The flexibility regulation processes in power plants possibly yield significant impacts on ash deposition behaviors and fuel nitrogen migration, thereby affecting boiler safety and impeding efforts to reduce nitrogen oxide emissions. However, existing studies have primarily focused on steady conditions, with limited mechanistic understanding under dynamic parameter operations. Hence, this study employs a vertical two-stage furnace experimental system to investigate the ash deposition behavior and NO x emission characteristics of high-alkali coal combustion under air-staged, dynamic parameter conditions, with particular emphasis on the previously overlooked influence mechanisms of dynamic variations in temperature and coal feeding rate. Experimental results indicate that dynamically increasing the feeding rate reduces the ash deposition propensity ( DP ), but slightly increases the nitrogen conversion rate ( X NO ). The ramp-down condition enhances calcium sulfation at normal load and also promotes Fe 2 O 3 generation at low load compared to the ramp-up condition, leading to the easier formation of adhesive ash. The variable temperature conditions show increases in both X NO and DP compared to the stable temperature condition, while the combustion efficiency significantly decreases. The combustion efficiency declines even further when the excess air ratio is further increased to 1.4. In comparison to stable conditions, the temperature ramp-down condition promotes calcium sulfation, and the temperature ramp-up condition further facilitates the generation of sodium-containing crystalline minerals, exacerbating the ash deposition issue. This research can provide instructive significance for the low-carbon development and the safe variable load operation of power plants.
Centrifugal pumps in waste heat recovery systems frequently operate under off-design conditions, where complex turbulent structures induce significant irreversible energy dissipation and flow-borne noise. However, distinguishing the specific contributions of rigid vortex rotation from boundary layer shear remains a challenge for classical identification methods. This study elucidates the mechanisms coupling vortex dynamics, entropy production, and acoustic radiation in a low-specific-speed pump using the Liutex-shear decomposition method. A hybrid Detached Eddy Simulation (DES), rigorously validated against experimental hydraulic performance and pressure pulsation data, is employed to resolve transient turbulent structures. Results demonstrate that high-magnitude Liutex vectors, rather than general shear layers, exhibit the strongest spatiotemporal correlation with local entropy production rates. Quantitatively, vortex-induced dissipation accounts for approximately 35% of the total hydraulic loss under overload conditions, directly impacting the system’s parasitic power consumption. Furthermore, acoustic analogy analysis shows that coherent Liutex structures organize the wake–tongue interaction and localize the blade-passing-frequency-related dipole source region. Crucially, the results indicate that while shear deformation dominates viscous dissipation and directly amplifies the acoustic source intensity, rigid rotation primarily modulates the coherent source topology and phase relationship. These findings establish a direct physical link between specific vortex topologies and performance degradation, providing a theoretical basis for targeted flow control in energy conservation equipment.
Conventional solar-powered battery charging systems tend to operate at their rated capacity; however, at low solar insolation conditions, efficiency reduces. To ensure maximum energy extraction, an energy optimization system is implemented in our research, comprising an impedance-matching mechanism, a battery model by means of a digital twin, and an offset transformer. Furthermore, the machine learning-based method is used to precisely predict the battery state of charge (SoC), State of Health (SoH), and internal resistance, thus enhancing the accuracy of the real-time charging control. An experimental validation was provided by developing a prototype at a laboratory scale. However, the findings reveal a remarkable energy extraction with a gain of 16.68% in the conditions of low irradiance and 11.25% in conditions of moderate irradiance. The suggested system is a good option to incorporate renewable energy sources into the infrastructure of the electric vehicle (EV) battery charging process, significantly increase the efficiency of the energy expenditure, and minimize the total time of charge.
To solve the problem that distributed denial-of-service attacks hinder the normal operation and affect stability of smart grid, a detection and classification method based on improved residual network and attention mechanism is proposed. Based on Residual Network 50 (ResNet50), the Convolutional Block Attention Mechanism (CBAM) is introduced to establish the detection model called ResNet50-CBAM. This model is suitable for the detection and classification of distributed denial of service attacks in interactive traffic with many features and a large amount of computation during training, and solves the gradient disappearance, gradient explosion and “degradation phenomena” existing in ResNet50. The experimental results show that in the multi-classification task such as detecting and classifying of 11 attack types, the overall accuracy is significantly improved.
Accurate electric vehicle (EV) charging load forecasting is important for grid dispatch, peak-load management, and charging infrastructure planning. However, residential EV charging loads exhibit both multi-scale periodicity and stochastic fluctuations, making it difficult for fixed-window models to capture dynamic temporal patterns. To address this issue, this study proposes an Adaptive Time-Frequency Fusion PatchTST (ATF-PatchTST) model for EV charging load forecasting. The model combines an adaptive patch partitioning module with a time-frequency fusion module to jointly capture short-term variations and long-term periodic features. Experiments are conducted using real-world residential charging load data from Shanghai in August 2023 with a 15-min sampling interval. Compared with PatchTST, ATF-PatchTST reduces MAE, RMSE, RAE, and RSE by 6.6%, 10.4%, 6.9%, and 5.5% in the 8–30 day forecasting task, and by 7.6%, 10.2%, 11.9%, and 4.3% in the 7-day forecasting task, respectively. Time-series cross-validation further shows that ATF-PatchTST achieves the lowest average MAE of 0.372 kWh and average RMSE of 1.205 kWh, with an RMSE coefficient of variation of 3.2%. These results demonstrate that the proposed model provides a more accurate and stable forecasting framework for residential EV charging load management and distribution network operation.
This study presents an integrated assessment of waste heat recovery from an existing Algerian gas turbine (GT) power plant retrofitted as a Combined Heat and Power (CHP) system for District Heating (DH) applications. Unlike conventional studies that assess thermodynamic performance and heat demand separately, this work proposes an integrated framework that combines process simulation, demand estimation, and sustainability evaluation. The system is modeled using Aspen HYSYS, while the DH demand of the Aïn Beïda urban area is estimated using the degree-day method. The results demonstrate that upgrading the F’Kirina power plant to CHP mode significantly enhances overall efficiency from 33.6% to 88.1%. The integrated analysis indicates that, from an annual energy-balance perspective, the recovered waste heat is theoretically sufficient to satisfy the estimated heating demand of Aïn Beïda. Practical implementation will require dedicated studies on District Heating Network (DHN) design, thermal storage, and peak-load management. Sensitivity analysis reveals that electricity generation is strongly affected by ambient temperature, whereas heat recovery remains comparatively stable. Exergy analysis identifies the combustion chamber and Waste Heat Recovery Exchanger (WHRE) as the dominant sources of irreversibility. Furthermore, a sustainability assessment based on the Sustainability Index (SI) shows a marked improvement in exergetic performance, with SI rising from 1.46 to 1.80 after CHP integration. Environmentally, the CHP configuration reduces specific fuel consumption from 243.2 to 189.8 kg/MWh and CO 2 emissions per MWh by 29% (from 616.8 to 437 kg CO 2 /MWh). Annual avoided emissions through the substitution of conventional heating amount to 58.6 kt CO 2 /year. The proposed framework provides a comprehensive tool for evaluating CHP retrofitting strategies by linking energy production, demand profiles, and thermodynamic efficiency. These findings underscore the theoretical feasibility of CHP-based DH as a pathway to improve energy efficiency, reduce environmental impacts, and provide benchmarks to support future large-scale feasibility and infrastructure planning studies.
To address the growing power demand of airborne systems, the Ducted Ram Air Turbine (DRAT) generator is investigated for its high efficiency. This paper proposes a wide-envelope adaptive control strategy to ensure its stable operation. A component-level model captures the system’s coupling dynamics, while a composite controller combining feedforward compensation and gain-scheduled Proportional-Integral (PI) control is designed to handle load transients and flight condition variations. Simulations demonstrate that the proposed controller suppresses the peak speed fluctuation during load transients from 2.70% (with traditional PI) to 0.71%. Monte Carlo analyses confirm enhanced robustness, reducing average speed overshoot by 33.9% and significantly improving stability margins. Finally, Hardware-in-the-Loop tests verify the strategy’s real-time feasibility for airborne applications.
This work introduces an innovative method for intelligent control and optimization of the electric drive system, utilizing an integrated combination of photovoltaic (PV), fuel cell (FC), battery, and supercapacitor sources. The system is designed to provide and regulate power for a brushless DC (BLDC) motor in an Electric Vehicle (EV), aiming for optimal operational efficiency under fluctuating irradiance, particularly in Partial Shading Conditions (PSC). This paper presents an adaptive hybrid maximum power point tracking (MPPT) methodology that integrates a pretrained neural network (NN) with the perturbation and observation (P&O) method. The system in development employs an intelligent decision engine and a MATLAB function block to manage the dynamic exchange of solar irradiance variations from irradiation difference and the output power levels from associated plants/sites. This technique attains superior tracking precision with little oscillation by enabling context-sensitive, fluid transitions between P&O and NN control operations. The hybrid energy system of the proposed vehicle consists of four power sources connected to a DC bus through the DC-DC power converters. A DC bus is further connected to a DC-AC inverter used to run the electric motor of a vehicle. A Fractional Order PI (FOPI) controller, tuned with the Chaotic JAYA algorithm, is compared to a Proportional-Integral (PI) controller optimized through Particle Swarm Optimization (PSO) for motor speed control. The FOPI controller displays enhanced stability and transient response. The simulation results across diverse PSC scenarios illustrate that the proposed hybrid MPPT and energy control architecture exhibits resilience. The proposed method achieved superior MPPT performance, with an average efficiency under partial shading. It significantly reduces and improves convergence speed compared to conventional methods, yielding performance metrics such as MeanSquare Error (MSE) of = 0.22. These results demonstrate the method’s high accuracy, stability, dependable motor performance and practical viability for real-world EV applications.
This study investigates the application of Organic Rankine Cycle (ORC) technology for recovering waste energy from the intercooler in the Siemens SGT-A65 gas turbine. ORC demonstrates high energy recovery potential for mid-grade waste heat within the 100-200 degrees C range, which aligns with the temperature range available from the intercooler. Using EES software for thermodynamic modeling and analysis to evaluate various working fluids and operating conditions, comparing the performance of the standard gas turbine cycle with modified configurations. Findings indicate that ORC integration significantly enhances energy recovery efficiency, achieving an improvement of up to 7.51% in overall thermal performance while using R123 as the working fluid, along with a reduction of 9.65 $/MWh in LCOE compared to the baseline while using Ethanol as the working fluid. Furthermore, environmental analysis reveals a 14.85% reduction in carbon emissions (0.354 kg CO2/kWh) while using R123 at 3.5 MPa HRVG pressure, enhancing the system's environmental sustainability. Economic feasibility is also examined, emphasizing the potential for sustainable energy management. Recommendations focus on optimizing performance and minimizing environmental impact to encourage broader adoption.
Micro heat pipes (MHPs) are compact, passive, two-phase thermal management devices that enable high heat transport capability through capillary-driven phase change. Their small footprint, low thermal resistance, and rapid thermal response make them increasingly relevant for modern systems experiencing escalating heat fluxes due to miniaturization. This review synthesizes recent advancements in MHP design, fabrication, working fluid optimization, and application-specific performance. Key application domains electronic cooling, renewable and solar energy systems, biomedical instruments, battery thermal management, and emerging nuclear and aerospace platforms are critically examined with emphasis on quantitative performance indicators such as maximum heat flux, temperature uniformity, and thermal resistance. Geometric innovations (micro-grooves, star-shaped channels, hybrid structures), nanofluid-enhanced working media, and integration with PCMs are highlighted as major breakthroughs that continue to expand the operational envelope of MHPs. Despite wide applicability, several challenges remain, including orientation sensitivity, fill-ratio selection, material compatibility, nanofluid stability, and limited long-term reliability data. Comparative insights reveal that well-optimized MHPs outperform conventional heat spreaders and certain two-phase cooling devices in high-power, space-constrained environments. This review identifies critical research gaps and outlines opportunities for next-generation thermal management in areas such as EV batteries, 5G/6G electronics, micro-reactors, and space systems. The article consolidates fragmented existing knowledge to support future design and deployment of MHP-based technologies.
The improvement of solar air heater (SAH) performance has gained greater importance with the increasing demand for clean thermal energy. This review discusses the main techniques for increasing their efficiency, including jet impingement for targeted heat transfer, adding ribs for creating local turbulence, and nanocoating for improved thermal and optical properties. Ribs significantly increase the heat transfer by 2-6 times with thermo-hydraulic performance parameter (THPP) values rising to 3.4; however, at the expense of increased friction and pumping power. Jet impingement enhances heat exchange, achieving THPP values of 3.5-4.1, but with considerable pressure losses and auxiliary power requirements. In contrast, the nanocoating increases the thermal efficiency by 8–25% and raises the outlet temperature by 5-20°C with negligible hydraulic penalties. The review covers experimental and computational fluid dynamics (CFD) studies, durability investigations, and optimization strategies. Although these techniques have been established effectively in laboratory-scale tests and confirmed by CFD analyses, their incorporation into commercial SAHs has been low. To date, applications are mostly limited to prototype systems; recent techno-economic analysis indicates payback periods ranging from 0.3 to 6 years and annual CO 2 emission reduction of up to 0.7-44 tons. Future research efforts have been proposed, including the shift to field validation, design of hybrid systems, long-term performance observation, and techno-economic studies, to enable large-scale implementation and make next-generation solar air heaters feasible and cost-effective.
NH 3 /CO 2 cascade systems have become one of the most widely used kinds of industrial and commercial refrigeration systems, in which the middle temperature and condensing temperature are the most important parameters affecting the system performance, and hence chosen as control variables in optimization control. However, the existing optimization control based on data-driven models still encounters the problem of how to obtain complete data covering a large number and wide range of operating conditions as soon and low cost as possible. To solve this problem, a novel energy-saving control method of NH 3 /CO 2 cascade refrigeration systems by data-driven models with varying searching boundaries was proposed innovatively. The potential of the proposed methods was evaluated from the precision and power perspectives. Results showed that the proposed energy-saving control method had the lowest RMSEs of control variables and system power, which indicated its very high optimization precision. Besides, it provided a reduction in energy consumption by 10.62% compared to the conventional constant-variable control approach and by 1.2% compared to the data-driven model with fixed search boundaries, which proved the huge potential of the proposed control method.
Bolted joints are critical for maintaining the sealing and internal contact pressure of automotive proton exchange membrane fuel cell (PEMFC) stacks. However, existing durability models typically assume static mechanical boundary conditions, neglecting the dynamic decay of clamping force induced by road excitation and its cascading impact on electrochemical performance. To bridge this gap, this study proposes a novel comprehensive degradation framework that explicitly couples vehicle-level vibration, fastener loosening dynamics, and functional layer damage mechanisms. Unlike traditional approaches, a system-level PEMFC model is integrated with a mixed fastener loosening model accounting for both frictional rotational slip and accumulated plastic deformation under real-world proving ground road spectra. The time-varying clamping force is mapped to the stress evolution within the stack, driving physics-based damage accumulation in the gas diffusion layer (GDL), membrane (PEM), and catalyst layer (CL). Numerical results reveal a non-linear sensitivity of stack life to preload: insufficient preload triggers rapid rotational loosening. In contrast, excessive preload accelerates thread plasticity, creating a mechanistic trade-off that defines a narrow optimal design window. Crucially, the study quantifies that moderate preload loss can lead to significant power capability reduction before any structural failure occurs. This work provides a pioneering quantitative tool for automotive engineers to optimize stack assembly strategies and predict on-board durability by linking chassis vibration directly to electrochemical aging.
Indonesia’s vast hydropower potential remains significantly untapped, despite the relevance of low-head water resources for decentralised rural electrification. This study investigates the effect of the blade thickness ratio (L/H) of a centre-convex upper blade profile on the hydraulic performance of a pico-hydro crossflow turbine. The novelty of this work lies in isolating L/H as a manufacturable blade-profile variable and linking its influence to velocity-triangle design, transient CFD results, benchmark comparison, and flow-field mechanisms. The numerical model was assessed through mesh independence, time-step independence, analytical benchmark comparison, and comparison with relevant literature. The reported efficiency is therefore interpreted as runner-domain hydraulic efficiency rather than complete electro-mechanical system efficiency. The optimum L/H ratio of 0.5 achieved 81.80% hydraulic efficiency and 210.07 W mechanical power at 600 rpm under a 3 m head. This performance is associated with a favourable pressure difference, smoother streamlines, and delayed separation, whereas the thickest blade (L/H of 1.5) produced stronger blockage, recirculation, and efficiency reduction to 56.31%. The results provide design guidance for locally manufacturable pico-hydro crossflow turbines and establish a basis for future prototype validation and field installation in remote Indonesian communities.
A combined cycle integrating a gas turbine (GT) with supercritical carbon dioxide (sCO 2 ) and organic Rankine cycles (ORC) can recover waste heat from the GT’s flue gas. To evaluate the exergoeconomic performance of this GT/sCO 2 /ORC system, both conventional and advanced exergoeconomic analyses were performed. The results indicate that 27.12% of the combined cycle’s exergy destruction costs(EDC) are avoidable, with endogenous avoidable and exogenous avoidable costs accounting for 13.96% and 13.16%, respectively. Within the individual cycles, the combustion chamber(CC), high-temperature turbine(HT), and ORC turbine exhibit the highest endogenous exergy destruction(ED) costs for the GT, sCO2, and ORC cycles, respectively. A comparative assessment of the two exergoeconomic approaches revealed that the conventional method overestimates the limited optimization potential of the combustion chamber. Prioritizing components based on the advanced exergoeconomic factor suggests the following optimization order for the combined cycle: air compressor(AC), low-temperature turbine(LT), high-temperature turbine, ORC turbine, and then pumps. Therefore, it is recommended that these rotating components be optimized to improve the system.
This study presents a comprehensive theoretical and experimental evaluation of Photovoltaic–Thermal (PVT.) systems, focusing on the performance trade-offs between water and high-viscosity heat transfer fluids. Two identical custom-designed PVT. collectors were constructed to compare the thermodynamic behavior of water (SET-1) and Mobiltherm 605 thermal oil (SET-2) against a conventional uncooled PV panel. A steady-state mathematical model was developed to predict system performance, and its accuracy was rigorously validated against experimental data, achieving Root Mean Square Error (RMSE) values of 0.93°C for water and 2.27°C for thermal oil. Experimental results obtained under clear-sky conditions revealed that the conventional PV panel exhibited approximately 6% higher electrical efficiency compared to the PVT. systems. This disparity is attributed to optical losses caused by the additional glazing layer and heat accumulation due to rear thermal insulation in the PVT. design. However, among the PVT. configurations, the water-based system demonstrated superior performance. At a mass flow rate of 0.007 kg/s, the water-based system achieved an electrical efficiency 4% higher and a thermal efficiency nearly double that of the oil-based system approximately 8.6%. This performance gap is governed by the lower thermal conductivity and higher viscosity of the thermal oil, which suppresses turbulent flow and heat transfer coefficients at low velocities. Conversely, the oil-based system provided higher outlet temperatures, validating its potential for specific high-temperature applications where freeze protection is critical. The study concludes that while water is thermodynamically superior for maximizing instantaneous efficiency, thermal oils offer operational stability for harsh climates, provided that flow rates are optimized to compensate for their viscous nature.
Methods for increasing the operating range in compressors are classified into two categories: active and passive. Among the passive methods, one can mention casing treatment, recirculation, tip squealer, and so on. This study reports on the effect of hole blade treatment on the stability and endwall flow field of a high-speed compressor rotor. The hole blade treatment method can achieve a similar operating range compared to casing treatment, while exhibiting lower total pressure ratio and efficiency loss. Furthermore, the hole-blade treatment can achieve a comparable operating range to recirculation methods by sucking passage shock downstream and reducing blade loading. However, the selection of the hole design parameters is of great importance, as an incorrect choice will not only fail to expand the operating range, but may even lead to its reduction. The parameters investigated include hole angle, location, and its chordwise diameter. Simulation results indicate that the location, angle and diameter of the holes play a crucial role in rotor operating range.
Buildings account for a substantial share of global energy consumption, yet significant uncertainty persists in predicting energy savings following retrofit interventions, contributing to the widely reported energy performance gap. Existing prediction approaches primarily rely on physics-based simulation or short-term operational forecasting, with limited research addressing cross-building prediction of post-retrofit energy performance. This study develops a data-driven cross-building machine learning framework to forecast post-retrofit heating, ventilation, and air conditioning (HVAC) and lighting energy consumption using pre-retrofit building characteristics and operational variables. A Random Forest regression model was trained on a large-scale dataset comprising energy and operational records from 9450 commercial buildings, incorporating building characteristics, occupancy patterns, system specifications, and climatic variables. The model was validated using two independent commercial office buildings excluded from training to evaluate predictive generalization under realistic deployment conditions. Model performance was assessed using mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of variation of RMSE (CVRMSE), and coefficient of determination. The proposed framework achieved strong predictive accuracy, with MAPE values below 7% and CVRMSE below 10% across validation cases, outperforming commonly reported benchmarks in building energy forecasting. Results demonstrate that cross-building learning captures transferable relationships between pre-retrofit conditions and post-retrofit energy performance without requiring building-specific calibration. Feature importance analysis identified baseline HVAC consumption, occupancy-related variables, building size, and climatic factors as dominant drivers of post-retrofit energy demand. This study contributes a scalable and reliable predictive approach for retrofit performance forecasting, reducing uncertainty in energy savings estimation and supporting evidence-based investment and measurement and verification processes. The findings highlight the potential of data-driven cross-building models to enhance the credibility and effectiveness of large-scale building energy retrofit programs.
This study presents the modeling, control, and performance evaluation of a newly developed single-mode power split drive (PSD) for hybrid electric vehicles (HEVs). The model integrates key components including an internal combustion engine (ICE), motor/generator units, battery, DC-DC converter, and planetary gear-based PSD using MATLAB/Simulink. An intelligent stateflow-based control logic governs mode selection across start, cruise, acceleration, and braking phases. The system is evaluated under an urban driving cycle to validate speed tracking, energy flow, and charge management. Simulation results show accurate tracking of the drive cycle profile, with effective transitions between electric, hybrid, and regenerative braking modes. State of Charge (SOC) is effectively maintained between 96.5% and 100%, validating the robustness of the energy management strategy. The engine, motor, and generator exhibit coordinated ON/OFF behavior based on real-time torque and SOC demands. The PSD enables dynamic torque distribution, ensuring fuel efficiency and reduced emissions. This research demonstrates the feasibility of the proposed power split strategy in practical HEV applications and provides a foundation for future hardware implementation and optimization.
The unconventional V-riblets structure originated from the shark skin has excellent performance in drag-reduction, and is applied to the suction surface of the airfoil. The blade trailing-edge thickening delays the flow separation, increases the pressure difference, and reduces the roughness sensitivity, but it also causes an increase in drag. To achieve both the optimal drag-reduction and the optimal lift enhancement of H-type vertical axis wind turbines (VAWTs), a new optimization method is developed for the design of the blunt trailing-edge wind wheel with a bionic V-riblets structure. The blunt trailing-edge airfoil is obtained using the coordinate rotation-scaling method, and then its parametric representation is realized by the mean camber and thickness functions. The V-riblets structure is arranged from the flow separation point of the airfoil, and its expression is constructed through the vector and coordinate transformation. The optimizer, based on the particle swarm optimization (PSO) algorithm integrated with the computational fluid dynamics (CFD) method, seeks the solutions maximizing the wind energy utilization, where the airfoil shape control factors, chord length, blunt trailing-edge thickness, V-riblets structure dimensions, wind wheel radius, and blade length are the design variables. The power and flow characteristics of un-optimized and optimized wind wheels are analyzed to further understand the improvement effect of the simultaneous optimization of V-riblets structure and blunt trailing-edge wind wheel. The results show that after the optimization, the maximum instantaneous torque coefficient of every blade increases by 32.8%, the torque coefficient of the wind wheel improves within the azimuth angle range of 62.5%, and the instantaneous torque of three blades and wind wheel improves significantly. The pressure difference of upper and lower surfaces and the wake region of every blade respectively increases and decreases in most upwind regions and partial downwind regions. The shedding vortex strength of the wind wheel obviously reduces during the operating cycle.
