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The results of an experimental study of nitrogen oxide (NO) and particulate matter (PM) concentrations in the exhaust gas of a compression-ignition engine used in agricultural tractors and other commercial vehicles are presented. The engine was fueled with second-generation biodiesel obtained from used frying oils (classified as waste) and first-generation biodiesel produced from rapeseed oil as well as, comparatively, diesel fuel. Tests were conducted on a dynamometer bench at a variable load and a variable engine speed. The levels of PM and NO emissions in the exhaust gas were determined. The study showed significant environmental benefits of using first- and second-generation biodiesel to power the engine due to the level of PM emissions. The PM content, when burning ester biofuel compared to diesel fuel, was reduced by 45–70% on average under the speed and load conditions implemented. As for the concentration of nitrogen oxide in the exhaust gas, no clear trend of change was shown for the biodiesel in relation to the diesel fuel. The level of NO emissions in the range of full-power characteristics was found to be lower for both tested biofuels compared to diesel fuel at lower engine speeds by an average of 7–8%, while in the range of a higher rotation speed, the NO content in the exhaust gases was higher for the tested biofuels compared to diesel oil by an average of 4–5%. The realized engine performance tests, moreover, showed an unfavorable effect of the biodiesel on the engine energy parameters. In the case of biofuels, this was by more than 4% compared to diesel fuel.

Abstract This paper proposes a hybrid deep learning framework (HDLF) that combines improved convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformer models. First, feature selection and dimensionality reduction are performed using XGBoost and principal component analysis, respectively. Secondly, CNN is enhanced by multiscale convolution, residual connection, and attention mechanism. Then, the bidirectional LSTM is combined with temporal convolutional network to improve the LSTM. Then, an improved dynamic focusing mechanism of transformer is introduced. The experimental results show that the HDLF has an accuracy of 0.945 in identifying low-pressure load characteristics.

Geopolymer concrete is one of the directions of green development in the construction industry, and casting geopolymer concrete inside steel tubes can effectively play the respective advantages of both. In order to study the axial compression performance and failure mechanism of geopolymer concrete-filled steel tubular (GCFST) columns, two hollow steel tubular columns were designed as the control group and eight geopolymer concrete-filled steel tubular columns were tested in axial compression with concrete strength grade, length-to-diameter ratio, and steel tube wall thickness as parameters. The load–displacement curve, skeleton curve, and stiffness degradation curve of each specimen were obtained by observing the force process and failure mode of each specimen, analyzing the characteristic load, and characteristic displacement, stiffness degradation, ductility, and energy dissipation capacity, and deriving the method of calculating the axial compression load-bearing capacity of GCFST columns, and comparing with several codes commonly used in the world. The results show that the length-diameter ratio has a significant effect on the failure mode of GCFST columns. The peak load and initial stiffness of each group of specimens are different, but their load–displacement curves, skeleton curves, and stiffness degradation trend are the same. The increase of geopolymer concrete strength and steel tube wall thickness can improve the ultimate load-bearing capacity, average compression capacity, and energy dissipation capacity of GCFST columns. The axial compression load-bearing capacity calculation formula proposed in this paper is in good agreement with the test results, and the calculated values of the formula are compared with the calculation results of the international codes to verify the accuracy and applicability of the axial compression load calculation formula proposed in this paper, and the results of the study can provide reference for the theoretical research and engineering application of geopolymer concrete-filled steel tube composite structure.

To solve the problem of a large amount of fly ash accumulation and study the axial compression and bearing capacity prediction of the self-compacting fly ash concrete filled circle steel tube (SCCFST) columns, eight specimens are designed to explore the impact of concrete strength grade, internal structural measures, and additional parameters. The stress, progression of deformation, and failure mode of each specimen are observed during the loading process. The load–displacement curves, load-strain curves, characteristic load and displacement, ductility, and stiffness degradation are analyzed. The findings revealed that shear deformation occurred predominantly in the middle and upper portions of the steel tubes. Enhancing the strength of the concrete or adopting internal structural measures could increase the bearing capacity and ductility of the specimens. The peak load and ductility could be increased by up to 17.6 and 53.6%, respectively. The proposed unified calculation equation for the axial compression bearing capacity of SCCFST columns demonstrates notable reliability and precision. Furthermore, these tests offer valuable references for the engineering application of various forms of SCCFST columns, which are of significant importance in practical engineering.

Hydrostatic bearings for liquid rocket engine turbopumps provide distinctive advantages, including high load capacity even with low viscosity cryogenic fluid and extending life span by minimizing friction and wear between rotor and bearing surfaces. Application of hydrostatic bearings into turbopumps demands a reliable test database with well-quantified operating parameters and experimentally validated accurate performance predictive tools. The present paper shows the comprehensive experimental data and validation of predicted static load characteristics of hydrostatic journal bearings lubricated with air, water, and liquid nitrogen. Extensive experiments for static load characteristics of hydrostatic bearings are conducted using a turbopump-rotor-bearing system simulator while increasing supply pressure (Ps) into the test bearings. The test results demonstrate notable effects of the test fluids and their temperatures, as well as Ps, on the bearing performance. In general, the measured bearing flow rate, rotor displacement, and stiffness of the test bearings steadily increase with Ps. The static load bearing characteristics predictions considering flow turbulence and compressibility matched well with the experimental results. The work with independent test data and engineering computational programs will further the implementation of hydrostatic bearings in high-performance turbopump shaft systems with improved efficiency and enhanced reusability of liquid rocket engine sub-systems.

Stress concentration in partially caved goaf is the main cause of dynamic pressure accident in the lower seam mining. Aimed at the characteristics of caving in shallow partially caved goaf (PCG) and the specificity of interior load‐bearing structures, a classification criterion of goaf caving was proposed. The characteristics of cooperative load‐bearing in pillar group and gob pile were revealed by numerical calculation, physical simulation, and theoretical analysis. Taking intermittent mining as an example, combined with the deformation behaviors of the main roof and the loading characteristics of waster rock mass, the characteristics of the load‐bearing of gob piles were described in the form of a piecewise function. A FLAC3D model of intermittent mining is modeled to evaluate the stability of pillars, the long‐term stability and distribution laws of overlying loads were revealed through a refined model. The results show that the width of the caved zone bearing the load in the intermittent goaf is about 20 m, and the maximum load‐bearing capacity is 1.055 MPa. The maximum depth of the plastic region in one side of the pillar is 1.48 m, and the load on elastic zone is about 11.33 MPa. This study provides a method for the study of the caving characteristics and load uncertainty in PCG, and the results provide important theoretical values for the safe mining of lower coal seams. Definition of the PCG (partially caved goaf) and analysis of the bearing structure in goaf. Modeling and calculation the Load‐bearing characteristics of broken rock mass. Stability of pillars and cooperative load‐bearing characteristics of pillar group–gob pile in PCG are revealed by large‐scale numerical models (3DEC and FLAC3D).

In marine environments, strong nonlinear internal solitary waves (ISWs) frequently occur. Due to their significant energy, ISWs have a substantial impact on the deep submergence rescue vehicle (DSRV). This study uses computational fluid dynamics (CFD) methods to establish a numerical model for the stable propagation of ISWs and their interaction with a fixed DSRV, aiming to clarify the effects on the DSRV. Changes in depth lead to transitions in the flow field experienced by the structure. Initially, the flow is dominated by the upper fluid layer, then by both the upper and lower layers, and finally by the lower layer. Consequently, the load characteristics of the structure are complex and variable. Additionally, the drag and lift forces on the DSRV change significantly as it moves through ISWs. Changes in yaw angle result in an exponential increase in drag, according to the sine of the yaw angle. They also cause varying degrees of lift attenuation, complicating the maneuverability of the DSRV when encountering ISWs. Therefore, the effects of depth and yaw angle on the DSRV’s performance should not be overlooked when encountering ISWs.

This paper analyzes the working behavior characteristics of a prestressed concrete transverse large cantilever continuous (PCTLCC) box girder bridge model based on structural stressing state theory and the numerical shape function (NSF) method. At first, the normalized generalized strain energy density (GSED) is established to model the stressing state of the bridge model. Subsequently, the Mann Kendall (M–K) criterion is applied to detect three characteristic loads, respectively, elastic–plastic branch load P (200 kN), failure load Q (300 kN), and progressive failure load H (340 kN), and the failure load Q is found to be the starting load of the damage process of the bridge model, rather than the ultimate load where the structure has been destroyed. Finally, the NSF method is adopted to interpolate the test data, and a detailed analysis for the variation characteristics of the working behavior of the bridge model under loads is performed based on the interpolation results. The characteristic load detection method and experimental data extension method for PCTLCC box girder bridge established in this study can provide valuable references for the design and analysis of such bridges.

In this paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in three-phase networks with the voltage level below 0.4 kV (low voltage—LV) is presented. In the first stage, the input data regarding the hourly active and reactive powers of the consumers and producers are introduced. The powers are loaded from the database of the smart metering system (SMS) for the consumers and producers integrated in this system or files containing the characteristic load profiles established by the Distribution Network Operator for the consumers, which have installed the conventional meters non-integrated in the SMS. In the second stage, a function, which is based on the work with the structure vectors, was implemented to easily identify the configuration of analysed networks. In the third stage, an improved version of a forward/backward sweep-based algorithm was proposed to quickly calculate the power/energy losses to three-phase LV distribution networks in a balanced and unbalanced regime. A real LV rural distribution network from a pilot zone belonging to a Distribution Network Operator from Romania was used to confirm the accuracy of the proposed algorithm. The comparison with the results obtained using the DigSilent PowerFactory Simulation Package certified the performance of the algorithm, with the mean absolute percentage error (MAPE) being 0.94%.