Eng, Volume 7, Issue 8 (August 2026) – 65 articles

Green hydrogen is a key energy carrier in Power-to-Liquid (PtL) pathways for the production of sustainable synthetic fuels, contributing to the decarbonization of the industrial and transport sectors. However, the intermittent nature of renewable energy sources and the variable hydrogen requirements needed to maintain the appropriate stoichiometric ratio for synthesis processes necessitate regulating hydrogen production according to process demand, rather than maximizing its generation. This article proposes an intelligent control strategy for alkaline water electrolysis, in which the hydrogen production target is determined from the stoichiometric requirements of synthetic methanol production, based on available carbon dioxide. ANN models were developed using the experimental data, incorporating both classical and conformable activation functions in the hidden layer. Based on the selected models, the ANNi was formulated, and PSO and GA were used to determine the required feed current according to hydrogen demand. The proposed methodology was evaluated under a dynamic hydrogen-demand profile derived from the stoichiometric requirements of methanol synthesis. The results show that the proposed controllers closely track changes in hydrogen demand. After each change in the setpoint, the H₂/CO₂ ratio returned to a $\pm 2\%$ band around the stoichiometric setpoint in approximately 0.98 s for ICANNi-PSO and 0.96 s for ICANNi-GA. Furthermore, some conformable activation functions achieved performance comparable to that of classical activation functions while using fewer neurons in the hidden layer. Both optimization algorithms provided comparable tracking performance under the evaluated conditions. Full article

To address the challenges associated with operational ventilation in high-altitude tunnels, this study investigates the distribution patterns of ventilation flow fields and optimizes the spatial layout parameters of jet fans to determine the most effective configuration. Using a case study from the Zhuohe Expressway tunnel, numerical simulations were conducted to analyze four key design parameters: the lateral clear distance (L) between two jet fans in a single group, the vertical distance (H) from the fan center to the tunnel lining, the axial distance (T) from the fan to the tunnel entrance, and the longitudinal spacing (S) between two groups of fans. The results indicate that for a single-fan group, when the parameter L is 1.25D (D is the fan diameter), pressure rise and comprehensive influence coefficients reach peak values of 20.090 Pa and 0.886, respectively. As well as the parameter H between 1.20 m and 1.25 m, the diffusion of the vertical wind field velocity is continuously reduced due to the constraint of the tunnel lining on Section BB of the tunnel fan’s symmetry axis, and the interference of the tunnel lining on the stable flow state of the fan’s outlet airflow is relatively small. Moreover, parameter T has a relatively low sensitivity impact on the increase in pressure and the variation of the influence coefficient. When the parameter T is within the range of 50 m to 100 m, the airflow at the entrance of the tunnel is smoothly connected with the airflow at the suction section of the fan. Additionally, the pressure rise and the influence coefficient increase by the parameter T. Both the fan’s pressure rise and the influence coefficient reach their maximum values when the parameter T is 100 m. Furthermore, in the case of two-fan groups, the gas is fully mixed in the tunnel when the parameter S is 150 m, and the fan pressure rise and the influence coefficient increase as well as parameter S. The gas between the two sets of fans has been fully mixed in the parameter S at 175 m, and the pressure rise and the coefficient influence reach their maximum values of 40.231 Pa and 0.887, respectively. In light of these findings, the following optimal parameters ranges are recommended for similar tunnel ventilation designs: parameter L is 1.25D for two jet fans within a single group, parameter H is between 1.20 m and 1.25 m, parameter T is 100 m from the tunnel entrance, and parameter S is between 150 m and 175 m for two groups of fans. Full article

As a key energy-conversion component of tower gravity energy storage systems (T-SGESs), the hoisting structure is susceptible to large-amplitude coupled vibrations when the dominant frequency of a continuous external excitation approaches one of its natural frequencies, potentially compromising operational stability and safety. To characterize this behavior, a two-degree-of-freedom nonlinear dynamic model is developed based on Hamilton’s principle. Eigenvalue and modal analyses are performed to determine the natural frequencies and modal characteristics of the coupled system, while the second-mode primary resonance is further analyzed using the method of multiple scales and validated through numerical frequency-sweep simulations. Near the second-mode primary resonance, the system exhibits a pronounced hardening-type nonlinear response characterized by multistability, saddle-node bifurcations, jump transitions, and hysteresis. Parametric analysis indicates that greater attention should be paid to short-rope and low-payload operating conditions, under which the system tends to exhibit stronger nonlinear responses and larger payload swing amplitudes near the second-mode primary resonance. Meanwhile, the nonlinear resonance response of the hoisting structure can be effectively mitigated through enhanced equivalent stiffness and damping, which substantially narrow the multistable frequency interval. At a damping ratio of 0.04, the system transitions from a multivalued response to a single stable branch, with a marked reduction in payload swing amplitude. These findings identify the second-mode primary resonance as a critical nonlinear operating regime and provide a quantitative basis for resonance avoidance and parameter regulation in T-SGES hoisting systems. Full article

Enhanced circular tubes are widely employed in shell-and-tube heat exchangers, power-generation condensers, chemical reactors, refrigeration systems, and air-cooled heat exchangers, where improved convective performance can reduce the heat-transfer area required for a specified thermal duty. Helical screw tapes (HSTs) are passive inserts that promote sustained swirling motion and enhance convective heat transfer within such tubes. Although helical screw tapes and multiple-insert arrangements have been investigated previously, the combined thermohydraulic and second-law effects of increasing the number of co-rotating HSTs under fixed geometric ratios remain insufficiently quantified. In this investigation, turbulent airflow in a heated round tube was numerically investigated to examine the effect of tape number on heat transfer, pressure drop, thermal performance, total entropy generation (S_total), and exergy destruction (Ex_D). Six HST configurations containing one to six tapes were examined over a Reynolds-number range of Re = 5000–20,000 in a circular tube with an inner diameter of D_T = 31 mm, which was also adopted as the characteristic length for the Reynolds number, Nusselt number, and friction factor. The helical pitch P, screw diameter D_s, tape width W, and tape thickness t were 60 mm, 30 mm, 4.5 mm, and 0.2 mm, respectively, giving a pitch ratio of P/D_s = 2.0 and a width ratio of W/D_s = 0.15. A plain tube (PT) served as the baseline case. The results show that increasing the number of tapes intensifies swirl flow and enhances heat transfer but also leads to a continuous increase in pressure loss. For the optimum three-tape arrangement, the Nusselt number is increased by 126.0–158.8% and the thermal performance factor by 4.5–19.5% relative to the plain tube, while the total entropy generation and exergy destruction are simultaneously reduced by 7.9–61.0%. Among the configurations examined, HST-P2.0-W0.150-3, comprising three tapes at a pitch ratio of P/D_s = 2.0 and a width ratio of W/D_s = 0.15, achieved the best overall performance by delivering the highest thermal performance factor and the lowest total entropy generation and exergy destruction among the HST cases. Full article

Sixteen reinforced concrete beams were tested under symmetric concentrated loading to investigate the mechanical behavior of beams designed using the compressive force path (CFP) method, in comparison with specimens designed according to the Chinese Code for Design of Concrete Structures (GB 50010-2010). The test variables included shear-span ratios (4.0, 3.0, 2.5, and 2.0) and sectional dimensions (150 × 300 mm and 250 × 550 mm). The test process and test results were systematically analyzed. The results show that the stress transmitted along the compressive force path is the main factor governing the shear capacity. The CFP beams achieved peak loads comparable to those of the GB beams while using 5.88–39.99% fewer stirrups, with larger savings observed for smaller shear-span ratios. The CFP method predicted the shear capacity with an error of approximately 10% (ranging from 2.24% to 12.45%). The shear strength of the CFP beams decreased with increasing shear-span ratio and effective depth. Overall, the CFP-designed specimens met the expected mechanical performance requirements, verifying the accuracy and applicability of the CFP method. Full article

Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of a hydrogen production system was used, featuring a functioning electrolyzer that generates pure hydrogen by splitting water. To perform efficient leakage mapping, the leak location and watch time were varied, and six readings from commercial hydrogen gas sensors were recorded for better analysis. The relative sensor responses recorded by the six hydrogen sensors were used as input features for the machine learning models. The model accuracy was approximately 88.13%. LoRa communication technology was also used to demonstrate its use in harsh conditions, along with the IoT protocol, to deliver data over the Internet for better accessibility and monitoring. The developed localization technology enables safe monitoring of hazardous, highly flammable hydrogen gas, and machine learning can help prevent fatal accidents. Full article

The industrial sector faces one of the biggest challenges in decarbonization, mainly due to the high costs associated with the development and implementation of low-carbon, energy-efficient technologies and solutions. The long lifespan of industrial assets and infrequent replacement contribute to maintaining high levels of energy consumption and emissions. As electric motors represent a significant portion of energy consumption in industries, improving their efficiency generates substantial reductions in consumption, energy demand, and emissions, thus optimizing overall energy performance. This article proposes an integrated guideline for the application of multi-criteria decision-making (MCDM) methods, computational thinking (CT), and technical standards in industrial energy management problems. To validate this proposal, the guidelines were applied to a real-world case of electric motor selection in an industrial complex. In this context, the structured analysis of the problem, when based on computational thinking, MCDM methods, and technical standards, provides transparency and traceability to decisions. The motor-selection case study, which incorporated computational thinking and MCDM tools (AHP/TOPSIS) aligned with technical standards, demonstrated that these integrated guidelines can substantially improve decision-making in industrial contexts by structuring selection problems and aligning them with the strategic objectives of organizations. Full article

Methane emission estimates from offshore facilities are increasingly used for regulatory reporting and climate assessment, yet it remains unclear under what atmospheric conditions such estimates are physically meaningful. This study defines three necessary conditions for valid offshore methane quantification: plume detectability, adequate sampling (interception), and reliable inference. A simplified Monte Carlo modelling framework was used to examine how these conditions are affected by atmospheric regime. Results suggest that the ability to obtain a physically meaningful emission estimate is strongly regime dependent. Under well-mixed conditions, successful quantification is achieved in most simulations, with uncertainty dominated by limitations in the inversion method. Under shallow marine boundary layers, quantification becomes increasingly conditional, with success probabilities reduced to approximately 15–20% depending on sampling configuration. Under strongly stratified conditions, plume observability is limited and valid emission estimates are not obtained within the illustrative model framework. To place these findings in context, ERA5 reanalysis data were used to assess atmospheric regime occurrence at representative offshore locations. Well-mixed and neutral conditions occur approximately 70% of the time in the North Sea, whereas the Gulf of Mexico is dominated by shallow boundary layer conditions (~90%), with stratified conditions occurring more frequently (~5%). These results suggest that offshore methane quantification is not a universally achievable measurement capability, but a regime-dependent and probabilistic outcome controlled by atmospheric structure. Atmospheric conditions therefore determine when physically meaningful emission estimates can be obtained and when measurement results should be interpreted with caution. Full article

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform. Full article

This experimental work aims at the study by non-destructive and destructive testing of the mechanical and acoustical properties of cold-setting epoxy resins plasticized with amounts of plasticizer and of PMMA (Plexiglas), both belonging to the two basic categories (thermosetting and thermoplastics respectively) of polymeric materials, which usually can be modified because of polymerization rate and curing, change in temperature and frequency, by the addition of plasticizers and/or inclusions as well as due to discontinuities (defects, voids and porosity) where stress concentration exists. On the other hand, ultrasound is a mechanical, elastic wave of very high frequency, and can be used for material testing. Using ultrasounds, defects, discontinuities, and damage can be detected, and moduli can be evaluated accurately. It should be noted that the moduli determined in this way are the dynamic moduli and differ from the static ones for any material. Here, the authors focus their study on plasticized epoxy resins and PMMA and apply this NDT method to estimate mechanical properties and correlate the results with those from destructive tests. Finally, the glass-transition temperature of plasticized epoxies was also evaluated from thermal experiments to determine the effect of the plasticizer. Full article

This study presents a numerical model of electromagnetic and thermal processes characteristic of a submerged arc furnace. Because direct modeling of a full-scale industrial furnace is complex and difficult to validate experimentally, a laboratory system without an electric arc is considered at this stage. The system reproduces the main features of current supply and energy distribution in the conductive region of the furnace bath. The model is implemented in ANSYS Fluent 2020 R1 using user-defined scalar equations for the electric potential, the components of the magnetic vector potential, and their time derivatives. The implementation was assessed in terms of mesh independence, time-step sensitivity, current and energy balances. The calculations yielded consistent distributions of electric potential, current density, magnetic flux density, Joule heat generation, and temperature. Heating was described using a two-stage scheme: the transient electromagnetic problem is first solved to obtain period-averaged Joule heat generation, which is then used as a source term in the energy equation. The model represents the first stage of a computational framework for submerged arc furnace modeling: at this stage, it is developed and assessed using a simplified laboratory configuration without an electric arc, while in future work it can be supplemented with an arc-channel description and extended to industrial furnace conditions. Full article

Overpressure prediction is critical for safe and efficient drilling, yet remains challenging in complex basins with multiple genetic mechanisms. This study systematically investigates the overpressure origins in the Xihu Sag, East China Sea, a prolific hydrocarbon-bearing sag with widespread overpressure and complex pressure regimes. By integrating well logging data and direct pore pressure measurements from nine wells across three major structural units, the Western Slope Belt, the Western Sub-sag and the Central Inversion Belt, a multi-method diagnostic framework is employed. This combines Bowers’ effective stress analysis with sonic-density cross-plots to discriminate between loading and unloading mechanisms. Results show obvious vertical zoning of pore pressure—normal-pressure zone, overpressure zone, and pressure reversal zone—with distinct horizontal heterogeneity. Results reveal a distinct spatial differentiation in dominant overpressure mechanisms. In the Western Slope Belt, overpressure in the deep Pinghu Formation primarily results from a composite of undercompaction (creating initial pressure seals) and subsequent hydrocarbon generation-induced fluid expansion. In contrast, in the Central Inversion Belt and Western Sub-sag, overpressure is predominantly driven by hydrocarbon charging along faults coupled with tectonic compression, with minimal undercompaction signatures. Previous studies on overpressure genesis in the Xihu Sag have largely focused on the Western Slope Belt. This study expands the analytical scope to the Western Sub-sag and Central Inversion Belt, and conducts a systematic comparative analysis of overpressure genesis across multiple tectonic units. The value of this work lies in the systematic application of classical diagnostic methods to fill the regional research gap regarding the overpressure characteristics of the Huagang Formation and the composite nature of overpressure. With accurately constrained genetic mechanisms, the findings can provide support for optimized drilling fluid design and wellbore stability management, and effectively mitigate deep hydrocarbon exploration risks in this sag and analogous overpressured basins. Full article

The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R² of 0.868 and the 1 h model an R² of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R² of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R² 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H₂/CH₄ blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO₂ emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications. Full article

Small- and medium-sized enterprises (SMEs) face significant challenges in adopting robotic solutions due to limited financial resources, insufficient technical expertise, and uncertainty regarding operational and economic outcomes. Existing automation approaches are often technologydriven and provide limited support for systematic decisionmaking. This study proposes the Agile Robotics Implementation Model (ARIM), an iterative framework integrating Lean Manufacturing, Lean Robotics, and Lean Startup principles. ARIM combines process assessment, key performance indicator (KPI)-based evaluation, and iterative experimentation within the Robotic Startup Cycle, supported by a decision-support software tool. The framework was developed using a Design Science Research (DSR) approach and validated through an industrial case study. Results demonstrate strong agreement between predicted and realized KPI values. The implemented solution achieved a 24.5% return on investment (ROI), with a payback period of approximately 2.1 years, reduced labor demand by 3900 h, and improved productivity, ergonomics, and quality. The findings indicate that ARIM supports reliable and data-driven robotics implementation in the studied SMEs; broader transferability requires validation across multiple cases. Full article

The rise in the need for sustainable energy has facilitated the advancement of perovskite solar cells (PSCs) as potential substitutes for traditional photovoltaic technologies. Nevertheless, their performance is very sensitive to environmental factors, especially temperature, which influences the charge transport and recombination processes. This paper examines the thermal effect on the electrical characteristics and impedance response of lead-free PSCs in accordance with the FTO/ETL (C₆₀, PCBM, SnS₂, ZnSe)/CH₃NH₃SnBr₃/Cu₂O configuration. The experiments were performed with SCAPS-1D under usual illumination, using a combination of current-voltage analysis and impedance spectroscopy between 270 and 400 K. The findings indicate that there is a significant reduction in open-circuit voltage with higher temperature, whereas the short-circuit current density does not change much. The enhancement of the fill factor increases and then decreases with increased temperature, leading to a net decrease in power conversion efficiency because of the increased recombination. The impedance analysis is also an indicator of lower recombination resistance and accelerated charge carrier dynamics. These results demonstrate that thermal control and interface optimization can be important for enhancing PSC performance. Full article

Cold-climate dwellings can face coincident electricity and domestic hot-water shortfalls in winter, when solar availability is at its lowest. This study evaluates an integrated residential system for Aomori, Japan, combining photovoltaics, evacuated-tube solar water heating, and battery storage with electrolysis, compressed-hydrogen storage, and a PEM fuel cell operated in combined-heat-and-power mode. Building on a screening-level annual-balance analysis, a coupled annual TRNSYS simulation with a 0.125 h time step resolved battery dispatch, electrolyzer part-load operation, hydrogen compression and finite storage, seasonal fuel-cell operation, and heat recovery. The results show that the principal value of seasonal hydrogen lies in improving winter supply adequacy, dispatchability, and heat recovery rather than annual conversion efficiency. Fuel-cell heat recovery increased the number of days satisfying the hot-water screening indicator—a daily mean tank temperature of at least 43 °C—from 221 to 332. A reserve-aware criterion identified a 225 W electrolyzer operating-power cap as the positive-reserve case; 205 W was near-cyclic with a negligible margin, whereas the original 475 W cap was substantially oversized. The hydrogen pathway remained markedly less efficient than direct photovoltaic and solar-thermal use, and the estimated storage hardware’s lower bound substantially exceeded the break-even capital ceiling supported by the annual operating value. Seasonal hydrogen can therefore strengthen winter energy adequacy and heat recovery but is not yet cost-effective at the single-dwelling scale under the investigated conditions. Full article

The aim of this study was to investigate the effect of reduction roasting parameters on the phase transformations of the Ushkatyn-III ferruginous manganese ore and the efficiency of subsequent magnetic separation. The experimental procedure included preliminary high-intensity magnetic separation, reduction roasting at 650 °C for 3–5 h using 20–30 wt.% coal as the reducing agent, and low-intensity dry magnetic separation at magnetic field intensities of 0.1–0.6 T. Chemical composition was determined by standard analytical methods, while phase composition was analyzed by X-ray diffraction (XRD). Preliminary magnetic separation increased the manganese content in the magnetic pre-concentrate to 30.71–35.09 wt.%. The optimum results were obtained after roasting for 5 h with 30 wt.% coal, followed by magnetic separation at 0.2 T, producing a low-iron concentrate containing 33.26 wt.% Mn and 0.67 wt.% Fe, with a manganese recovery of 87.79% and an Mn/Fe ratio of 49.6. XRD analysis confirmed the partial reduction of hematite to magnetite (Fe₃O₄), providing the basis for efficient magnetic separation. The proposed process offers an effective approach for upgrading low-grade ferruginous manganese ores for manganese ferroalloy production. Full article

This article presents the results of a computational parametric study, a global sensitivity analysis, multi-objective optimization, and a technical and economic evaluation of the parameters of phase-change materials (PCMs) incorporated into the building envelope of an office building in a sharply continental climate (using Astana, Kazakhstan, as an example). The study was conducted using simulation modeling, incorporating dynamic thermal calculations in the EnergyPlus software package and the NSGA-II genetic algorithm. The CondFD algorithm was used, for which results of independent verification and experimental validation conducted by other researchers have previously been published. This study used this validated implementation without conducting additional experimental verification of the structure under consideration. Based on the results of a parametric analysis (1232 calculations) and an optimization run (≈25,000 calculations), the range of quasi-optimal phase transition temperatures for the PCM was determined to be 23–25 °C. For further analysis and a technical–economic evaluation, a value of 24 °C was selected as the recommended compromise solution, with a PCM layer thickness of 16 mm and a distance of 15 mm from the inner surface of the wall. This compromise solution reduces annual specific energy consumption for heating and cooling by 22% and hours of thermal discomfort by 42% compared to a reference concrete wall without PCM. A technical and economic assessment, based on post-processing of the simulation results using current electricity rates and market data on the cost of PCM, shows a simple payback period ranging from 3.8 to 38 years, depending on the assumed cost of the encapsulated PCM layer. The results are limited to the specific case considered (south-facing orientation, standalone office module, and continuous ventilation) and are intended for subsequent experimental verification. The information in this article can be used by architects and engineers in the early stages of designing energy-efficient office buildings in regions with a sharply continental climate. Full article

The article is aimed at studying the features of the hot rolling mill CWBRM-1700 of JSC “Qarmet”, which negatively affect the operation of the distribution network of the workshop. Such factors are frequent shock loads of technological mechanisms with high installed capacity of the equipment. Experimental studies of the distribution network of the rolling production on the buses of the 10 kV substation showed that shock loads of synchronous electric drives of roughing stands lead to periodic voltage drops of up to 13% lasting 5–6 s. Mathematical modeling in the MATLAB/Simscape/Electrical environment, the results of which coincide with the data of the experimental study, showed that the most significant factor affecting the quality of electricity are abrupt changes in the reactive power of the synchronous motor from −0.5 to +0.5 MVAR. To solve the problem, it is proposed to use a controlled filter-compensating device. Variants of circuit solutions for such devices are considered. The choice was made in favor of a three-phase adjustable LLC filter with diode–transistor keys. The article develops a method for calculating the electromagnetic parameters of such a filter and establishes that in order to reduce the level of harmonic distortion of voltage, it is necessary to use a triangle connection of the controlled reactive compensator and select the PWM frequency of the transistors, a multiple of the tripled frequency of the power grid. Two options for creating a closed-loop control system for energy modes are studied: a reactive power stabilization system and a voltage stabilization system in a distribution network node, which reduce the duration of transient processes to 0.5 s and reduce the voltage drop in the network node to −4 to + 1% in the first case and to −4 to + 3% in the second, also reducing reactive power consumption to 0.02 MVAR and 0.25 MVAR, respectively. The advantage of a closed-loop control system with voltage stabilization is the ability to use a technically less complex voltage sensor. Full article

Sensorless speed estimation replaces fragile shaft encoders in cost-sensitive Permanent Magnet Direct Current (PMDC) motor drives, but classical model-based observers degrade under brush friction, commutation ripple, and thermal drift, while purely data-driven estimators sacrifice physical interpretability. This paper presents a Hybrid Physics-Data-Driven Observer (HPDDO) that couples an identified lumped-parameter electrical model with a compact multilayer-perceptron residual compensator, which is executed in real time on a low-cost ESP8266 microcontroller. Global parameters are identified from a short labeled recording, after which the network corrects only the nonlinear residual that the physics model cannot explain. Under a strictly time-series-aware evaluation (chronological 80/20 split), the proposed estimator achieves an average root mean square error (RMSE) of 4.11 RPM across dynamic PWM sweeps, abrupt load transitions, and a long-duration thermal-drift test, outperforming an extended Kalman filter (9.49 RPM), a sliding mode observer (10.89 RPM), and a pure neural-network estimator (7.14 RPM) implemented on the identical dataset. An ablation study shows that accuracy is insensitive to network size, with a 0.9 kB variant matching the deployed model, and a residual-clamping safeguard bounds the estimation error under unseen operating conditions. The framework provides an accurate, interpretable, and computationally lightweight solution for industrial PMDC drives without dedicated speed sensors. Full article

To address delayed attitude correction, limited adaptability of single-actuator systems, and reduced tunneling efficiency in full-face shaft-boring machines (SBMs), this study proposes a PSO-LSTM-based hybrid steel strand–support shoe attitude correction strategy. A coupled dynamic model with a 45° offset configuration is developed to enable coordinated multi-actuator control. A PSO-optimized Long Short-Term Memory (PSO-LSTM) network is employed to predict inclination deviation over a 5 s horizon, providing anticipatory information for proactive control. Based on this prediction, a hierarchical control strategy with adaptive torque allocation is designed to seamlessly coordinate fine correction via steel strand cables and high-torque correction via support shoes. Simulation results demonstrate that the proposed model achieves a prediction accuracy within ±0.02°. Under inclination conditions of 0.05°, 0.3°, and 1.0°, rapid attitude correction is achieved. Compared with independent support shoe control, the maximum horizontal displacement is reduced from 64 mm, 131 mm, and 160 mm to 6.3 mm, 65 mm, and 100 mm, corresponding to reductions of 90.2%, 50.4%, and 37.5%, respectively. The results further indicate that small-angle deviations can be compensated by the steel-strand system without additional support-shoe operations, while medium- and large-angle deviations can be regulated through coordinated actuation of multiple correction systems according to deviation magnitude. Simulation results demonstrate that the proposed method improves attitude correction performance and dynamic response under the investigated simulation conditions. The proposed framework provides a potential solution for intelligent attitude control of SBMs, while further field validation is required before practical engineering deployment. Full article

Three-level neutral-point-clamped (NPC) inverters are widely used in medium- and high-power drives and grid-connected applications due to their reduced device voltage stress, improved output power quality, and lower switching losses relative to conventional two-level topologies. Among the potential failure modes, clamping diode open-circuit faults are difficult to detect because the clamping diodes conduct only during the zero-voltage states, and their failure produces only subtle distortions in the phase current waveform. This paper proposes a fault diagnosis method for clamping diode open-circuit faults in three-level NPC inverters. The method is based on a current asymmetry index defined as the ratio of the per-cycle mean phase current to the per-cycle mean absolute phase current. During healthy operation, this index is approximately zero in all phases. A fault causes the index to deviate markedly from zero, while the polarity of this deviation identifies the failed diode. The method requires only phase-current measurements already available in the inverter control system. Consequently, no additional sensors, hardware modifications, or changes to inverter operation are required. Simulation results obtained for a 10 kW three-level NPC inverter demonstrate successful fault detection within approximately one to two fundamental cycles for open-circuit failures of both the upper and lower clamping diodes in all three phases. Full article

Super duplex stainless steels are widely used in seawater desalination plants due to their high mechanical strength and excellent corrosion resistance in chloride-rich environments. However, during reverse osmosis processes, the salinity of the reject stream increases progressively, generating concentrated brines with concentrations close to 7 wt.% NaCl, which represent a chloride-rich service environment that may affect passive film stability and promote localized corrosion. This study investigates the effect of novel La Geria-inspired microstructures (LGMs) generated by laser surface texturing on the microstructure, microhardness, and electrochemical behavior of UNS S32750 super duplex stainless steel in 3.5 wt.% and 7.0 wt.% NaCl solutions, simulating seawater and concentrated desalination brine. Electrochemical results show that textured surfaces exhibit improved corrosion resistance, with more stable corrosion potentials, lower corrosion current densities, and higher impedance values. Microhardness measurements revealed a homogeneous mechanical response, confirming that laser texturing does not alter the mechanical integrity of the material. Microstructural observations showed reduced surface degradation and improved preservation of the duplex ferrite–austenite structure in textured samples after exposure to chloride solutions. These findings demonstrate that biomimetic laser surface texturing enhances corrosion resistance by modifying interfacial conditions and stabilizing the passive film, providing experimental evidence of the beneficial effect of LGMs in aggressive desalination environments. Full article

The Greek power system includes 42 non-interconnected islands grouped into 28 autonomous electrical systems operated by the Hellenic Electricity Distribution Network Operator. Although these systems possess substantial wind and solar potential, the technical constraints of isolated microgrids lead to systematic renewable energy curtailment. Building on our previous methodology for estimating curtailed wind energy and hydrogen production, this study develops and evaluates a dispatch-oriented power-to-power pathway in which curtailed wind electricity is converted into hydrogen and subsequently reconverted into electricity. The study integrates hydrogen-to-power technology selection, annual energy recovery, dispatch strategy, and operational environmental and economic benefits for a representative non-interconnected island. A comparative assessment of commercially relevant hydrogen-to-power technologies identified proton exchange membrane fuel cells as the most suitable option because of their absence of direct CO₂ and NO_x emissions, rapid start-up, load-following performance, modularity, and compatibility with remote island operation. Applying the previously developed curtailment methodology to 2024 data yielded 9334.5 MWh of exploitable curtailed wind energy. This energy could produce 155.6–233.4 tonnes of hydrogen and recover 2437.1–4277.9 MWh of electricity annually. Two dispatch strategies were evaluated: continuous integration of hydrogen-derived electricity into the island’s generation mix, and strategic hydrogen storage with priority dispatch during periods of emergency diesel generator operation. Under the reference case, both strategies recovered approximately 2935.1 MWh annually, avoided 1868.9 tonnes of CO₂ emissions, and reduced fuel expenditure by €359,000. Full article

Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness. Full article

Accurate pressure measurements in soils and other particulate media are essential for the development and validation of engineering models. Pressure transducers based on deflecting membranes remain widely used despite longstanding recognition that sensor deformation may influence the measured response. This study investigates the extent to which hysteresis observed during load–unload cycles originates from sensor–material interaction rather than intrinsic material behavior. Controlled experiments were conducted on dune sand and uniform glass beads using sensors operating in membrane-deflection mode and Null mode, together with direct measurements of local material deformation. The results show that even small membrane deflections generate pronounced apparent hysteresis and that conventional calibration procedures cannot reliably eliminate this effect, even when performed under matching test conditions. The resulting measurement errors become particularly significant during unloading. Direct observations reveal that during loading, the surrounding soil accommodates the elastic deflection of the membrane. During unloading, however, the surrounding soil does not accommodate membrane recovery, preventing the membrane from rebounding along its elastic path. In contrast, Null-mode measurements exhibit a nearly unique response with substantially reduced hysteresis and little dependence on sensor stiffness. The findings indicate that a significant portion of hysteresis commonly reported in pressure measurements may reflect measurement-induced effects rather than intrinsic material behavior, highlighting the importance of accounting for sensor–material interaction when interpreting experimental data and validating engineering models. Full article

The paper is focused on the problem of increasing the reliability of determining the critical speed of railway vehicles during measurements in bench tests, due to the difference in the geometry of the contact interaction in the “wheel-roller” and “wheel-rail” systems. It is shown that existing approaches in dynamic stability analysis and the processing of measurement signals do not consider the systematic influence of equivalent conicity on the measurement results, which leads to a shift in the assessment of the threshold for loss of stability even with the high accuracy of the measuring equipment. An information-measuring approach is proposed, which is based on an analytical description of the relationship between the equivalent conicity of the wheel/rail contact and the critical speed. A generalized error model is developed integrating geometric, metrological and random components. A method for correcting the results of bench tests based on a coefficient considering the ratio of equivalent conicities is also proposed. It was found through the results of the research that neglecting the geometric mismatch of the contact interaction leads to an error in determining the critical speed at the level of 8 to 15%, while the application of the proposed approach reduces it to less than 1 to 2%. Additionally, frequency analysis of the oscillatory process was used. It allowed us to identify the critical mode of the frequency range of about 8 Hz and to establish its connection with the critical speed of movement. It was experimentally confirmed that the corrected results correspond with the calculated values within 1%. The obtained results provide an increase in the metrological consistency of bench and operational studies, expand the possibilities of interpreting test results and can be used in the development of methods for diagnostics, certification and prediction of the limit modes of operation of railway vehicles. The proposed approach provides increased reliability in determining the critical speed during bench tests and it can be used for analytical compensation of a systematic error without upgrading the test equipment. Full article

Background: Traditional osteopathic manipulative medicine (OMM) instruction relies on laboratory sessions constrained by scheduling, faculty availability, and practice partner variability. Virtual reality (VR) may address these limitations by providing on-demand access to standardized training scenarios. This pilot feasibility and acceptability study evaluated first-year osteopathic medical students’ motivational responses and perceptions of a novel VR training module focused on thoracic spine assessment techniques. Methods: This pilot single-arm observational study enrolled 45 first-year students at the New York Institute of Technology College of Osteopathic Medicine to assess the feasibility and acceptability of VR-based OMM instruction. A VR training module developed using Unity 3D and deployed on Oculus Quest headsets included 40 interactive assessment items covering thoracic diagnostic techniques. Participants received one-week access to the program then completed the Reduced Instructional Materials Motivation Survey (RIMMS) based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and a custom 10-item feedback survey. Session duration and assessment scores were automatically recorded. Descriptive statistics and Pearson correlation analysis were performed. Results: The Relevance domain achieved the highest RIMMS composite mean (3.98), while Attention demonstrated the greatest opportunity for enhancement (3.49). The overall RIMMS composite score was 3.81, indicating favorable motivational reception. Feedback survey results showed that 89.2 percent of participants endorsed the VR experience as educationally positive, and 81.1 percent supported expansion to additional OMM procedures. Performance analysis revealed minimal correlation between session duration and assessment scores (R² = 0.0169). Conclusions: First-year osteopathic medical students demonstrated positive motivational responses and favorable perceptions toward VR-based OMM training. Based on student perception and feasibility, these findings suggest VR is well-received and perceived by students as a potentially viable supplement to traditional OMM instruction, though attention-capturing elements and interface usability warrant refinement. This pilot study did not measure learning outcomes or skill acquisition. Full article

Belt conveyors are essential systems for the continuous transport of various materials in many sectors and applications, including heavy industry or mining, which are characterized by demanding environmental and operational conditions. Our research is focused on the development of the system based on magnetic sensors for the detection of ferromagnetic objects. These detection systems are designed to prevent damage to conveyor belts and the downstream vehicles, machines, and processing equipment involved in material transport and processing. By detecting hazardous foreign objects, they help avoid belt damage or tearing, thereby reducing operational disruptions and the associated maintenance and repair costs. Our study confirmed that in addition to the development of the hardware and software solutions, it is also necessary to develop methods for the processing and evaluation of the data recorded by the detection system, as the data represent a valuable source of information not only for the operational workers but also for the managers and are very helpful in the creation of the sustainable transportation system. The article describes an innovative application of the Weibull distribution for the operational enhancement of the system and its comparison to the conventionally used histograms. In addition to that, the utilization possibilities of the obtained statistical data to evaluate the belt conveyor loading for a better planning of the process, to monitor the work of the operational or other employees’ quality of the supported material, or to reveal failures of the detection system or even of the belt conveyor are overviewed and discussed. Full article

This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is deployed on the physical system, produces an experimental dataset, and is evaluated through a performance index. The objective function combines tracking error, control effort, and control-signal variation, while penalty terms identify and penalize actuator saturation, excessive overshoot, settling-time violations, steady-state error, and divergent responses. Candidate controllers are generated using a constrained Gaussian Cross-Entropy Method and evaluated directly on the physical actuator. The proposed host-embedded architecture separates two computational time scales: PID execution, encoder processing, and data acquisition are performed in real time on the embedded platform, whereas population sampling, candidate ranking, and distribution updates are executed on the host computer between experiments. Within the reported experimental campaign, the sampling distribution progressively concentrates toward gain regions associated with lower closed-loop cost under the prescribed admissibility criteria. The resulting framework provides a structured and traceable procedure for physical controller deployment, data acquisition, constrained performance evaluation, and episodic PID retuning without explicit plant identification. The reported results demonstrate the feasibility of the architecture on the considered platform, without implying comparative superiority, run-to-run statistical convergence, or analytical closed-loop stability. Full article