Let u and v be plurifine plurisubharmonic functions. In this paper, we investigate sufficient conditions on u and v under which they can be compared.
Determining the residual compressive strength (CS) of concrete after exposure to elevated temperatures poses a significant metrological challenge, as direct measurements are often hazardous and impractical. To address this gap, an interpretable indirect measurement system based on machine learning was developed and validated. Using a dataset of 207 experimental observations, five advanced boosting algorithms were systematically compared. A Gradient Boosting model optimized with the Slime Mould Algorithm (GB-SMA) was identified as the most effective, yielding the highest accuracy (test RMSE of 6.199 MPa). The focus on metrological reliability in this work constitutes a primary contribution. SHapley Additive exPlanations (SHAP) were used to provide model transparency by quantifying the influence of input parameters, such as temperature and aggregate content, on the final measurement. To transition the validated model into a practical tool, a graphical user interface (GUI) was developed. This interface allows engineers to perform efficient, code-free estimations of post-fire concrete strength. The outcome is therefore not simply a predictive model but an interpretable and validated measurement system for a fundamental parameter in civil engineering.
Reducing harmful emissions from diesel engines has become an urgent requirement in the transportation sector of all countries. In this context, compressed natural gas (CNG) is considered a promising alternative fuel for existing diesel engines due to its clean combustion characteristics and emission reduction potential. This study focuses on the development and evaluation of a simulation model for a diesel engine converted to operate on CNG, aiming to comprehensively compare its economic, technical, and emission performance with those of the original diesel engine. The engine model was developed using AVL Boost software and calibrated and validated with experimental data to ensure the reliability of the results. Simulations were conducted over a wide range of engine operating conditions, with simultaneous variations in engine speed and load, allowing for a detailed assessment of the effects of fuel type on engine performance characteristics. The results show that the CNG-fueled engine achieves a maximum power increase of 7.69 % and a reduction in brake specific fuel consumption of 8.5-10.12 % compared to the diesel engine. In terms of emissions, nitrogen oxides (NOx) are significantly reduced by 49.63-95 %, and soot emissions are completely eliminated. However, under rich mixture operating conditions, carbon monoxide (CO) emissions increase by 3.77-5.85 times, accompanied by a considerable amount of unburned hydrocarbons (HC). Therefore, the obtained results can serve as a basis for applying CNGconverted diesel engines in buses, urban trucks, agricultural tractors, or power generators to improve energy efficiency and meet stringent environmental standards. This study focuses on evaluating the applicability of CNG fuel in existing diesel engines rather than newly designed engines, while also extending the investigation across a wide range of engine operating conditions. In addition, the simulation model is calibrated using experimental data, thereby enhancing the reliability and practical applicability of the obtained results.
This article aims to analyze and evaluate the roll safety thresholds (RSTs) and roll safety zones of tractor semi-trailer vehicles during turning maneuvers, using the roll safety factor (RSF) and yaw rate of the vehicle bodies. To achieve this, a full dynamics model is established using the multibody system method. This model is then used to survey and evaluate the vehicle's motion state, using ramp steer maneuver (RSM) steering rules. In each survey case, the maximum values of RSF and yaw rate of vehicle bodies are synthesized in 3D data, with an initial velocity range of 40 km/h to 80 km/h and a magnitude of steering wheel angle range of 12.5 degrees to 300 degrees. These 3D data are used to determine the proposed values of RSF, which can be used as examples to set the threshold values of the yaw rate of vehicle bodies and roll safety zones. At a velocity of 60 km/h, the dynamic rollover threshold for proposed roll safety factor (RSFprop) is equal to 1, with corresponding values of 15.718 degrees/s and 14.962 degrees/s. Similarly, the warning threshold for RSFprop is equal to 0.6, with values of 9.514 degrees/s and 9.404 degrees/s, and for RSFprop equal to 0.7, the values are 10.705 degrees/s and 10.625 degrees/s. The control threshold for a vehicle velocity of 60 km/h and RSFprop equal to 0.9 is calculated as 13.588 degrees/s and 13.339 degrees/s. These results can be used as a basis for developing early warning and control systems for various vehicle operating modes.
Optimizing liquid-cooled battery modules using high-fidelity computational fluid dynamics (CFD) remains computationally demanding, particularly when multiple design variables and competing performance objectives must be considered simultaneously. To address this issue, this study develops a surrogate-assisted multi-objective framework for the thermal-hydraulic optimization of a serpentine liquid-cooling system for cylindrical lithium-ion battery modules. First, a three-dimensional CFD model is established and benchmarked against published results. Latin hypercube sampling is then used to generate representative design points, and Gaussian process regression (GPR) models are constructed to relate three design variables, namely channel wall thickness, coolant inlet velocity, and inlet temperature, to three performance metrics: maximum temperature, maximum temperature difference, and pressure drop. Morris global sensitivity analysis is further employed to quantify the relative importance of the design variables. The surrogate models are subsequently coupled with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal solutions. The results indicate that inlet temperature has the strongest influence on the maximum temperature, inlet velocity predominantly affects temperature uniformity, and channel wall thickness mainly governs pressure drop. Compared with the baseline design, the selected Pareto solution reduces the pressure drop from 539.0 Pa to 208.8 Pa, corresponding to a 61.26% reduction, while increasing the maximum temperature and maximum temperature difference by only 0.21% and 1.00%, respectively. A full CFD re-evaluation of the selected optimum yields relative errors below 2.5%, supporting the reliability of the proposed framework. The proposed approach provides a practical and computationally efficient tool for balancing thermal and hydraulic performance in liquid-cooled battery modules.
