Search Results (51)

Low regression rate is the most critical issue for the development and application of hybrid rocket motors (HRMs). Paraffin-based fuels are potential candidates for HRMs due to their high regression rates but adding polymers to improve strength results in insufficient regression rates for HRMs applications. In this work, Computational Fluid Dynamics (CFD) modeling and analysis were used to investigate the mixing and combustion of gaseous fuels and oxidizers in HRMs for various fuel grains and injector combinations. In addition, the regression rate characteristics and combustion efficiency were evaluated using a ground test. The results showed that the swirling flow with both high mixing intensity and high velocity could be formed by using the swirl injector. The highest mixing degree attained for the star-swirl grain and swirl injector was 86%. The reported combustion efficiency calculated by the CFD model attained a maximum of 93% at the nozzle throat. In addition, a spatially averaged regression rate of 1.40 mm·s⁻¹ was achieved for the star-swirl grain and swirl injector combination when the mass flux of N₂O was 89.94 kg·m⁻²·s⁻¹. This is around 191% higher than the case of non-swirling flow. However, there were obvious local regression rate differences between the root of the star and the slot. The regression rate increase was accompanied by a decrease in the combustion efficiency for the strong swirling flow condition due to the remarkable higher mass flow rate of gasified fuels. It was shown that the nano-sized aluminum was unfavorable for the combustion efficiency, especially under extreme fuel-rich conditions. Full article

The production of very-low-sulfur residual fuel oil is a great challenge for modern petroleum refining because of the instability issues caused by blending incompatible relatively high-sulfur residual oils and ultra-low-sulfur light distillates. Another obstacle in the production of very-low-sulfur residual fuel oil using hydroprocessing technology is the contradiction of hydrodesulfurization with hydrodemetallization, as well as the hydrodeasphaltization functions of the catalytic system used. Therefore, the production of very-low-sulfur residual fuel oil by employing hydroprocessing could be achieved by finding an appropriate residual oil to be hydroprocessed and optimal operating conditions and by controlling catalyst system condition management. In the current study, data on the characteristics of 120 samples of heavy fuel oils produced regularly over a period of 10 years from a high-complexity refinery utilizing H–oil vacuum residue hydrocrackers in its processing scheme, the crude oils refined during their production, the recipes of the heavy fuel oils, and the level of H–oil vacuum residue conversion have been analyzed by using intercriteria and regression analyses. Artificial neural network models were developed to predict the characteristics of hydrocracked vacuum residues, the main component for the production of heavy fuel oil. It was found that stable very-low-sulfur residual fuel oil can be manufactured from crude oils whose sulfur content is no higher than 0.9 wt.% by using ebullated bed hydrocracking technology. The diluents used to reduce residue viscosity were highly aromatic FCC gas oils, and the hydrodemetallization rate was higher than 93%. Full article

Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest fires in the Ghalahmah Mountains, Saudi Arabia, using remote sensing data and fire impact models to assess fire severity, environmental drivers, and post-fire vegetation recovery. The research integrates Landsat 8, Sentinel-2, and DEM data to analyze the spatial extent and severity of a 2020 fire event using the Relativized Burn Ratio (RBR). Results reveal that high-severity burns covered 49.9% of the affected area, with pre-fire vegetation density (NDVI) and moisture (NDWI) identified as key drivers of fire severity through correlation analysis and Random Forest regression. Post-fire vegetation recovery, assessed using NDVI trends from 2021 to 2024, demonstrated varying recovery rates across vegetation types. Medium NDVI areas (0.2–0.3) recovered fastest, with 134.46 hectares exceeding pre-fire conditions by 2024, while high NDVI areas (>0.3) exhibited slower recovery, with 26.55 hectares still recovering. These findings underscore the resilience of grasslands and shrubs compared to dense woody vegetation, which remains vulnerable to high-severity fires. The study advances fire ecology research by combining multi-source remote sensing data and machine learning techniques to provide a comprehensive understanding of fire impacts and recovery processes in semi-arid mountainous regions. The results suggest valuable insights for sustainable land management and conservation, emphasizing the need for targeted fuel management and protection of ecologically sensitive areas. This research contributes to the broader understanding of fire ecology and supports efforts to post-fire management. Full article

Ebullated bed vacuum residue hydrocracking is a well-established technology providing a high conversion level of low-value residue fractions in high-value light fuels. The main challenge in this technology when processing vacuum residues derived from different crude oils is the sediment formation rate that leads to equipment fouling and cycle length shortening. With the severity enhancement, the asphaltenes become more aromatic and less soluble which leads to sediment formation when the difference between solubility parameters of asphaltenes and maltenes goes beyond a threshold value. Although theoretical models have been developed to predict asphaltene precipitation, the great diversity of oils makes it impossible to embrace the full complexity of oil chemistry by any theoretical model making it impractical for using it in all applications. The evaluation of process data of a commercial ebullated bed vacuum residue hydrocracker, properties of different feeds, and product streams by intercriteria and regression analyses enabled us to decipher the reason for hydrocracked oil sediment content rising from 0.06 to 1.15 wt.%. The ICrA identified the presence of statistically meaningful relations between the single variables, while the regression analysis revealed the combination of variables having a statistically meaningful effect on sediment formation rate. In this study, vacuum residues derived from 16 crude oils have been hydrocracked as blends, which also contain fluid catalytic cracking heavy cycle oil and slurry oil (SLO), in a commercial H-Oil plant. It was found that the hydrocracked oil sediment content decreased exponentially with fluid catalytic cracking slurry oil augmentation. It was also established that it increased with the magnification of resin and asphaltene and the reduction in sulfur contents in the H-Oil feed. Full article

As emission regulations become increasingly stringent, diesel/natural gas dual-fuel engines are regarded as a promising solution and have attracted extensive research attention. However, their complex combustion processes pose significant challenges to traditional combustion modeling approaches. Data-driven modeling methods offer an effective way to capture the complexity of combustion processes, but their performance is critically constrained by the quantity and quality of the test data. To address these limitations, this study proposes a combustion prediction model framework for dual-fuel engines based on neural networks and data augmentation, aiming to achieve high-quality and fast predictions of the heat release rate curve. First, a hybrid regression data augmentation architecture based on an improved Generative Adversarial Network (GAN) is introduced to enable high-quality dataset augmentation. Subsequently, a Bayesian Neural Network (BNN) is employed to construct a Wiebe parameter prediction model for dual-fuel engines with an accelerated and optimized training model. Meanwhile, an adaptive weight allocation method is proposed based on the model’s precision performance, achieving balanced accuracy distribution across multiple output dimensions and further enhancing the model’s generalization ability. Overall, the proposed modeling approach introduces tradeoff optimizations in both data and model dimensions, enhancing the training and learning efficiency, which offers a valuable direction for data-driven prediction models with practical significance. Full article

Given the intricate combustion process and the multitude of control parameters inherent to the high-pressure direct injection (HPDI) diesel/natural gas dual-fuel engine, achieving precise combustion control represents a significant challenge. It is imperative to develop a high-precision engine model and integrate it with advanced control algorithms to achieve an optimal combustion strategy. In this study, a system-level engine plant model with high accuracy and real-time performance was developed using a modular modeling method through the calibration of experimental data and the simplification of model calculations. In this model, the relative error of the model simulation is controlled to be less than 5%, and the real-time factor (RTF) is less than 1. The multi-stage combustion process was parameterized by performing piecewise linear fitting of the heat release rate curve, and the relationship between injection parameters and combustion parameters was established using multiple regression analysis. On this basis, a model predictive control (MPC) algorithm was designed and verified in the constructed model-in-the-loop (MiL) platform. The results demonstrate that the designed MPC algorithm can accurately track the combustion phasing CA50 and the indicated mean effective pressure (IMEP) targets with a maximum error of 0.0624° and 0.046% within 6 and 8 cycles while ensuring the stability of the control process. The MiL platform not only meets the current combustion control requirements but also provides a general basis for the development of subsequent engine multi-control strategies and cooperative control optimization. Full article

With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis for the optimal design and operation of engines to promote the sustainable development of the transportation industry. The performance of an engine is essentially determined by the combustion process, which in turn depends on the fuel characteristics and the work cycle mode suitability of the technical architecture of the engine itself (oil-engine synergy). Currently, there is a lack of theoretical support and means of reference for the sensitivity analysis of the core parameters of oil–engine synergy. Recognizing the problems of unclear methods of defining sensitivity parameters, unclear influence mechanisms, and imperfect model construction, this paper proposes an evaluation method system composed of oil–engine synergistic sensitivity factor determination and quantitative analysis of contribution. The system contains characteristic data acquisition, model construction and research, and sensitivity analysis and application. In this paper, a hierarchical SVM regression model is constructed, with fuel physicochemical characteristics and engine control parameters as input variables, combustion process parameters as an intermediate layer, and diesel engine performance as output parameters. After substituting the characteristic data into the model, the following results were obtained, R² > 0.9, MSE < 0.014, MAPE < 3.5%, indicating the model has high accuracy. On this basis, a sensitivity analysis was performed using the Sobol sensitivity analysis algorithm. It was concluded that the load parameters had the highest influence on the ID (ignition delay time), combustion duration (CD), and combustion temperature parameters of the combustion elements, reaching 0.24 and above. The influence weight of the main spray strategy was greater than that of the pre-injection strategy. For the sensitivity analysis of the premix ratio, the injection timing, EGR (exhaust gas recirculation) rate, and load have significant influence weights on the premix ratio, while the influence weights of the other parameters are not more than 0.10. In addition, the combustion temperature among the combustion elements has the highest influence weights on the NOx, PM (particulate matter) concentration, and mass, as well as on the BTE (brake thermal efficiency) and BSFC (brake specific fuel consumption). The ID has the highest influence weight on HC and CO at 0.35. Analysis of the influence weights of the index parameters shows that the influence weights of the fuel physicochemical parameters are much lower than those of the engine control parameters, and the influence weights of the fuel CN (cetane number) are about 5% greater than those of the volatility, which is about 3%. From the analysis of the proportion of index parameters, the engine control parameter influence weights are in the following order: load > EGR > injection timing > injection pressure > pre-injection timing> pre-injection ratio. Full article

To achieve low-cost and on-demand launches of microsatellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar–turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of a low fuel regression rate; i.e., it achieved a higher fuel gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up, and its total impulse was increased to over 50 kNs; it reached an altitude of 15 km. In addition to the engine, other components were also improved, such as through the incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device, and a data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. This presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments. Full article

Traditional computational methods for pressurized water reactors are unable to handle dispersed fuel particles as the double heterogeneity and the direct volumetric homogenization can result in significant errors. In contrast, reactivity-equivalent physical transformation techniques offer high precision for addressing the double heterogeneity introduced by dispersed fuel particles. This approach converts the double heterogeneity problem into a single heterogeneity problem, which is then subsequently investigated by using the conventional pressurized water reactor computational procedure. However, it is currently empirical and takes a lot of time to obtain the right k_∞. In this paper, we train the RPT model by using the existing dataset of plate-dispersed fuel and rod-dispersed fuel by a machine learning method based on a linear regression model, and we then use the new data to make predictions and derive the corresponding similarity ratios. The burnup verification, density verification, fission rate verification, and neutron energy spectrum analysis are calculated through the OpenMC program. For plate-type fuel elements, the method maintains an accuracy within 200 pcm during depletion, with deviations in the ²³⁵U density and ²³⁵U fission rate within 0.1% and neutron energy spectrum errors within 6%. For rod-type fuel elements, the method maintains an accuracy within 100 pcm during depletion, with deviations in ²³⁵U and ²³⁹Pu density within 1.5% and neutron energy spectrum errors within 1%. The numerical validation indicates that the reactivity-equivalent physical transformation method based on the linear regression model not only greatly improves the computational efficiency, but also ensures a very high accuracy to deal with double heterogeneity in nuclear reactors. Full article

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations. Full article

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) and false-negative (FN) rates. This research proposes a novel machine learning (ML)-based approach for robust XSS attack detection. We evaluate various models including Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVMs), Decision Trees (DTs), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning. The models are trained on a real-world dataset categorized into benign and malicious traffic, incorporating feature selection methods like Information Gain (IG) and Analysis of Variance (ANOVA) for optimal performance. Our findings reveal exceptional accuracy, with the RF model achieving 99.78% and ensemble models exceeding 99.64%. These results surpass existing methods, demonstrating the effectiveness of the proposed approach in securing web applications while minimizing FPs and FNs. This research offers a significant contribution to the field of web application security by providing a highly accurate and robust ML-based solution for XSS attack detection. Full article

Hybrid rockets have very interesting characteristics like simplicity, reliability, safety, thrust modulation, environmental friendliness and lower costs, which make them very attractive for several applications like sounding rockets, small launch vehicles, upper stages, hypersonic test-beds and planetary landers. In recent years, advancements have been made to increase hybrid motor performance, and two of the most promising solutions are vortex injection and paraffin-based fuels. Moreover, both technologies can be also used to tailor the fuel regression rate, in the first case varying the swirl intensity, and in the second case with the amount and type of additives. In this way, it is possible not only to design high-performing hybrid motors, but also to adjust their grain and chamber geometries to different mission requirements, particularly regarding thrust and burning time. In this paper, the knowledge about these two technical solutions and their coupling is extended. Three sets of experimental campaigns were performed in the frame of the Italian Space Agency-sponsored PHAEDRA program. The first one investigated a reference paraffin fuel with axial and standard vortex injection. The second campaign tested vortex injection with low values of swirl numbers down to 0.5 with a conventional plastic fuel, namely polyethylene. Finally, the last campaign tested another, lower regressing, paraffin-based fuel with the same low swirl numbers as the second campaign. Full article

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH₄ increased by 3.7%, on average. Full article

This study investigated combustion characteristics of composite fuel grains designed based on a modular fuel unit strategy. The modular fuel unit comprised a periodical helical structure with nine acrylonitrile–butadiene–styrene helical blades. A paraffin-based fuel was embedded between adjacent blades. Two modifications of the helical structure framework were researched. One mirrored the helical blades, and the other periodically extended the helical blades by perforation. A laboratory-scale hybrid rocket engine was used to investigate combustion characteristics of the fuel grains at an oxygen mass flux of 2.1–6.0 g/(s·cm²). Compared with the composite fuel grain with periodically extended helical blades, the modified composite fuel grains exhibited higher regression rates and a faster rise of regression rates as the oxygen mass flux increased. At an oxygen mass flux of 6.0 g/(s·cm²), the regression rate of the composite fuel grains with perforation and mirrored helical blades increased by 8.0% and 14.1%, respectively. The oxygen-to-fuel distribution of the composite fuel grain with mirrored helical blades was more concentrated, and its combustion efficiency was stable. Flame structure characteristics in the combustion chamber were visualized using a radiation imaging technique. A rapid increase in flame thickness of the composite fuel grains based on the modular unit was observed, which was consistent with their high regression rates. A simplified numerical simulation was carried out to elucidate the mechanism of the modified modular units on performance enhancement of the composite hybrid rocket grains. Full article

This study compares how crude oil and ethanol price changes are passed through to the wholesale prices of a conventional fuel (E10), which contains 10% ethanol, and a high-blend ethanol fuel (E85), which contains 51% to 83% ethanol. Daily observations from October 2017 to June 2019 were obtained from a large market in the United States that provided wholesale fuel prices and ethanol prices. The Error Correction Model (ECM) was applied to each fuel specification using Seemingly Unrelated Regressions (SURs) in order to improve the efficiency of the estimates. Comparable to prior research, the long-run pass-through coefficient for E10 with respect to crude oil was 1.13. In contrast, the E85 long-run pass-through coefficient with respect to crude oil was 0.74. Estimates for the short-run analysis indicated asymmetry in the transmission of crude oil price changes to E10, with crude price increases passing through at greater rates compared to crude price decreases. Symmetry was found in the transmission of ethanol price changes to E85, indicating the same response to rising and falling ethanol costs. Despite the differences in ethanol requirements, the relative prices of crude oil and ethanol are still important for both fuels. Full article