Engineering reports : open access最新文献

The significance of this study is to understand the complex interplay between fluid flow and surface roughness. Modeling surface roughness adds a new dimension for examining fluid dynamics, which is essential for understanding phenomena like drag force, heat transfer, and mass transfer. In this context, the aim of the present work focuses on modeling the magnetohydrodynamic peristaltic slip flow of Casson nanofluid and analyzing the role of multiple slip effects over a non-uniform rough channel. A novel rough non-uniform model is effectively governed by a set of nonlinear coupled governing partial differential equations, which are simplified under long wavelength and creeping flow approximations. The resulting simplified equations are solved numerically using Mathematica's built-in ND-Solve tool. The study primarily examines the velocity, temperature, and concentration profiles graphically for various pertinent physiological parameters. Additionally, engineering interests like skin friction coefficients, Nusselt numbers, and Sherwood numbers are reported in tabular form, revealing intrinsic flow oscillations. The results are further explored by analyzing pressure drop, friction force, and bolus shapes created by the sinusoidal motion of the fluid. Such insights are vital for comprehending internal fluctuations during peristaltic transport. In summary, skin friction and Nusselt numbers are typically higher for rough versus smooth surfaces. Also, roughness induces stresses, conductive-convective heat transfer, and viscous effects. Further, magnetically activated rough surfaces and nanoparticle interactions create flux balances. Magnetic effects reduce bolus size due to resistive forces. The findings of this study have important applications in biomedical engineering, aerospace engineering, heat transfer enhancement, and environmental remediation.

Smart cities amalgamate technologies such as Internet of Things, big data analytics, and cloud computing to collect and analyze large volumes of data from varied sources which facilitate intelligent surveillance, enhanced energy management systems, and environmental monitoring. The ultimate goal of these smart cities is to offer city residents with better services, opportunities, and quality of life. However, the vulnerabilities in the underlying smart city technologies, interconnection of heterogeneous devices, and transfer of data over the open public channels expose these networks to a myriad of security and privacy threats. Therefore, many security solutions have been presented in the literature. However, the majority of these techniques still have numerous performance, privacy, and security challenges that need to be addressed. To this end, we present an anonymous authentication scheme for the smart cities based on physically unclonable function and user biometrics. Its formal security analysis using the Real-Or-Random (ROR) model demonstrates the robustness of the negotiated session key against active and passive attacks. In addition, the informal security analysis shows that it supports salient functional and security features such as mutual authentication, key agreement, perfect key secrecy, anonymity, and untraceability. It is also shown to withstand typical smart city threats such as side-channeling, offline guessing, session key disclosure, eavesdropping, session hijacking, privileged insider, and impersonation attacks. Moreover, comparative performance shows that it incurs the lowest energy and computation costs at relatively low communication overheads.

Tuberculosis (TB) remains a significant global health challenge, claiming over 2 million lives annually, predominantly among adults. Existing TB models often neglect seasonal variations, optimal control, and reinfection, limiting their accuracy in predicting disease dynamics. This study presents a novel data-driven SVEITRS mathematical model incorporating these factors to analyze TB transmission dynamics. Employing the next-generation matrix approach, a basic reproduction number $��\left(R_0\right)�$ of 1.005341 was calculated, suggesting that without robust public health interventions, TB disease may persist in Kenya. The model equations were solved numerically using fourth- and fifth-order Runge–Kutta methods, with the forward–backward sweep technique applied to the optimal control problem. The model was fitted to historical TB incidence data for Kenya from 2000 to 2022 using lsqcurvefit algorithm in MATLAB software. The fitting algorithm yielded a mean absolute error (MAE) of 0.0069, demonstrating a close alignment between simulated and observed data. The optimized parameter values were used to project future TB dynamics. Key findings indicate that a 20% decrease in transmission rate coupled with a 5% increase in vaccine efficacy, while maintaining other parameters constant, would result in a 32.60% reduction in TB transmission in Kenya. Moreover, the incidence of TB in Kenya is expected to decrease to an estimated 17 cases per 100,000 people by 2045 with sustained efforts in vaccine development and public awareness campaigns. The development of highly efficacious vaccines emerges as the most cost-effective strategy in combating TB transmission in Kenya. Policymakers should prioritize investing in the development and deployment of highly efficacious vaccines to achieve optimal public health outcomes and economic benefits, aligning with Kenya's Vision 2030.

The optimization of parameters in proton exchange membrane fuel cell (PEMFC) models is essential for enhancing the design and control of fuel cells and is currently a vibrant area of research. This involves a complex, nonlinear, and multivariable numerical optimization challenge. Recently, various metaheuristic approaches have been applied to efficiently identify optimal configurations for PEMFC models, capable of exploring a broad search space to locate ideal solutions promptly. In this study, the recently developed hierarchical population-based differential evolution (HPDE) was employed for parameter optimization of PEMFCs due to its robustness and demonstrated superiority over other optimization algorithms. This research tested the proposed optimization algorithm by identifying parameters for 12 distinct PEMFCs, including BCS 500 W PEMFC, Nedstack 600 W PS6 PEMFC, SR-12500 W PEMFC, H-12 PEMFC, STD 250 W PEMFC, and HORIZON 500 W PEMFC, four variants of 250 W PEMFC, and two variants of H-12 12 W PEMFC. The performance of HPDE was also benchmarked against other advanced evolutionary algorithms (EAs), such as E-QUATRE, iLSHADE, CRADE, L-SHADE, jSO, HARD-DE, LSHADE-cnEpSin, DE, and PCM-DE. Despite its simplicity, the results reveal that HPDE can precisely and swiftly extract the parameters of PEMFC models. Furthermore, the voltage–current (V–I), power-current (P–I), and error characteristics derived from the HPDE algorithm consistently align with both simulated and experimental data across all seven models of PEMFCs. Additionally, HPDE has shown to outperform various versions of DE algorithms, providing superior results.

The accurate prediction of the output power of each power plant is crucial for effective resource deployment. This paper proposes a convolutional neural network-long short-term memory (CNN-LSTM) network integration model based on spatio-temporal feature fusion. Firstly, the temporal correlation of the PV features of the target power plant and the spatial correlation between the PV power of the target power plant and the PV power of the neighboring power plants are computed. The features are then fused according to the strength of the correlation, which allows the features to be combined with spatial and temporal attributes, which promotes faster and more effective training of the model. Subsequently, an integrated network architecture comprising three individual models, CNN, LSTM, and CNN-LSTM, is designed. The SENet attention mechanism is utilized to add non-linear integration weights to the outputs of the individual models. Due to the variability of different neural networks, the prediction results of the integrated model are often higher than the best-performing individual model. Additionally, we designed different case studies to compare model performance under sunny, rainy, and cloudy conditions. Extensive simulation experiments demonstrate the effectiveness of our proposed integrated approach. When the prediction interval is set to 5 min, the RMSE loss of the integrated model on the test set is reduced by 13.5%, 6.9%, and 5.1% compared to the CNN, LSTM, and CNN-LSTM models included in the ensemble, respectively.

Data reconstruction is essential in enhancing the quality of vehicle trajectory data. Previous studies have identified the location of abnormal data inaccurately, resulting in poor trajectory reconstruction results. This study proposed a two-step reconstruction method. The first step detected the locations of obviously abnormal speed data using wavelet transform. Then, the abnormal data were repaired by the cubic spline curve interpolation algorithm. The second stage identified the locations of abnormal acceleration data based on the general acceleration value. And the vehicle trajectory data were reconstructed using Lagrange interpolation and Kalman filter algorithms. The approach was utilized on NGSIM trajectory data. The results show that the acceleration values of the proposed method range from −6.69 m/s² to 4.96 m/s², with a standard deviation of 0.87. The reconstructed results are more closely matching drivers' physiological capabilities compared to other methods. These findings verify the reliability of the proposed approach and notably improve the quality of the trajectory data. It provides critical foundational data support for traffic planning, design, and management.

This paper develops a modified strain-wedge (MSW) model that accounts for the resistance to bending moments caused by the lateral frictional resistance and axial forces of piles in the small-strain regime in sands and algorithmizes a finite differential analysis program. Two cases of rigid pile tests, one in Blessington and another in Shenton Park were used to calibrate the finite element analysis model and verify the proposed modified model. The results show that the analysis of pile-side resistance is pivotal for rigid horizontally loaded monopiles, and the MSW model is the most accurate at predicting lateral displacement and the moments. Its accuracy in a small-strain regime to predict the pile rotation points and the maximum bending moment is almost three times that of other models. With the increase of the soil strain, the accuracy of the MSW decreases. On the contrary, the prediction accuracy of numerical analysis using the UBC3D-PLM constitutive model increases.

This study investigates the integration of the supercritical carbon dioxide (S-CO₂) cycle with V18 MAN 51/60DF engines for waste heat recovery in powerships, representing a significant advancement in energy production efficiency. Detailed analysis focuses on the micro S-CO₂ cycle's potential in terms of energy efficiency, environmental sustainability, and economic benefits. The results demonstrate the system's capability to utilize 374.4 kW of heat provided by 1 kg/s air flow, achieving an exergy efficiency of 9.7% and an energy efficiency of 21.8%. The compressor requires 35.51 kW of work, while the turbine produces 89.62 kW, resulting in a net work output of 54.11 kW. The CO₂ mass flow rate is 0.9988 kg/s, and 320.3 kW of heat is transferred to sea water through a flow rate of 0.1231 kg/s. These studies show that the micro supercritical carbon dioxide cycle has great potential in energy production and waste heat recovery and may offer an important innovation to increase energy efficiency, especially in powerships. It highlights the potential of this innovative technology to deliver higher efficiency, lower carbon emissions, and more compact designs than traditional energy conversion systems. These findings indicate that the S-CO₂ cycle can effectively enhance energy production in future projects, offering a promising solution for sustainable power generation.

Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and poses significant health risks, such as cardiovascular disease and cognitive damage. Understanding the causes of diabetes is crucial to managing it and preventing complications. The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. To tackle the challenge of designing an improved diabetes classification algorithm that is more accurate, random oversampling and hyper-tuning parameter techniques have been used in this study. Whereas most of the existing methods were built upon considering any single dataset, for getting more acceptability in general, our proposed model has been designed based on two benchmark datasets: the BRFSS dataset, which has multiple classes, and the Diabetes 2019 dataset, which has binary classes. What is more, to improve the comprehensibility of the proposed model, a variety of explainability methodologies such as SHapley Additive Explanations (SHAP), Partial Dependency, and Local Interpretable Model-agnostic Explanations (LIME) have been implemented which are not often noticed in the previous works. The detailed explainability charts will enable the end users or practitioners to understand the exact factors of any given diagnostic report. This research focused on classifying type 2 diabetes using machine learning and providing an explanation for the outcomes derived from the model predictions. Random oversampling and quantile transform are used to rectify imbalances in the dataset and guarantee the resilience of model training. By meticulously adjusting parameters with gridsearchCV, we successfully optimized our models to attain exceptional accuracy across binary and multi-class datasets. We evaluate the proposed model using two datasets and performance metrics. The extra trees classifier (ET) performed exceptionally, achieving 97.23% accuracy on the multi-class dataset and 97.45% on the binary dataset.

A novel algorithm for thermal runaway detection embedded in the electric bicycle charging system is proposed. The battery's internal temperature is a key indicator to diagnose battery safety and monitor the charging state. First, the relationship between the battery's internal temperature and the impedance's phase shift is derived theoretically. Second, given that random interference in the charging current, the current area integration algorithm (CAIA) is presented to measure the phase shift, so the internal temperature is estimated. Third, the smart charging system for electric bicycles is presented and results are discussed. The results show that the phase shift increases with the internal temperature in the range of 200–600 Hz when the internal temperature is 20°C–35°C. When the internal temperature reaches 45°C, the phase shift decreases sharply with the internal temperature. Therefore, smart electric bicycle charger realizes thermal runaway detection of electric bicycle.