Search Results (406)

Introducing an additional term into the thermodynamic potential density of the quark matter system, as required for thermodynamic consistency, resolves the inconsistency that arises in the conventional perturbative quantum chromodynamics (QCD) model. In this work, we use a revised, thermodynamically consistent perturbative QCD model to compute the stability window and equation of state of up-down ($ud$) quark matter at zero temperature. Our results indicate that the measured tidal deformability for GW170817 places an upper limit on the maximum mass of $ud$ quark stars, but does not rule out the possibility of such stars with a mass of about two solar masses. However, when the maximum mass of $ud$ quark stars significantly exceeds two solar masses, such as the compact object with a mass in the range of 2.50–2.67 $M_{\odot }$ observed in the GW190814 event, it cannot be identified as a $ud$ quark star according to the revised perturbative QCD model. Full article

Distributed photovoltaic (PV) generation is increasingly important for urban energy systems amid global climate change and the shift to renewable energy. Traditional PV deployment prioritizes maximizing energy output, often neglecting electricity price variability caused by time-of-use tariffs. This study develops a high-resolution planning and economic assessment model for building-integrated PV (BIPV) systems, incorporating hourly electricity real-time market prices, solar geometry, and submeter building spatial data. Wuhan (30.60° N, 114.05° E) serves as the case study to evaluate optimal PV placement and tilt angles on rooftops and façades, focusing on maximizing economic returns rather than energy production alone. The results indicate that adjusting rooftop PV tilt from a maximum generation angle (30°) to a maximum revenue angle (15°) slightly lowers generation but increases revenue, with west-facing orientations further improving returns by aligning output with peak electricity prices. For façades, south-facing panels yielded the highest output, while north-facing panels with tilt angles above 20° also showed significant potential. Façade PV systems demonstrated substantially higher generation potential—about 5 to 15 times that of rooftop PV systems under certain conditions. This model provides a spatially detailed, market-responsive framework supporting sustainable urban energy planning, quantifying economic and environmental benefits, and aligning with integrated approaches to urban sustainability. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

As renewable energy technologies advance, identifying efficient photocatalytic materials for water splitting to produce hydrogen has become an important research focus in materials science. This study presents a multi-task regression model (MTRM) designed to predict the conduction band minimum (CBM), valence band maximum (VBM), and solar-to-hydrogen efficiency (STH) of inorganic materials. Utilizing crystallographic and band gap data from over 15,000 materials in the SNUMAT database, machine-learning methods are applied to predict CBM and VBM, which are subsequently used as additional features to estimate STH. A deep neural network framework with a multi-branch, multi-task regression structure is employed to address the issue of error propagation in traditional cascading models by enabling feature sharing and joint optimization of the tasks. The calculated results show that, while traditional tree-based models perform well in single-task predictions, MTRM achieves superior performance in the multi-task setting, particularly for STH prediction, with an MSE of 0.0001 and an R² of 0.8265, significantly outperforming cascading approaches. This research provides a new approach to predicting photocatalytic material performance and demonstrates the potential of multi-task learning in materials science. Full article

The disposal of wastewater resulting from petroleum industries presents a major environmental challenge due to the presence of hard-to-degrade organic pollutants, such as oils and hydrocarbons, and high chemical oxygen demand (COD). In this study, an efficient and eco-friendly method was developed to treat such wastewater using a photocatalyst composed of biochar derived from pistachio shells and loaded with zinc oxide (ZnO) nanoparticles. The biochar-ZnO composite was prepared via a co-precipitation-assisted pyrolysis method to evaluate its efficiency in the photocatalytic degradation of petroleum wastewater (PW). The synthesized material was characterized using various techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy, to determine surface morphology, crystal structure, and functional groups present on the catalyst surface. Photocatalytic degradation experiments were conducted under UV and sunlight for 90 h of irradiation to evaluate the performance of the proposed system in removing oil and reducing COD levels. Key operational parameters, such as pH (2–10), catalyst dosage (0–0.1) g/50 mL, and oil and COD concentrations (50–500) ppm and (125–1252) ppm, were optimized by response surface methodology (RSM) to obtain the maximum oil and COD removal efficiency. The oil and COD were removed from PW (90.20% and 88.80%) at 0.1 g/50 mL of PS/ZnO, a pH of 2, and 50 ppm oil concentration (125 ppm of COD concentration) under UV light. The results show that pollutant removal is slightly better when using sunlight (80.00% oil removal, 78.28% COD removal) than when using four lamps of UV light (77.50% oil removal, 75.52% COD removal) at 0.055 g/50 mL of PS/ZnO, a pH of 6.8, and 100 ppm of oil concentration (290 ppm of COD concentration). The degradation rates of the PS/ZnO supported a pseudo-first-order kinetic model with R² values of 0.9960 and 0.9922 for oil and COD. This work indicates the potential use of agricultural waste, such as pistachio shells, as a sustainable source for producing effective catalysts for industrial wastewater treatment, opening broad prospects in the field of green and nanotechnology-based environmental solutions in the development of eco-friendly and effective wastewater treatment technologies under solar light. Full article

This study explores the impact of using water-Al₂O₃ nanofluids, at different nanoparticle concentrations, in solar thermal collectors for solar cooling applications. Improving the seasonal energy performance of solar cooling systems is a current research priority, and this work investigates whether nanofluids can significantly enhance system efficiency compared to traditional heat transfer fluids. A transient simulation was carried out using a dynamic model developed in TRNSYS (TRANsient SYstem Simulation), evaluating the system performance throughout the cooling season. The results show that in July, under low volumetric flow conditions and with nanoparticle concentrations of 0.6% and 0.3%, the solar fraction reaches a maximum value of 1. Using a nanofluid at 0.6% concentration leads to significantly higher fractional energy savings compared to pure water. Despite increased pumping energy, the overall energy savings—which include the contribution from an auxiliary boiler—exceed 80% when nanofluids are used. This study goes beyond previous work by providing a dynamic, system-level simulation of nanofluid-enhanced solar cooling performance under realistic operating conditions. The findings demonstrate the practical potential of nanofluids as a valid and more energy-efficient alternative in solar thermal applications. Full article

This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the southern and western regions, including Surkhandarya, Kashkadarya, Bukhara, Khorezm, and Republic of Karakalpakstan. In the most vulnerable areas, such as Karakalpakstan, Surkhandarya, and Kashkadarya, the annual number of SDS days can exceed 80 in certain years, reflecting a high recurrence of extreme dust events in certain climatic zones. About 53% of the SDS events were regional, affecting several stations, while 47% were localized, indicating a combination of large-scale dust transport and localized emissions. Seasonal patterns showed a peak SDS activity between March and August, coinciding with the dry season characterized by elevated temperatures, reduced soil moisture, and intense agricultural activity, all of which contribute to the surface exposure and increased vulnerability. This study found a significant variation in the event duration across regions, with Karakalpakstan and Surkhandarya experiencing the highest proportion of prolonged events due to its orography and persistent southerly wind patterns. Using ERA5 data and a decision tree regressor, the analysis identified the wind direction and mean wind speed as the most influential meteorological factors, followed by the maximum wind speed and soil temperature, with other variables such as solar radiation and soil moisture playing moderate roles. This study highlights the importance of regional wind patterns and geomorphology in SDS formation, with prevailing wind directions from the northwest, west, and south. The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article

The requirement for energy transition through the residential sector has increased research on the dissemination of solar thermal power systems in this area. Parabolic Trough Collector (PTC), as one of the matured solar technologies for thermal power generation, has shown huge potential in meeting demands for heating and domestic hot water systems. In this experimental study, several small-scale PTCs have been developed with four alternative absorber shapes: a simple cylindrical absorber, a spiral absorber, and two different configurations of a sinusoidal absorber to examine their performance under domestic application (non-evacuated and non-tracking). The study aims to analyze the applicability of such systems to be used as a water-heating source in buildings and compare the performance of the proposed configurations in terms of thermal efficiency to find the most appropriate design. The experimental results revealed that the simple shape provides a minimum average thermal efficiency of 24%, while the maximum thermal efficiency of 32% is obtained with the spiral shape. Studying various orientations of the sinusoidal shape revealed that thermal efficiencies of 30% and 20% could be achieved using the parallel and the perpendicular shapes, respectively. Finally, a concise economic and environmental analysis is performed to study the proposed systems as solutions for domestic water heating applications, which highlights the suitability of PTCs for integration with future sustainable buildings. Full article

Applying the holographic 2-flavor Einstein–Maxwell-dilaton model, the parameters of which are fixed by lattice QCD, we extract the equations of state for hot quark–gluon plasma around the critical point at $T=182$ MeV, and have corresponding quark star cores constructed. By further adding hadron shells, the mass range of the whole stars spans from 2 to 17 solar masses, with the maximum compactness around 0.22. This result allows them to be black hole mimickers and candidates for gap events. The I–Love–Q–C relations are also analyzed, which show consistency with the neutron star cases when the discontinuity at the quark–hadron interface is not large. Furthermore, we illustrate the full parameter maps of the energy density and pressure as functions of the temperature and chemical potential and discuss the constant thermal conductivity case supposing a heat source inside. Full article

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element. Full article

Despite the convenience of solar potential and the magnitude of energy received by the Earth from the sun, solar photovoltaic systems have failed to meet the growing energy demand. This can be attributed to various factors such as low cell efficiency, environmental conditions, and improper tracking of operating points, which further worsen the system’s performance. Various advanced metaheuristic-based Maximum Power Point Tracking (MPPT) techniques were reported in the literature. Most available techniques were designed and tested in subscription-based/paid software such as MATLAB/Simulink, PSIM simulator, etc. Due to this, the simulation and analysis of these MPPT algorithms for developing and underdeveloped countries added an extra economic burden. Many open-source PV libraries are computationally intensive, lack active support, and prove impractical for MPPT testing on resource-constrained hardware. Their complexity and absence of optimization for edge devices limit their viability for the edge device. This issue is addressed in this research by designing a robust framework using an open-source programming language i.e., Python. For demonstration purposes, we simulated and analyzed a solar PV system and benchmarked its performance against the JAP6 solar panel. We implemented multiple metaheuristic MPPT algorithms including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), evaluating their efficacy under both Standard Test Conditions (STC) and complex partial shading scenarios. The results obtained validate the feasibility of the implementation in Python. Therefore, this research provides a comprehensive framework that can be utilized to implement sophisticated designs in a cost-effective manner for developing and underdeveloped nations. Full article

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

COVID-19 restricted the number of employees. Operational data showed that traditional methods of modeling heat consumption are not correct anymore. The aim is to model the energy demand of an office building during COVID-19 limitations and showcase improvements after a new controller or suggested alternatives are applied. After an actual heat consumption profile was simulated, energy conservation scenarios were considered: the usage of thermostatic radiator valves (TRVs); accounting impacts of solar radiation and wind; changing mass flow rates based on the indoor temperature; adopting an additional control, changing the temperature setpoint; introducing night and day setbacks. After implementing new design and operational methods, the overheating of indoor spaces was alleviated, and the average indoor temperature was reduced from 23.5 °C to 20.4 °C. The annual specific heat consumption decreased to 174 kWh/m² (20.2% lower). The methodology ensured thermal comfort and high energy-saving potential. If operating parameters were adjusted, the total saving effect in energy demand was 119.8 MWh, with an energy-saving rate of 19.8%. Employing TRV-related savings and considering thermal inertia provided more stable indoor temperatures and higher energy performance. The minimum saving effect corresponded to the optimal operation and ensuring the indoor environment by considering wind and the maximum one-to-night setbacks. The fluctuations in indoor temperature became smoother. Full article

This study presents a comprehensive dynamic model of a small-scale, solar-powered hydraulic gas compression energy storage system tailored for renewable energy applications. Addressing the intermittency of renewable energy sources, the model incorporates mass, momentum, and energy conservation principles and is implemented using GT-Suite simulation software v2025.0. The system, based on a liquid piston mechanism, was analyzed under both adiabatic and isothermal compression scenarios. Validation against experimental data showed maximum deviations under 10% for pressure and 5 °C for temperature. Under ideal isothermal conditions, the system stored up to 8 MJ and recovered 6.1 MJ of energy, achieving a round-trip efficiency of 76.3%. In contrast, adiabatic operation yielded 52.6% efficiency due to thermal losses. Sensitivity analyses revealed the importance of heat transfer enhancement, with performance varying by over 15% depending on spray cooling intensity. These findings underscore the potential of thermally integrated hydraulic systems for efficient, scalable, and cost-effective energy storage in distributed renewable energy networks. Full article