Engineering reports : open access最新文献

This study addresses a gap in existing literature by exploring the impact of an aerodynamics laboratory course on students' readiness for senior design projects in Aerospace Engineering at North Carolina State University. The redesigned curriculum focuses on propellers, aligning with the increasing popularity of senior design projects involving fixed-wing aircraft and multi-rotor systems in the Mechanical and Aerospace Engineering Department. The updated course incorporates Additive Manufacturing (AM) and an anechoic chamber for the first time, outlining its structure, objectives, and pivotal concepts. The use of data acquisition codes streamlines experimentation while integrating AM accelerates practical applications of blade element theory (BET). Through an assessment of lab report grades, this initiative significantly enhances students' grasp of propeller concepts, marking a pivotal step in their preparation for senior design projects.

The air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far-reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter ($��\mu �$g/m$��{�\hspace{0.17em}�}^{�3�}�$). The dataset is then preprocessed by cleaning and normalizing it before using K-means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K-means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K-nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1-score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1-score of 0.99, 0.99, 0.97, and 0.99, respectively.

This paper investigates the flow of a second-grade viscoelastic fluid with dust particles under hydromagnetic effects between vertical plates. This study investigates the effects of the left plate's oscillations, which induce fluid motion, on heat and mass transfer, and particle temperature. The study also considers the variable temperature and concentration. Mathematical models are developed using partial differential equations to represent the flow regime. To generalize energy and concentration Fick's and Fourier's laws are employed. Laplace and finite Fourier-Sine transforms are then used to solve the resulting system of dimensionless equations. Finally, Zakian's numerical technique is used in MATHCAD software to compute the Laplace inverse and obtain the final solution. The research concludes that the fractional approach is more realistic and practical than the classical approach. Changes in mass and heat transfer rates, as well as skin friction on the left plate, are observed over time across various physical parameters. Additionally, dust particles can be employed in various applications, including agriculture. In this sector, they can be mixed with water to create a dust suspension, which is subsequently sprayed over crops to enhance the effectiveness of pesticide application.

Material identification based on R_value (Transparency natural logarithm ratio of low-energy to high-energy.) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation within the material range cannot be directly measured, whereas their precision has essential effect on R_value. Therefore, a vertical-horizontal-context-based signal synthesis method was proposed to rebuilt incomplete/masked image, which takes the filtered signals outside the material range as the reference context, and takes into account the vertical (forward/column/Y) and horizontal (scanning/row/X) anisotropy. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The vertical curves are synthesized one by one, thus extending to the whole surface. The special rigorous synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only around 0.0007, and it only takes 35 ms to complete the surface synthesis of 119 × 119 pixels on the CPU with 3.4 GHz main frequency. This high numerical precision can match the similarly filtered signals after attenuation so as to improve the accuracy of R_value. And this, together with calculation real-time, can promote the application of industrial inline material identification.

This article examines the fire safety of rubber flooring used in an ice sports center in China. The fire characteristics and parameters of the rubber floor were determined through material fire tests. Subsequently, a fire safety analysis was conducted using the fire intrinsic safety method. Additionally, numerical simulation was employed to investigate fire and evacuation scenarios, particularly when the safety grade of the floor decoration materials falls below standard requirements. The results indicate that, under specific safety conditions, the ice sports center can utilize B1 grade rubber flooring in the required area while ensuring compliance with fire safety standards.

Epilepsy is a brain disorder that causes patients to have multiple seizures. About 30% of patients with epilepsy are not treated with medication or surgery. The abnormal activity of brain before occurring of a seizure (about a few minutes before a seizure occurs) are known as the preictal area. Therefore, if we can predict this state, we can control possible seizures by using appropriate medications. In this study, we present a method for predicting epileptic seizures using electroencephalogram (EEG) signals. The method can identify the preictal region that occurs before the onset of seizures. In our proposed method, first the noise removal of EEG signals is performed, and then the necessary features are extracted using a convolution neural network. Finally, we use the feature vectors in order to train multiple classifiers, fully connected layer, random forest, and support vector machines with linear kernel. Additionally, we apply maximum voting, which is an ensemble method, to classify preictal segments from interictal ones. In this study, using EEG signals of patients from CHB-MIT dataset, we were able to achieve sensitivity of 90.76%.

Wire arc additive manufacturing (WAAM) is an efficient technique for producing medium to large-size components, due to its accessibility and sustainability in fabricating large-scale parts with high deposition rates, employing low-cost and simple equipment, and achieving high material efficiency. Consequently, WAAM has garnered attention across various industrial sectors and experienced significant growth, particularly over the last decade, as it addresses and mitigates challenges within production markets. One of the primary limitations of WAAM is its thermal history during the process, which directly influences grain formation and microstructure heterogeneity in the resulting part. Understanding the thermal cycle of the WAAM process is thus crucial for process improvement. Typically, fabricating a part using WAAM results in a microstructure with three distinct zones along the build direction: an upper zone (thin surface layer) with fine grains, a middle zone dominated by undesirably long and large columnar grains covering more than 90% of the produced part, and a lower zone with smaller to intermediate columnar grains closer to the substrate material. These zones arise from variations in cooling rates, with the middle zone exhibiting the lowest cooling rate due to 2D conduction heat transfer. Consequently, producing a component with a microstructure comprising three different zones, with a high fraction of large and long columnar grains, significantly impacts the final mechanical properties. Therefore, controlling the size and formation of these grain zones plays a key role in improving WAAM. The aim of this work is to investigate the formation of undesired columnar grains in austenitic stainless steel 316L during WAAM and propose a simple hybrid technique by combining WAAM with a hot forging process (with or without interlayer cooling time). This approach targets the disruption of the solidification pattern of columnar grain growth during deposition progression and aims to enhance the microstructure of WAAM components.

Globally, the encouragement of using renewable fuels like biodiesel for diesel engines is driven by concerns over the fossil fuel depletion and harmful emissions. Additionally, the utilization of renewable fuel additives like diethyl ether has the potential to enhance fuel properties and boost engine performance. The aim of this paper was to construct a computer simulation using Ricardo Wave program in order to predict the performance and nitrogen oxides (NOx) emission of a diesel engine fuelled by a diesel-biodiesel blend and a diethyl ether (DEE) as a fuel additive. The computer model was validated by comparing the simulation engine performance and NOx emission results against the corresponding experimental data for diesel, diesel-biodiesel blend with 30% biodiesel proportion (B30), and two blends of diesel-biodiesel-DEE with DEE proportions of 5% and 10% on a volume basis. Also, the effect of varying the inlet air pressure on engine performance and NOx emission was compared for all investigated fuels. It was numerically demonstrated that using the DEE with an optimum proportion of 5% enhanced engine performance as it decreased engine fuel consumption by 5.9% and increased engine thermal efficiency by 9.6% compared to diesel fuel at engine full load condition. Also, a significant reduction of 20.5% in NOx emission resulted from the addition of DEE. Increasing the inlet air pressure increased engine power and decreased engine fuel consumption for all investigated fuels. Increasing the inlet air pressure from 1 to 3 bar increased engine brake thermal efficiency by almost 20% for all tested fuels. However, NOx emission increased slightly within a range from 1.7% to 7% for the different investigated fuels.

The goal was to evaluate the hysteretic performance of buckling-resistant braces with low yield point steel LYP160, the monotonic tensile and cyclic loading tests of LYP160 test specimens were conducted and the cyclic constitutive relationship was obtained. According to the load–displacement curves of the specimens, the low-yield point steel was characterized by good ductility and energy absorption ability. With consideration of the Chaboche model for the materials, the cyclic hardening parameters of low-yield point steel were obtained. On this basis, the hysteretic properties of buckling-resistant braces under cyclic loads were simulated and analyzed. After the analysis and comparison of buckling-resistant braces specimens with isotropic core plate and segmented variable section core plate, it can be found that: when the conventional buckling-resistant braces with an isotropic core plate were loaded to L/100, the lateral deformation of the buckling-resistant brace (BRB) would reach 17 mm. Additionally, serious squeezing could be observed on the lateral restraining members. The conventional BRB would become ineffective due to the accumulation of deformation at both ends of the BRB. When the segmented buckling-resistant brace was applied, the core plate with variable section would buckle first in the middle area, other parts could continue to consume energy thanks to the action of the limit plate. It would avoid the situation that other areas would be unable to consume energy after the core plate yields at one area first. Under the action of cyclic loads, no stiffness degradation was noted in the segmented buckling-resistant brace. Segmented buckling-resistant braces demonstrated superior ductility and energy dissipation capacity.

A Crimp Force Monitor (CFM) detects defects by whether the crimp force curve of the force sensor exceeds the set tolerance, with the goal of low defect miss rate and false alarm rate. In this study, typical defects made by hand are crimped with an automatic crimping machine together with good products to obtain continuous data of the crimping force curves and to process the data. For the peak and area deviations of the crimp force curve, the good products show skew-normal distribution, and the defective samples show outliers and different deviation intensities in the positive and negative directions. Furthermore, by manually intervening in the batch production of multiple groups of defects and comparing the defect miss rate and false alarm rate of CFM with different tolerance settings, the peak skewed tolerance, joint loose tolerance and joint skewed tolerance tree settings can meet a low defect miss rate and low false alarm rate at the same time. The group test results verify the skew-normal distribution of crimping force curve deviation rate and the characteristics of deviation intensities, providing reference for the tolerance setting of CFM in automated terminal crimping production.