Search Results (82)

This study investigates bolted connections in open-section glass fiber-reinforced polymer (GFRP) composite cross-arms for 34.5 kV energy distribution towers. Six GFRP angle sections (L50 × 5 to L120 × 12) were tested under tensile loading using a constant edge distance-to-bolt diameter ratio (e/d = 5), and the connection performance was evaluated based on general maximum and deformation-based criteria (4% and 1 mm hole elongation). Connection capacities ranged from 14.65 to 36.68 kN for single-bolt configurations. Results from multi-bolt connections tests indicated strong influence of bolt layout on connection performance. The highest load capacities of 46.45 kN and 45.93 kN were obtained, respectively, with the two-row bolt configuration and staggered configuration. Comparison of the measured load capacities with ASCE/SEI 74-23 predictions revealed significant discrepancies depending on the assumed failure mode of the connection. A simplified finite element model was developed to predict load–displacement response, capturing initial stiffness and overall trends with reasonable agreement, particularly for connections exhibiting similar failure modes. The findings provide a reliable basis for selecting appropriate bolted connection details in open-section GFRP cross-arm systems. Full article

Studying the slumping disintegration, movement speed, impact intensity, accumulation characteristics, and energy conversion laws of tower-column unstable rock masses (TCURM) is crucial for high-altitude rockfall hazard risk evaluation. Existing PFC-based rockfall simulations rarely target the unique “top-hard-bottom-weak” structural characteristics of TCURM and lack in-depth integration of on-site monitoring videos to verify dynamic evolution processes. Taking the large-scale collapse of W12^# unstable rock mass at Zengziyan, Jinfo Mountain in Chongqing as an example, a combination method of orthogonal test and PFC^3D discrete element simulation is used. Mesoscopic parameters are calibrated via comparison with on-site video and investigation data, accurately reproducing the entire slumping disintegration process and revealing its dynamic characteristics. Results confirm the simulation is basically consistent with field data, verifying the model and parameter rationality. The total duration from instability to stagnation is 121 s (15 s to impact the secondary steep cliff base, 106 s for debris accumulation). Movement speed time-histories of deteriorated and non-deteriorated zones are generally consistent, both exhibiting a “double-peak” feature. Rockfall impact force first increases, stabilizes in the middle, and declines to stability afterward, with a maximum of 2.1 × 10⁹ N. The kinetic energy curve also shows a “double-peak” distribution, closely related to the on-site two-level steep cliff morphology. The findings provide important references for analyzing the dynamic evolution of such rockfalls and designing disaster prevention/mitigation engineering. Full article

In high-density urban environments, residential design often faces a conflict between maximizing landscape access and maintaining energy-oriented compactness. This study proposes a target-based visibility analysis framework to optimize high-rise forms under strict performance constraints. Utilizing a Quad-mesh reconstruction strategy and Inverse Targeted Ray-Casting, the method accurately quantifies visibility via the cumulative Landscape Visible Surface (LVS) on the target building and Viewpoint-Specific Surface Visibility Rate (R_v) for precise verification against specific landscape targets. The framework is applied to evaluate three morphological prototypes: Compact Tower, Dispersed Tower, and Slab–Tower Hybrid. Quantitative simulations identified the Slab–Tower Hybrid as the optimal solution, demonstrating superior “Visual Morphological Efficiency.” While maintaining a moderate Shape Coefficient (SC = 0.326) to satisfy energy standards, the Hybrid achieved a cumulative Park-View LVS approximately 1.8 times that of the Compact Tower. Furthermore, environmental simulations indicated the Hybrid fosters stable wind environments (0.4–0.7 m/s) and equitable sunlight distribution. The research concluded that through differentiated massing, high-rise architecture can achieve a synergistic balance between visual openness and physical compactness, transforming view analysis from a passive check into an active design driver. Full article

This study addresses the need for flexible and high-toughness materials for transmission tower pile foundations subjected to typhoons and earthquakes by investigating the static and dynamic mechanical behavior of rubberized concrete prepared using vibratory mixing. The objectives are to assess how vibratory mixing influences strength evolution, failure modes, strain rate sensitivity, and energy absorption of rubberized concrete compared with conventional mixing at 0%, 20%, and 30% rubber contents. Quasi-static compression tests and Split Hopkinson Pressure Bar (SHPB) dynamic compression tests were conducted to quantify these effects. The results show that vibratory mixing significantly improves the paste–aggregate–rubber interfacial structure. It increases the compressive strength by 8.4–30% compared with conventional mixing and reduces the strength loss at the 30% rubber content from 51.12% to 38.98%. Under high-speed impact loading, vibratory mixed rubber concrete exhibits higher peak strength, stronger energy absorption capacity, and a more stable strain rate response. The mixture with 20% rubber content shows the best comprehensive performance and is suitable for impact-resistant design of transmission tower foundations. Future research should extend this work by considering different rubber particle sizes and vibratory mixing frequencies to identify optimal combinations, and by incorporating quantitative fragment size distribution analysis under impact loading to further clarify the fracture mechanisms and enhance the application of rubberized concrete. Full article

Optimizing chiller load (OCL) distribution in multi-chiller HVAC systems is critical for energy efficiency, yet existing algorithms often struggle with accuracy and convergence. This challenge is compounded by the fact that existing research predominantly focuses on chiller-centric optimization, often neglecting the significant energy consumption of auxiliary components. To address this gap, this study proposes a novel method utilizing Modelica/Simulink co-simulation to accurately model the entire refrigeration system, including chillers, pumps and cooling towers, thereby eliminating complex mathematical derivations and enhancing real-world applicability. To solve this holistic optimization problem, an Improved Particle Swarm Optimization (IPSO) algorithm is developed, which integrates a Phased Adaptive Decreasing Inertia Weight (PADIW) strategy, adaptive learning factors, and a mutation operator to enhance its global search capability and robustness. A case study of a shopping mall demonstrates the approach’s efficacy: over a six-month period, the optimization method reduces total refrigeration system consumption by 25.5% compared to the strategy of distributing the load equally and 15.5% compared to the human experience strategy. Notably, this case revealed that the water pumps, while accounting for less than 20% of total consumption, held a disproportionately large energy-saving potential of over 25%. Comparative experiments and Monte Carlo simulations further confirm the proposed IPSO’s superior convergence and robustness over standard PSO and other common metaheuristics. This study demonstrates that the synergy of Modelica/Simulink co-simulation and the IPSO algorithm is crucial for realizing the full energy-saving potential of the entire system, particularly from previously overlooked components like the water pumps. Full article

Evapotranspiration (ET) plays a vital role in understanding water and energy cycles in forest ecosystems, particularly in tropical regions where rubber plantations are widespread. In this study, a rubber plantation system was used. By combining meteorological data from flux towers and 30 periods of Landsat-8 image data, we estimated the daily ET of a rubber plantation from 2022 to 2024 using the Surface Energy Balance System (SEBS) model. Additionally, the study employed the eddy covariance method to validate the accuracy of the daily average ET estimated by the SEBS model in different source areas, in order to explore the model’s applicability. Simultaneously, we examined the key drivers influencing ET in rubber plantations by analyzing meteorological factors and physiological growth indicators. The results indicated that the SEBS model exhibited the highest estimation accuracy (R² = 0.90, RMSE = 0.43 mm, RE = 15.23%) for the rubber plantation ET in the region 1.5 km away from the flux tower, and the retrieval accuracy of 30 periods of ET was higher (RMSE ≤ 1 mm, RE ≤ 46.84%), indicating that the SEBS model was well-suited for estimating ET in rubber plantations. From 2022 to 2024, the daily average and monthly cumulative ET showed a unimodal distribution, with high summer and low winter values; the average monthly accumulated ET during the wet season (102.75 mm) was found to be significantly greater than that during the dry season (50.61 mm). On the daily and monthly scales, the correlation between atmospheric pressure, temperature, and ET was the most significant. These findings enhance our understanding of rubber plantation water use patterns and support the application of remote sensing models for regional water resource management, offering valuable insights for optimizing irrigation strategies and ensuring sustainable rubber production in tropical regions. Full article

Climate change has increased the frequency and severity of extreme weather events such as wildfires, storms, high winds, and floods. Overhead lines are particularly vulnerable to these hazards, prompting utilities to consider reinforcement solutions through undergrounding overhead lines or structural hardening. However, these mitigation strategies are expensive and should be used selectively, prioritized for areas that are most at risk. This necessitates a framework to concurrently balance cost and resilience. In addition, the adopted reinforcement strategy must consider the consequences of possible outages on communities. This paper presents a multi-objective optimization framework to identify overhead line reinforcement strategies in a distribution system exposed to different hazards. A case study is presented for the city of Greeley, CO, which is prone to both wildfire and flood risks. Undergrounding overhead lines and reinforcing tower structures are considered as possible solutions for wildfire-prone areas and flood-prone areas, respectively. The proposed model is adaptable and can be applied to other hazard types and/or geographic regions. The proposed framework incorporates energy justice by prioritizing vulnerable populations and ensuring equitable distribution of reinforcement benefits. The results indicate that targeted hardening can reduce load shedding, improve outage response, and support equitable resilience planning. Full article

Urea production plays a crucial part in the worldwide agricultural economy, providing a primary supply of nitrogen for fertilizers. For storage and transport, urea is synthesized in granular form, and the prilling technology is frequently employed. In this technique, the hot liquid feed passes through an atomizer to produce small droplets, which then fall along the high tower. During the falling process, the liquid droplets gradually become solid because the internal energy is removed by the cooling air, which flows upward from the bottom. Typically, three consecutive thermal phases are analyzed for the solidification process: the liquid droplet cooling, solidification when the surface reaches freezing point, and the solid particle cooling. In this paper, the temperature distribution across the radius of the urea particles was analyzed using a heat transfer equation, which is considered a two-phase Stefan problem. The system of partial differential equations is solved numerically using the finite difference method and the enthalpy method. The temperature of the cooling air at various heights of the tower and the degree of solidification of different particle sizes were estimated and compared with data obtained from the urea factory to assess their reliability. The validation demonstrated a strong correlation between the model estimates and the real plant observations. Full article

The structural safety of multi-span ultra-high-voltage (UHV) substation gantries is a cornerstone for the reliability and resilience of sustainable energy grids. The wind-resistant design of the structures is complicated by their complex modal behaviors and highly non-uniform wind load distributions. This study proposes a novel hybrid framework that integrates segmented high frequency force balance (HFFB) testing with a multi-modal stochastic vibration analysis, enabling the precise assessment of wind load distribution and dynamic response. Five representative segment models are tested to quantify both mean and dynamic wind loads, a strategy rigorously validated against whole-model HFFB tests. Key findings reveal significant aerodynamic disparities among structural segments. The long-span beam, Segment 5, exhibits markedly higher and direction-dependent responses. Its mean base shear coefficient reaches 4.34 at β = 75°, which is more than twice the values of 1.74 to 2.27 for typical tower segments. Furthermore, its RMS wind force coefficient peaks at 0.65 at β = 60°, a value 2.5 to 4 times higher than those of the tower segments, all of which remained below 0.26. Furthermore, a computational model incorporating structural modes, spatial coherence, and cross-modal contributions is developed to predict wind-induced responses, validated through aeroelastic model tests. The proposed framework accurately resolves spatial wind load distribution and dynamic wind-induced response, providing a reliable and efficient tool for the wind-resistant design of multi-span UHV lattice gantries. Full article

Daily evapotranspiration (ET) estimated from an unoccupied aerial system (UAS) could help improve irrigation practices, but its spatial resolution needs to be upscaled to coarser pixel resolutions before applying surface energy balance models. The purpose of this study was to evaluate the impact of various energy balance-based aggregation schemes on generating spatially distributed latent heat flux (LE), and, in comparison, to existing occupied aircraft and satellite remote sensing platforms. In 2017, UAS multispectral and thermal imagery, along with ground truth data, were collected at various cotton growth stages. These data sources were combined to model LE using a Two-Source Energy Balance Priestley–Taylor (TSEB-PT) model. Several UAS aggregation schemes were tested, including the mode of aggregation (i.e., input image and output flux) as well as the averaging scheme (i.e., simple aggregation vs. Box–Cox). Results indicate that output flux aggregation with Box–Cox averaging produced the lowest relative upscaling pixel-scale variability in LE and the lowest absolute prediction errors (relative to eddy covariance flux tower measurements). Output flux aggregation with simple averaging was also more accurate in reproducing LE from occupied aircraft and satellite imagery. Although results are limited to a single site, UAS LE estimates were reliably aggregated to coarser pixel resolutions, which made for faster image processing for operational applications. Full article

To address the issue of premature wear and tear in wind turbines due to uneven fatigue load distribution within wind farms, this study proposes an optimal active power allocation method that considers unit fatigue loads. First, the fatigue load expressions for wind turbine shafts and tower systems with two degrees of freedom are derived, and a quantitative relationship between turbine fatigue load and active power output variations is established. Subsequently, the optimization objective is set as minimizing the total fatigue load in the wind farm during frequency regulation. This model incorporates the fatigue load differences among different turbines and ensures that the sum of the power adjustments across all turbines meets the frequency regulation power demand, resulting in an active power allocation model. To solve this optimization model, an improved Firefly Algorithm (IFA), integrating Logistic mapping and an adaptive weight strategy, is employed. Aligned with the recommended goals of sustainable development, this approach not only reduces fatigue loads, enhancing the lifespan and efficiency of wind turbines, but also ensures that the wind farm retains strong frequency regulation performance. By optimizing turbine performance and promoting a more balanced load distribution, the proposed method significantly contributes to the overall reliability and economic sustainability of renewable energy systems. Finally, a case study system consisting of nine 5 MW turbines is established to validate the proposed method, demonstrating its ability to evenly distribute the fatigue load across turbines while effectively tracking higher-level dispatch commands and reducing the same fatigue loads. Full article

Against the backdrop of global climate change, extreme weather events are increasingly challenging the safe and stable operation of power distribution networks. These events can cause sudden load fluctuations, equipment failures, and disruptions in power transfer. To address these, this paper proposes an optimal control strategy for distribution network power transfer, integrating Long Short-Term Memory (LSTM) networks and dynamic optimization models. By fusing meteorological data with grid characteristics, the LSTM model predicts load demand and fault probability, capturing complex system behaviors under extreme conditions. Combined with Mixed-Integer Linear Programming (MILP), a decision-making model is developed, and a deep-reinforcement-learning-based algorithm handles uncertainties in weather, load, and equipment faults, enabling accurate control. Validation on a 33-bus system shows the method enhances reliability under extreme weather, providing practical value. Furthermore, typhoons, as extreme weather events, can severely damage infrastructure, disrupt power lines, and affect grid stability. In the 33-bus system, typhoons can cause tower collapses and line failures, impacting power transfer. This paper explores the impact of typhoons on a bus model integrated with renewable energy, proposing optimal control strategies to ensure power supply to critical loads while minimizing equipment damage. Full article

Offshore wind farms are rapidly expanding worldwide, and the submerged structures supporting wind turbines have the potential to function as artificial reefs for marine organisms. Quantitative visualization of fish aggregations around these foundations can provide valuable information for promoting collaboration between fisheries and offshore wind energy development. This study explored the use of multibeam sonar to detect spatial distributions and estimate the biomass of pelagic fish aggregations around the foundations of offshore wind power facilities. Fish distribution was extracted from multibeam water column image data using an automated sequence of filtering steps, ending with a spatial filter designed to remove common noise artifacts in multibeam sonar data. The resulting fish aggregations were visualized in three dimensions, revealing a tendency to cluster leeward of turbine and observation tower foundations, and fish biomass was successfully estimated from beam backscatter strength. The developed method can be applied to other offshore wind farms to demonstrate the role of turbine foundations as artificial reefs for fish. Full article

As the world increasingly gravitates towards green, environmentally friendly and low-carbon lifestyles, wind power has become one of the most technologically established renewable energy sources. However, with the continuous increase in their output power and height, wind turbine towers are subjected to higher-intensity alternating wind loads. This makes critical components more prone to fatigue failure, potentially leading to major accidents such as tower buckling or turbine collapse. High-strength bolts play a vital role in supporting towers but are susceptible to fatigue crack initiation under long-term cyclic loading, necessitating regular inspection. Types of wind turbine bolts mainly include high-strength bolts, stainless steel bolts, anchor bolts, titanium alloy bolts, and adjustable bolts. These bolts are distributed across different parts of the turbine and perform distinct functions. Among them, high-strength bolts in the tower are particularly critical for structural support, demanding prioritized periodic inspection. Compared to destructive offline inspection methods requiring bolt disassembly, non-destructive testing (NDT) has emerged as a trend in defect detection technologies. Therefore, this review comprehensively examines various types of NDT techniques for wind turbine towers’ high-strength bolts, including disassembly inspection techniques (magnetic particle inspection, penetration inspection, intelligent torque inspection, etc.) and non-disassembly inspection techniques (ultrasonic inspection, radiographic inspection, infrared thermographic inspection, etc.). For each technique, we analyze the fundamental principles, technical characteristics, and limitations, while emphasizing the interconnections between the methodologies. Finally, we discuss potential future research directions for bolt defect NDT technologies. Full article

Water distribution and pumping systems consume a large share of energy in metro HVAC operations and remain a major challenge to energy-efficient performance. This study, grounded in a practical metro project, investigates four control strategies for chilled water systems, focusing on chiller sequencing, pump frequency modulation, and variable flow regulation. A dynamic system model was developed using Dymola to simulate and evaluate the performance of each strategy. The results indicate that Strategy 2, which integrates real-time outdoor weather parameters into the frequency control logic, enhances operational stability and maintainability while achieving a 4.42% reduction in total energy consumption compared to the baseline. Strategy 4 employs a genetic algorithm to optimize chiller load distribution, resulting in improved system efficiency and energy savings of up to 8.62%. Further analysis reveals that chillers account for approximately 80% of the system’s total energy consumption, underscoring their central importance in system-wide energy optimization. Additionally, cooling towers show significant energy-saving potential under low wet-bulb temperatures. A 1 °C decrease in wet-bulb temperature results in an estimated 7% reduction in energy use. These findings offer quantitative insights and practical guidance for the low-carbon optimization of metro chilled water systems. Full article