Journal of Engineering Research and Reports

Research on the Diagnosis of Circulating Current Faults in the Metal Sheath of Cross-linked XLPE Cables under Different Working Conditions

Zhixian Zhu — 2025-09-20

This article deeply analyzes the distribution characteristics and variation laws of metal sheath circulating current under various typical fault conditions when XLPE high-voltage single core cables are connected by cross interconnection at 110 kV level. By comparing the different distribution characteristics of circulating current under various working conditions, the fault mechanism is revealed and theoretical basis is provided for state monitoring and protection strategies. By establishing a theoretical model of a cross connected system and based on the principle of electromagnetic induction, five typical fault scenarios are theoretically derived and quantitatively estimated. This article uses PSCAD/EMTDC simulation software to simulate the current values of the sheath ring during normal operating conditions, single-phase grounding short circuit of A-phase core, two-phase short circuit of BC two-phase core, open circuit of A-phase metal sheath, and two-phase short circuit of AB two-phase metal sheath. Compared with theoretical calculation values, it is found that the short-circuit fault of the core will excite a huge sheath current of kA level, and the fault phase current is highly concentrated. The fault of the metal sheath itself will cause abnormal current of 10A level. Among them, the short-circuit between sheath phases significantly increases the fault phase current, while the open circuit of the sheath causes the fault phase current to drop sharply to zero and produce slight disturbance in the non fault phase.

Prediction of Nanofluid Specific Heat Capacity using Supervised Regression Models

Yomna Zakarya Abo Amra — 2025-09-25

The accurate prediction of the specific heat capacity (SHC) of nanofluids has become increasingly important for industrial and scientific applications. However, traditional experimental methods of determining SHC are often costly, labor-intensive, and subject to measurement uncertainties. Consequently, there is a need for reliable and efficient predictive models. The aim of this study is to develop and evaluate supervised machine learning regression models capable of predicting the SHC of nanofluids based on their key thermophysical features. A data set of 517 records containing the thermophysical features of nanofluids is collected and preprocessed. The dataset features include nanoparticle type, base fluid, base fluid temperature, and nanoparticle volume fraction. Supervised regression models such as Gradient Boosting, XGBoost, AdaBoost and Decision Tree Regressor, were applied and evaluated. The Gradient Boosting model showed the best performance with R² score of 99.60%, followed by XGBoost (97.43%), AdaBoost (97.07%) and the Decision Tree Regressor (86.73%). These findings demonstrate the capability of machine learning regression models in predicting SHC, offering a cost-effective and rapid alternative to experimental measurements. The results highlight the potential of such approaches to support the design and optimization of advanced thermal management and energy systems.

Unlocking the Potential of Collaborative Digital Tools: Key Factors Influencing Their Usage among Workers in the Ghanaian Construction Industry

Emmanuel Tekpe — 2025-09-25

Collaborative Digital Tools (CDTs) are meant to solve key issues such as communication breakdowns, delays in workflows, and misunderstandings of project plans. By allowing real-time updates, progress tracking, and smooth coordination across different locations, they improve how teams work together. Despite their recognised benefits of reducing errors and improving productivity, CDTs usage remains inconsistent across construction firms in Ghana. This study therefore, establishes the key factors influencing the usage of CDTs within the Ghanaian construction industry. A structured questionnaire survey was administered to workers of 20 registered construction firms across Accra, Kumasi, and Takoradi, resulting in 545 valid responses out of the 650 distributed questionnaires. The firms included in the study ranged from small- to large-scale firms (D4K4 to D1K1). A purposive sampling technique was employed in the study, ensuring that respondents had prior experience with the use of CDTs. Data was analysed using the Statistical Package for the Social Sciences (SPSS), version 20. The F-statistic of 1459.594 with a significance value (p) of 0.000 (less than 0.05) indicates that the model explains a significant proportion of the variance in the CDTs. The high t-value (t=38.205) and low p-value (p<0.05) further underline the statistical significance of these factors. The data analysis comprised both descriptive and inferential statistical methods. WhatsApp, Microsoft Teams, AutoCAD, and BIM were identified as the most widely used CDTs for enhancing communication, data management, and project monitoring in the Ghanaian construction industry. However, factors such as security and data privacy concerns, recurring costs of data subscriptions and software updates, technological challenges, post- maintenance expenses, and training requirements hinder the widespread use of CDTs. Hence, ineffective usage of CDTs in the Ghanaian construction industry could be attributed to these inhibiting factors. The study found that training, leadership engagement, and cost-mitigation strategies are vital for CDT adoption. The findings offer insights for construction firms, developers, and policymakers to accelerate digital transformation in Ghana's construction sector.

Bio-oil Production from Paper Waste and Paper Cups via Pyrolysis: A Sustainable Approach to Waste-to-Energy Conversion

Hycent Jacob — 2025-09-26

This paper examines the paper waste and disposable paper cup pyrolysis to production of bio-oil which provides a viable waste-to-energy option. Traditional ways of disposal like landfill and incineration cause pollution and emission of greenhouse gases to the Environment. Pyrolysis transforms paper wastes into bio-oil, bio-char and syngas. The quality and bio-oil yield were systematically tested with respect to temperature (400-600°C) and reaction time (10-60 minutes) on the quality of bio-oils. The highest yield of bio-oil of 47.3wt% with a heating value of 24.6 MJ/kg was at optimal conditions (500°C, 20 minutes) comprising phenolic, organic acids, and levoglucosan that could be used in heating and chemical feedstock. The process cuts down the garbage of the landfill by about 66%, encourages renewable energy, and circular economy principles.

Grid Integration of Renewable Energy for Reliable and Efficient Power Supply in Nigeria

Ubong J. Joseph — 2025-09-27

This research investigates the integration of renewable energy sources such as solar and wind into Nigeria’s power grid. The main objective is to develop a simulation model that examines how renewable energy systems affect power reliability and supply efficiency under different load and generation conditions using MATLAB. The simulation setup includes solar photovoltaic arrays and wind turbines connected to the grid through inverter systems and controlled using standard power system techniques. Changes in solar radiation and wind speed are introduced during the simulation to reflect real-life variations in renewable energy generation. The results show that, despite their variable nature, solar and wind systems can support the grid effectively when combined with proper control, forecasting, and storage technologies. The study demonstrates that integrating renewable energy into the grid can reduce power shortages, improve electricity access, support a more stable and reliable power supply as well as contribute to sustainable development goals by promoting the use of renewable energy in Nigeria.

Modelling of a Steady Micropolar Nanofluid Flow along a Wedge

J.B. Mwamunga — 2025-09-28

Micropolar fluids are polarized fluids hence have better thermal conductivity properties. The incorporation of nanoparticles into micropolar fluids further enhances their thermal conductivity performance. The gyration characteristic of these fluids is significant in fields such as astrophysics, stellar dynamics, and dynamic theory. Flow along wedge structures has important application in aerodynamics, hydrodynamics heat transfer and industrial processes. The main aim of the study is to investigate the steady micropolar nanofluid flows along a wedge and determine the effect of microparameter, wedge parameter, concentration and unsteadiness, magnetic field on the fluid flow. In this study, a two-dimensional steady flow of a micropolar nanofluid along the surface of a wedge with a uniform surface temperature, a uniform upstream velocity, pressure, temperature with a perpendicularly applied magnetic field was considered. By incorporating gyration and inertial effects into the Navier-Stokes equations, the flow is modeled and converted to ordinary differential equations through similarity transformation. Then solved numerically by Fourth-Order Runge-Kutta method, in combination with the shooting technique and the bvp5c solver in MATLAB. Results reveal that increase in magnetic and micropolar parameters reduces the fluid velocity due to higher rotational viscosity, however, micropolar effects increases the temperature, solute concentration, energy and mass transfer. Additionally, skin friction is highest at the least wedge parameter and magnetic parameter, fluid velocity and enhances mass heat and mass transfer regardless of the magnetic field strength. Higher values of the micropolar parameter led to reduced velocities in both primary and secondary directions. An increased micropolar effects raised the temperature and solute concentration. Heat and mass transfer is highest at the highest wedge parameter irrespective to the strength of the magnetic field. Skin friction increases with an increase in wedge parameter. Sherwood numbers resulted in decreased solute concentration and elevated temperatures. The reduced solute dispersion and enhanced temperature retention.

Enhancing Soil Stability: Evaluating the Effectiveness of Terrasil Nanotechnology in Soil Stabilization

Akolade A. S. — 2025-10-03

Background: Conventional techniques for stabilizing soil frequently use expensive and occasionally hazardous materials like cement and artificial polymers. Thus, stabilization of soils with chemical additives has drawn up a lot of interest from construction sector, because it can improve the engineering qualities of problematic soils. Nanomaterials have the potential to improve soil qualities without the disadvantages of conventional additives because of their special qualities and high surface area to volume ratio. Terrasil is a water-soluble nanochemical that has demonstrated promise in improving soil qualities by decreasing permeability and boosting strength, thus making it an appealing choice for soil stabilization.

Aims: In this study, the performance and resilience of soils stabilized with Terrasil based on nanotechnology was investigated

Methodology: Two soil samples, Sample A (cohesive) and Sample B (non-cohesive) were treated with Terrasil Nano chemical, at dosages of 0%, 3%, 6%, and 9%. The additive, predetermined dosages of Terrasil, were first diluted in water and then thoroughly mixed with the soil samples to ensure uniform distribution. Geotechnical properties of soil such as CBR, shear strength and consolidation were investigated. The effectiveness of Terrasil stabilization was then evaluated through these geotechnical tests, and the results were compared with untreated soils (0%) and benchmarked against typical performance ranges reported for traditional stabilizers such as lime and cement.

Results: The results shows that there is significant improvement in both CBR and shear strength with increasing Terrasil dosage, particularly for Sample A, where cohesion increased markedly from 23 kN/m² at 0% to 75 kN/m² at 9% dosage. Total settlement decreased significantly with increasing dosage, according to settlement analysis, falling from 0.0369 mm to 0.02405 mm for Sample A and from 0.03117 mm to 0.02428 mm for Sample B. The coefficient of consolidation also demonstrated a downward trend, indicating improved soil stability and reduced compressibility.

Conclusion: This confirms that Terrasil is a dependable additive improving the resilience of soils for performance applications related to highway construction and civil engineering.

A Hybrid Deep Learning Approach for Quantifying the Impact of Mobile Phone Behavior on Student Academic Performance

S. Vimala — 2025-10-03

The study aimed to quantify the influence of students’ mobile phone behaviors—including daily screen time, non‐educational versus educational app usage, study‐to‐phone ratios, and nighttime device checks—on academic performance and to develop a real‐time digital wellness alert system for early identification of at‐risk learners. To achieve this, researchers collected digital activity logs and academic records from 5,000 consenting students across middle school, high school, and university during the 2023–24 academic year, engineered features such as screen time totals, app category hours, study‐to‐phone ratios, and sleep metrics, and then trained a hybrid deep learning model that combines convolutional neural networks, long short‐term memory units, and an attention mechanism. This CNN‐LSTM with attention was benchmarked against four traditional classifiers (Random Forest, Gradient Boosting, Support Vector Machine, and Naive Bayes), each optimized via grid search and validated through five‐fold cross‐validation, with all models evaluated on accuracy, precision, recall, F1‐score, and ROC‐AUC. The deep learning approach outperformed all baselines, achieving 92% accuracy and over 91% on every other metric compared to the best traditional model’s 88% accuracy—and revealed clear behavioral thresholds: over four hours of non‐educational app use corresponded to a 20% performance drop, while maintaining a study‐to‐phone ratio above 2 : 1 yielded a 15% grade improvement; heavy social media and gaming use led to declines of 14% and 16%, respectively, whereas educational app engagement produced a modest 3.5% boost. The novelty of this work lies in its integration of convolutional, sequential, and attention layers to detect critical usage spikes, the establishment of precise intervention benchmarks, and the demonstration of how embedding such predictive models into educational dashboards can shift student support from reactive remediation to proactive wellness promotion.

The Potential of Hydrogen Gas in Gas Lifting Oil Well

Livinus, A. — 2025-10-03

Hydrogen gas usage is expanding into various sector of our economy due to its physio-chemical properties, as well as political and economic attentions in response to rising energy demand and low carbon requirements. During the net-zero carbon transition period, oil and gas industries will continue to devise new strategies to improve oil production and also meet the low carbon requirements. Firstly, this work presents a brief overview of the production, transportation, compression, storage, deblending and usage of hydrogen gas. The potential of using hydrogen-natural gas blends for gas lifting oil wells was studied, considering Well-X (that has experienced natural gas lift for years, and a foam-assisted gas lift trial was currently conducted) n the Niger Delta. The gas lifted well was modelled to match the production data from the test conducted on Well-X pre-foam assisted gas lift field trial on the well. Then, further analysis of the well performance was carried out. Oil rate increment between 10 and 22 stb/d was obtained from the well performance simulations for various hydrogen-natural blends, which is insignificant when compared to the reported 280 bbls/d oil rate increment of the foam-assisted gas lift field trial. Furthermore, irrespective of the hydrogen-natural gas blends, gas injection rate has significant influence on the profitability of the production strategy. However, the 30 % hydrogen-natural gas lift case has the least positive NPV values, due to the high electrolytic hydrogen production costs of $1.11 per kg considered.

RV Reducer Key Component Dynamic Simulation and Stress Analysis

Pang Bo — 2025-09-29

The RV reducer has a series of advantages such as high transmission efficiency, small size, light weight, large load-bearing capacity, smooth movement, low noise, and long service life, which is of great significance for studying its dynamic characteristics. This article takes the RV-110E type reducer as the research object, establishes a parametric 3D model in SolidWorks, and uses Adams to establish the dynamic model of the RV reducer. Through simulation analysis, the force analysis of key components such as the crankshaft is obtained. This research method has certain guiding significance for the force on the key component crankshaft of the RV reducer and the prediction of the component's service life.

Dynamic Simulation Analysis of Wind Power Crane Multi-body System

Hongze Liu — 2025-09-22

As the wind power industry expands into low-wind-speed regions, traditional wind turbine installation equipment struggles to adapt to complex working conditions such as rugged terrain, weak ground-bearing foundations, and confined construction spaces. Developing new-type non-attached wind turbine cranes suitable for these special environments has become an urgent engineering challenge.

A wind turbine crane is specialized equipment used for the loading and unloading of wind turbine components, as well as an indispensable piece of engineering machinery in the development of modern green energy. During hoisting operations, the wind turbine blade inevitably experiences pendulum motion due to influences such as inertial force and centrifugal force.Aiming at the special construction conditions of low-wind-speed wind farms and the hoisting requirements of high-tower wind power equipment, this paper focuses on the research of a wind turbine crane that is efficient, safe, non-attached, adaptable to weak ground-bearing foundations, and suitable for limited construction areas. Using co-simulation technology with ANSYS and ADAMS, a rigid-flexible coupled dynamics model of the truss structure and wire rope was established: the boom and wire rope were flexibly processed in ANSYS to generate modal neutral files, which were then imported into ADAMS to complete the multi-body system modeling and simulation analysis. Through dynamic response analysis under typical working conditions such as slewing, hoisting, and luffing, it was found that the maximum dynamic load on the boom is 520,000 N (during hoisting) and the maximum partial load is 26,000 N (during operation). Moreover, the displacement fluctuation of the load during slewing does not exceed 1 m, verifying the overall stability of the non-attached tower crane boom during hoisting operations. This study provides key technical references for the optimized design of new-type wind turbine cranes.

Research on the Principle and Application of Vortex-Induced Vibration Control Based on Nonlinear Energy Sink

ZhuoXian Wang — 2025-09-28

In order to solve the harm of vortex-induced vibration (VIV), this paper studies the VIV control performance of multi-type nonlinear energy sink (NES) and tube-in-tube (PIP) structures. The multi-degree-of-freedom NES (MDOF-NES) with low linear stiffness coupling control is the best, and the mass block affects the key characteristics of the limit cycle vibration (LCO), and the performance is better than that of the classical type I NES. Rotary NES (R-NES) has better suppression of large mass ratio cylinders, and the mass ratio and rotation radius improve the performance. The damping parameters need to be designed in combination with the mass ratio of the cylinder. The NES with combined nonlinear damping explicitly emphasizes the influence of the control response condition and the amplitude of the external excitation on the frequency detuning coefficient interval. The vertical vibration control performance of NES with cubic stiffness is related to the natural frequency of the main structure. When the frequency is ≥ 3 Hz, the self-weight can be ignored. When the frequency is ≤ 0.8 Hz, the static vertical displacement of the tuned mass damper (TMD) is greater than that of the TMD. The NES stability emphasizes that the system response is caused by the saddle-node bifurcation of the limit cycle of the coupled system, and two conditions need to be satisfied to strengthen the energy transfer. The optimized PIP structure optimizes the flow field and VIV suppression through three-dimensional fluid-solid coupling and computational fluid dynamics simulation, and has the function of heat preservation, which provides a ' heat preservation + vibration reduction ' scheme for marine engineering. Key conclusions: Low linear stiffness MDOF-NES is suitable for nonlinear energy transfer vibration control, and reasonable parameter PIP is preferred for marine engineering.

AI-Powered Digital Twin Platforms for Next-Generation Structural Health Monitoring: From Concept to Intelligent Decision-Making

Toheeb Abbey Animashaun — 2025-09-22

Combining the Artificial Intelligence (AI) together with Digital Twin (DT) technologies is redefining Structural Health Monitoring (SHM) to shift maintenance of the infrastructure to proactive across aspects. The design of a digital twin framework that incorporates a cadre of machine learning and neural-network-based tools into a predictive maintenance approach that can continuously sense, learn, and respond through closed-loop feedback. The framework is based on sensor networks using the IoT, sophisticated models of AI, and immersive visualizations and can provide real-time knowledge about the structural state. Field applications within civil infrastructure, aerospace and renewable energy have proven it to be effective at predicting remaining useful life and limit downtime, improve safety and minimize costs of operations. The results indicate the potential of AI-powered digital twins to establish self-sustaining SHM systems and lead to more resistant and intelligent infrastructure.

AI-driven Innovations in the Indian Healthcare System: An Update (2025)

Srivarshan M — 2025-09-22

AI shines like a ray of hope. AI, which is powered by enormous medical data, makes a richer picture of one's own health. AI is transforming the Indian healthcare system by enabling early disease detection, personalised treatment, and operational efficiency. Artificial Intelligence (AI) is revolutionising the Indian healthcare system by enabling early disease detection, personalised treatment, and operational efficiency. The review aims to investigate AI-driven innovations in the Indian Healthcare System. With India's vast population, shortage of healthcare professionals, and limited resources, AI offers innovative solutions to bridge the healthcare gap, particularly in rural and underserved areas. AI can identify latent cancers and tuberculosis early, save lives, and screen populations to detect risk factors and forecast outbreaks. It can also tailor healthcare based on a patient's lifestyle and medical history, optimising treatment effectiveness and reducing side effects. However, AI faces challenges such as data privacy, robust infrastructure, and biased algorithms. AI integrated with mobile-based diagnostic kits helps Acculi Labs and similar startups deliver affordable healthcare in rural India. AI-powered portable devices can test blood, detect infections, and recommend treatments even without specialised doctors. India, as a healthy tech ecosystem, can address these issues by investing in AI research, building data infrastructure, and setting up ethical frameworks. AI applications in healthcare include early diagnosis, screening, treatment, and rehabilitation. With a population of over 1.4 billion and increasing mobile and internet penetration, India can emerge as a global leader in AI-driven healthcare innovation if these challenges are systematically addressed.

Characteristics and Control of Nonlinear Flutter in Extra-Large-Span Bridges

Xin Li — 2025-09-29

As main spans of bridges approach the 2,000 m class, flutter exhibits significant structural and aerodynamic nonlinearities. Traditional linear theories, which focus on a single critical wind speed, fail to explain post-critical behaviors such as limit-cycle oscillations, hysteresis, and multistability. This review synthesizes recent experimental, theoretical, and computational advances to elucidate key characteristics and fluid-structure interaction mechanisms in nonlinear flutter. It highlights amplitude-dependent aerodynamic and structural damping, flutter derivatives, multi-modal interactions, and vortex-induced–flutter coupling. The paper summarizes modern methods for identifying amplitude-varying parameters through free and forced vibration tests, nonlinear frequency- and time-domain analyses in 2D and 3D, and multi-modal coupling solvers, including two-layer iterative eigen analyses and rational-function–based approaches. Control strategies—such as central stabilizers, inverted-L guide vanes, and bio-inspired flexible devices—are reviewed, along with TMD optimization that explicitly incorporates aero-structural nonlinearities. Flow-field diagnostics provide mechanistic insights, while deep learning and reinforcement learning show potential for nonlinear aerodynamic modeling and aerodynamic-shape optimization. Future challenges include developing generalizable self-excited force models, efficient 3D multi-modal solvers, addressing complex non-uniform mountainous wind fields and multi-physics coupling, and transitioning from response prediction to resilience-based design and reliability standards, supported by open benchmarks and data sharing. This review aims to facilitate mechanism discovery, modeling, and stabilization of nonlinear flutter in long-span bridges.