Search Results (6,560)

To improve the aerodynamic design efficiency of nacelle intake systems for wing-mounted civil aero-engines under multiple operating conditions, an integrated multi-objective optimization method was developed to address the limited optimization efficiency and robustness encountered in conventional approaches. The proposed method employed parametric techniques to construct three-dimensional non-axisymmetric nacelle geometries and integrated flow-field simulations with performance evaluation modules, forming a hybrid optimization framework based on a Kriging surrogate model coupled with the NSGA-II genetic algorithm. Two-dimensional numerical analyses were employed to rapidly evaluate inlet profiles and constrain the three-dimensional design space. Following the reduction in the design space, the three-dimensional optimization simultaneously accounted for multiple performance objectives, including nacelle drag and block fuel consumption during cruise conditions, as well as inlet distortion and flow separation under off-design conditions. A set of Pareto-optimal solutions was obtained through surrogate-based prediction and validated using high-fidelity CFD simulations. The results indicate that the optimized nacelle configuration achieves a 0.933% reduction in drag coefficient and a 0.628% decrease in block fuel consumption under cruise conditions. Under crosswind conditions, the inlet total pressure recovery coefficient is increased by 2.76%, accompanied by a pronounced reduction in flow separation, while under maximum-lift coefficient conditions, the total pressure recovery remains above 99%. These results demonstrate that the proposed optimization approach enables coordinated aerodynamic performance improvements across multiple operating conditions while simultaneously enhancing overall aircraft fuel efficiency, providing an effective strategy for advanced nacelle aerodynamic shape design. Full article

A major development direction for next-generation civil aircraft is to significantly reduce fuel consumption through the integration of high-bypass-ratio engines. However, the large diameter of high BPR engines will cause traditional aircraft to face the dilemma of ground clearance. The over-the-wing engine mount configuration avoids ground clearance constraints by installing the engines over the wings, which is conducive to the integration of high BPR engines. However, the sensitivity of the flow on the upper surface of the wing makes this configuration more likely to cause strong interference between the engine and the wing than the traditional configuration. During the design, the important interaction of the wing shapes, the wing static elastic deformation, the engine installation position and the engine inlet and exhaust effect should be fully considered, which brings great challenges to the traditional design method. An automatic multidisciplinary coupled optimization method based on the discrete adjoint approach and gradient-based optimization is proposed for this configuration. A corresponding framework is established based on the open-source multidisciplinary optimization platform OpenMDAO; the CFD solution and the adjoint solution are carried out using the open-source CFD solver DAFoam; the structural finite element solution and the structural adjoint solution are carried out using the open-source FEM solver TACS; and the engine power effect is solved by coupling the intake and exhaust boundary conditions into the CFD solver. This method can comprehensively consider the changes in the wing shapes, the static aeroelastic deformation of the wing, the intake and exhaust effects of the engine, and the positional movement of the engine along the spanwise, chordwise and vertical directions of the wing. The optimization results show that the optimized configuration eliminates the strong shock interaction between the nacelle and the wing, enhances the favorable pressure gradient on the upper surface of the wing, and reduces the drag by 9.51%, thereby demonstrating the effectiveness of the proposed multidisciplinary coupled adjoint optimization method for this configuration. Full article

One of the main goals of heat and electricity producers in Latvia is to reduce the use of fossil fuels and introduce alternative fuel types that could help in reducing carbon dioxide emissions. This work focuses on addressing the set issue for a medium-capacity automated gas boiler plant, which provides heat for a local residential district. The following solutions were selected for boiler plant optimization: an electric boiler, a heat storage system, and solar collectors. Operating mode simulations were conducted for the electric boiler and solar collectors using Excel and Polysun (Standard) software. Simulations were created based on energy resource demand data obtained from a residential district located in Latvia and local energy resource prices/heat energy tariffs for the year 2024. The results from the simulations were used for technical and economic calculations to determine the payback period of the project. The electric boiler, together with the thermal energy storage tank and solar collectors, can produce 5903.04 MWh/year (~70% of local district heat demand) of thermal energy. This reduces the CO₂ emissions of the boiler plant by at least 1186.51 tCO₂ per year, which, at an emission quota price of 63.80 EUR/tCO₂, allows for savings of 75,699.34 EUR per year (12.82 EUR/MWh heat energy). The project’s discounted payback period is 4.12 years, considering the reduction in the cost of the CO₂ emission quota. The results of this study show that the chosen technologies are straightforward solutions that can be used to optimize existing boiler plants with limited space and can provide financial benefits to heat energy producers. Full article

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

Nuclear energy has emerged as a crucial technological solution for ensuring energy security and achieving carbon neutrality goals, given its ultra-high energy density and near-zero carbon emissions against the backdrop of rapid socioeconomic development, increasing energy demands, and accelerated global transition toward low-carbon energy structures. As the core component for energy conversion in nuclear reactors, fuel elements critically determine reactor efficiency and safety performance, with the fission product retention capability of silicon carbide layers in multilayer-coated fuel particles having been thoroughly validated through high-temperature gas-cooled reactor irradiation tests. The precise sphericity control of large-sized UO₂ fuel kernels represents a fundamental requirement for enhancing tristructural isotropic (TRISO) fuel particle performance and advancing Generation IV nuclear power plant development. This study presents a sphericity control strategy based on sol–gel processing that synergistically integrates physicochemical regulation of gelling media with multi-field washing flow field optimization. By implementing silicone oil-mediated interfacial tension gradient control, we effectively suppressed gel sphere destabilization while developing an innovative three-phase sequential washing technique involving kerosene washing, anhydrous ethanol interfacial transition, and ammonia solution replacement, which significantly enhanced mass transfer diffusion in stagnant liquid films and revolutionized fuel microsphere washing technology with improved efficiency and quality. Experimental results demonstrate that this integrated approach increases kernel sphericity qualification to 99.8%, reduces washing solution consumption by 79%, and achieves an average sphericity of 1.03. The research establishes a coupling mechanism between gelling media and multi-field washing processes, elucidating the synergistic effect between interfacial tension regulation and washing optimization, thereby providing both theoretical foundations and engineering application basis for the precision manufacturing of high-performance nuclear fuels. Full article

The increasing generation of organic and liquid wastes calls for sustainable strategies to convert residues into valuable energy resources. This study investigates waste Oxytree biomass (Paulownia Clon In Vitro 112^®) as a sorbent for producing impregnated solid fuels from selected liquid wastes, including used cooking oil, spent mineral oil, and pyrolysis condensate, targeting industrial energy applications. Oxytree biomass was selected due to its high and predictable yield, uniform composition, and favorable physical properties compared to conventional lignocellulosic residues such as pine sawdust. Biomass and liquid wastes were characterized in terms of fuel properties and elemental composition. Several empirical combinations of sorbent and liquid fractions were tested to optimize homogeneity and fuel quality, resulting in a final composition of sorbent:used cooking oil:used machine oil:pyrolytic condensate equal to 3:1:1:3. The temporal stability of this selected fuel was verified over 24 h, 3 days, and 1 week. The resulting fuels exhibited an energy value of approximately 15 MJ/kg, low ash content (<1%), and minimal concentrations of chlorine and sulfur (<0.08%). Overall, the findings demonstrate that Oxytree waste biomass can serve as an effective sorbent for integrating problematic liquid wastes into solid fuels, providing a practical route for waste valorization and supporting circular economy principles, and establishing a foundation for further research on sustainable energy applications of biomass and industrial residues. Full article

Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative. However, effective planning requires accurate prediction of the amount of solar energy that can be produced. Prediction accuracy directly depends on two factors: the model’s hyperparameters and the feature set. In this study, we use boosting models, such as LightGBM, XGBoost, and CatBoost, to forecast solar power production. The prediction horizon is 60 min, which corresponds to short-term forecasting. Model tuning is performed using the NSGA-II multi-objective optimization algorithm. In this study, NSGA-II simultaneously tunes hyperparameters and a feature set of boosting models. We aim to enhance the performance of the NSGA-II algorithm in the early stages using the proposed method to generate the initial population. The initialization is based on an ensemble of filtering methods. The proposed approach promotes faster convergence in the early stages of the algorithm compared to the traditional initialization method. The results of numerical experiments are proven by the Wilcoxon test. Full article

As the key technology of space exploration, space power has been a major area of international research focus. A lot of research work has been carried out around the world for the space nuclear reactor using the heat pipe, liquid metal and gas cooling methods. With the development of molten salt reactor in the Generation IV reactor system, molten salt dissolving fissile material and acting as a coolant at the same time has become a new cooling scheme, which provides new ideas for the design of space nuclear reactors. In this study, a novel reactor, the liquid-solid dual-fuel space nuclear reactor (LSSNR) was preliminarily proposed, combining the molten salt fuel and cross-shaped spiral solid fuel to achieve the design goals of 30-year lifetime and an active core weight of less than 200 kg. Monte Carlo neutron transport code OpenMC based on ENDF/B-VII.1 library was employed for neutronics design in the aspect of fuel type, cladding material, reflector material and the spectral shift absorber. Then, the thickness of the control drum absorber was optimized to meet the requirement of the sufficient shutdown margin, lower solid fuel enrichment, and 30-effective-full power-years (EFPY) operation lifetime. Finally, UC solid fuel with U-235 enrichment of 80.98 wt.% and B₄C thickness of 0.75 cm were adopted in LSSNR, and BeO was adopted as the reflector and the matrix material of the control drum. A spectral shift absorber Gd₂O₃ was used to avoid the subcritical LSSNR returning to criticality in a launch accident. The k_eff with the control drum in the innermost position is 0.954949, and the k_eff reaches 1.00592 after 30 EFPY of operation. The total mass of the active core is 158.11 kg. In addition, the thermal-hydraulic feasibility of LSSNR using cross-shaped spiral fuel was analyzed based on a 4/61 reactor core model. The structure of cross-shaped spiral fuel achieves enhanced heat transfer by generating turbulence, which leads to a uniform temperature distribution of the coolant flow field and reduces local temperature peaks. Based on the LSSNR scheme, some neutronic characteristics were analyzed. Results demonstrate that the LSSNR has strongly negative reactivity coefficients due to the thermal expansion of liquid fuel, and the fission gas-induced pressure meets safety requirements. One hundred years after the end of core life, the total radioactivity of reactor core is reduced by 99% and is 7.1305 Ci. Full article

Against the backdrop of the dual-carbon strategy promoting the green and low-carbon transformation of the shipping industry, pollutant emissions generated during vessel berthing operations have become a critical challenge in port environmental governance. To address the combined effects of the priority berthing policy for new energy vessels and time-of-use electricity pricing, a joint optimization model for berth and shore power allocation is developed with the objectives of minimizing the total economic cost of vessels and the environmental tax cost associated with pollutant emissions. An improved Adaptive Large Neighborhood Search algorithm (ALNS-II) is further designed to solve the model. Numerical experiments based on actual port data verify the effectiveness of the proposed model and the superiority of the algorithm. The results indicate that, under time-of-use electricity pricing, the priority berthing policy for new energy vessels can shorten their waiting time at anchorage and encourage fuel-powered vessels to shift toward electrification. When the peak-to-valley electricity price ratio increases from 4.1:1 to 7.5:1, the environmental tax cost of pollutant emissions decreases slightly, whereas the total economic cost of vessels rises by 4.17%, suggesting that the peak-to-valley electricity price ratio should not be set excessively high. In addition, increasing the proportion of new energy vessels to 70% is more conducive to improving the overall economic and environmental performance of ports. The findings provide a theoretical basis and decision support for the optimal allocation of port resources under the coordination of multiple policies. Full article

The higher heating value (HHV), sometimes referred to as the gross calorific value, is a crucial metric for determining a fuel’s primary energy potential in energy production systems. By combining extreme gradient boosting (XGBoost) with the differential evolution (DE) optimizer, an innovative machine learning-based model was created in this study to forecast the HHV (dependent variable). As input variables, the model included the constituents of the coal’s ultimate analysis: carbon (C), oxygen (O), hydrogen (H), nitrogen (N), and sulfur (S). For comparative purposes, random forest regression (RFR), M5 model tree, multivariate linear regression (MLR), and previously reported empirical correlations were also applied to the experimental dataset. The results showed that the XGBoost strategy produced the most accurate predictions. An initial XGBoost analysis was carried out to identify the relative contribution of the input variables to coal HHV prediction. In particular, for coal HHV estimates reliant on experimental samples, the XGBoost regression produced a correlation coefficient of 0.9858 and a coefficient of determination of 0.9691. The excellent agreement between observed and anticipated values shows that the DE/XGBoost-based approximation performed satisfactorily. Lastly, a synopsis of the investigation’s key conclusions is provided. Full article

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related emissions, making sustainability necessary across all modes of transport. Based on the relevant literature, this study examines the role and potential of railways in decarbonising the EU transport sector. Railway is highly efficient, consuming just 1.9% of transport sector energy while handling 16.9% of freight and 5.1% of passenger transport in the EU, yet is responsible for only 0.4% of total emissions. According to studies, greenhouse gas emissions can be reduced by improving energy efficiency, using low-carbon or renewable energy, and expanding train electrification. The greatest potential for decarbonisation lies in a modal shift to rail. However, this requires significant infrastructure investment: raising line speeds to at least 160 km/h, expanding networks, building terminals, digitalisation, and alignment with TEN-T standards. Although the EU supports the modal shift with funding programmes, the transition is not progressing as expected—the share of road freight transport increased from 74% in 2013 to 78% in 2023. Stronger investment is needed in Member States’ national policies for the development and modernisation of railways. The authors developed a Path Evaluation Matrix (PEM), a quantitative decision framework integrating the fields of energy, transport, politics, and economics. The PEM results indicate that BEMU (battery electric multiple units) is optimal for 68% of secondary lines in south-eastern Europe. Full article

Aviation heavy-fuel piston engines are widely used in UAVs, general aviation, and military platforms due to their fuel efficiency and adaptability. However, emissions of NO_x, PM, and other pollutants pose significant environmental challenges. This paper reviews emission-reduction strategies, including combustion-chamber optimization, fuel-injection control, alternative fuels, and exhaust after-treatment technologies. Research indicates that optimizing combustion-chamber geometry, high-pressure common-rail injection, and turbulence enhancement improve combustion efficiency and reduce emissions. Biofuels, synthetic aviation fuels (SAF), and hydrogen-based fuels demonstrate strong potential for low-carbon emissions, while after-treatment technologies such as SCR, DPF, and EGR effectively mitigate NO_x and PM emissions. Despite technological advancements, challenges remain in balancing combustion efficiency with NO_x control and ensuring compatibility between EGR and combustion stability. Future advancements in intelligent combustion control, novel catalytic materials, low-temperature combustion, and high-efficiency after-treatment systems will drive aviation diesel engines toward lower emissions, higher efficiency, and greater intelligence, contributing to the green and sustainable transformation of aviation propulsion systems. Full article

As a crucial initial step in humanity’s quest to explore deep space, lunar transfer missions have garnered significant attention. The escalating demand for increased payload capacity and mission flexibility have presented challenges in terms of mission fuel costs. In response, the design of low-energy lunar transfer trajectories, rooted in multibody dynamics, has become paramount for deep space exploration trajectory design. This paper summarizes the design methods for transfer trajectories from the Earth to the Moon and even deeper space that consume low energy at the expense of expanded transfer time. The fundamental design methods include the weak stability boundary method, the chaos control method, and the invariant manifold theory, which are primarily determined by dynamical mechanisms. Additionally, the paper discusses the low-thrust technique, formulating trajectory design as an optimization problem to tailor thrust profiles for minimum fuel consumption. Finally, landmark missions are discussed to demonstrate the practical applications and advantages of low-energy trajectories, spanning lunar missions to exploration within deeper space regions. Full article

With the growth of global maritime transportation volume and fuel shortages caused by excessive oil consumption, energy conservation and emission reduction technologies for marine diesel engines have become a core research focus. A three-dimensional (3D) CFD model of a methanol–diesel dual-fuel marine diesel engine was developed in AVL-FIRE and coupled with a CHEMKIN reaction mechanism. The model was validated against experimental data, with errors in cylinder pressure, heat release rate, and major emissions below 5%. Based on the validated model, the effects of the methanol blending ratio (0–30%), injection advance angle, intake temperature, intake pressure, and EGR rate on combustion and emissions were investigated. The results show that increasing the methanol blending ratio reduced cylinder pressure, in-cylinder temperature, and NO and soot emissions, while increasing the peak heat release rate. Advancing injection timing improved combustion and reduced CO and soot emissions but increased NO formation. Higher intake temperature worsened combustion performance and increased NO, CO, and soot emissions. Orthogonal analysis and regression-based optimization identified an optimal condition with a methanol blending ratio of 27%, an EGR of 12.5%, an injection advance angle of 21.2 °CA, an intake temperature of 319.05 K, and an intake pressure of 0.223 MPa. Under this condition, the NOx mass fraction was 1.65 × 10⁻⁵. Full article