Search Results (302)

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

The study examines the influence of diverse flue gas compositions on the operational parameters and efficiency of MCFCs (molten carbonate fuel cells) as CO₂ separation devices to provide foundational knowledge on MCFC operation under various industrial conditions. MCFCs inherently rely on the presence of CO₂ at the cathode, where it combines with oxygen to form carbonate ions that migrate through the electrolyte; thus, CO₂ acts as a carrier species rather than a fuel, enabling simultaneous electricity generation and CO₂ separation. The findings indicate that MCFCs are most effective when operated with CO₂-rich flue gases, such as those from coal and lignite-fired power plants with CO₂ contents of roughly 12–15 vol.% and O₂ contents of 2–6 vol.%. In these cases, CO₂ reduction rates of up to 80% can be achieved while maintaining favorable cell voltages. Under such conditions, relevant also for the cement industry (CO₂ between 15 and 35 vol.%), the Nernst voltage can reach about 1.18 V. In contrast, flue gases from gas turbines, which typically contain only 4–6 vol.% CO₂ and 11–13 vol.% O₂, result in lower Nernst voltages (0.6–0.7 V) and a decrease in efficiency. To address this issue, potential modifications to the MCFC electrolyte are suggested to enhance oxygen-ion conductivity and improve performance. By quantifying the operational window and CO₂-reduction potential for different sectors at 650 °C and 1 atm using a reduced-order model, the paper provides a technology assessment that supports sustainable industrial operation and the design of CCS (carbon capture and sequestration) strategies in line with climate goals. Full article

Remote islands face persistent challenges in achieving secure, sustainable and affordable energy supply due to their geographic isolation, fragile ecosystems and dependence on imported fossil fuels. Hybrid renewable energy systems (HRES)—typically combining photovoltaics (PV), wind turbines and battery energy storage systems (BESS)—have emerged as the dominant off-grid solution, demonstrating their potential to reduce fossil fuel dependence and greenhouse gas emissions. Yet, empirical case studies from Zanzibar, Thailand, Malaysia, the Galápagos, the Azores and Greece confirm that current systems remain transitional, relying on oversized storage and fossil backup during low-resource periods. Comparative analysis highlights both technical advances and persistent limitations, including seasonal variability, socio-economic barriers and governance gaps. Future directions for PV—wind-based (non-dispatchable) island microgrids point toward long-term hydrogen storage, artificial intelligence (AI)-driven predictive energy management and sector coupling—alongside participatory planning frameworks that enhance social acceptance and community ownership. By synthesizing technical, economic and social perspectives, this study provides a roadmap for advancing resilient, autonomous and socially embedded hybrid off-grid systems for remote islands. Full article

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (${\mathrm{M}}_{\mathrm{A}\mathrm{P}\mathrm{E}}$) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs. Full article

The study of the dynamics during droplet breakup is fascinating to engineers. Some industrial applications include fire extinguishing by sprinkler systems, painting of various components, washing processes, and fuel spraying in internal combustion engines, which involve the interaction between liquid droplets, gaseous flow field, and walls. In this work, washing operations effectiveness of civil aviation aircraft engines is analyzed. Periodic washing operations are necessary to slow down the effects of particle deposition, e.g., gas turbine fouling, to reduce the specific fuel consumption and the environmental impact of the gas turbine operation. This analysis describes the dynamics in the primary breakup, related to the breakup of droplets due to aerodynamic forces, which occur when the droplets are set in motion in a fluid domain. The secondary breakup is also considered, which more generally refers to the impact of droplets on surfaces. The latter is studied with particular attention to dry surfaces, investigating the limits for different breakup regimes and how these limits change when the impact occurs with surfaces characterized by different wettability. Surfaces with different roughness are also compared. All the tested cases are referred to surfaces at ambient temperature. Dimensionless numbers generalize the analysis to describe the droplet behavior. The analysis is based on several data reported in the open literature, demonstrating how different washing operations involve different droplet breakup regimes, generating a non-trivial data interpretation. Impact dynamics, droplet characteristics, and erosion issues are analyzed, showing differences and similarities between the literature data proposed in the last twenty years. Washing operation and the effects of gas turbine fouling on the aero-engine performance are still under investigation, demonstrating how experiments and numerical simulations are needed to tackle this detrimental issue. Full article

A clearance necessarily exists between the blade tip and the casing in turbines. A leakage flow, formed by the accelerated gas through the tip clearance, is a major cause of turbine stage efficiency loss. Severe heat loads on the blade tip surface also result from a leakage flow, a primary cause of blade damage. Although the understanding of leakage flow mechanisms is mature after years of research, the continuous rise in turbine inlet temperature, pursuing higher engine thrust, requires more effective cooling methods for the blade tip region. This paper presents a review of research on three fundamental tip structures (flat tip, squealer tip, and winglet tip) to explain their design concepts, analyze their respective flow mechanisms as well as heat transfer characteristics, and introduce various modified designs. Various film cooling arrangements applied to these tip structures are examined to identify effective strategies that strengthen the advantages of structural optimization. In view of engineering applications, this paper reviews research on unsteady wake interactions as the aforementioned framework, hoping to provide readers a more comprehensive understanding. Full article

This review surveys recent progress in hybrid artificial intelligence (AI) approaches for gas turbine intelligent digital twins, with an emphasis on models that integrate physics-based simulations and machine learning. The main contribution is the introduction of a structured classification of hybrid AI methods tailored to gas turbine applications, the development of a novel comparative maturity framework, and the proposal of a layered roadmap for integration. The classification organizes hybrid AI approaches into four categories: (1) artificial neural network (ANN)-augmented thermodynamic models, (2) physics-integrated operational architectures, (3) physics-constrained neural networks (PcNNs) with computational fluid dynamics (CFD) surrogates, and (4) generative and model discovery approaches. The maturity framework evaluates these categories across five criteria: data dependency, interpretability, deployment complexity, workflow integration, and real-time capability. Industrial case studies—including General Electric (GE) Vernova’s SmartSignal, Siemens’ Autonomous Turbine Operation and Maintenance (ATOM), and the Electric Power Research Institute (EPRI) turbine digital twin—illustrate applications in real-time diagnostics, predictive maintenance, and performance optimization. Together, the classification and maturity framework provide the means for systematic assessment of hybrid AI methods in gas turbine intelligent digital twins. The review concludes by identifying key challenges and outlining a roadmap for the future development of scalable, interpretable, and operationally robust intelligent digital twins for gas turbines. Full article

Combined Cycle Power Plants (CCPP) suffer from drops in power and efficiency due to summer time ambient conditions. This power reduction is especially important in regions with extreme summer ambient conditions. Given the substantial investment and labor involved in the establishment and operation of these power plants, mitigating power loss using various methods emerges as a promising solution. In this context, the integration of Concentrated Solar Power (CSP) technologies has been proposed in this research not primarily to improve the overall performance efficiency of power plants as other recent studies entail, but to ensure continuous power generation throughout summer days, improving stability. This research aims to address this issue by conducting an extensive study covering the different scenarios in which Concentrated Solar Power (CSP) can be integrated into the power plant. Multiple scenarios for integration were defined including CSP integration in the Heat Recovery Steam Generator, CSP-powered chiller for Gas Turbine Compressor Cooling and Gas Turbine Combustion Chamber Preheating using CSP, and scenarios with inlet air fog cooling and hybrid scenarios were studied. This systematic analysis resulted in the selection of the scenario where the CSP is integrated into the combined cycle power plant in the HRSG section as the best case. The selected scenario was benchmarked against its equivalent model operating in Seville’s ambient conditions. By comparing the final selected model, both Yazd and Seville experience a noticeable boost in power and efficiency while reaching the maximum integration capacity at different reflector lengths (800 m for Seville and 900 m for Yazd). However, both cities reach their minimum fuel consumption at an approximate 300 m total reflector length. Full article

Modern gas turbines for aeroengines operate at ever-increasing inlet temperatures to maximize thermal efficiency, power, output and thrust, subjecting turbine blades to severe thermal and mechanical stresses. To ensure component durability, effective cooling strategies are indispensable, yet they strongly influence the underlying aerothermal behavior, particularly in transonic regimes where shock–boundary layer interactions are critical. In this work, a comprehensive Reynolds-Averaged Navier–Stokes (RANS) investigation is carried out on the LS89 transonic turbine cascade, considering both adiabatic and cooled wall conditions. Three operating cases, spanning progressively higher outlet Mach numbers (0.84, 0.875, and 1.020), are analyzed using multiple turbulence closures. To mitigate the well-known model dependence of RANS predictions, a model-averaging strategy is introduced, providing a more robust prediction framework and reducing the uncertainty associated with single-model results. A systematic mesh convergence study is also performed to ensure grid-independent solutions. The results show that while wall pressure and isentropic Mach number remain largely unaffected by wall cooling, viscous near-wall quantities and wake characteristics exhibit a pronounced sensitivity to the wall-to-recovery temperature ratio. To support further research and model benchmarking, the complete RANS database generated in this work is released as an open-source resource and made publicly. Full article

Pressure Gain Combustion (PGC) is an interesting emerging concept to enhance the performance of gas turbines currently based on the Brayton–Joule cycle. Focusing on a F-class gas turbine for land-based power generation, the current work investigates PGC potential in both simple and combined cycle operations by means of an in-house simulation software. The PGC cycle lay-out specifically includes a booster compressor for delivering cooling air to the blades at the first stage of the gas turbine expander. The effects of different amounts of air from the same booster to the PGC system for cooling requirements are also analyzed. Considering reasonable PGC values based on literature data, the efficiency of the gas turbine simple cycle rises by 2.85–3.40 percentage points in the case of no combustor cooling, or 1.85–2.25 percentage points for the most extensive cooling at the combustor, compared to the reference case. The combined cycle efficiency increases too, despite the almost equal power generation at the bottoming steam cycle. Ultimately, a revised parametric analysis with reduced efficiency at the first stage of the gas turbine expander is carried out as well to account for the losses induced by the PGC on the fluid dynamics of the expansion. In this new scenario, the risk of nullifying the advantages related to PGC is real, because of specific combinations of lower expansion efficiency at the gas turbine expander and extensive cooling at the combustor. Thus, better turbine design and effective thermal management at the combustor are fundamental to achieve the highest efficiency. Full article

The evolution of the generation mix towards deep decarbonization poses pressing questions about the role of hydropower and its possible share in the future mix. Most technical–economic analyses of deeply decarbonized systems either rule out hydropower growth due to lack of additional hydro resources or take it into account in terms of additional reservoir capacity. This paper analyzes a generation mix made of photovoltaic, wind, open-cycle gas turbines, electrochemical storage and hydroelectricity, focusing on the optimal generation mix’s reaction to different methane gas prices, hydroelectricity availabilities, pumped hydro reservoir capacities, and mean filling durations for hydro reservoirs. The key feature of the developed model is the sizing of both optimal peak power and reservoir energy content for hydropower. The results of the study point out two main insights. The first one, rather widely accepted, is that cost-effective decarbonization requires the greatest possible amount of hydro reservoirs. The second one is that, even in the case of totally exploited reservoirs, there is a strong case for increasing hydro peak power. Application of the model to the Italian generation mix (with 9500 MWp and 7250 MWp of non-pumped and pumped hydro fleets, respectively) suggests that it is possible to achieve methane shares of less than 10% if the operating costs of open-cycle gas turbines exceed 160 EUR/MWh and with non-pumped and pumped hydro fleets of at least 9200 MWp and 28,400 MWp, respectively. Full article

The use of additive manufacturing (AM) has seen increased utilization over the last decade, thanks to well-documented advantages such as lower startup costs, reduced wastage, and the ability to rapidly prototype. The poor surface finish of unprocessed AM components is one of the major drawbacks of this technology, with the research literature suggesting a measurable impact on flow characteristics and burner operability. For instance, surface roughness has been shown to potentially increase resistance to boundary layer flashback—an area of high concern, particularly when utilizing fuels with high hydrogen content. A more detailed understanding of the underlying thermophysical mechanisms is, therefore, required. Computational fluid dynamics can help elucidate the impact of these roughness effects by enabling detailed data interrogation in locations not easily accessible experimentally. In this study, roughness effects on a generic gas turbine swirler were numerically modeled using a low-y+ detached eddy simulation (DES) approach. Three DES models were investigated utilizing a smooth reference case and two rough cases, the latter employing a literature-based and novel equivalent sand-grain roughness (k_s) correlation developed for this work. Existing experimental isothermal and CH₄ data were used to validate the numerical simulations. Detailed investigations into the effects of roughness on flow characteristics, such as swirl number and recirculation zone position, were subsequently performed. The results show that literature-based k_s correlations are unsuitable for the current application. The novel correlation yields more promising outcomes, though its effectiveness depends on the chosen turbulence model. Moreover, it was demonstrated that, for identical k_s values, while trends remained consistent, the extent to which they manifested differed under reacting and isothermal conditions. Full article

This study investigates the feasibility of green hydrogen as an alternative to natural gas for power generation. In doing so, it contributes to the broader discourse surrounding hydrogen’s potential role in the transition of the energy sector. Our case study is of Ontario, Canada, where natural gas serves as the sole remaining carbon-emitting energy source for the generation of electricity. Through this, we present a practical reference and methodology that energy planners, policymakers, and researchers can use to analyze fuel consumption patterns and their costs. Our research involves estimating the volume of hydrogen required to support the conversion of natural gas-powered turbines. At present, electrical power in Ontario generated by natural gas may reach 39 TWh annually by 2035. Our findings suggest that the cost of hydrogen to generate that volume of electricity will range between USD 1.8 billion and USD 23.2 billion, contingent upon turbine efficiency and fluctuations in hydrogen prices. Moreover, if hydrogen prices remain elevated (up to USD 8/kg), the annual premium for hydrogen-generated electricity compared to natural gas could reach USD 20.604 billion, a significant deterrence for energy planners in Ontario from adopting hydrogen at scale. Thus, the added costs of hydrogen, along with challenges related to infrastructure requirements, safety, and technological considerations, render a potential transition to hydrogen, and to green hydrogen specifically, a complex undertaking. Ultimately, insights derived here enhance understanding of hydrogen’s potential within the context of power generation and may be applicable to other regions considering similar transitions toward hydrogen-based energy systems. Full article

To effectively address the issues of curtailed wind and photovoltaic (PV) power caused by the high proportion of renewable energy integration and to promote the clean and low-carbon transformation of the energy system, this paper proposes a “chemical–mechanical” dual-pathway synergistic mechanism for the reversible solid oxide cell (RSOC) and flywheel energy storage system (FESS) electricity–hydrogen hybrid system. This mechanism aims to address both short-term and long-term energy storage fluctuations, thereby minimizing economic costs and curtailed wind and PV power. This synergistic mechanism is applied to regulate system operations under varying wind and PV power output and electricity–hydrogen load fluctuations across different seasons, thereby enhancing the power generation system’s ability to integrate wind and PV energy. An economic operation model is then established with the objective of minimizing the economic costs of the electricity–hydrogen hybrid system incorporating RSOC and FESS. Finally, taking a large-scale new energy industrial park in the northwest region as an example, case studies of different schemes were conducted on the MATLAB platform. Simulation results demonstrate that the reversible solid oxide cell (RSOC) system—integrated with a FESS and operating under the dual-path coordination mechanism—achieves a 14.32% reduction in wind and solar curtailment costs and a 1.16% decrease in total system costs. Furthermore, this hybrid system exhibits excellent adaptability to the dynamic fluctuations in electricity–hydrogen energy demand, which is accompanied by a 5.41% reduction in the output of gas turbine units. Notably, it also maintains strong adaptability under extreme weather conditions, with particular effectiveness in scenarios characterized by PV power shortage. Full article

This work focuses on modeling the performance of the natural gas-fired Allam cycle under off-design conditions. Key thermodynamic parameters, such as turbine inlet pressure (TIP), turbine inlet temperature (TIT), and turbine outlet temperature (TOT), were evaluated at part-load and varying environmental conditions. In the former case, different control strategies were implemented in the simulation code (Thermoflex^®) to reduce the power output. In the latter case, the impact of ambient temperature (T_amb) on the minimum cycle temperature (T_min) was evaluated. The ultimate goal is to predict the thermal efficiency (η_th) and its decrease due to partial load operation and warm climate, without thermal recovery from the air separation unit (ASU). With the most efficient partial load strategy, η_th decreased from 50.4% at full load to 40.3% at about 30% load, at nominal T_min. The penalty caused by the increase in T_min due to hot weather, up to T_amb = 30 °C, was significant at loads above 60%, but limited to 0.5 percentage points (pp). Full article