Search Results (1,635)

This study investigates the effect of rotation step size on the performance of flat-plate solar collectors (FPSC) equipped with single-axis tracking. Numerical simulations were carried out in EnergyPlus, coupled with a custom Python interface enabling dynamic control of collector orientation. The analysis was carried out for the city of Kragujevac in Serbia, located in a temperate continental climate zone, based on five representative summer days (3 July–29 September) to account for seasonal variability. Three collector types with different efficiency parameters were considered, and inlet water temperatures of 20 °C, 30 °C, and 40 °C were applied to represent typical operating conditions. The results show that single-axis tracking increased the incident irradiance by up to 28% and the useful seasonal heat gain by up to 25% compared to the fixed configuration. Continuous tracking (ψ = 1°) achieved the highest energy yield but required 181 daily movements, which makes it mechanically demanding. Stepwise tracking with ψ = 10–15° retained more than 90–95% of the energy benefit of continuous tracking while reducing the number of daily movements to 13–19. For larger steps (ψ = 45–90°), the advantage of tracking decreased sharply, with thermal output only 5–10% higher than the fixed case. Increasing the inlet temperature from 20 °C to 40 °C reduced seasonal heat gain by approximately 30% across all scenarios. Overall, the findings indicate that relative single-axis tracking with ψ between 10° and 15° provides the most practical balance between energy efficiency, reliability, and economic viability, making it well-suited for residential-scale solar thermal systems. This is the first study to quantify how discrete rotation steps in single-axis tracking affect both thermal and economic performance of flat-plate collectors. The proposed EnergyPlus–Python model demonstrates that a 10–15° step offers 90–95% of the continuous-tracking energy gain while reducing actuator motion by ~85%. The results provide practical guidance for optimizing low-cost solar-thermal tracking in continental climates. Full article

Multi-story residential buildings present distinct challenges for demand-side management due to shared infrastructure, diverse occupant behaviors, and complex load profiles. Although demand-side management strategies are well established in industrial sectors, their application in high-density residential communities remains limited. This study proposes a cost-optimized energy management framework for urban multi-story apartment buildings, integrating rooftop solar photovoltaic (PV) generation, shared battery energy storage, and flexible electric vehicle (EV) charging. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. EVs are modeled as flexible common loads under strata ownership, alongside shared facilities such as hot water systems and pool pumps. The optimization framework ensures equitable access to battery storage and prioritizes energy allocation from the most cost-effective source solar, battery, or grid on an hourly basis. Two seasonal scenarios, representing summer (February) and spring (September), are evaluated using location-specific irradiance data from Joondalup, Western Australia. The results demonstrate that flexible EV charging enhances solar utilization, mitigates peak grid demand, and supports fairness in shared energy usage. In the high-solar summer scenario, the total building energy cost was reduced to AUD 29.95/day, while in the spring scenario with lower solar availability, the cost remained moderate at AUD 31.92/day. At the apartment level, energy bills were reduced by approximately 34–38% compared to a grid-only baseline. Additionally, the system achieved solar export revenues of up to AUD 4.19/day. These findings underscore the techno-economic effectiveness of the proposed optimization framework in enabling cost-efficient, low-carbon, and grid-friendly energy management in multi-residential urban settings. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

Northeastern Brazil (NEB) has a high potential for wind energy generation, making it a strategic area for the development of this renewable source. However, the region’s complex wind regime, driven by interactions between large-scale atmospheric systems, local circulations, and coastal topography, presents significant challenges for weather forecasting and wind energy applications. Despite this, detailed assessments of forecast performance using mesoscale models remain limited. The main objective was to develop an efficient strategy that enables satisfactory results by optimizing data assimilation, land use and topography information as well as improvements in physical parameterizations and post-processing, optimizing computational effort. Forecasting conducted during the year 2020 were validated with data from 20 anemometric measurement towers (AMTs), located at strategic points across various wind power complexes. The model’s performance was evaluated using statistical metrics such as MBE, MAE, nRMSE, standard deviation ratio, and correlation. Additionally, the impact of bias removal was assessed using two approaches: one that eliminates the mean error per forecasted time step and another employing artificial intelligence for bias removal training. The results revealed distinct characteristics for each analyzed location, with errors of diverse nature due to the local nuances of the measurements. However, both bias removal approaches showed significant improvements in wind characterization across all complexes. Full article

This article addresses the storage of carbon dioxide [CO₂] in underground geological formations. It presents the results of a preliminary assessment of the feasibility of sequestering CO₂ in Cambrian aquifer units located within the Polish Exclusive Economic Zone of the Baltic Sea. The northern segment of a structure within the Rozewie tectonic block was selected as the research and test site. The aim was to determine the sequestration capacity and select optimal locations for injection wells, taking into account storage safety. The results and conclusions are based on numerical simulations of CO₂ injection and plume migration within a brine-filled structure using Petromod software v. 2024. A geological model of the site was developed representing the spatial distribution of petrophysical parameters (porosity and permeability) of the reservoir and sealing horizons. Fault zones were also mapped and parameterised in order to evaluate the structural integrity and identify potential migration barriers for the injected gas. An initial assessment assumed the possibility of injecting 100 Mt of CO₂ into the analyzed structure over a 30-year period using ten wells. However, simulation results based on the current state of geological characterization demonstrated that injection performance may vary considerably between individual wells. Wells situated within zones of highest reservoir capacity were estimated to sustain injection rates of 6–7 Mt of CO₂ over 30 years, implying that a greater number of injection wells would be required to accommodate the target storage amount. Fault seal capacity was evaluated using an algorithm based on the Shale Gouge Ratio (SGR) criterion, which enabled the assessment of fault permeability and revealed potential risks of CO₂ leakage. Numerical simulations further facilitated the estimation of the reservoir’s storage potential and the optimization of injection well placement, considering both injection efficiency and the risk associated with CO₂ migration and leakage. Full article

Misiones province covers < 1% of Argentina’s land area yet harbors > 50% of the country’s biodiversity, with a significant remnant of Atlantic Forest, a global biodiversity hotspot. Approximately 540,000 ha of this native forest is protected, with the remaining areas facing threats from ongoing land conversion, an expanding road network, and a growing rural population. A prior study incorporated noninvasive data on five carnivores into a multifaceted cost analysis to define the optimal location for a multispecies biological corridor, with the goal of enhancing landscape connectivity among protected areas. Subsequent analyses, with an updated framework, emphasized management strategies that balanced human–wildlife coexistence and habitat needs. Building on these efforts, our study applied ecological niche modeling to data located by conservation detection dogs, with genetics used to confirm species identity, and two land-use scenarios, to predict potential distributions of three game species—lowland tapir (Tapirus terrestris), white-lipped peccary (Tayassu pecari), and collared peccary (Pecari tajacu)—that are not only threatened by poaching, road mortality, and habitat loss but also serve as essential prey for carnivores. We assessed the suitability of unique and overlapping vegetation types, within and outside of protected areas, as well as within this multispecies corridor, identifying zones of high conservation concern that underscore the need for integrated planning of predators and prey. These results highlight that ensuring the long-term viability of wildlife across the heterogeneous land-use matrices of Misiones requires going beyond protected areas to promote functional connectivity, restore degraded habitats, and balance human–wildlife needs. Full article

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

The lithological composition of deep coal rock reservoirs in the Ordos Block is complex. The characteristics of hydraulic fracture propagation directly impact reservoir stimulation effectiveness. Therefore, efficient development requires an in-depth understanding of the cross-layer propagation mechanisms of fractures in deep coal rock. To clarify the cross-layer patterns and explore the controlling factors in deep coal rock, large-scale laboratory true triaxial hydraulic fracturing physical simulation experiments were conducted. These experiments, combined with CT scanning and post-fracture 3D reconstruction technology, investigated Ordos Block deep coal rock under different perforation locations, and the complexity of fractures was quantitatively characterized. Due to the well-developed weak planes such as natural fractures in coal rock, perforations in coal rock significantly reduce the breakdown pressure compared to perforations in sandstone. The complexity of perforation fractures in coal rock is far greater than in sandstone. Quantitative characterization of fracture complexity shows that the number of perforation fractures in coal rock fracturing reached 450% of that in sandstone, and the fracture area ratio reached 131.7%. Under high-rate and high-viscosity fracturing conditions, dominant hydraulic fractures tend to form, while the well-developed natural fractures in the coal rock interact with each other, resulting in a complex fracture network. Perforations in coal rock can effectively connect adjacent sandstone layers through cross-layer propagation, whereas perforations in sandstone form dominant hydraulic fractures without connecting the adjacent coal rock layers. The findings can provide operational guidance for optimizing field fracturing operations. Full article

Judging from the current global exploration trend, ultra-deep layers have become the main battlefield for energy exploration. China has made great progress in the ultra-deep field in recent decades, with the Tarim Basin and Sichuan Basin as the focus of exploration. The Sichuan Basin is a large superimposed gas-bearing basin that has experienced multiple tectonic movements and has developed multiple sets of reservoir–caprock combinations vertically. Notably, the multi-stage platform margin belt-type reservoirs of the Sinian–Lower Paleozoic exhibit inherited and superimposed development. Source rocks from the Qiongzhusi, Doushantuo, and Maidiping formations are located in close proximity to reservoirs, creating a complex hydrocarbon supply system, resulting in vertical and lateral migration paths. The structural faults connect the source and reservoir, and the source–reservoir–caprock combination is complete, with huge exploration potential. At the same time, the ultra-deep carbonate rock structure in the basin is weakly deformed, the ancient closures are well preserved, and the ancient oil reservoirs are cracked into gas reservoirs in situ, with little loss, which is conducive to the large-scale accumulation of natural gas. Since the Nvji well produced 18,500 cubic meters of gas per day in 1979, the study of ultra-deep layers in the Sichuan Basin has begun. Subsequently, further achievements have been made in the Guanji, Jiulongshan, Longgang, Shuangyushi, Wutan and Penglai gas fields. Since 2000, two trillion cubic meters of exploration areas have been discovered, with huge exploration potential, which is an important area for increasing production by trillion cubic meters in the future. Faced with the ultra-deep high-temperature and high-pressure geological environment and the complex geological conditions formed by multi-stage superimposed tectonic movements, how do we understand the special geological environment of ultra-deep layers? What geological processes have the generation, migration and enrichment of ultra-deep hydrocarbons experienced? What are the laws of distribution of ultra-deep oil and gas reservoirs? Based on the major achievements and important discoveries made in ultra-deep oil and gas exploration in recent years, this paper discusses the formation and enrichment status of ultra-deep oil and gas reservoirs in the Sichuan Basin from the perspective of basin structure, source rocks, reservoirs, caprocks, closures and preservation conditions, and provides support for the optimization of favorable exploration areas in the future. Full article

The large-scale integration of renewable energy sources has made power generation highly susceptible to climate variability, increasing operational risks within power systems. The growing frequency of extreme weather events has further intensified uncertainty and stochasticity, thereby elevating risks to supply security. To enhance the operational resilience of modern power systems under extreme weather conditions, this study proposes a multi-temporal and multi-spatial power supply planning model that explicitly incorporates the impacts of such events. First, the effects of extreme weather on the source–grid–load framework are analyzed, and a radiation attenuation model for the rainy season as well as a spatiotemporal evolution model for hurricanes are developed. Subsequently, a climate-dependent power output model is established, employing the Finkelstein–Schafer statistical method to construct a Typical Meteorological Year, which serves as input for the reliable power source modeling. Furthermore, a two-stage power supply planning model based on generation adequacy was established to optimize the location and capacity of various types of backup power sources. Case studies conducted on the IEEE 24-bus system demonstrate that optimized planning of thermal power units and energy storage systems can mitigate the overall power shortfall during extreme weather events, thereby improving the system’s ability to maintain a reliable electricity supply under adverse climate conditions. Full article

This paper proposes a new family of robust estimators of means, depending on the Orthogonalized Gnanadesikan–Kettenring (OGK) covariance matrix. These estimators are computationally feasible and robust replacements of the Minimum Covariance Determinant (MCD) estimator in survey sampling contexts involving auxiliary information. With the growing popularity of outliers in environmental data, as in the case of measuring solar radiation, conventional estimators like the sample mean or the Ordinary Least Squares (OLS) regression-based estimators are both biased and unreliable. The suggested OGK-based exponential-type estimators combine robust measures of location and dispersion and have a considerable advantage in the estimation of the population mean when auxiliary variables such as temperature are highly correlated with the variable of interest. The MSE property of OGK-based estimators is also obtained through a detailed theoretical derivation with the expressions of optimal weights. Performance was further proved using real-world and simulated data on solar radiation, as well as by demonstrating lower MSEs and higher PREs in comparison to MCD-based estimators. These results show that OGK-based estimators are highly efficient and robust in actual and artificially contaminated situations and hence are a good option in robust survey sampling and environmental data analysis. Full article

The prediction of thin-bedded, favorable sand bodies within the Triassic Baikouquan Formation fan delta on the western slope of the Mahu Sag is challenging due to their strong spatial heterogeneity. To address this, we propose an integrated workflow that synergizes seismic sedimentology with geologically constrained seismic inversion. This study leverages well logging, core data, and 3D seismic surveys. Initially, seismic attribute analysis and stratal slicing were employed to delineate sedimentary microfacies, revealing that the fan delta front subfacies comprises subaqueous distributary channels, interdistributary bays, and distal bars. Subsequently, the planform distribution of these microfacies served as a critical constraint for the Seismic Waveform Indicative Inversion (SWII), effectively enhancing the resolution for thin sand body identification. The results demonstrate the following: (1). Two NW-SE trending subaqueous distributary channel systems, converging near the BAI65 well, form the primary reservoirs. (2). The SWII, optimized by our workflow, successfully predicts high-quality sand bodies with a cumulative area of 159.2 km², primarily located in the MAXI1, AIHU10, and AICAN1 well areas, as well as west of the MA18 well. This study highlights the value of integrating sedimentary facies boundaries as a geological constraint in seismic inversion, providing a more reliable method for predicting heterogeneous thin sand bodies and delineating future exploration targets in the Mahu Sag. Full article

With the frequent occurrence of stuck pipe incidents during the ultra-deep well drilling operation, the hydraulic-while-drilling (HWD) jar has become a critical component of the bottom hole assembly (BHA). However, during jarring operations for stuck pipe release, the drill string experiences severe vibrations induced by the impact loads from the jar, which significantly alter the stress state and dynamic response of the threaded connections—the structurally weakest elements—under cyclic dynamic loading, often leading to fracture failures. here, a thread failure incident of a hydraulic jar in an ultra-deep well in the Tarim Basin, Xinjiang, is investigated. A drill string dynamic impact model incorporating the actual three-dimensional wellbore trajectory is established to capture the time-history characteristics of multi-axial loads at the threaded connection during up and down jarring. Meanwhile, a three-dimensional finite element model of a double-shouldered threaded connection with helix angle is developed, and the stress distribution of the joint thread is analyzed on the boundary condition acquired from the time-history characteristics of multi-axial loads. Numerical results indicate that the axial compression induces local bending of the drill string during down jarring, resulting in significantly greater bending moment fluctuations than in up jarring and a correspondingly higher amplitude of circumferential acceleration at the thread location. Among all thread positions, the first thread root at the pin end consistently experiences the highest average stress and stress variation, rendering it most susceptible to fatigue failure. This study provides theoretical and practical insights for optimizing drill string design and enhancing the reliability of threaded connections in deep and ultra-deep well drilling. Full article

Accurate assessment of coalbed methane (CBM) content is essential for characterizing subsurface reservoir distribution, guiding well placement, and estimating reserves. Current methods for determining coal seam gas content mainly rely on direct laboratory measurements of core samples or indirect interpretations derived from well log data. However, conventional coring is costly, while log-based approaches often depend on linear empirical formulas and are restricted to near-wellbore regions. In practice, the relationships between elastic properties and gas content are highly complex and nonlinear, leading conventional linear models to produce substantial prediction errors and inadequate performance. This study introduces a novel method for predicting gas content in deep coal seams using a Conditional Generative Adversarial Network (CGAN). First, elastic parameters are obtained through pre-stack inversion. Next, sensitivity analysis and attribute optimization are applied to identify elastic attributes that are most sensitive to gas content. A CGAN is then employed to learn the nonlinear mapping between multiple fluid-sensitive seismic attributes and gas content distribution. By integrating multiple constraints to refine the discriminator and guide generator training, the model achieves accurate gas content prediction directly from seismic data. Applied to a real dataset from a CBM block in the Ordos Basin, China, the proposed CGAN-based method produces predictions that align closely with measured gas content trends at well locations. Validation at blind wells shows an average prediction error of 1.6 m³/t, with 83% of samples exhibiting errors less than 3 m³/t. This research presents an effective and innovative deep learning approach for predicting coalbed methane content. Full article

Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices capable of performing spatially selective stimulation with multi-contact cuff electrodes in vivo; however, optimizing the technique is difficult due to the large possibility space granted by a multi-electrode cuff. Our work attempts to elucidate the most valuable stimulation montages (current ratios between stimulating electrodes) provided by a multi-contact cuff. We characterized the performance of five different montage types when stimulating fibers in different “electrode configurations”, with configurations including up to three rings of electrode contacts, 13 different counts of electrodes per ring, and five electrode arc lengths per electrode count (for 195 unique configurations). Selected montages included several methods from prior art, as well as our own. Among montage types, the most spatially selective stimulation was one we refer to as “X-Adjacent” stimulation, in which three adjacent electrodes are active per ring. Optimized X-adjacent montages achieved an average fiber specificity of 71.9% for single-ring electrode configurations when stimulating fibers located at a depth of two-thirds of the nerve radius, and an average fiber specificity of 77.2% for two-ring configurations. These values were the highest among montages tested, and in combination with our other metrics, led these montages to perform best in the majority of cost functions investigated. This success leads us to recommend X-Adjacent montages to researchers exploring spatially selective stimulation. Full article