Wiley

Engineering Reports

Published by Wiley

Online ISSN: 2577-8196

Disciplines: General engineering

Journal websiteAuthor guidelines

Top read articles

341 reads in the past 30 days

Block diagram of WPT.
Systematic review through PRISMA guideline.
Classifications of WPT.
Diagram of LPT.
Transition and absorption in earth's atmosphere; image: NASA.

+9

A comprehensive review of wireless power transfer methods, applications, and challenges

June 2024

·

990 Reads

·

1 Citation

·

·

Nayan Sarker

·

[...]

·

Download

Aims and scope


Engineering Reports is an open access engineering and computer science journal with a broad scope, addressing questions relevant to academia, industry or government. We quickly publish rigorously peer reviewed, well-conducted scientific research, covering areas including biological, chemical, civil, software, electrical, industrial, and mechanical engineering, as well as other engineering sciences and technologies, and applied sciences for engineering.
As part of Wiley’s Forward Series, this journal offers a streamlined, faster publication experience with a strong emphasis on integrity. Authors receive practical support to maximize the reach and discoverability of their work.

Recent articles


Optimizing Heat Transfer in Heat Pipes Using Hybrid Nanofluids With Multi‐Walled Carbon Nanotubes and Alumina
  • Article
  • Full-text available

October 2024

·

12 Reads

This study investigates the thermal performance of heat pipes using hybrid nanofluids composed of multi‐walled carbon nanotubes (MWCNT) and aluminum oxide (Al2O3) nanoparticles. The aim was to assess the effects of nanoparticle concentration (0.1%–0.5%), filling ratio (60%–90%), heat input (50–80 W), and inclination angle (0°–90°) on thermal resistance and heat transfer coefficient (HTC). Hybrid nanofluids were prepared using ultrasonic homogenization, and their stability was confirmed by zeta potential analysis, showing a reduction from −60 to −48 mV over 30 days. Experimental results revealed that the thermal resistance decreased with increasing filling ratio and inclination angle, reaching a minimum of 0.80 K/W at a 90° angle, 90% filling ratio, and 80 W heat input. Similarly, the overall HTC increased with these parameters, peaking at 2250 W/m² K under the same conditions. At a 0.5% nanoparticle concentration, the HTC improved by up to 40% compared with conventional fluids. The thermal conductivity of the hybrid nanofluid also rose significantly, from 0.7 W/m K at 30°C to 1.5 W/m K at 90°C, outperforming distilled water. These findings highlight the potential of hybrid nanofluids to enhance heat pipe efficiency, particularly in high‐power applications, by optimizing nanoparticle concentration, filling ratio, and inclination angle.


Development, implementation, and evaluation of a 3D‐printed high‐fidelity pediatric mannequin with expected hard‐to‐intubate airway

October 2024

·

3 Reads

Clinical simulation is fundamental for the healthcare staff to learn and enhance their procedural skills without causing harm to the patients. Despite its importance, in literature appears a deficiency of pediatric pathological mannequins, especially those simulating difficult airway management due to the obstruction of the passage of tubes, fiberscopes, or catheters. Given the importance of simulating complex scenarios in the medical staff's training, the authors decided to realize a modular high‐fidelity pathological mannequin with nasal access using reverse engineering and additive manufacturing techniques within T3Ddy, a joint laboratory between Meyer Children's Hospital of Florence and the Department of Industrial Engineering of the University of Florence. The mannequin is developed from diagnostic images of a significant 30‐month‐old polymalformative patient also affected by Pierre‐Robin syndrome modifying the tracheobronchial tree to reproduce an abnormal status. Rigid parts and silicone cast molds are manufactured using 3D‐printed ABS/ASA while platinum‐cure‐silicones are used for the soft ones. Meyer's anesthesiologists collaborated to the realization providing feedback during design and production. The device is evaluated with a 5‐point Likert scale questionnaire and results in a useful tool for the training of procedural skills related to difficult intubation as its realism, anatomical geometry, and tactic feedback are positively evaluated.


Experimental Study on Quick‐Locking Reinforcement Model for Local Defects in Pipelines in the Rich Water Area

October 2024

·

15 Reads

The management of local defects in municipal pipelines in water‐rich areas remains a significant challenge, particularly under high‐pressure conditions. This study investigates the performance of quick‐lock steel sleeves as a trenchless repair method through full‐scale experiments on five different pipeline diameters. The experiments focus on the failure modes and critical buckling pressure of the sleeves under external pressure. A novel design model based on structural reliability theory was developed and validated against experimental results. The results show a close match between the calculated and experimental buckling pressures, with a ratio ranging from 0.87 to 1.15. These findings provide valuable insights for the design and application of quick‐lock sleeves in high‐pressure municipal networks. This study contributes to improving the reliability and effectiveness of pipeline repair technologies, offering practical solutions for addressing pipeline leakage and instability in challenging environments.


Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

October 2024

·

20 Reads

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to 50%50% 50\% and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.


Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm

October 2024

·

28 Reads

Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of developing heart disease. However, most ML algorithms require more accurate data in order to build an accurate prediction model and do not tolerate missing values. Handling missing risk factors is critical during dataset preprocessing and becomes more difficult when the risk factors are completely missing. Removing this completely missing feature may result in the loss of critical information, but there are no readily available imputation methods, which presents a significant challenge. To overcome this difficulty, in this study, we take an attempt to impute using statistical multiple linear regression and Huber regression (HR) methods using four blended datasets (Statlog, Cleveland, Hungarian, and Switzerland) sourced from the UCI ML repository. The entire dataset comprises 14 attributes, including one target variable; however, in the Switzerland dataset, one feature value (“serum cholesterol”) is entirely missing. Missing “serum cholesterol” is recognized as a predisposing factor including “chest pain,” “supreme heartbeat rate,” “type of defect,” “exercise induced ST stress related to rest,” and “exercise generated angina” in the proposed imputation methods. We also proposed applying the majority voting ensemble technique in an individual's and integrated dataset using ML algorithms as part of the risk factor identification strategy. The results show that our proposed stacked algorithm for the combined dataset with the ensemble features significantly improved accuracy by 93.47%, and an AUC score of 94.50% demonstrated more accurate and early prediction than the previous research and also provided the model's diversity, resilience, generalization, and adaptability to varied datasets.


A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm

October 2024

·

2 Reads

Accurate and highly efficient construction of numerical concrete models with different gradations is important to analyze the acoustic properties of ultrasonic waves in concrete. The random aggregates‐based method is a classic approach to meso‐structural modeling of concrete and is particularly suitable for generating models with low‐density aggregates. However, when facing the simulation requirements of concrete models with large volumes, small particle sizes, and high aggregate content, traditional random aggregates‐based methods encounter challenges in satisfying aggregate packing density and modeling efficiency. In order to improve the efficiency of aggregate placement in modeling concrete meso‐models, we proposed a hybrid‐strategy point cloud placement algorithm that combines the re‐placement and sedimentation strategies. Then, the algorithm's effectiveness in improving the modeling efficiency is verified by reconstruction experiments on random aggregate models with different aggregate grades. Finally, the model's reliability and validity is further confirmed by simulation analysis of the ultrasonic attenuation characteristics of the model. The results show that the algorithm can rapidly generate more than 75% aggregate packing density concrete specimens and significantly outperforms similar algorithms regarding aggregate placement efficiency and model generation time. In addition, the model generated by the proposed algorithm can accurately simulate the attenuation characteristics of ultrasonic waves in concrete.


Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

October 2024

·

17 Reads

The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.


Impact of Carbon Fiber‐Reinforced Polymer Sheets and Bolt Diameter on the Seismic Performance Enhancement of Steel Beam‐Column Joints

October 2024

·

32 Reads

Beam‐column joints are pivotal for ensuring the resilience of prefabricated steel structures under various loading conditions. Following the major earthquakes of the 1990s, semi‐rigid bolted connections emerged as a promising alternative to traditional welded connections. This study investigates a fully prefabricated Intermediate Beam‐Column Joint (IBCJ) with extended endplates, renowned for its excellent seismic resistance. While significant progress has been made in existing research, there is still a need to thoroughly examine the tension capacity of IBCJs concerning bolt size and explore the potential of Carbon Fiber‐Reinforced Polymer (CFRP) sheets to enhance joint performance under seismic loading. Using the finite element method, this research evaluates the performance of IBCJ under both monotonic and cyclic loading conditions. After validation with experimental data, the study examines various bolt diameters to assess their tension capacity, ductility ratio, secant stiffness, and energy dissipation capacity. The findings indicate that larger bolts exhibit higher ultimate capacities and reduced deformation at failure. Additionally, the study investigates the optimal placement and configuration of CFRP sheets, identifying the backside of the endplates as the most effective location. The application of CFRP significantly enhances bolt tension capacity by up to 1.2 to 1.3 times, demonstrating its potential in reducing bolt failure risk and improving structural reliability under seismic conditions. The superior performance of CFRP‐strengthened bolts can play a crucial role in the design and retrofitting of prefabricated steel structures, potentially contributing to the improvement of existing standards and practices of seismic enhancement of IBCJ.


5G Network Slicing as a Service Enabler for the Automotive Sector

October 2024

·

15 Reads

Network slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range of heterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of network slicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the state of the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such as automotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previous cellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enabler to face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers, and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.


Optimization of White Tea Flavanol Extraction Process by the Ultrasonic‐Assisted Deep Eutectic Solvent Method and Determination of the Antioxidant and Antibacterial Activity

Flavanols have a variety of health benefits and biological activities and become the hotspot of the food development and research. In this research, flavanols were extracted from white tea by ultrasonic‐assisted deep eutectic solvent (DES) method. High‐performance liquid chromatography was used to determine the content of flavanols. The influence of four factors on the flavanol extraction yield was analyzed by single factor experiments, including deep eutectic solvent mole ratio (the mole of hydrogen bond acceptors: the mole of hydrogen bond donors), solid–liquid ratio (white tea sample weight/g: the volume of DES/mL), extraction time and extraction temperature. On the basis of single factor experiments, the factors which had significant effects on the flavanol extraction yield were further optimized by the response surface test. The results showed that the optimized extraction conditions were as follows: the mole ratio of DES 1: 4 (choline chloride: 1,2‐propanediol), the solid–liquid ratio 1:30, the extraction temperature 56°C, the extraction time 53 min, and the ultrasonic power 341 W. Under the above parameters, the extraction yield of white tea flavanols was 9.63 mg/g. After purification with macroporous resin, antioxidation and antibacterial experiments were carried out. The results showed that the antioxidant and antibacterial effects of white tea flavanols were remarkable. The antibacterial effects performed best on Escherichia coli, followed by Bacillus subtilis and Staphylococcus aureus. The maximum diameter of the antibacterial zone on Escherichia coli was 0.95 ± 0.11 mm. The best radical scavenging rates of OH, ABTS+ and DPPH were 97.1%, 97.5%, and 88.4%, respectively, when the flavanol concentration was 9.5 mg/mL. The conclusions of this research can provide theoretical foundation for the further study of white tea flavanols.


A Theoretical and Experimental Investigation of the Effects of Inverted Wings Modifications on the Stability and Aerodynamic Performance of a Sedan Car at Cornering

October 2024

·

30 Reads

This research examines the impact of cornering on the aerodynamic forces and stability of a Nissan Versa (Almera) passenger sedan car by introducing novel modifications. These modifications included single inverted wings with end plates as a front spoiler, double‐element inverted wings with end plates as a rear spoiler, and incorporating the ground as a diffuser under the car trunk. The goal is to enhance the performance and stability of conventional passenger cars. To ensure the accuracy of the numerical data, the study utilized multiple methodologies to model the turbulence model, ultimately selecting the most suitable option. This involved comparing numerical data with wind tunnel experimental data using force balance and pressure distribution. Once validated, the computational fluid dynamics (CFD) was employed to analyze the vehicle's aerodynamic performance relative to the straight‐line condition under cornering conditions. The car simulation in a cornering condition was conducted at a representative Reynolds number based on the vehicle length of about 1.3 × 10⁷. The study discovered that asymmetry was a recurring theme regarding surface pressure distribution, with greater prominence under cornering conditions. All modified models exhibited a more favorable lift‐to‐drag ratio than the baseline, indicating improved aerodynamic efficiency. The underbody double‐element diffuser proved most effective for enhancing fuel efficiency and stability. Mesh refinement with a polyhedral algorithm consisting of 11.27 million elements and a computational domain with a frontal area of 91.8 m² and a curved length of 31 m (˜7 times car length) was crucial for achieving accurate and repeatable results. The study employed multiple turbulence models within the CFD framework. The realizable k‐ε model was chosen due to its balance between accuracy and computational cost for all Nissan Versa models. These findings are limited to the selected parameters and wind tunnel conditions, and further investigations might be needed for extreme driving scenarios.


Gypsum Binder With Increased Water Resistance Derived From Membrane Water Desalination Waste

October 2024

·

43 Reads

A method has been developed for separating a mixture of calcium, magnesium and sodium sulfates obtained through the interaction of sulfuric acid and waste from the water purification process generated by using membrane filters. The primary goal of this method is to extract gypsum and produce gypsum‐based binders. Patterns were identified regarding how various types, ratio and quantities of additives: blast furnace slag, granite screenings, portland cement, electric steel smelting slag affect the water‐gypsum ratio, strength properties, and water resistance of high‐strength gypsum binders. It was found that adding a single‐component additive specifically to enhance water resistance does not significantly impact these properties. Complex additives have been developed based on Portland cement, granulated blast furnace slag, electric furnace slag, expanded clay dust, and granite screenings of various fractions. These additives are designed to maximize the water resistance of high‐strength gypsum binder based on synthetic calcium sulfate dihydrate. As a result, the water resistance coefficient increased from 0.45 to 0.52. Additionally, a technological block diagram of the process has been proposed.


Formation of the product quality DNA model.
Flow of the quality diagnosis method for the rocket body structure manufacturing process based on product quality DNA.
Similarity of the weld quality DNA to be diagnosed.
Quality Diagnosis for Rocket Body Structure Manufacturing Process Based on Product Quality DNA

October 2024

·

3 Reads

In the current multivariety and small‐batch production mode, it is difficult to fully reflect the evolution law of the sampling population due to incomplete sample information in the manufacturing of the rocket body structure. By referring to the principle of biological DNA, a knowledge‐based quality diagnosis strategy is adopted, and a quality diagnosis method for the rocket body structure manufacturing process based on product quality DNA is proposed in this paper. The accumulated experience or knowledge in the past is stored in the product quality DNA knowledge base in the form of cases. Through knowledge retrieval, historical cases with the highest similarity to the product quality DNA to be diagnosed are found, and reasonable control and rectification schemes are provided for the quality problems in the production process, so as to avoid the same quality problems in the future. The feasibility of the method is verified by two case studies of two welds. The results show that the method can effectively and accurately diagnose weld quality.


Understanding the Effects of Manufacturing Attributes on Damage Tolerance of Additively Manufactured Parts and Exploring Synergy Among Process‐Structure‐Properties. A Comprehensive Review

October 2024

·

57 Reads

Additive Manufacturing (AM) has revolutionized the production industry by offering design freedom with shorter lead times and reduced material wastage. However, the damage tolerance (DT) of AM parts is a significant concern due to their microstructural and geometric complexities, which affect their mechanical performance. This article aims to provide a comprehensive overview of the manufacturing parameters affecting the components produced by AM specifically selective laser melting (SLM). Detailed discussions are presented on the effects of manufacturing attributes on the microstructure, defects, and mechanical characteristics of AM parts. Depending on these aspects, basic concepts are studied and critically explained specifically for AM materials. The basic criterion for damage‐tolerant component design, the criterion for fatigue and fracture properties, and the effect of the defects on fatigue life are critically presented. In addition, the effect of different types of gradation on the crack growth behavior of samples processed by SLM is also investigated in depth. There is currently a lack of a specific review study in the literature that establishes a connection between process attributes and metallographic properties, and their impact on the damage behavior of additively manufactured parts. This gap in research highlights the need for a comprehensive review to bridge this knowledge deficit and provide valuable insights for understanding the relationships between manufacturing processes, material characteristics, and the structural integrity of additively manufactured components. This review concludes by addressing the challenges and opportunities in designing and qualifying AM parts for damage tolerance.


Design and Development of a Wayside AI‐Assisted Vision System for Online Train Wheel Inspection

October 2024

·

50 Reads

Wayside inspection of rolling stock has been around for some time and wheel impact load and fiber‐grating sensors are actively explored for getting high‐fidelity data. Visual inspection from wayside provides the opportunity to gain high‐resolution data, which can help in the early diagnosis of potential faults. It is rarely explored due to complexities associated with calibration, moving and rotating targets, and difficulties associated with data acquisition. This paper explores and presents an in‐depth design and development strategy for such systems. It presents the development steps, implementation, and results of a vision inspection system for regular and automated inspection of train wheels. First, various configurations for positioning of the cameras in a three‐dimensional setting are considered and discussed, followed by online data acquisition for establishing a data set. Later, a comprehensive comparative analysis was conducted on several object detection algorithms for wheel segmentation task. Different algorithms are evaluated using COCO evaluation metrics, and the best‐performing model, YOLOv9, achieves a mAP50 of 0.94 and a recall of 0.91. The developed system has produced satisfactory results in acquiring proper wheel tread images and segmenting the wheel. Further avenues for countering lighting issues and defect detection are provided.


Modeling of Inscribed Dual Band Circular Fractal Antenna for Wi‐Fi Application Using Descartes Circle Theorem

October 2024

·

24 Reads

This study focuses on the modeling of a dual‐band circular fractal antenna designed for Wi‐Fi applications by utilizing the Descartes Circle Theorem. The antenna's geometry is characterized by self‐similar fractal patterns, enabling enhanced performance in dual frequency bands relevant to Wi‐Fi communication. Current research is trending towards the development of antennas capable of operating across various Wi‐Fi bands, and the emerging 6 GHz band. In this article, there is also a focus on achieving ultra‐wideband functionality to cater to the requirements of future wireless technologies. Incorporation with Circuits and Systems: Ongoing efforts are directed at seamlessly integrating these antennas with RF circuits and communication systems to enhance their practical utility and applicability. The exploration of unconventional fractal shapes and the utilization of advanced optimization algorithms present promising avenues for enhancing antenna performance and achieving miniaturization. This research contributes to the advancement of compact and efficient antenna designs for wireless communication systems. Detailed considerations are given to the 2.4 and 5.55 GHz bands to ensure compatibility with standard Wi‐Fi protocols. The designed circular fractal antenna is compared with the conventional circular patch antenna and the results were analyzed. At the resonating frequency of 2.4 and 5.55 GHz, circular patch antenna has a reflection coefficient (S11) of −18.1 and −13.51, respectively with a peak gain of 3.6 dBi, whereas, the designed circular fractal antenna shows an improved reflection coefficient, S11 of −22.0 and −15.5 dB at the same resonating frequency with a peak gain of 11.7920 dBi. The radiation pattern shows that the antenna radiated in unidirectional pattern with the front‐to‐back ratio of 101.4 which is higher than circular patch antenna. The miniaturized antenna is fabricated through photo etching process, tested, and validated.


Numerical Analysis of Optical Fiber Refractive Index in the Miniaturized Graded‐Index Fiber Probe

October 2024

·

12 Reads

We analyze the impact of the refractive index of optical fibers on the focusing properties of a miniaturized graded‐index (GRIN) fiber probe. The ABCD ray transfer matrix and characteristic parameters are employed to characterize the working distance, focusing spot size, and the depth of field effectively. The three‐dimensional (3‐D) function diagrams and two‐dimensional (2‐D) graphs are used to illustrate the impact of the no‐core fiber (NCF) refractive index and the center refractive index of GRIN fiber on the focusing properties. Numerical analysis results show that when the length of the NCF is 0.36 mm and the GRIN fiber is 0.1 mm, the variation of the refractive index of the NCF between 1.44 and 1.52 leads to the variation of the working distance in the range of 0.02 mm, while the focusing spot size varies in the range of 5 μm. A comparison of 12 probe samples reveals that a 0.02 difference in the center refractive index of GRIN fiber could result in a 0.16 mm variation in working distance and a variation in focusing spot size of over 3 μm. The experimental results indicate that alterations in the length of the fibers have a considerable effect on the focusing properties of the probe. In contrast, the range of variation in focusing properties with fiber refractive index is relatively limited.


Evaluation, tribological examination, and multi‐objective optimization of aluminum‐fly ash‐egg shell composites for sustainability

October 2024

·

42 Reads

This study addresses the technological challenge of enhancing the mechanical and tribological properties of Al 6082 alloy by incorporating fly ash (FA) and eggshell (ES) as reinforcing materials. The objective is to fabricate hybrid metal matrix composites using sand casting, with varying weight percentages of eggshell and fly ash: 4 wt% eggshell with 6 wt% fly ash, 5 wt% eggshell with 5 wt% fly ash, and 6 wt% eggshell with 4 wt% fly ash. The experimental methodology involved designing experiments with response surface methodology (RSM), considering applied load, sliding velocity, and sliding distance as variables. The outcomes showed that the composite with 6 wt% eggshell and 4 wt% fly ash exhibited superior hardness, tensile strength, and impact resistance compared to pure Al 6082 and other composite variations. Additionally, this composite reduced the coefficient of friction (COF) from 0.489 for the base Al 6082 to 0.265. ANOVA based on RSM identified sliding distance as the most influential factor affecting COF. Optimal conditions for minimizing COF were determined to be a 40 N load, 0.215 m/s sliding velocity, and 800 m sliding distance through confirmatory trials. Scanning electron microscopy (SEM) analysis of the worn surfaces provided further insights into the wear mechanisms. The research reveals that integrating the addition of eggshell and fly ash significantly enhances the mechanical properties and reduces the friction of Al 6082, with sliding distance being a critical factor in tribological performance. This investigation is distinctive due to its innovative use of dual reinforcement with fly ash and eggshell, which are abundant, cost‐effective, and rarely employed together in this context. The research thoroughly investigates various reinforcement ratios, providing vital insights into how they affect the composite's properties, particularly in terms of improving tribological properties. Using RSM and ANOVA makes the findings more precise and reliable. The investigation highlights sliding distance as the key factor affecting the tribological behavior of the composites.


Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform

October 2024

·

41 Reads

Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.


Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors

October 2024

·

5 Reads

Agricultural Internet of Things has become one of the most important data sources of agricultural big data. However, the wireless sensors equipped with agricultural Internet of Things is affected by many factors, and anomalous data inevitably exists during the data collection process. The existence of anomalous data leads to the deterioration of the agricultural data quality, which hinders the efficient development of agricultural data analysis. In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. The model first adds Gaussian noise to the input sequence and converts it into standard time series and image data. Subsequently, it is processed by different modal encoders and fuses the image and sequence modal data using Temporal Cross‐modal Attention module to enhance the perception of anomalous modal information. Finally, the fused data is reconstructed, and anomalous data are identified by the image and sequence decoders. Comparison experiments with several baselines prove the validity of the model proposed in this article, ablation experiments prove the necessity of the key modules in the model, and multiple sets of experiments are also designed to discuss the effects of hyperparameters.


Feasibility and design of nonlinear negative‐stiffness isolating approach for solid‐rocket‐motor‐type structures

October 2024

·

29 Reads

Solid rocket motor (SRM)‐type structures are popular due to their reliability, considering that service safety during transportation can be improved by applying advanced vibration control technologies. In this study, a negative‐stiffness‐enhanced isolation system (NSeIS) with appropriately designed linear and nonlinear properties was developed to vertically isolate SRMs subjected to transportation‐ and deployment‐induced vibrations. The NSeIS design, based on the combination of a negative‐stiffness device and vertical isolator, involved a clear mechanical model, physical realization, and mechanical properties. Parametric analyses were performed on a typical SRM controlled with a linear and nonlinear NSeIS and a conventional isolation system. Subsequently, a feasible parameter domain and design recommendations were deduced. Finally, design cases for the SRM for time‐domain verification were considered. The results revealed that the NSeIS offers a flexible and enhanced isolating effect through the parallel arrangement of the negative‐stiffness device and conventional isolators. For the motor‐type structure, NSeIS ensures marked enhancements in performance and multiple levels of mitigation effects. Thus, compared with a conventional isolator with the same damping, NSeIS achieves a more substantial negative‐stiffness effect for a large displacement response range owing to its nonlinear property. NSeIS can isolate more vibration‐induced energy, thereby suppressing the interface Mises stress, which is essential for SRM‐type structures.


Digital teaching resource management system for higher education

October 2024

·

16 Reads

At present, the management and utilization of teaching resources in higher education is characterized by problems, such as single function, simple structure, poor sharing, and weak management capacity. Based on this, this study proposes a multimedia information processing technique based on computer technology. The system utilizes Java programming language, Springboot framework, Servlet and other technologies to convert the data information through MySQL database. The main modules are student module, teacher module and administrator module. The established digital teaching resource management system is stable, simple to operate, clear interface and can meet the teaching needs of colleges and universities. The system is developed and utilized to maximize the sharing and integration of university teaching resources. This not only puts an innovative approach to teaching and learning, but also has very important practical implications. image


Experimental study on electricity generation performance of T‐type direct‐expansion solar PVT heat pump system

October 2024

·

10 Reads

Focusing on the electricity generation performance of a direct expansion solar PVT heat pump system, a novel T‐type optimized collector/evaporator was designed. The experiment used refrigerant R410a as the working medium and was conducted under the average winter ambient temperature of 16°C in Fuzhou. Through experimental comparison, the differences in panel temperature, electricity generation, and efficiency under different radiation intensities were tested against traditional PV panels and honeycomb PVT panels. The experimental results showed that the T‐type PVT system exhibited significant advantages under various weather conditions. The back panel temperature was reduced by 12.74, 8.52, and 13.06°C compared to traditional PV panels and honeycomb PVT panels, respectively, and it maintained a stable increase in electricity generation. The maximum instantaneous electricity output increased by 56.6% relatively, and the total electricity generation increased by 9.3% to 14.5% compared to traditional PV panels. The photoelectric efficiency reached 21.54% and 22.6%, surpassing traditional PV panels and honeycomb PVT panels, with measured values relatively increased by 1.7%, 2.38%, and 2.76%, respectively. Economic evaluation indicated that the T‐type PVT system could save on self‐generated electricity costs, achieving savings of up to 386.50 and 210.81 yuan/year compared to traditional PV panels and honeycomb PVT panels. The T‐type PVT system demonstrated clear advantages in self‐generated electricity costs, significantly reducing costs for application scenarios and providing strong support for the practical application of renewable energy technologies. image


On the mechanical behavior of polymeric lattice structures fabricated by stereolithography 3D printing

September 2024

·

54 Reads

The utilization of 3D printing technology has transformed the possibilities for design adaptability and manufacturability. This study delves into the mechanical response and energy absorptivity of resin‐based lattice structures when subjected to compression, specifically examining structures fabricated from Tough 2000 (ductile) and Rigid 10K (brittle) resin materials using a stereolithography 3D printer. The analysis encompasses various types of lattice designs (such as cubic‐primitive, circular, triangular, and hexagonal), gradient structures, and combined shape configurations with varying strut dimensions. The primary objective is to provide significant findings regarding the compressive performance of these resin lattice structures produced through 3D printing. Analysis results show that graded and combined lattice designs have better compressive behavior compared to regular shapes with the same strut thickness. In addition, and for strut thickness of 0.5 mm, combined lattice designs show better energy absorption capabilities compared to regular shapes.


A pore network modeling approach to bridge void ratio‐dependent soil water retention and unsaturated hydraulic conductivity curves

September 2024

·

58 Reads

In geotechnical engineering, understanding the relationship between soil permeability and deformation is essential, particularly for applications like earth dams, where compaction‐induced permeability reduction is crucial for performance optimization. In unsaturated soils, soil moisture content significantly impacts hydraulic conductivity. Traditionally, changes in unsaturated hydraulic conductivity have been linked to soil void ratios. However, a pore network modeling perspective reveals the significance of structural parameters, such as pore and throat size distribution and pore coordination number. This study introduces a pore network model to estimate unsaturated hydraulic conductivity based on void ratio‐dependent soil‐water retention curves. It examines how soil deformation at varying stress levels affects structural parameters and the water phase continuity. The model shows strong potential in predicting unsaturated hydraulic conductivity across different stress levels, aligning well with experimental data and established equations. Notably, the aspect ratio and coordination number parameters are most affected by stress levels. The study also presents relationships to describe changes in pore network structural parameters with soil void ratio, which can be used to predict soil‐water retention curves and unsaturated hydraulic conductivity at various stress levels.


Journal metrics


1.8 (2023)

Journal Impact Factor™


35%

Acceptance rate


5.1 (2023)

CiteScore™


27 days

Submission to first decision


$1,970 / £1,490 / €1,660

Article processing charges