Search Results (95)

Subsurface deposition determines whether soils, aquifers, or ocean sediment represent a sink or temporary reservoir for microplastics. Deposition is generally studied by applying the Smoluchowski–Levich equation to determine a particle’s sticking efficiency, which relates the number of particles filtered by sediment to the probability of attachment occurring from an interaction between particles and sediment. Sticking efficiency is typically measured using column experiments or estimated from theory using the Interaction Force Boundary Layer (IFBL) model. However, there is generally a large discrepancy (orders of magnitude) between the values predicted from IFBL theory and the experimental column measurements. One way to bridge this gap is to directly measure a microparticle’s interaction forces using Atomic Force Microscopy (AFM). Herein, an AFM method is presented to measure sticking efficiency for a model polystyrene microparticle (2 μm) on a model geomaterial surface (glass or quartz) in environmentally relevant, synthetic freshwaters of varying ionic strength (de-ionized water, soft water, hard water). These data, collected over nanometer length scales, are compared to sticking efficiencies determined through traditional approaches. Force measurement results show that AFM can detect extremely low sticking efficiencies, surpassing the sensitivity of column studies. These data also demonstrate that the 75th to 95th percentile, rather than the mean or median force values, provides a better approximation to values measured in model column experiments or field settings. This variability of the methods provides insight into the fundamental mechanics of microplastic deposition and suggests AFM is isolating the physicochemical interactions, while column experiments also include physical interactions like straining. Advantages of AFM over traditional column/field experiments include high throughput, small volumes, and speed of data collection. For example, at a ramp rate of 1 Hz, 60 sticking efficiency measurements could be made in only a minute. Compared to column or field experiments, the AFM requires much less liquid (μL volume) making it effortless to examine the impact of solution chemistry (temperature, pH, ionic strength, valency of dissolved ions, presence of organics, etc.). Potential limitations of this AFM approach are presented alongside possible solutions (e.g., baseline correction, numerical integration). If these challenges are successfully addressed, then AFM would provide a completely new approach to help elucidate which subsurface minerals represent a sink or temporary storage site for microparticles on their journey from terrestrial to oceanic environments. Full article

Reducing CO₂ emissions in transport requires sustainable alternatives such as fuel cell electric vehicles. A critical challenge is the efficient and safe storage and fast refueling of hydrogen at 70 MPa. This study proposes a practical design-support tool to optimize hydrogen storage systems for heavy-duty vehicles with capacities up to 100 kg. A customizable, dynamic Matlab-Simulink model was developed, including all components from dispenser to onboard tanks, enabling evaluation of multiple design options. The aim is to provide clear guidelines to ensure fast, safe, and complete refueling compliant with SAE J2601-5 limits. Simulations showed Type III tanks deliver the best performance. The fastest refueling (~10 min) was achieved with shorter pipes, larger diameters and low temperatures (20 °C ambient, −40 °C dispenser), while Average Pressure Ramp Rate was maximized up to 9 MPa/min (220 g/s of hydrogen from the dispenser) without exceeding SAE limits for pressure and temperature. Full article

The proliferation of photovoltaic energy in the electricity grid presents a significant challenge in terms of management, control, and optimization, especially due to its dependence on weather behavior and cloud passing. Even if there are a great number of articles centered on study cloud passing effects, such as voltage flickers, voltage fluctuations, or ramping events, the approaches are quite heterogeneous and lack a broader perspective. A key factor might be the limiting data sets, as wide power generation data sets often omit meteorological data and vice versa. This study uses an advanced monitoring system based on phasor measurement units (PMUs), developed by the authors. The monitoring system is installed at a photovoltaic plant and generates high-quality synchronous irradiance and power data, enabling the joint analysis of irradiance transients, solar power ramp rates, and voltage fluctuations. Therefore, the results of this article present a detailed analysis of the production parameters of photovoltaic plants, focusing on the effects of passing clouds on the photovoltaic plant’s power, current, and voltage. To that end, compression algorithms such as the Swinging Door Algorithm (SDA), commonly used to detect ramp events, were employed. It was found that SDA produces a similar ramp rate output with power and irradiance data, suggesting that both data sets may be complementary. In addition, voltage fluctuations attributable to passing clouds were analyzed. Full article

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance. Full article

Three-dimensional NAND Flash has adopted the cell-over-peripheral (COP) structure to increase storage density. Unlike the conventional structure, the COP structure cannot directly increase the channel potential via substrate bias during the erase operation. Therefore, the gate-induced drain leakage (GIDL) erase method, which utilizes band-to-band tunneling (BTBT) to raise the channel potential, is employed. However, compared to bulk erase, the BTBT-based erase method requires a longer time to generate holes in the channel, leading to erase speed degradation. To address this issue, we propose a structure which enhances the erase speed by surrounding the bitline (BL) PAD with SiGe. In the case of a SiGe thickness (t_SiGe) of 13 nm, the lower bandgap of SiGe increases the BTBT generation rate, boosting the channel potential rise at the end of the erase voltage ramp-up by 861% compared to the Si-only structure, while limiting the reduction in read on-current to within 4%. We modeled the voltage and electric field across the SiGe layer, as well as BTBT generation rate and GIDL current in the SiGe layer, by varying t_SiGe, Ge composition ratio (SiGe_X), and the voltage difference between V_BL and V_{GIDL_TR}. Full article

This paper presents a novel approach to economic dispatch (ED) optimization in power systems through the application of Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm. The economic dispatch problem, a fundamental challenge in power system operations, involves optimizing the generation output of multiple units to minimize operational costs while satisfying load demands and technical constraints. Traditional methods often struggle with the non-linear, non-convex nature of modern ED problems, especially with increasing penetration of renewable energy sources. Our PPO-based methodology transforms the ED problem into a reinforcement learning framework where an agent learns optimal generator scheduling policies through continuous interaction with a simulated power system environment. The proposed approach is validated on a 15-generator test system with varying load demands and operational constraints. Experimental results demonstrate that the PPO algorithm achieves superior performance compared to conventional techniques, with cost reductions of up to 7.3% and enhanced convergence stability. The algorithm successfully handles complex constraints including generator limits, ramp rates, and spinning reserve requirements, while maintaining power balance with negligible error margins. Furthermore, the computational efficiency of the PPO approach allows for real-time adjustments to rapidly changing system conditions, making it particularly suitable for modern power grids with high renewable energy penetration. Full article

Congestion on port motorways often leads to reduced capacity and traffic efficiency, while the growing prevalence of connected vehicles (CVs) offers new opportunities for improving traffic control. This paper proposes a hierarchical control method integrating ramp metering (RM) and variable speed limits (VSLs) explicitly designed for port motorway environments dominated by CVs. The method uses real-time CV data to reduce congestion through a hierarchical control framework in which the upper-level optimization determines system-wide parameters, and the lower-level execution translates them into local control commands. A microscopic simulation using SUMO in the Guoju area of the Chuanshan Port Motorway demonstrated that the proposed method increases traffic capacity by approximately 16% compared to the no-control scenario and improves traffic efficiency by 4.8% and 4.5% compared to PI-ALINEA and MTFC-FB, respectively. Further experiments in varying CV penetration rates (MPRs) from 60% to 100% revealed that while lower MPRs result in higher traffic fluctuations, the method remains effective and robust, particularly when MPRs exceed 80%. This highlights its ability to mitigate congestion and enhance the utilization of the existing infrastructure. Full article

With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NO_x emissions by 9.42%, CO₂ emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article