marked by the decarbonization of the power system and a high degree of electrification in end-use sectors. The decarbonization of the power system primarily relies on large-scale renewable energy, nuclear power, and fossil fuel-based power with carbon capture technologies. This structure of power supply introduces significant uncertainty in electricity supply. Due to the technological progress in end-use sectors and spatial reallocation of industries in China, the load curve and power supply curve is very different today. However, most studies’ analyses of future electricity systems are based on today’s load curve, which could be misleading when seeking to understand future electricity systems. Therefore, it is essential to thoroughly analyze changes in end-use load curves to better align electricity demand with supply. This paper analyzes the characteristics of electricity demand load under China’s future energy transition and economic transformation pathways using the Integrated Energy and Environment Policy Assessment model of China (IPAC). It examines the electricity and energy usage characteristics of various sectors in six typical regions, provides 24-h load curves for two representative days, and evaluates the effectiveness of demand-side response in selected provinces in 2050. The study reveals that, with the transition of the energy system and the industrial relocation during economic transformation, the load curves in China’s major regions by 2050 will differ notably from those of today, with distinct characteristics emerging across different regions. With the costs of solar photovoltaic (PV) and wind power declining in the future, the resulting electricity price will also differ significantly from today. Daytime electricity prices will be notably lower than those during the evening peak, as the decrease in solar PV and wind power output leads to a significant increase in electricity costs. This pricing structure is expected to drive a strong demand-side response. Demand-side response can significantly improve the alignment between load curves and power supply. Full article