Search Results (8,291)

Abstract: This study evaluates the effects of laser parameters on the surface remelting of the Ti-3Al-2.5V alloy. A ms-laser equipped with a coaxial gas-pressure head integrated into a Swiss-type turning machine is used for the laser remelting process of cylindrical parts. The influence of different pulse frequencies, as well as varying intensities, is investigated. The results reveal that surface micro-cracks can be eliminated through laser remelting. Increasing the input laser intensity also increases the size of the melting pool. A similar effect is observed with higher pulse frequencies. The metallurgical microstructure and the size of the heat-affected zone of the remelted surface at different input laser energy levels are also examined. The results indicate that input laser energy influences phase transformation in the metallurgical microstructure, which correspondingly results in variations in micro-hardness within the heat-affected zone. The variations in laser fluence lead to a surface hardness improvement of approximately 15%. Full article

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles’ sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors’ data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window ($L_w$), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms. Full article

The horizontal-transverse coherence of low-frequency (300 Hz) and long-range (10–40 km) acoustic fields near the bottom in deep water is investigated based on experimental data obtained from the South China Sea. The results indicate that the horizontal-transverse coherence length exhibits a strong dependence on the source-receiver distance, with fluctuations consistent with sound intensity trends. In high-intensity regions, the horizontal-transverse coherence is relatively high, with a coherence length exceeding 600 λ, where λ is the acoustic wavelength, whereas in low-intensity regions, the horizontal-transverse coherence decreases significantly, with the coherence length shortening to 10–30 λ. The physical mechanisms underlying the horizontal-transverse coherence are analyzed using the ray theory. In high-intensity regions, the energy of the dominant ray (the ray with the highest energy) accounts for over 70% of the total energy of the rays, exerting a decisive influence on the coherence coefficient and leading to stable horizontal-transverse coherence in the received acoustic field. In contrast, in low-intensity regions, the energy distribution is dispersed, and when amplitude and phase disturbances due to spatial inhomogeneity are introduced, the horizontal coherence deteriorates significantly. The numerical simulations are also performed, and the results are consistent with the experimental observations. Full article

The spectra of internal friction and temperature dependencies of the frequency of a free-damped oscillation process excited in the specimens of an amorphous–crystalline copolymer of polyoxymethylene with the co-monomer trioxane (POM-C) with a degree of crystallinity ~60% in the temperature range from −150 °C to +170 °C has been studied. It has been established that the spectra of internal friction show five local dissipative processes of varying intensity, manifested in different temperature ranges of the spectrum. An anomalous decrease in the frequency of the oscillatory process was detected in the temperature ranges where the most intense dissipative losses appear on the spectrum of internal friction. Based on phenomenological model representations of a standard linear solid, the physical–mechanical (shear modulus defect, temperature position of local regions of inelasticity) and physical–chemical (activation energy, discrete relaxation time, intensities of detected dissipative processes) characteristics of each local dissipative process were calculated. It was found that the intensities of dissipative processes remain virtually unchanged for both annealed and non-annealed samples. The maximum variation in the shear modulus defect is 0.06%. Additionally, according to computational data, small changes are also characteristic of the following parameters: the activation energy varies from 0.5 to 1.4 kJ/mol and the relaxation time changes from 0.002 to 0.007 s, depending on the presence or absence of annealing. As a result of annealing, there is a significant increase in the relaxation microinheterogenity of the polymer system across the entire temperature range (250% for the low-temperature region and 115% for the high-temperature region). Full article

A marble processing plant (MPP) can achieve sustainable development by implementing energy-saving and consumption-reduction technology. Reducing energy loss in such an energy-intensive plant is crucial for overall energy savings. This study establishes an MPP optimization model based on the second law of thermodynamics and the law of conservation of mass. Marble is an aesthetically pleasing and long-lasting building material that has boosted economies in European and sub-Saharan African nations. However, high energy costs and scarcity have constrained the industry’s economic potential and hindered the achievement of optimal levels of production. The second law of thermodynamics is adopted to study the irreversibilities, inefficiencies, and exergetic performance of a marble processing plant. The Aspen Plus commercial software application is used to model and generate thermodynamic data, determine energy flow streams and conduct sensitivity and optimization analysis to improve data quality and energetic performance outcomes. From the results, the various scales of the exergetic destruction, efficiencies, and exergetic losses are determined, and recommendations are established. The overall energy and exergy efficiency levels were determined to be 87.43% and 86.84%, respectively, with a total exergetic destruction of 200.61 kW. The reported methodologies, cutting-edge ideas, and solutions will give industrialists and other significant stakeholders in the global manufacturing sector cutting-edge information about energy usage and ways to cut energy losses in both new and existing factory designs, manage energy cost components, and adjust energy efficiency to maximize productivity. Full article

Spent coffee grounds (SCGs), a by-product of coffee brewing, have high application potential. However, their high moisture content complicates conventional conversion without energy-intensive drying. This study explores a new route to convert SCGs to high-carbon bioproducts, such as hydrochar and bio-oil, through hydrothermal processing. The effect of the processing variables, i.e., temperature, residence time, and the application of the binary solvent as a reaction medium, on the distribution of the resultant bioproducts was investigated. The quality of the fabricated bioproducts was analyzed by means of instrumental techniques such as EA, ATR-FTIR, GC-MS, and GC-TCD-FID. Two dominant fractions were liquid bio-oil and solid hydrochar. The highest char yield (39 wt.%) was observed under milder conditions (low T and short residence times), while more severe conditions led to an increase in bio-oil formation, which reached a maximum of 46 wt.%. The resulting bio-oils were of similar quality, presenting high carbon content (71–74 wt.%) and energetic values (approximately 35 MJ/kg). Also, hydrochars showed a noticeable energy densification compared to raw materials, where the C content and HHV reached up to 73.8 wt.% and 30 MJ/kg, respectively. The addition of co-solvent to water improves the bio-oil yield as a result of the enhanced stabilization of reactive intermediates. Full article

As a sustainable alternative technology to the cost- and energy-intensive Haber–Bosch method, electrochemical nitrogen (N₂) reduction offers direct conversion of N₂ to NH₃ under ambient conditions. Direct use of noble metals or non-noble metals as electrocatalytic materials results in unsatisfactory electrocatalytic properties because of their low electrical conductivity and stability. Herein, three-dimensional flexible carbon nanofiber (CNF/TiO₂@CoS) nanostructures were prepared on the surface of CNF by using electrospinning, a hydrothermal method, and in situ growth. We investigated the behavior of CNFs/TiO₂@CoS as an electrocatalytic material in 0.1 M sodium sulfate. The highest ammonia yield of the material was 4.61 × 10⁻¹¹ mol s⁻¹ cm⁻² at −0.45 V vs. RHE, and the highest Faraday efficiency, as well as superior long-term durability, was 8.3% at −0.45 V vs. RHE. This study demonstrates the potential of firecracker-shaped nanofiber templates for loading varied noble metals or non-noble metals as a novel development of hybrid composites for electrocatalytic nitrogen reduction. Full article

As large-scale depletion of shallow coal seams and increasing mining depths intensify, the frequency and intensity of mining-induced earthquake events have significantly risen. Due to the complex formation mechanisms of high-energy mining-induced earthquakes, precise identification and early warning cannot be achieved with a single monitoring method, posing severe challenges to coal mine safety. Therefore, this study conducts an in-depth risk analysis of two high-energy mining-induced earthquake events at the 3308 working face of Yangcheng Coal Mine, integrating microseismic monitoring, stress monitoring, and seismic source mechanism analysis. The results show that, by combining microseismic monitoring, seismic source mechanism inversion, and dynamic stress analysis, critical disaster-inducing factors such as fault activation, high-stress concentration zones, and remnant coal pillars were successfully identified, further revealing the roles these factors play in triggering mining-induced earthquakes. Through multi-dimensional data integration, especially the effective detection of the microseismic “silent period” as a key precursor signal before high-energy mining-induced earthquake events, a critical basis for early warning is provided. Additionally, by analyzing the spatiotemporal distribution patterns of different risk factors, high-risk areas within the mining region were identified and delineated, laying a foundation for formulating precise prevention and control strategies. The findings of this study are of significant importance for mining-induced earthquake risk management, providing effective assurance for safe production in coal mines and other mining environments with high seismic risks. The proposed analysis methods and control strategies also offer valuable insights for seismic risk management in other mining industries, ensuring safe operations and minimizing potential losses. Full article

The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications. Full article

Gold nanoclusters (AuNCs) have been widely investigated because of their unique photoluminescence properties. However, the applications of AuNCs are limited by their poor stability and relatively low fluorescence. In the present work, we developed nanocomposites (L-Cys-AuNCs@ZIF-8) with high fluorescence and stability, which were constructed by encapsulating the water-dispersible L-Cys-AuNCs into a ZIF-8 via Zn²⁺-triggered growth strategy without high temperature and pressure. The maximum emission wavelength of the L-Cys-AuNCs@ZIF-8 composite was at 868 nm, and the fluorescence intensity of L-Cys-AuNCs@ZIF-8 was nearly nine-fold compared with L-Cys-AuNCs without the ZIF-8 package. The mechanism investigation by fluorescence spectroscopy and X-ray photoelectron spectroscopy showed that L-Cys-AuNCs@ZIF-8 impeded ligand rotation, induced energy dissipation, and diminished the self-quenching effect, attributing to the spatial distribution of L-Cys-AuNCs. Based on the high fluorescence efficiency of L-Cys-AuNCs@ZIF-8, a “signal off” detective platform was proposed with copper ions as a model analyte, achieving a sensitive detection limit of Cu²⁺ at 16.7 nM. The quenching mechanism was confirmed, showing that the structure of the L-Cys-AuNCs@ZIF-8 nanocomposites was collapsed by the addition of Cu²⁺. Attributing to the strong adsorption ability between copper ions and pyridyl nitrogen, the as-prepared L-Cys-AuNCs@ZIF-8 was shown to accumulate Cu²⁺, and the Zn²⁺ in ZIF-8 was replaced by Cu²⁺. Full article

The rising demand for both water and energy has intensified the urgency of addressing the water–energy nexus. Energy is required for water treatment and distribution, and energy production processes require water. The increasing demand for energy requires substantial amounts of water, primarily for cooling. The emergence of new persistent contaminants has necessitated the use of advanced, energy-intensive water treatment methods. Coupled with the energy demands of water distribution, this has significantly strained the already limited energy resources. Regrettably, no straightforward, universal model exists for estimating water usage and energy consumption in power and water treatment plants, respectively. Current approaches rely on data from direct surveys of plant operators, which are often unreliable and incomplete. This has significantly undermined the efficiency of the plants as these surveys often miss out on complex interactions, lack robust predictive power and fail to account for dynamic temporal changes. The study thus aims to evaluate the potential of mathematical modeling and simulation in the water–energy nexus. It formulates a mathematical framework and subsequent simulation in Java programming to estimate the water use in hydroelectric power and geothermal energy, the energy consumption of the advanced water treatment processes focusing on advanced oxidation processes and membrane separation processes and energy demands of water distribution. The importance of mathematical modeling and simulation in the water–energy nexus has been extensively discussed. The paper then addresses the challenges and prospects and provides a way forward. The findings of this study strongly demonstrate the effectiveness of mathematical modeling and simulation in navigating the complexities of the water–energy nexus. Full article

The fractional Brownian field is often used to reproduce the fractal properties of complex heterogeneous media, which closely represent real-world geological materials. Studying elastic wave transport in this type of heterogeneous media is essential for advancing knowledge in geophysics, seismology, and rock mechanics. In this paper, we numerically investigate the wavefield evolution and arrival behavior of elastic wave propagation in a two-dimensional fractional Brownian field characterized by the standard deviation (σ) and the Hurst exponent (H). Using a high-fidelity finite element model, we quantify the influence of these parameters on wavefront morphology, wave arrival synchronization, and energy decay. Our results reveal that increased matrix heterogeneity with higher σ and lower H values leads to pronounced wavefront roughness, asynchronous arrival phenomena, and increscent energy decay, attributed to enhanced scattering and modulus variability. For smaller H values, rougher modulus distributions scatter wave energy more intensely, producing more coda waves and distorted wavefronts, while smoother fields with larger H fields promote smoother wave propagation. Higher σ amplifies these effects by increasing modulus variability, resulting in more attenuated wave energy and substantial wavefield disturbance. This study contributes to a quantitative understanding of how fractal heterogeneity modulates wave transport and energy attenuation in random media. Our findings hold practical significance for geophysical exploration and seismic tomography, as well as aiding in subsurface imaging and structural evaluation within fractured or stratified rock formations. Full article

The inner core of a typhoon plays a crucial role in storm intensification and is especially critical for rapid increases in storm intensity. Most of the energy exchange occurs in the inner core, including the eyewall. Moist air rising from the warm ocean releases latent heat, increasing wind speeds and sustaining the warm-core structure through secondary circulations. A deeper understanding of the physical processes in the inner core is essential for improving intensity forecasts and disaster preparedness and mitigation. This paper reviews key studies on the inner core. We focus on lead–lag relationships, eyewall replacement cycles, and waves and oscillations, which are topics that can greatly enhance forecasting capabilities. We highlight limitations of current research and propose key scientific questions that would provide essential insights to improve forecasts and support disaster reduction strategies. These include: (1) what are the physical processes that drive the lead–lag relationship between eyewall convection and intensity changes, and how does the time lag vary across typhoons? (2) What conditions favor merging of the inner and outer eyewalls and completion of the eyewall replacement cycle, potentially leading to rapid intensification before landfall? (3) How do waves and oscillations in the eyewall influence typhoon intensity variations? Full article

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals. Full article

With the deep implementation of the national “dual carbon” strategy, the development of a new power system dominated by renewable energy has accelerated significantly. Electrolytic aluminum load, as an important energy-intensive industrial resource, possesses response flexibility, providing a critical pathway for the efficient utilization of renewable energy. However, ensuring the safety of its production process during demand-side response remains a key challenge. This study systematically investigates the core production constraint of electrolytic aluminum load—electrolytic bath temperature—and its impacts on chemical reaction rates, current efficiency, and production equipment. A detailed coupling relationship between core production constraints and active power regulation is established. By quantifying the effects of temperature variation on the electrolytic aluminum production process, a demand-side response control cost model for electrolytic aluminum load is proposed. Additionally, a day-ahead scheduling model is developed with the objective of minimizing system operating costs while considering the participation of electrolytic aluminum load. Simulation results demonstrate that this method significantly reduces wind curtailment and load shedding while ensuring the safety of electrolytic aluminum production. It provides a novel approach for enhancing system economic efficiency, improving renewable energy utilization, and promoting the deep integration of power systems with industrial loads. Full article