Search Results (946)

To address the real-time performance issue of the zero-dimensional nozzle model for gas turbine engines, a non-iterative computational method is proposed that determines the flow regime (subcritical vs. choked) via characteristic Mach number and characteristic flow factor. This method eliminates iterative solution procedures, thereby reducing computational time, and solves the problem of discontinuous throat mass flow rate calculation at the transition flow regime from subcritical to choked in traditional nozzle models. The method is applied to improve a component-level turbofan engine model and is validated through numerical simulation. Simulation results indicate that, compared with traditional nozzle models requiring two and eight iterations, the non-iterative nozzle model reduces computation time by $69.7\%$ and $85.71\%$, respectively. The turbofan engine model incorporating the non-iterative nozzle model achieves a 24.58% reduction in maximum per-step computation time and a 13.7% reduction in average per-step computation time compared with the traditional model, while maintaining comparable simulation accuracy. The proposed method substantially enhances the real-time simulation performance of the component-level turbofan engine model, and can be readily extended to other component-level models—whether based on iterative-solution schemes or on volume-based modeling approaches. Full article

One of the most critical challenges in gas turbine design is preventing the ingestion of hot mainstream gases into the disk space between the stator and rotor disks. Rim seals and superposed sealant flows are commonly used to mitigate the risk of component overheating. However, leakage paths inevitably form between the mating interfaces of adjacent components due to the complex architecture of the engine. Therefore, the interaction between the different flows present within the disk space complicates the accurate determination of the optimal sealing flow quantity. For this reason, this study experimentally investigates fluid dynamics inside a stator–rotor cavity, with a particular focus on leakage flows. In particular, this work examines the impact of multiple parameters, including injection radius position, number of leakage holes, and injection angle, on the sealing effectiveness values measured on the stator side of the cavity through CO₂ gas sampling measurements. By comparing the effectiveness values with the swirl measurements derived from static and total pressure readings, the development of flow structures and the impact of leakage injection on sealing performance were finally evaluated. The results indicate that leakage injection has a minimal effect on the sealing effectiveness above the injection point, but significantly improves the performance at a lower radius. Moreover, it was observed that for a given mass flow rate, using a lower number of holes results in worse sealing performance due to a higher jet momentum, which causes the leakage flow to penetrate through the cavity toward the rotor side. In the end, employing two distinct injection angles—both aligned with the rotor’s direction of rotation—showed no substantial impact on sealing effectiveness. Full article

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Full article

Supercritical carbon dioxide (SC-CO₂) power cycles offer a promising solution for offshore platforms’ gas turbine waste heat recovery due to their compact design and high thermal efficiency. This study proposes a novel parallel-preheating recuperated Brayton cycle (PBC) using SC-CO₂ for waste heat recovery on offshore gas turbines. An integrated energy, exergy, and economic (3E) model was developed and showed good predictive accuracy (deviations < 3%). The comparative analysis indicates that the PBC significantly outperforms the simple recuperated Brayton cycle (SBC). Under 100% load conditions, the PBC achieves a net power output of 4.55 MW, while the SBC reaches 3.28 MW, representing a power output increase of approximately 27.9%. In terms of thermal efficiency, the PBC reaches 36.7%, compared to 21.5% for the SBC, marking an improvement of about 41.4%. Additionally, the electricity generation cost of the PBC is 0.391 CNY/kWh, whereas that of the SBC is 0.43 CNY/kWh, corresponding to a cost reduction of approximately 21.23%. Even at 30% gas turbine load, the PBC maintains high thermoelectric and exergy efficiencies of 30.54% and 35.43%, respectively, despite a 50.8% reduction in net power from full load. The results demonstrate that the integrated preheater effectively recovers residual flue gas heat, enhancing overall performance. To meet the spatial constraints of offshore platforms, we maintained a pinch-point temperature difference of approximately 20 K in both the preheater and heater by adjusting the flow split ratio. This approach ensures a compact system layout while balancing cycle thermal efficiency with economic viability. This study offers valuable insights into the PBC’s variable-load performance and provides theoretical guidance for its practical optimization in engineering applications. Full article

This study addresses the thermal management challenges of marine gas turbine enclosures by proposing an innovative optimization of the air intake design, enhancing thermal management capabilities without mechanical restructuring. Through Computational Fluid Dynamics (CFD), the research systematically optimizes key parameters including cooling air inlet pressure, positioning, and enclosure inlet diameter. The results demonstrate that elevating the cooling air inlet pressure to 300 Pa enhanced the entrainment ratio (η) by 9.55% and increased the pressure loss coefficient (PLC) by 2.06% compared to the baseline case (P_in = 0 Pa). An enclosure inlet diameter of 1100 mm optimizes entrainment efficiency (η = 0.331) and minimizes internal temperatures. The multi-objective optimization identifies the globally optimal configuration (D = 800 mm, P_in = 300 Pa, L = 1.6 m), which improves the entrainment ratio by 31.7% (η = 0.399) and reduces the average temperature at key monitoring points (T₁–T₅) by up to 14 K compared to the baseline, albeit with a marginal increase in PLC. This optimal configuration ensures that all local temperatures remain within the operational limit of 355 K. This research provides a theoretical foundation for enhancing marine power system performance and offers evidence-based guidance for engineering applications. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

This paper introduces a novel approach for designing a Fractional Order Proportional-Integral-Derivative (FOPID) controller for the Hydraulic Turbine Regulating System (HTRS), aiming to overcome the challenge of tuning its five complex parameters ($K_p,K_i,K_d$, λ and μ). The design is formulated as a multi-objective optimization problem, minimized using the Multi-Objective Imperialist Competitive Algorithm (MOICA). The goal is to minimize two key transient performance metrics: the Integral of Squared Error (ISE) and the Integral of the Time Multiplied Squared Error (ITSE). MOICA efficiently generates a Pareto-front of non-dominated solutions, providing control system designers with diverse trade-off options. The resulting optimal FOPID controller demonstrated superior robustness when evaluated against simulated variations in key HTRS parameters (mg, eg and Tw). Comparative simulations against an optimally tuned integer-order PID and established literature methods (FOPID-GA, FOPID-MOPSO and FOPID-MOHHO) confirm the enhanced dynamic response and stable operation of the MOICA-based FOPID. The MOICA-tuned FOPID demonstrated superior performance for Setpoint Tracking, achieving up to a 26% faster settling speed (ITSE) and an 8% higher accuracy (ISE). Furthermore, for Disturbance Rejection, it showed enhanced robustness, leading to up to a 23% quicker recovery speed (ITSE) and an 18.9% greater error suppression (ISE). Full article

The frictional resistance of impeller machinery blades such as aircraft engines, gas turbines, and wind turbines has a decisive impact on their efficiency and energy consumption. Inspired by the micro-tooth structure on the surface of shark skin, microstructural drag reduction technology has become a cutting-edge research direction for improving aerodynamic performance and a continuous focus of researchers over the past 20 years. However, the significant difficulty in fabricating microstructures on three-dimensional curved surfaces has led to the limited widespread application of this technology in engineering. Addressing the issue of drag reduction and efficiency improvement for small axial flow fans (local Reynolds number range: (36,327–40,330), this paper employs Design of Experiments (DOE) combined with high-precision numerical simulation to clarify the drag reduction law of bionic microgroove surfaces and determine the dimensions of bionic microstructures on fan blade surfaces. The steady-state calculation uses the standard k-ω model and simpleFoam solver, while the unsteady Large Eddy Simulation (LES) employs the pimpleFoam solver and WALE subgrid-scale model. The dimensionless height (h⁺) and width (s⁺) of microgrooves are in the range of 8.50–29.75, and the micro-grooved structure achieves effective drag reduction. The microstructured surface is fabricated on the suction surface of the blade via a spray coating process, and the dimensions of the microstructures are determined according to the drag reduction law of grooved flat plates. Aerodynamic performance tests indicate that the shaft power consumed by the bionic fan blades during the tests is significantly reduced. The maximum static pressure efficiency of the bionic fan with micro-dimples is increased by 2.33%, while that of the bionic fan with micro-grooves is increased by 3.46%. The fabrication method of the bionic microstructured surface proposed in this paper is expected to promote the engineering application of bionic drag reduction technology. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

The present work adopts a cooled cooling air (CCA) technology based on the integrated aircraft/engine thermal management concept, by coupling an air-kerosene heat exchanger with a high-temperature combustor. Using the heat exchanger, kerosene is preheated to near-critical and supercritical conditions, and the combustion characteristics of kerosene at ambient, near-critical, and supercritical states were investigated. The combustion performance tests were carried out in a model combustor under varying fuel-to-air ratios (FARs) and different kerosene injection conditions. The experimental results show that when the combustor’s FAR is increased to 0.055, the supercritical kerosene exhibits significant advantages over kerosene of the ambient state. The comparison of the combustion performance parameters shows that the combustor outlet temperature distribution factor (OTDF) and radial temperature distribution factor (RTDF) decrease by 52.26% and 51.07%, respectively; in terms of the pollutant emissions, the CO emission index (EI_CO) and unburned hydrocarbon emission index (EI_UHC) are reduced by 66.63% and 68.33%, respectively, while the NOx emission index (EI_NOx) increases by 76.26%, and the combustion efficiency improves by 2.0%. It is noteworthy that once the kerosene reaches the supercritical state, the threshold for the optimal FAR in the combustor rises to 0.055, which carries the significant engineering value for enhancing an aero-engine combustor’s operability across variable conditions and its low-emission combustion performance. Full article

Integrating electrochemical fuel cells and internal combustion engines can enhance the total efficiency and sustainability of power systems. This study presents a promising solution by integrating a Proton Exchange Membrane Fuel Cell (PEMFC) with a mini gas turbine, forming a hybrid system called the “Oya System.” This approach aims to mitigate the efficiency losses of gas turbines during high ambient temperatures. The hybrid model was designed using Aspen Plus for modelling and the EES simulation program for solving mathematical equations. The primary objective of this research is to enhance the efficiency of gas turbine systems, particularly under elevated ambient temperatures. The results demonstrate a notable increase in efficiency, rising from 37.97% to 43.06% at 10 °C (winter) and from 31.98% to 40.33% at 40 °C (summer). This improvement, ranging from 5.09% in winter to 8.35% in summer, represents a significant achievement aligned with the goals of the Oya System. Furthermore, integrating PEMFC contributes to environmental sustainability by utilising hydrogen, a clean energy source, and reducing greenhouse gas emissions. The system also enhances efficiency through waste heat recovery, further optimising performance and reducing energy losses. This research highlights the critical role of interface engineering in the hybrid system, particularly the interaction between the PEMFC and the gas turbine. Integrating these two systems involves complex interfaces that facilitate the transfer of electrochemistry, energy, and materials, optimising the overall performance. This aligns with the conference session’s focus on green technologies and resource efficiency. The Oya System exemplifies how innovative hybrid systems can enhance performance while promoting environmentally friendly processes. Full article

This study presents the operational assessment of a pilot-scale power-to-gas (PtG) facility located in La Guajira, Colombia, which integrates a 10 kW photovoltaic array and a 5 kW wind turbine to power a system with two anion exchange membrane (AEM) electrolyzer of 4.8 kW in total for green hydrogen production. Unlike most studies that rely on simulations or short-term evaluations, this study analyzes nine months of real operating data to quantify renewable energy availability, system capacity factors, and effective hydrogen output under tropical conditions. The results show that the hybrid system generated 7111 kWh during the monitoring period. The comparison of theoretical models with real-time energy production shows a low correlation between the data. The MBE ranged from 1253 to 2988 for the solar system, from −814 to 1013 for the wind system, and from 338 to 2714 for the hybrid system. The RMSE values obtained for each evaluated month ranged from 3179 to 3811 for the solar system, from 928 to 1910 for the wind system, and from 2310 to 4327 for the hybrid system, suggesting that the theoretical models tend to overestimate the energy production of the hybrid system in general terms. From the renewable energy produced in real conditions, 92 kg of hydrogen was produced at an average rate of 9 kg/month, considering the availability of wind and solar resources. However, approximately 300 kWh/month of renewable electricity remained unused because the removable generation did not meet the operating conditions of the electrolyzers, highlighting the importance of improved energy management and storage strategies. These findings provide a real scenario of power-to-gas system performance under Caribbean climatic conditions in Colombia, demonstrate the challenges of resource intermittency and system underutilization, and underline the importance of design systems that allow these intermittencies to be managed for the more optimal production of hydrogen from renewable sources. The outcomes contribute to the understanding of small-scale PtG systems in developing regions and support decision making for future scaling and replication of hybrid renewable–hydrogen infrastructures. Full article

To achieve low-carbon collaborative operation of active distribution networks (ADNs) and microgrid clusters, this paper proposes a signal-guided collaborative optimization method. Firstly, a spatiotemporal carbon intensity equilibrium model (STCIEM) is constructed, overcoming the limitations of centralized carbon emission flow models in terms of data privacy and equitable distribution, and enabling distributed and precise carbon emission measurement. Secondly, a dual-market mechanism for carbon and electricity is designed to support peer-to-peer (P2P) carbon quota trading between microgrids and ADN-backed clearing, enhancing market liquidity and flexibility. In terms of scheduling strategy optimization, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is incorporated into the carbon-electricity cooperative game framework, enabling differentiated energy scheduling under constraints. Simulation results demonstrate that the proposed method can effectively coordinate the operation of energy storage, gas turbines, and demand response, reduce system carbon intensity, improve market fairness, and enhance overall economic performance and robustness. The study shows that this framework provides theoretical support and practical reference for future distributed energy consumption and carbon neutrality paths. Full article

Global carbon emissions are driving the maritime industry toward cleaner fuels, with LNG already established as a transitional option that reduces SOx, NOx, and particulate emissions relative to conventional marine fuels and in line with decarbonisation strategies. This research aimed to explore the transition of offshore and marine platforms from conventional marine fuels to cleaner alternatives, with liquefied natural gas (LNG) emerging as the principal transitional fuel. Subsequently, floating liquefied natural gas (FLNG) platforms are increasingly being deployed to harness offshore gas resources, yet they face critical challenges related to weight, space, and energy efficiency. The study proposes pathways for transitioning FLNG energy systems from LNG to zero-carbon fuels, such as hydrogen derived directly from LNG resources, to optimise fuel supply under the unique operational constraints of FLNG units. The work unifies the independent domains of pure-fuel and blending-fuel processes for LNG and hydrogen, viewed in the context of thermodynamic processes, to optimise hydrogen–LNG co-firing gas turbine performance and meet the base power line of 50 MW. Furthermore, the research article will contribute to the development of other floating production platforms, such as FPSOs and FSRUs. It will be committed to clean energy policies that mandate support for green alternatives to hydrocarbon fuels. Full article