Engineering Research: Perspectives on Recent Advances Vol. 9

Optimizing Quarry Equipment Maintenance Through Reconditioning of Components Subject to Abrasive Wear

Mihaela TODERAȘ — 2025-07-12

This study rigorously analyses the operational reliability and maintainability of critical metal components susceptible to abrasive wear within the technological equipment of the Pătârș basalt quarry. By integrating tribological wear parameter analysis with robust reliability and maintainability modelling, the research quantifies their direct impact on equipment efficiency and lifespan. We assessed components such as the Komatsu WA470 front loader bucket knife, Komatsu HB365 excavator bucket teeth, and the sorting station trough, alongside evaluating alternative materials like Hardox 400 steel and Pucest panels with perforated metallic inserts. Our methodology employed statistical distributions (lognormal and three-parameter Weibull) for reliability and maintainability, complemented by laboratory tribological tests on rock-metal pairs to quantify wear. Key findings reveal significant reliability degradation across all components with increased operating time, necessitating proactive maintenance. Notably, weld reconditioning proved highly effective, achieving a 68% increase in the operational lifespan of reconditioned bucket teeth. Furthermore, an economic analysis demonstrated that while alternative materials like Hardox 400 and Pucest have higher initial costs, their significantly extended operational lifespans (achieving 80% reliability for 157 and 251 days, respectively, compared to 105 days for the original material) promise substantial long-term cost-effectiveness through reduced replacement frequency and minimised downtime. This comprehensive study provides a robust framework for optimising quarry operations by strategically evaluating critical component reliability and implementing targeted maintenance interventions.

Modelling and Sustainable Management of Sludge in Drinking Water Treatment Plants: A Case Study from Meknes, Morocco

M.Farhaoui — 2025-07-12

Water management is a key pillar of sustainable development. Indeed, the rational use of water has become a condition for new investments in the water sector, as in many sectors. Optimising the production of drinking water is one aspect. This optimisation involves not only the choice of water resource use but also the management of by-products of the water treatment process to manage sustainably the exploited water resources. The objective of this study is to provide water treatment operators with a tool to attain the most effective management of the facility's by-products and in consequently optimise the cubic meter price of the treated water.

The city of Meknes is watered from two sources and a set of holes (14), The turbidity of water sources can vary depending on rainfall recorded in the region. A water treatment plant (600 l/s) was performed for the purification of water sources. Through this study, we focus on the modelling of the sludge volume produced by this plant. As in the construction field, WTP sludge is very important in the pottery sector. A study proposed the use of a mixture consisting of sludge (85%) and sand (silicon dioxide), 15%, in pottery manufacturing.

The objective is to design a model for calculating the sludge volume from the actual data recorded in the plant. The model can be used by the operator to predict the sludge volume and can also be used by the designers.

The results of this study demonstrated that the volumes calculated from the model constructed considering the data recorded at the station perfectly match the volumes produced, with a determination coefficient of 100%.

This paper has presented some preliminary results concerning the challenging task of modelling the sludge volume produced by the water treatment plant using a model. The application of this model can not only provide the operator with an effective tool for managing the station by-products but also provide designers with a formula to prevent over-/under design of structures. Therefore, these measures help to optimise the cost of production of drinking water and will play an important role in the sustainable development of water resources. Therefore, it is reasonable to consider this approach to be applied in the treatment plant for water with a similar turbidity level.

Nonlinear Analysis of PCC Bridge Abutment-Layered Soil Interaction System Subjected to Different Seismic Ground Motions

Rishav Kumar — 2025-07-12

The present study investigates the structural behaviour of a PCC bridge abutment-layered soil system subjected to seismic ground accelerations due to different time histories. The linear and nonlinear soil-structure interaction analyses are carried out using the latest version of ABAQUS/CAE 2024, considering a 2D plane strain approach. The material of PCC abutment is considered to behave in a linear manner, whereas the layered soil mass is considered to be a nonlinear material. To capture the real-world complexity of subsurface conditions, a layered soil profile with varying soil types is modelled, incorporating both linear elastic and nonlinear Mohr-Coulomb soil properties. This simulation also accounts for detailed soil-structure interaction (SSI) through surface-to-surface contact interfaces between the abutment and surrounding soils. To assess seismic performance, three notable Indian earthquakes of varying ground accelerations [India-Burma (1988), Chamba (1995), and Bhuj (2001)] are selected for dynamic time-history analysis. The dynamic simulations are conducted to evaluate the displacement response and stress distribution in the abutment and underlying soil strata. The applied boundary conditions and loading scenarios are defined to closely reflect realistic field conditions.

The results of analyses reveal that horizontal displacements are significantly amplified under dynamic loading, especially when nonlinear soil behaviour is considered. For instance, under the India-Burma earthquake, horizontal displacement due to linear analysis is 175mm, whereas the nonlinear analysis, it to be 482 mm. On the contrary, the vertical displacements remained relatively consistent, with minor variations across all cases, indicating a limited impact of seismic motion on vertical settlements.

The comparison between linear and nonlinear analyses reveals that nonlinear soil characteristics have a significant influence on structural behaviour and cause reduced stress concentrations and altered displacement patterns. The present investigations underscore the necessity of incorporating advanced soil models and SSI effects for reliable seismic assessment of bridge abutments. The findings offer valuable insights for engineers and researchers, supporting the design of safer and more resilient infrastructure in earthquake-prone regions.

Analysis of Diffusion Models with a Focus on Stable Diffusion Image Generation

Babychen Kunnel Mathew — 2025-07-12

Diffusion models have emerged as a powerful class of generative models that revolutionise image synthesis. By iteratively adding and subsequently removing noise from data, these models learn to generate high-quality images with remarkable detail and realism. Models like Stable Diffusion, leveraging U-Net architectures, demonstrate the efficacy of this approach, producing impressive results in image generation tasks. While potentially computationally more intensive than some other generative models, diffusion models exhibit notable advantages, including enhanced stability and a reduced propensity for mode collapse. The incorporation of positional encoding further enhances their ability to generate high-quality images by enabling the model to effectively process images at varying noise levels. Powerful, open-source diffusion model like Stable diffusion runs efficiently on consumer-grade hardware. It can generate photorealistic images from text descriptions and offers additional capabilities like image-to-image style transfer and upscaling. Stable Diffusion excels at transforming text prompts into visually stunning images. The latest version, Stable Diffusion 3, further enhances the model's ability to handle complex prompts and generate high-quality images. Additionally, Stable Diffusion's outpainting feature allows users to extend images beyond their original boundaries. This paper explores the basic diffusion concepts in generative AI, the mathematical formulation of forward and reverse diffusion, the structure of Denoising Diffusion Probabilistic Models (DDPMs), stable diffusion frameworks, comparative advantages of stable diffusion and other popular diffusion models (Eshratifar et al. A. E. 2024, C., Wu, H. et al. 2024, Pan, X. et al. 2022, Yang, J. et al. 2024).

Improved Path Planning Techniques for Ambient Energy-powered IoT Collection Systems

Pradeep Mullangi — 2025-07-12

Energy harvesting systems, when combined with Internet of Things (IoT) technologies, present a promising path toward self-sustaining and environmentally friendly power solutions. These systems harness ambient energy—such as solar radiation, wind, mechanical vibrations, and thermal gradients—and convert it into electrical energy to power IoT devices. This paper explores recent innovations in circuit design that support energy harvesting in IoT applications, emphasising improvements in energy capture, power management, and storage. Particular attention is given to Maximum Power Point Tracking (MPPT) algorithms, which optimise the energy extraction process from diverse and variable sources. The development of ultra-low-power circuit architectures is discussed as a means to enhance the performance of energy harvesting systems under minimal energy availability. The roles of supercapacitors and rechargeable batteries are examined as energy storage solutions, with a focus on their capabilities for buffering and maintaining consistent power delivery. Advanced battery management techniques—including dynamic charging methods and real-time capacity estimation—are also analysed. Voltage regulation, a key aspect of stable power supply in IoT systems, is addressed through recent advancements in low-dropout regulators (LDOs) and high-efficiency voltage converters. The integration of these energy harvesting systems with IoT platforms is explored, highlighting the design challenges and benefits in building energy-aware IoT applications. Finally, the importance of energy-efficient communication protocols and adaptive data processing algorithms is emphasised as essential components for minimising energy consumption in IoT networks.

Assessing the Trends towards Predictive Mapping of Graduates’ Skills to Industry Roles in Kenya: A Case Study of Software Engineering

Fullgence Mwachoo Mwakondo — 2025-07-12

Background: Long-term unemployment (LTU) has been a major concern in many countries, including Kenya. LTU would not be a trouble if job characteristics for each kind of worker, levels of education and skills, experience, and occupation were precisely known by new graduates. Employability of skilled graduates in the industry is a challenge not only because of the effect of unemployment duration, but also due to increased skills variation among both graduates and industry roles, emanating from the industry-academia gap.

Aim: The aim of this study is to investigate whether industry roles in the same occupation have similar academic requirements and establish learning trends in academia towards occupational industry roles.

Methodology: This study was conducted in the Kenyan software engineering industry and universities in academia in the month of May 2016. A descriptive survey research design was adopted in this study. Perception from 113 employees used as respondents and 24 examinations past papers from 5 Kenyan universities, both in the domain of software engineering, were involved. Two experts, a software engineering lecturer and a pedagogy lecturer, were used to extract data from the exam past papers after their reliability test was confirmed. Both descriptive procedures and non-parametric tests of hypotheses were conducted using SPSS version 16 software and .05 as the test limit for significance. A proposed model for mapping graduates’ skills to industry roles was used as the research model for the study, while for academic requirements analysis purposes, the model’s variables were double classified into two dimensions, i.e. knowledge or skill type and domain-specific or domain-general.

Results: This study showed that the most common job entry roles for software engineers after graduation are 'web programmer' and 'analyst programmer'. A frequency analysis of 17 role performance activities (RPAs) performed by software engineers at entry-level industry positions reveals that ‘design database’ is the most frequently performed activity (11%), while ‘manage project workflows’ is the least performed (2%). Regarding the proposed model, findings indicated that while domain-specific knowledge (χ²=2.44, P=.87) and skills (χ²=1.86, P=.93) for industry roles in the same occupation are similar, domain general knowledge (χ²=13.10, P=.04) and skills (χ²=16.151, P=.01) are significantly different for these industry roles. Further revelation indicated that, while academia trends towards various industry roles within the same occupation are fairly good for knowledge (80%) and poor for skills (45.7%), trends towards various industry roles within the same occupation are not uniform among universities.

Conclusion: Academic knowledge and skills requirements for occupational industry roles are not similar, and trends towards occupational industry roles are not uniform among universities. Therefore, students should select universities that have a higher trending profile for industry roles in order to increase their chances. It is also recommended that domain-specific knowledge and skills must be covered well during learning in academia.

Numerical Investigation of Ice Accretion Effects on Aircraft Wing Aerodynamics

Swetha S — 2025-07-12

A significant weather-related hazard to safe flight operations since the dawn of civil aviation has been aircraft icing. The term "aircraft icing" refers to the formation of ice on exposed wing surfaces when an aircraft travels through clouds of supercooled liquid droplets. This phenomenon mainly impacts flight safety as ice accumulation adversely affects the aerodynamic performance and controllability of aircraft. In particular, ice buildup on aerofoils reduces lift and the wing's stall angle, which may result in longitudinal instability of the aircraft.

This study numerically simulates ice formation on the NACA0012 airfoil to evaluate its impact on aerodynamic performance, focusing on lift, stall angle, and aircraft stability.

Three sophisticated mathematical tools—FENSAP-ICE, DROP3D, and ICE3D—were used to model the formation of ice over an airfoil. A Reynolds-Averaged Navier-Stokes (RANS) model was used to solve for the aerodynamic airflow field. an Eulerian approach was used to represent the ingestion of droplets and ice crystals, and the Boundary conditions used for the current ice accumulation simulations.

Simulation results, validated against experimental data, demonstrated that the mass of ice generated on the airfoil surface is greatly influenced by the period of ice accretion. It was shown that the pattern and magnitude of ice accretion were significantly influenced by the angle of attack (AOA). Length was found to be a major component in ice formation, as seen by the strong linear link between accretion time and ice mass.

The FENSAP-ICE system's three combined modules were successfully used to simulate ice accretion over an NACA 0012 airfoil. These results suggest that AOA and deposition time are important variables to be studied further, particularly in the context of blade cascades and cambered airfoils, where icing can have pronounced aerodynamic consequences.

Advanced Transformer Fault Prediction via LSTM and Digital Twin Integration

GVSSN Srirama Sarma — 2025-07-12

Transformers are the heart of electric power systems, and their operational state decides whether or not the power network is well-regulated. Electrical, mechanical, and thermal stresses cause some gases created during an operation to dissolve in insulating oil. The most significant tool for defect diagnostics in transformers is dissolved gas analysis (DGA). The time series prediction of dissolved gas levels in oil, when combined with dissolved gas analysis, provides a foundation for transformer fault diagnosis and an early warning. A long short-term memory (LSTM) based prediction model is developed in this paper to train the digital twin for identifying the essential fault in the transformer via DGA. The model is fed with three different gas concentrations as input. This study achieves the performance evaluation in terms of validation accuracy. The suggested model exhibits significant validation accuracy of 99.83%, as indicated by the analyses, thus aiding the early prediction of transformer maintenance. It can be validated that the LSTM model for fault identification and analysis using dissolved gas in the transformer has a lot of research potential. The study concluded that the trained digital twin integrated with the test transformer's condition monitoring system can precisely envisage the transformer's useful life. Its application in transformer online monitoring using a mobile device can be investigated.