Al-Noor Journal of Engineering Management and Computer Science
Al-Noor Journal of Engineering Management and Computer Science (NJEMCS) is an international scholarly refereed research journal which aims to promote the theory and practice of technology, innovation, and engineering management. The journal links engineering, science, and management disciplines. It addresses the issues involved in the planning, development, and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organization. It covers not only R&D management, but also the entire spectrum of managerial concerns in technology-based organizations. This includes issues relating to new product development, human resource management, innovation process management, project management, technological fusion, marketing, technological forecasting and strategic planning.
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Analysis Failure Modes for Design Reliability to Improve Performance of Mechanical Equipment (Case study of a water pump)
This paper presents a methodology for failure analysis and performance optimization of industrial components while ensuring optimal design reliability. This approach integrates Failure Pattern and Effects Analysis (FMEA) with Failure Tree Analysis (FTA) and is applied to an electric motor driving water pumps. The research aims to identify the most impactful failures, determine their causes, and propose alternatives that significantly reduce maintenance costs. An optimal distribution (exponential distribution) of the data sets was proposed using Minitab software. The design is validated using the proposed methodology to continuously evaluate the failure pattern. These results guide the intended design modifications, which are subsequently verified through iterative testing of the relevant equipment components.
Copyright (c) 2026 Al-Noor Journal of Engineering Management and Computer Science https://creativecommons.org/licenses/by-nc-sa/4.0/
An Engineering Approach to Explainable Artificial Intelligence for SQL Query Optimization: Design and Evaluation of the OptiMind Framework
Query optimization remains a critical challenge in Relational Database Management Systems (RDBMSs) because it directly affects execution efficiency, resource utilization, and overall database performance. Traditional cost-based and rule-based optimizers rely on statistical estimation and heuristic plan enumeration methods that often perform poorly when handling correlated predicates, skewed data distributions, and complex multi-join workloads. Although recent learned query optimization approaches have improved cost estimation accuracy, many existing solutions operate as black-box models with limited interpretability, weak workload adaptability, and insufficient support for database administrator (DBA) analysis and decision-making. This paper presents OptiMind, an explainable intelligent framework for SQL query optimization that integrates semantic SQL analysis, workload-aware behavior profiling, adaptive feature intelligence, and interpretable optimization scoring into a unified recommendation system. Unlike traditional optimization pipelines, OptiMind emphasizes transparency by generating human-readable optimization recommendations through clause dependency analysis, operator behavior profiling, workload-aware feature ranking, and feature-attribution scoring. The framework improves both optimization accuracy and explainability while maintaining adaptability across varying workload environments and query structures. OptiMind was evaluated using benchmark and enterprise workloads, including JOB, IMDB, SQLShare, Spider, and enterprise traces. Experimental results demonstrate that the framework reduces mean absolute error (MAE) by 18.4%–31.7% compared with PostgreSQL’s native optimizer and achieves execution speedups ranging from 1.31× to 2.14× on complex multi-join queries. Additionally, OptiMind achieved an interpretability score of 4.51/5.0, confirming strong explanation quality, recommendation usefulness, and practical effectiveness for transparent, workload-adaptive, and explainable SQL query optimization in modern database systems.
A Review of Feature Selection Techniques for Medical Datasets
Feature selection is an important part of machine learning, especially in medical research with high-dimensional datasets. Selecting relevant features can improve the accuracy, interpretability, and efficiency of prediction models. There are three feature selection methods, i.e., filter, wrapper, and embedding methods. In this paper, we analyze these algorithms applied to medical datasets; the basic idea of the most popular algorithms in each category is described, and advantages, efficiency, and disadvantages are discussed. The paper also describes the problem of complexity, the number of samples, and class imbalance in medical datasets. Finally, the analysis offers some clues for the most effective feature selection strategies and possible paths for future research in the medical field. This review is expected to serve as a useful guide for researchers and practitioners who are interested in choosing the best features to optimize the machine learning models of medical applications.
Feature selection, Filter methods, Wrapper methods, Embedded methods.
AI, image processing, deep learning, machine learning
Artificial Intelligence Integration in Enterprise Resource Planning Systems: Operational Efficiency and Strategic Organizational Performance in Manufacturing Environments
The rapid advancement of artificial intelligence (AI) has significantly transformed enterprise resource planning (ERP) systems, shifting them from traditional transactional platforms into intelligent decision-support environments. This study investigates the integration of AI technologies, including machine learning, predictive analytics, natural language processing, and generative AI—within ERP systems and evaluates their impact on both operational efficiency and strategic organizational performance. From a management perspective, AI-enabled ERP systems enhance decision-making quality, improve resource allocation, and support strategic planning by enabling real-time data analysis and predictive insights. In manufacturing environments, these systems contribute to reduced operational costs, improved productivity, optimized supply chain performance, and enhanced organizational agility. The study adopts a qualitative case study methodology based on secondary data from industry reports and documented ERP implementations, focusing on Champion Fiberglass as a representative manufacturing organization. The findings demonstrate that AI integration not only improves automation and forecasting accuracy but also strengthens organizational resilience, supports digital transformation, and enhances long-term competitiveness. Overall, the research highlights AI-powered ERP systems as strategic enablers of enterprise transformation, linking technological innovation with managerial effectiveness and operational excellence.
AI, ERP, Cloud computing, Generative artificial Intelligence
Information Systems Artificial Intelligence, Business Administration, Management Information Systems
ArSLT-RGBNet: RGB-Only Continuous Arabic Sign Language Translation via Spatiotemporal Encoding and Arabic LLM Decoding
Arabic Sign Language (ArSL) is the primary communication medium for an estimated 35 to 40 million deaf individuals across 22 Arab nations, yet no prior system has achieved gloss-free, end-to-end sentence-level translation of continuous ArSL video into fluent Arabic text via an Arabic-native large language model. Existing approaches depend on depth sensors unavailable in consumer hardware, require costly gloss annotations, or employ multilingual decoders ill-suited to Modern Standard Arabic morphology. We present ArSLT-RGBNet, the first annotation-efficient, RGB-only framework for continuous Arabic Sign Language translation. The architecture integrates three components: a three-stream spatiotemporal visual encoder combining ViT-B/16, VideoMAE-Base, and an Optical Flow CNN for spatial, temporal, and kinematic feature extraction, fused via Adaptive Cross-Modal Attention; a Q-Former Visual-Textual Alignment bridge with CTC-based temporal compression and 32 learnable query tokens; and AraGPT2-large fine-tuned through Low-Rank Adaptation, updating 0.49 percent of parameters. Under six-fold leave-one-signer-out cross-validation on ArabSign, the framework achieves BLEU-4 of 33.7 percent, ROUGE-L of 59.6 percent, METEOR of 47.1 percent, ChrF of 54.2 percent, BERTScore-F1 of 0.847, and WER of 0.48, surpassing all five adapted baseline systems with statistical significance and improving upon the published multi-modal benchmark despite relying solely on RGB input. The Arabic-native decoder contributes 12.9 ROUGE-L points over the strongest multilingual baseline. Human evaluation by two certified ArSL practitioners yields Adequacy of 3.84 out of 5 and Fluency of 4.01 out of 5, with agreement of 0.74. Ablation confirms the VideoMAE temporal stream as the largest individual contributor.
A Systematic Review of Integrated Multi-Physics and AI-Based Modeling of Photovoltaic–Thermal (PV/T) Systems Enhanced with Porous Fins Using ANSYS, Python, and MATLAB for Improved Energy Performance
Photovoltaic-thermal (PV/T) hybrid systems are among the most promising opportunities for high density solar energy conversion, as they can simultaneously extract electrical and thermal energy from a single collector area. Despite a long history of research, coupled thermo-fluid and photovoltaic transport processes underlying PV/T operation remain difficult to model accurately due to highly non-linear, multi-scale interactions between radiation absorption, charge carrier transport, heat transfer in the cooling channels, and deformation. This scoping review collates and critically synthesizes the latest advances in the integration of three computational approaches - finite-volume/finite-element-based multi-physics simulation (ANSYS), data-driven and physics-informed artificial intelligence (Python), and numerical optimization and signal processing (MATLAB) - for PV/T systems incorporating porous fin structures. Adhering to the PRISMA-2020 framework, 178 research articles from 2010-2024 were reviewed, with 94 primary studies included for comprehensive analysis. Major insights include that porous fin configurations, such as metal-foam and lattice-structured fins, can boost overall PV/T system energy efficiency by 8-22 percentage points relative to traditional channel designs, largely by raising the convective heat transfer coefficient on the absorber plate, while lowering parasitic pump power. ANSYS Fluent-Mechanical simulations show that thermal-mechanical stresses at fin-absorber interfaces can reduce PV/T durability without gradient porosity optimization. Python-based AI surrogates (neural networks, Gaussian processes, XGBoost) predict thermal and electrical outcomes with less than 2% error within six orders of magnitude faster than CFD simulations, allowing real-time design optimization. MATLAB genetic algorithms and particle swarm optimizers reliably converge to Pareto-optimal designs that achieve 8-31% higher exergy efficiency compared to single-objective designs. The review highlights key research opportunities, including the lack of standardized uncertainty-quantification methods, lack of experimental testing of AI surrogates under degradation conditions, and lack of multiyear durability experiments, and outlines a vision for digital-twin PV/T systems integrating all three simulation platforms
Analysis of the risks of stranded assets in the Iraqi oil sector under global climate change policies: An economic assessment for the period 2020–2040
This study discusses the economic threats facing Iraq because of the global shift towards a low-carbon economy. It focuses on the concept of "stranded assets"—oil resources or infrastructure that may lose their economic value before being fully depleted due to changes in climate policies, technology, or global demand. Oil constitutes more than 90% of Iraq's revenues, making the economy vulnerable to any decline in global oil demand. The study employs a theoretical and analytical approach, reviewing three potential climate scenarios and their projected impacts up to 2050. It indicates that the most severe scenario (a 1.5° trajectory) could lead to a collapse in oil revenues exceeding 80%, rendering a significant portion of Iraq's reserves unusable. The study recommends restructuring the Iraqi economy by diversifying income sources, investing in renewable energy, establishing a sovereign wealth fund to manage oil revenues, and restructuring oil contracts to align with the global shift. It also calls for the establishment of a national council for economic transformation. The study emphasizes that ignoring these transformations is a gamble with Iraq's future, and that successful models like Norway and Saudi Arabia should be studied as examples, while Nigeria serves as a cautionary tale about the failure of wealth management. This research constitutes a call for a radical rethinking of Iraq's development model to ensure the sustainability of its post-oil economy.
The Use of Spectral Methods in Solving Boundary Value Problems and the Study of Their Numerical Efficiency
This research aims to investigate the use of spectral methods in solving boundary value problems and to analyze their numerical efficiency in terms of accuracy, convergence rate, and numerical stability, with a comparison to some traditional numerical methods. Boundary value problems are among the fundamental topics in applied mathematics due to their central role in modeling many physical and engineering phenomena. However, the complexity of differential equations and the coupling of boundary conditions often make it difficult to obtain exact analytical solutions, which necessitates the use of numerical approaches. The study adopts an analytical approach to present the theoretical framework of boundary value problems, clarifying their concept and the types of associated boundary conditions, in addition to reviewing traditional numerical methods and the limitations of their efficiency. A numerical–applied approach is also employed to examine spectral methods, particularly those based on Fourier series and Chebyshev polynomials, by explaining their mathematical foundations and the mechanisms for implementing different boundary conditions. The applied part includes solving selected models of boundary value problems using spectral methods, conducting a detailed analysis of numerical error and convergence rate, studying numerical stability, and evaluating numerical efficiency through comparisons of accuracy and computational time with some conventional methods. The results demonstrate that spectral methods achieve exponential convergence when dealing with smooth solutions, enabling high accuracy to be obtained using a relatively small number of degrees of freedom, which positively affects computational cost. The study also shows that Fourier-based spectral methods are more suitable for problems with periodic boundary conditions, whereas Chebyshev-based methods exhibit higher efficiency in handling non-periodic problems. The research concludes that spectral methods represent an effective numerical option for solving high-accuracy boundary value problems, emphasizing the importance of proper mathematical formulation and accurate implementation of boundary conditions to ensure numerical stability and efficiency.
Video compression by hybrid neural networks (CNN+RNN(LSTM)) algorithm and generative adversarial network (GAN) algorithm
As the need for high-quality video increases it has become necessary to develop smarter compression methods. Artificial neural networks (ANNs) have emerged as an effective tool in this field enabling them to extract the most important features from video and minimize temporal and spatial redundancy resulting in an efficient and high-quality compressed representation. Some of the most prominent models used are: Hybrid neural networks (CNN+RNN(LSTM)) Hybrid neural networks combine convolutional neural networks (CNNs), recurrent neural networks (RNNs), and especially long short-term memory (LSTM) units to extract spatial and temporal features from data, GAN, GANs compress video files while maintaining high perceptual quality. They encode each frame into a compressed latent representation, allowing for realistic reconstruction. This method is efficient, making it ideal for video transmission applications where perceptual accuracy is more important than perfect pixel accuracy. These techniques enable efficient compression without significant quality loss, representing a paradigm shift in modern video compression techniques. After analyzing the results the highest compression ratio (94.75) was achieved by compressing using the GAN algorithm. The highest PSNR (36.08) was achieved by CNN+RNN(LSTM) compression.
Copyright (c) 2025 Al-Noor Journal of Engineering Management and Computer Science https://creativecommons.org/licenses/by-nc-sa/4.0/
Video Denoising Using Convolutional and Deep Neural Networks
Removal of noise from video files has become as one of the most important academic worries and especially with the growing reliance on video data in domains such as security, autonomous vehicles, and entertainment. Recent improvements in deep learning have lead to powerful neural network-based methods that go over traditional Denoising techniques. In this research, a number of deep learning algorithms were tested for video reduced noise on an array of video files. The results demonstrated the effectiveness of deep learning models in reducing noise artifacts, by on the mean square error (MSE) metric, which measures the difference between the original clean videos and the Denoise outputs. The research included processing (4) video files Which includes a number of videos of different lengths, and the number of frames in this work has become 1,604.through both (convolutional neural network ((CNN)) and (deep neural network (DNN)) and the results showed the superiority of the (CNN) method over the (DNN) method through the results of the (4) deference experiment. The best experiment with minimum MSE was (, μ=0, =0.02) with (MSE DNN =0.02001410), the methods can be applied to other multimedia files (audio, image).
Integrating Geographic Information Systems in Industrial Management: Key Drivers for Enhanced Performance and Sustainability
Geographic Information Systems (GIS) integration into the industrial management is a crucial aspect that has helped in boosting the operational performance and sustainability. The tools offered by GIS technology for spatial data analysis, real time data visualization and informed decision making add to greater efficiency of resource allocation and improved supply chain logistics for the industries. The aim of this paper is to identify the main reasons for the use of GIS in the industrial environments, which include the recent technological advances, the regulatory compliance, and the necessity of the operational efficiency. By illustrating how GIS tools help companies fulfill sustainability goals, including better tracking of environmental impacts, reduction of waste, and of energy, the document identifies industries that are immediately utilizing GIS tools. Additionally, the paper talks about the use of GIS in enabling competitive advantages, minimizing costs and work flow optimization. Real time data collection systems, predictive modeling and automation is studied as a feature of bringing about continuous improvement of industrial practice. Examples of applying the GIS approach to optimization of process in manufacturing, transportation and energy sectors are presented in case studies. It also addresses the challenges of GIS implementation, other than high initial costs and data integration, as the future trends and innovations that will influence the evolution from GIS in industrial management. In this paper, we stress the key role of GIS in making industrial management practices change in a way that provides organizational success in the long term due to increased efficiency and sustainability.
Revolutionizing Industrial Management: A Review of Artificial Intelligence Applications in Manufacturing Operations
Artificial Intelligence paved the path for integration into industrial management, which shook up the operational frontier within manufacturing and supply chain. Combining these with machine learning, predictive analytics, robotics and computer vision, this thought provoking, comprehensive review considers the transformative potential of mixed use AI in driving enhanced industrial productivity, better decisions, and smarter processes. The study describes how theoretical constructs of AI became practical applications capable of predictive maintenance, real time monitoring, and demand forecasting and flexible manufacturing systems. Dynamic resource allocation, as supported by AI, allows us to improve the operation of quality inspection systems to control quality and management of supply chain through intelligent logistics and inventory management to improve supply chain resilience. Besides that, these technologies not only lower the operational cost and downtime in production, but also allow for further customization, agility and innovativeness towards vary market needs. In the same vein, AI can also analyze big data in real time and help industries make concrete strategic decisions driven by the data. However, the paper argues in spite of these advantages the paper critically discusses several key barriers to adoption, which include legacy system integration, data privacy concern, workforce readiness and organizational resistance. It highlights the significance of cultural transformation, ethical AI practices, and up skilling construct in ensuring the full deployment of AI in an industrial environment. AI is expected to cause additional emerging trends like digital twins, generative AI and Industry 5.0 to meaningfully stir AI, which will create socio-technological synergies to foster efficiency while maintaining sustainability and human centric values. It concludes that AI will actually reshape operational frameworks as it will become a fundamental part of the digital transformation of industrial enterprises.
Leveraging Deep Learning for Accurate Classification of Skin Disease Images
This paper leverages deep learning techniques to address the complex task of classifying ten distinct types of skin diseases. A comprehensive dataset comprising 27,200 images was utilized to train and evaluate a convolutional neural network (CNN) model based on the ResNet50 architecture. To ensure a rigorous assessment, the dataset was split into training (80%), validation (10%), and testing (10%) subsets. The ResNet50-based CNN was trained extensively and achieved a classification accuracy of 92% on the test set, demonstrating strong predictive performance. These results highlight the potential of deep learning to support healthcare professionals by enabling automated and accurate skin disease classification. This paper contributes to the growing field of intelligent diagnostic systems aimed at improving early detection and treatment planning in dermatology.
Strategic Engineering Management for Renewable Energy Projects: Trends, Challenges, and Future Outlook
This comprehensive document examines strategic engineering management with respect to renewable energy projects. The urgency has increased for the global decarbonization and the race for the conventional fossil fuel to sustainable alternatives like solar, wind, geothermal and biomass energy has come to the center. The study stresses the importance of strategic planning, stakeholder involvement, and adaptive project management in successfully implementing renewable initiative. First, it outlines some reasons as to why the planning of the project requires to be structured; examining feasibility assessment, risk analysis and regulatory compliance, which all stand as the foundation to a successful energy transition. It describes a wide variety of engineering management models of traditional types (e.g., Waterfall, V-Model) and modern ones (e.g., Agile, hybrid systems) with specific examples of their use in a changing environment including the use in renewable environment. In addition, the text highlights that the emerging technologies like Artificial Intelligence, Blockchain and Smart Grids are leading to their transformational impact on operational efficiency, decision making and grid integration. A major portion is dedicated to solving the problems of financing, regulatory restrictions, and disconnections in communication for various stakeholders, especially in projects scattered or dispersed around. Global case studies and comparative insights into national policy frameworks enrich analysis of both best practices and common pitfalls. It also offers strategic recommendations for industry stakeholders, stressing the role that stakeholder inclusive governance, digital innovation and risk mitigation play in building a future landscape of clean energy development. Drawing from both analytical rigor and forward looking view, the document provides an interesting and so far unique blend to supplement guidance that policymakers, engineers and project managers should take into account when shaping sustainable energy infrastructures.
Adaptive Steganography Method Based on Localized ACO for Pixel-Level Image Enhancement
With the rapid proliferation of digital communication in critical domains such as healthcare, defense, and finance, ensuring secure and private data transmission has become paramount. Image steganography offers a promising solution by concealing sensitive information within digital images. Among conventional techniques, the Least Significant Bit (LSB) method is widely adopted due to its simplicity and high embedding capacity; however, its deterministic nature renders it vulnerable to visual and statistical steganalysis. This study introduces a novel enhancement to the classical LSB approach by integrating a localized Ant Colony Optimization (ACO) strategy within a block-wise embedding framework. The proposed method segments the host image into non-overlapping 5×5 blocks, where ACO is employed to select optimal embedding pixels. A hybrid fitness function, incorporating Mean Squared Error (MSE), local variance, and pixel energy, guides the selection process to ensure high imperceptibility and robustness. The framework was implemented in Python and validated using standard grayscale and RGB images. We found in experimental results that the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) of up to 50 dB at a 0.1 bpp payload with maintains a Structural Similarity Index (SSIM) of 0.99, displaying very little visual distortion. The proposed technique that we present in this paper achieves a detection rate of less than 4.81% when evaluated using the StegExpose tool also the developed method achieved a high ROC-AUC score of 0.91, indicating strong resistance to steganography. Furthermore, the suggested ACO-LSB technique can be utilized as a more secure and effective method for hiding secret information in different images which makes it suitable for real-world applications such as hiding metadata in medical images, securing biometric data, and digital watermarking.
INTELLIGENT INTERNATIONAL IDENTIFICATION CARD
This study will develop and implement the International Identification Card (IIC) to multi users (M-1). The IIC can be used in several methods such as an IC card, Passport, driver’s license, Visa card, Security Information system, Business part, all information about individuals/persons. The Smart Identification Card Technology (SICT) system will be using several new technology categories/tools such as Information Technology, Management Information Technology, Database management, internet service, Bluetooth service, NFC and mobile calling service. The target of SICT is to increase the efficiency of IC card to know the details for all citizens and foreigners from any country regardless their nationalities. What this means is the citizen’s national and/or international security check can be identifiable, accessible from anywhere and anytime without time and/or location considerations. Therefore, any criminal behavior records and any other required checks can be accessed by the governments and authorities. The advantages of implementing this technology are to reduce the cost and time of history checkup of the person and aggregate Multi-IDs to one SICT. This new technology system will result a magnificent change, this will includes the person's (passenger's) passport information as well. Therefore, passengers can use their IIC for check in and out processes instead of using traditional passport. This new implementation of technology will assist the governments and citizens around the world without any delay in the service securely.
Copyright (c) 2024 Ali Adham , Anwar Ja’afar Mohamad Jawad , Alyaa Al-Barrak https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis of Classification Algorithms in Wireless Body Area Networks: Advanced Techniques for Securing and Optimizing Trustworthy Node Identification
Wireless Body Area Networks (WBANs) have become essential in healthcare, fitness, and emergency monitoring, where they support real-time data collection and transmission from wearable or implantable sensors. However, the effectiveness of WBANs depends heavily on reliable and secure node classification, as these networks face numerous challenges, including data integrity issues, privacy concerns, and resource constraints. This study addresses the critical problem of accurately and securely classifying nodes in WBANs to ensure that only trusted data sources contribute to network operations, thus minimizing risks related to data breaches, energy inefficiency, and misclassification. The primary objective of this study is to analyze and evaluate various classification algorithms—ranging from traditional machine learning techniques to advanced AI models—within the context of WBANs. Additionally, this study explores methods to enhance WBAN security and optimize resource consumption, focusing on power efficiency, latency reduction, and error mitigation. Our contributions include a comparative analysis of different algorithms based on performance metrics such as accuracy, precision, recall, F1 score, energy efficiency, and robustness. We also introduce a framework for incorporating security-enhanced classification techniques, such as encryption, data anonymization, and authentication, to safeguard WBAN data and maintain network integrity. By providing a comprehensive overview of current challenges and advanced classification methods, this study aims to inform the development of scalable, secure, and efficient classification models tailored to WBANs. Our findings underscore the importance of balancing accuracy, energy efficiency, and security to create WBAN systems that are both trustworthy and adaptable. This research contributes to the ongoing evolution of WBAN technology, paving the way for improved reliability in applications that demand high standards of data integrity and real-time monitoring.
Israa Ibraheem Al Barazanchi Israa Ibraheem Al Barazanchi
Copyright (c) 2024 Israa Ibraheem Al Barazanchi, Ali Adham https://creativecommons.org/licenses/by/4.0
Validating the Channel Capacity in V2V Communication based on SCME Channel Model with Turbo-BLAST Coding
This paper presents spatial channel model extended with Turbo- Bell Laboratories Layer Space-Time coding to estimate the channel capacity for a wideband Line-of-Sight Vehicle-to-Vehicle communication environment for different propagation delays. The transmitter and receiver are equipped with massive Multiple-Input and Multiple-Output antenna arrays and different scenarios. Three velocities (20, 60 and 100 km/h) and three distances between vehicles (20, 60 and 100 meters) are considered. Furthermore, the performance of the suggested model is assessed using the Transmission Rate of R bps/Hz and the Cumulative Distribution Function. The simulation tests showed acceptable results that fit the theory results, which indicate that the proposed model could be suitable to be employed as a Vehicle-to-Vehicle channel model.
Copyright (c) 2024 Alyaa Al-Barrak, Ali Adham https://creativecommons.org/licenses/by/4.0
Investigational Survey of Heat Transfer in Heat Exchanger Containing Al2O3 as Nano-Fluid
In this work, the flow and the improvement of transference of heat features of distilled water and metal oxide of Al2O3 as Nanofluid are studied. The concentrations of nanoparticles in distilled water are 0.1, 1, 3, % by volume in a dual-pipe heat exchanger have been considered. It is composed of concentric tubes with a length of 82 cm. External tube from PVC with length (50.8 mm) and internal tube from Copper with length (12.7 mm). The cooling fluid flows in the annulus at atmospheric temperatures are (23-33) °C whereas the hot fluid flows in the internal tube at temperatures are (40, 50, 60 and 70) °C. The outcomes exhibit that the temperature rises as moving from the water to Nanofluid, whereas the average temperature alongside the inner pipe in common stay constant with the rise in the rate of flow. Likewise, the alteration in fluid characteristic raises the Nusselt number values. The Nanofluid has a Nusselt number higher than the water. The Nusselt number of Nanofluid rise with rising concentration (0.1, 1 and 3 %), as the concentration rises, the Nusselt number rises to (25.5) at (flow rate 1.5 L/min, T=70 oC, and ϕ=3 %). The pressure drop rise (164.8 pa) with rising concentration. Friction factor drops (0.04) at ϕ=3 % as Reynolds number rises.
Copyright (c) 2024 Nisreen Mezher Rahmah, Abdulhassan A. Karamallaha, Laith Jaafer Habeeb https://creativecommons.org/licenses/by/4.0
The Effects of Magnetic Nanofluid Flow with Different Heat Flux and Magnetic Field on Heat Transfer Through Horizontal Pipe
The influence of using nanoparticles with and without magnetic field on the characteristics of the flow and heat transfer rate is experimentally through a horizontal pipe. The base working fluid is distilled water, and the added nanoparticles are iron oxide (Fe3O4), with a volume fraction of (φ= 0.3, 0.6 and 0.9%). The intensity of the supplied magnetic field is (0.1 Tesla). The experiments are conducted at different heat fluxes (12.7, 15.9, 19.8 kW/m2), two different inlet temperatures (23, 45 oC), four different flow rates (2, 3, 3.5, 4, l/min) and range of Reynolds number (3930-7860). The results show that an increase in the concentration of nanofluids and heat flux give a boost in Nusselt number. Also, it is found that the use of a magnetic intensity enhances further heat transfer. The maximum enhancement in Nusselt number is about (18.3%) without using a magnetic field, and (20.1%) with the magnetic field, at a concentration of nanofluid of (φ = 0.9%) and lower heat flux.
Copyright (c) 2024 Laith Jaafer Habeeb, Abdulhassan A. Karamallah, Ali Habeeb Askar https://creativecommons.org/licenses/by/4.0