Al-Rafidain Journal of Engineering Sciences

This paper reviewed the application of basalt fiber-reinforced polymer sheets in enhancing the shear strength of reinforced concrete beams .However , traditional reinforcement methods are generally very limited regarding strength, durability, and environmental resistance. This has created a demand for advanced materials such as fiber-reinforced polymers. Basalt fibers have several advantages over conventional carbon and glass fibers owing to their superior mechanical properties, lower environmental impact, and cost-effectiveness. This review made an analysis of the experimental data on the effectiveness of BFRP sheets in enhancing shear capacity in RC beams, with a view to considering parameters such as shear span-to-depth ratios, material modulus of elasticity, and bonding configurations. The result showed that BFRP sheets remarkably enhanced shear resistance and structural ductility, particularly in configurations optimized to prevent premature failure. It also emphasized how further research was lacking, particularly in the performance of HSC reinforced with BFRP under different loading conditions. Results have shown that future studies must be conducted to develop the bonding and reinforcement techniques that can fully realize the benefits of BFRP in advanced structural applications . This work provides an insight into the structural potential of BFRP as a sustainable reinforcement material in modern concrete infrastructure.

This paper examines eco-friendly concrete, focusing specifically on geopolymer concrete and its performance in reinforced concrete circular slabs, with and without openings, as well as its strength when reinforced with CFRP sheets under punching shear conditions. Flat reinforced concrete slabs are frequently utilized in construction due to their efficiency and speed of installation, along with the continuous smoothness achieved in the arrangement of the elements. However, slab systems often lack adequate shear strength in both directions. As a result, they may suffer shear failure at the points where they intersect with columns, leading to the failure of a larger segment of the structure. Various factors can contribute to shear failure, including changes in the usage of the facility, design and construction errors, increased load, material degradation, and poor-quality standards. The placement of openings can occur in either the positive or negative moment area of the slab, giving rise to different challenges that cannot be addressed with a uniform approach. To correct structural deficiencies, carbon fiber-reinforced polymer (CFRP) sheets or strips are utilized as composite elements. The application of CFRP improves the punching shear resistance in both directions and also enhances flexural strength, ductility, and rigidity. This makes CFRP a more feasible option compared to other costly and complex methods, such as increasing the cross-sectional dimensions of columns. This paper reviews recent research on the application of Carbon Fiber Reinforced Polymer (CFRP) to boost the shear strength of flat slabs. It details the materials used for the reinforcement of these slabs and the techniques employed in their application. Furthermore, the paper summarizes the studies cited and proposes possible directions for future research.

This quantitative research explores features of impact of nanomaterials, hybridized nanomaterials, and composite nanomaterials on heat exchangers. Heat exchangers are used widely in various industrial processes and are considered important elements since they extend the lifespan of equipment through proper management of thermal loads. It has been evident that incorporation of TiO2 nanomaterials will improve progressively the thermal conductivity of heat exchange equipment and, thus, the overall efficiency of heat exchangers. In the present research, the findings have been derived through the use of computational fluid dynamics (CFD) analysis for studying heat exchange characteristics and the flow of fluids in heat exchangers incorporating various forms of nanomaterials. The study considered the influence of adding Al2O3 with CuO hybridized nanomaterials onto the heat exchanger as its material. The temperature got low at 43.3 °C, which showed the performance of the nanomaterials, and reduced further afterwards to 41.93 °C within just 10 minutes. The inclusion of the nanohybrid in combination approached the premium down to 36.6°C twelve minutes later. The result indicates that the use of the nanocomposite is well justified for improving the quality of the cooling process. Contour temperatures, velocities, and pressure show heat transfers and quantities of fluid density when hybrid nanomaterials are mixed with multiple substances. It was at 313.5 K that the maximum temperature was at, and the velocity arrived at 0.45 m/s, which subsequently decreased to 0.33 m/s. The increase in pressure was at 1035 pa. This translates to the difference in density of the absorbent fabric and the nanomaterials.

This research examines the reinforcement of deep hollow concrete beams with substantial openings by plate strengthening techniques, highlighting the benefits of these apertures and their major impact on structural performance, load-bearing capacity, and durability. It also examines several methods to improve the shear strength of deep reinforced guard beams with web openings, focusing on plate reinforcement. This method involves attaching plates to the beam's surface to enhance shear stress distribution alongside contemporary techniques that use metal plates to reinforce these openings. The dissertation assesses the existing literature on the behavior of deep hollow concrete beams with significant apertures, focusing on both laboratory tests and analytical methods used to evaluate the performance of different plate designs. This is achieved using finite element method (FEM) analysis utilizing specific applications, reinforcement techniques, and fastening processes. It concludes that aperture dimensions and location are crucial to reinforced concrete deep beam stability and effectiveness. It shows how the shear span-to-depth ratio (a/d) affects load-bearing and efficacy. Innovative reinforcing methods include steel plates and fiber-reinforced polymers to strengthen ductile beams with openings. Strategic aperture placement especially near shear spans can reduce stiffness and load distribution issues. Advanced finite element modeling predicts beam performance across configurations. The results emphasize the significance of carefully designing deep hollow beams to combine utility, material efficiency, and structural robustness, providing engineers in many applications with valuable insights and solutions. This detailed study helps improve deep beam structural performance at a low cost.

The application of 3D printing technology in the industry represents a substantial advancement in addressing longstanding issues in construction: labor shortages, sustainability, and cost reduction. In addition, it facilitates the generation of complex forms while consuming less material. Less labor could reduce construction costs by 40% and the time needed to build a structure by 70%. Still, there are hurdles to widespread 3D printing, such as material requirements, quality management systems, and regulations.
This study points to significant knowledge and technology gaps in material development, quality control, and compliance, which limit effective implementation. This stresses the demand for new construction materials and reliability standards for 3D-printed applications and structures. The research also investigates the novel development in material science, automation, and sustainability practices that augment the output of the 3D printed components.
The study employs a systematic literature evaluation and case studies to formulate recommendations for surmounting current obstacles and advancing the practical deployment of 3D printing in buildings. The findings demonstrate the possibility of developing new adaptive systems that foster more sustainable and efficient methodologies, thereby providing a framework for executing this technological shift. They also summarize how construction professionals, researchers, and policymakers might apply these formulas.

This paper looks at distributed solar cooling systems and assess the viability and the advantages of such systems under both the urban and rural contexts. This paper provides a detailed comparison of these systems, with special emphasis on energy performance, environmentally friendly features, and variation in climatic conditions. Specifically, the authors focused on the follow issues associated with cooling solutions in the framework of global warming and increasing urbanization: energy intensity, environmental footprint, and the dependency on fossil fuels. Most conventional cooling systems, including air conditioning, rely mainly on fossil energy and hence are a major source of greenhouse emissions. Decentralized solar cooling systems seem to offer a way by acting as eco-friendly cooling systems that are independent of the conventional energy supply. These systems capture and convert solar energy into usable cooling power for application in places with intermittent or constant high heat such as in urban as well as rural areas. The benefits are; less transmission loss, improved energy reliability, and less CO2 emission as compared to centralized system. Further, distributed structures enhance the availability of cooling in areas which are served by a weak grid power supply, especially in rural areas. The paper also explores various technologies that include photovoltaic panels and thermal collectors which make such systems functional in different circumstances. Nevertheless, several problems limit the application of the decentralized solar cooling systems. The problems associated with system complexity, maintenance, high costs of initial establishment, and absence of favorable policies remain critical for wider adoption. The last section of the paper briefly addresses the prospects of these systems, suggesting that future developments, supportive economic and policy frameworks will be conducive to the scaling up of such systems.

This review paper covers progressive developments in solar adsorption cooling technology with reference to new adsorbent materials and thermal cycle patterns for upgrading the performance of the system. An alternative for conventional cooling technologies, Solar adsorption cooling technology harnesses the heat from the sun to power the adsorption process, instead of electricity and destructive refrigerants. The first part of the review provides brief background information about the operation of solar adsorption cooling systems and the role of solar collectors as well as adsorbent materials in the process. Based on such categories the paper outlines the advancements in adsorbent materials including the metal-organic frameworks (MOFs), silica gel, activated carbon and zeolites that have enhanced efficiency, capacity and stability that work well under various conditions. Moreover, the review looks into the design improvements in adsorption cycles with the focus on new trends in adsorption in order to enhance the performances, increase the coefficient of performance (COP) and decrease the running costs. When solar thermal systems are combined with the adsorption technologies, the systems can provide cooling in an efficient way, and are ideal for areas that receive high levels of radiation. Furthermore, the externalities of these systems are rigorously examined to highlight their abilities to enable low carbon and fossil-free solutions. The paper also discusses the multipurpose use of solar adsorption cooling for residential air conditioning to industrial refrigeration which has been demonstrated in cases. Last, the difficulties associated with the choice of the working fluid, the system configuration, and the expansion of this technology are presented, after which the possible further studies that could improve the performance of the solar adsorption cooling systems and therefore guide their application are investigated.

This review investigates the use of vortex generators (VGs) in solar air heaters (SAHs) to enhance thermal performance and efficiency. Vortex generators, as passive elements, disrupt airflow to promote turbulence, thereby improving heat transfer and mitigating inherent inefficiencies in traditional SAHs. The review consolidates experimental findings and theoretical models to evaluate the influence of various VG configurations, such as delta-winglets and perforated ribs, on convective heat transfer. Insights into the role of parameters like geometry, pitch ratio, and placement are provided, highlighting their effects on thermal and hydraulic performance. Computational Fluid Dynamics (CFD) simulations are emphasized as a pivotal tool in analyzing fluid dynamics and optimizing VG design. Challenges like balancing pressure drops, material degradation, and scalability are explored, alongside potential solutions through hybrid systems and advanced design methodologies. Future research directions are outlined to address economic viability and long-term reliability, emphasizing the integration of VGs into more adaptive and modular SAH systems. This study underscores the critical role of innovative VG technology in advancing solar heating solutions to meet sustainability goals.

There is a growing concern over the use of Open-Graded Asphalt (OGA) mixes as a material for the wearing surface. This is due to the many advantages that may be acquired in terms of safety, economy, and the environment. In order to successfully remove water from the surface of the pavement in a short time, the OGA mix is particularly formulated to have high air void contents. A growing number of organizations and companies all around the entire world are beginning to acknowledge the benefits of these distinctive mixes. However, there is still some cause for worry
about the possible detrimental effects that surface water that has invaded the groundwater might have on the groundwater below. During the last three decades, a significant amount of research has been carried out on OGA mixes. The purpose of this paper was to examine certain issues related to design, construction, and performance that might maximize the benefits connected with the use of OGA mixes while simultaneously minimizing the negative consequences that are often associated with their utilization. It was decided to undertake a comprehensive analysis of the literature on OGA mix applications from all around the globe. Additionally, specifications for OGA mix from all over the world were assessed. As a result of the many advantages that OGA pavements offer, there is a great deal of opportunity for further studies to improve knowledge of the process, which will ultimately lead to it becoming a potential sustainability-oriented pavement material in the future. Providing a baseline for improvement of the existing design and conception of OGA mixes, the features that were summarized give directions for certain future research advances and provide a foundation for potential improvements.

This paper aims to reviewing the development and the prospects of the photovoltaic cooperating cooling systems. This topic covers the basics, new technologies, efficiency enhancement, and consequences of these systems in terms of environment and economy. The incorporation of photovoltaic (PV) technology into cooling system has also emerged widely for meeting the increasing demand for appropriate cooling methods especially in the regions that characterized by high temperatures. Photovoltaic air conditioning systems use electricity from solar power to support cooling processes, including vapor compression and absorption chillers with little or no inputs from fossil resources, thus reducing environmental effects. This paper provides a brief discussion of the various topologies of these systems; systems that incorporate DC compressor units, as well as solar-only systems that incorporate electric chillers and solar thermal systems. These designs are best used and suited for different uses and are more versatile in terms of functionality. The paper also explains the difficulties that arise when trying to implement the photovoltaic cooling technologies: high costs of investment, low storage capacity, and problems with fine-tuning. However, the combinations of these obstacles do not preclude the opportunities of energy conservation and the decrease of greenhouse gas emissions. As the final discussion of the paper, recommendations on future research and development are made with regard to the integration and control, cost effective measures and cooling technologies for enhancing the effectiveness of photovoltaic-driven cooling

The integration of Artificial Intelligence (AI) in civil engineering is reshaping traditional practices and driving innovation across the field. This comprehensive review explores emerging AI methods, including machine learning, deep learning, natural language processing, computer vision, generative AI, and reinforcement learning, highlighting their applications in key civil engineering domains. AI is revolutionizing structural engineering through predictive maintenance and design optimization, enhancing construction management with intelligent scheduling and automation, and transforming geotechnical, transportation, environmental, and water resources engineering with advanced modeling and predictive analytics. Despite its transformative potential, the adoption of AI in civil engineering faces significant challenges, such as data standardization, model interpretability, integration with established practices, and computational demands. Addressing these challenges requires continued research, ethical governance, and collaboration among academia, industry, and policymakers. This review underscores the importance of integrating AI with emerging technologies, such as IoT, blockchain, and digital twins, to unlock new possibilities for sustainable and resilient infrastructure. By addressing existing limitations and embracing advancements in AI algorithms, civil engineering is poised to achieve unprecedented levels of efficiency, sustainability, and innovation. This paper concludes with a call for ongoing research and development to fully harness the transformative potential of AI in building the infrastructure of the future.

Social engineering attacks have become one of the most sophisticated threats for modern cybersecurity, where social engineering itself is the best weapon for attackers. This is different from the conventional methods of cyber-attacks such as software and hardware through which the attackers deceived people and took their precious information through manipulation. From a cybersecurity engineering point of view, this paper presents the historical evolution, current trends, and implication of social engineering attacks. Various attack methods are examined: from phishing, vishing, baiting, pretexting to the tailgating, all them is analyzed in the context of their ability to bypass the security measure. It makes clear where the cybercriminals take advantage of technological advances like artificial intelligence (AI) these days, as well as deepfake technology to increase the precision and scalability of social engineering campaigns. AI driven reconnaissance tools helps attackers tailor their messages towards victors online behavior, enabling more deceptive try and more convincing. The study also assesses the consequences and impacts of social engineering attacks on the organizations by looking at possibilities of financial losses and reputation damage, among other things. In addition, this paper has detailed mitigation strategies; these include employee-training programs, multi factor authentication (MFA), email-filtering technologies and AI based threat detection systems. Case studies such as the Google and Facebook financial fraud scheme will show you how even well secured business are still susceptible to the social engineering tactics. Out of the findings numerous are there which urges for a multi layered approach towards the cybersecurity – both technological and human aspect. Recent studies with the rapid technology advancement suggest that cybersecurity experts collaborating with psychologists are urged to create more resilient defense systems against social engineering for a better resilience against cyber threats. Knowing what psychological manipulation techniques attackers use in order to attack is how organizations can proactively implement security by reducing the chance of a breach caused by human error.

By automating processes, optimizing allocation of resources and facilitating better
decision making based on Artificial Intelligence (AI), engineering management is
undergoing revolution. This paper discusses the role of Artificial Intelligence
technologies (AI), namely, Machine Learning (ML), Natural Language Processing
(NLP) and Predictive analytics in engineering management. Automation powered by
AI enables automation of processes leading to the reduction of the occurrence of
human errors, increase in timelines of a project and broadening the aspects of the
efficiency of operations. It then delves into the impact of AI on engineering
management‘s key functions, which include project planning, cost estimation,
operational efficiency as well as risk assessment. Depending on historical data, AI
powered predictive models predict project challenges so proactive risk manage
strategies can take place. Machine learning algorithms increase resource allocation to
give the best use of resources while reducing wastage. It simplifies NLP applications
for documentations and communications as well as collaboration in engineering teams.
In addition, AI enables on the real time monitoring of an engineering project; this
enables changes in the execution in a dynamic manner. In this, we highlight case
studies in which AI was successful in optimizing construction management,
infrastructure planning and energy efficiency in engineering projects. However, AI
based solutions like digital twins and robotic process automation (RPA) have further
enhanced the operational productivity. Nevertheless, while integrating AI into
engineering management poses such issues as ethical concerns, data privacy risks, and
algorithmic biases, our research has demonstrated significant successes. Finally, the
risks are discussed of these risks — which can be mitigated through, among others,
transparent AI governance, workforce training, and interdisciplinary collaboration.
One of the future directions is AI inspired innovations like smart infrastructure,
autonomous decision-making system, AI powered sustainability initiatives. Using AI,
engineering managers can improve productivities, fuel, introduce innovations, and
secure competitive benefits in the digital world. This underscores the need to integrate
AI adoption with engineering goals aiming at the strategic level whilst considering
technical and ethical considerations.

The impact of different fat contents and different degrees of hydrolysis on the
anaerobic digestion process and the production of biogas is the subject of this paper
and the evaluation is conducted with the aid of the ADM1 model. Anaerobic digestion
is one of the most important steps of the biogas production from organic wastes and it
contains mainly methane (CH₄) and carbon dioxide (CO₂). The study shows clearly
that each concentration of fat improves methane production with 15% fat
concentration giving the highest output at a rate of 0.75 m³/kg at day 20 as compared
to the 0.10 m³/kg from 0% fat concentrations. Also, the extent of hydrolysis
profoundly predicts the degradation of the complex organic material; the greatest
hydrolysis rate of 0.25h⁻¹ gave methane of 0.70 m³/kg, which is about 75% higher than
lesser rates. The existing study also shows that there was a direct relationship between
the density of organic acids, hydrogen and CO₂ with the degree of fat content and rates
of hydrolysis. Hence the results presented in this paper underscore the significance of
defining fat and its hydrolyses in relation to the biogas production so as to improve the
efficiency and stability of the anaerobic digestion process as the complex organic
matters diminish. What this research offers is knowledge indispensable to enhancing
the efficiency of energy extraction from organic waste, thereby enriching the
contemporary discourse on waste management.

The current need to fight evolving threats in the context of a fast-evolving threat
landscape makes the field of digital forensics extremely challenging, especially due to
the rapidly growing cybercrime. This paper looks at how artificial intelligence (AI)
and machine learning (ML) enhance the methods of AI and ML to help digital
forensics in cybercrime detection and evidence analysis, further improving the overall
years of a cyber-investigator. Cybercriminals are increasingly employing sophisticated
techniques such as ransomware, phishing, and deepfake technology, which are no
longer constant methods of operation. From the law enforcement and cybersecurity
professionals to forensic suits, AI-driven tools offer an easy way to analyze large data
sets efficiently, finding patterns and anomalies that would otherwise go unnoticed.
Machine learning algorithms help in automating the task of classifying digital
evidence and anomaly detection using supervised and unsupervised algorithms.
Additionally, the study also assesses the effects of engineering innovations, including
Radio Frequency Management (RFM) refinements, AI-based automation, and
predictive analytics in cyber vigilance. The paper discusses the ethical considerations
in this context, such as data privacy, algorithmic bias, and transparency while talking
about forensic AI applications. The paper also discusses how successful AI has been in
law enforcement and private-sector cybersecurity—two areas of life where AI is truly
transforming. However, lack of regulatory standards, low levels of interdisciplinary
teamwork, and insufficient training of relevant workforces are the main challenges that
face the development of AI's potential while minimizing risk. Two future heads
emphasize blockchain integration for evidence securely, quantum computing for rapid
encryption, and cross-sectoral partnerships to pioneer in the footprint. Engineering
principles and the application of AI-driven automation can be leveraged to advance the
discipline of digital forensics to a more proactive, adaptive state that furthers the
development of robust responses to cyber threats in a more and more interconnected
world.

Clean, safe, unpolluted water is a growing concern. The petrochemical,
pharmaceutical, and hazardous phenols are released into the river by the pulp and
paper industries .Therefore, phenol must be removed from polluted water due to its
hazardous effects on humans. This study will investigate how phenol can be removed
from wastewater by a support nanocomposite catalyst in a batch reactor. By sol-gel
technique, nickel and manganese with glycerol were utilized as solvent and Al(NO3)3
as active material to make the nanocatalyst locally and describe it with FTIR, BET,
XRD, TEM, and SEM. A novel catalyst was used for catalytic wet peroxide oxidation
(CWPO) of phenol. Catalytic tests were done in the batch reactor with an equivalent
amount of H2O2. The CWPO process\'s working conditions include temperature [40
°C, 50 °C, 60 °C, 70 °C], time [60 min, 80 min, 100 min, 120 min], and beginning
phenol concentration [200 ppm]. [1, 300, 400, 500 ppm] at 1 atm. The maximum
removal is 92.46% at 70 °C, 120 min, and 200 ppm using (Al2O3/NiMnO3) catalyst

Natural Fiber-Reinforced Polymer (NFRP) composites have garnered significant
attention as sustainable alternatives to synthetic composites, offering lightweight,
renewable, and environmentally friendly solutions for diverse engineering
applications. However, the hydrophilic nature of natural fibers poses challenges,
particularly in the form of water absorption, which compromises mechanical
properties and durability. This review comprehensively examines the role of fiber
treatments in mitigating these challenges, highlighting their impact on water
absorption behavior and composite performance. The study explores various fiber
treatment methods, including chemical (alkali, silane, acetylation), physical (plasma,
UV irradiation, heat), and biological (enzymatic, microbial) techniques. These
treatments enhance fiber hydrophobicity, improve fiber-matrix bonding, and reduce
moisture uptake, leading to composites with superior tensile strength, flexural
properties, and dimensional stability. Comparative analyses of treated versus untreated
fibers demonstrate the significant advantages of treated composites in moisture-
resistant applications. In addition to detailing experimental findings, the review
addresses the challenges associated with current treatment methods, such as cost,
scalability, and environmental impact, and identifies research gaps in understanding
the long-term behavior and biodegradability of treated composites. The discussion
extends to future directions, emphasizing the need for eco-friendly, cost-effective
treatments, hybrid approaches, nano-enhancements, and standardized protocols to
advance the field. This review underscores the critical role of fiber treatments in
optimizing NFRP composites for practical applications, contributing to the broader
goal of sustainable material development. It concludes with a call for continued
innovation and collaboration to overcome existing challenges and unlock the full
potential of NFRP composites as high-performance, environmentally conscious
engineering materials.

Artificial Intelligence (AI) is now a powerful tool in engineering with growing applications in civil engineering, mechanical engineering, structural engineering, electrical engineering, and environmental engineering. This review aims at assaying the impact of AI technologies in the areas of effectiveness, actions, and creation. AI uses machine learning or generative design, predictive analytics and other tools, to enhance design processes and make design work more efficient, to automate many of the repetitive tasks, and to predict when certain equipment will need maintenance, and that’s how AI boosts project outcomes. AI engineering enables various design solutions in a short time through generative design, and AI facilitates real-time decisions by predictive analytics to improve safety and resources. AI in smart infrastructure refers to the advanced use of artificial intelligence is new infrastructure systems such as IT systems, smart grid, and smart construction management that has resulted in enhanced project planning and risk management, energy optimization. In addition, artificial intelligence and machine learning technologies are used more frequently in real-time assessment of structure health and environmental conditions that lead to sustainable solutions and enhanced infrastructure life cycle. However, the use of AI in engineering also has its drawbacks like security issues, ethical issues and the last but not the least is the high-quality data required to train AI. Concerns pertaining to algorithmic bias and the accountability of resulting AI choices underscore the need for sound AI design practices and ethical guidelines. Also, the introduction of AI systems to the currently used engineering practices presents some essential challenges that involve interdisciplinary planning and coordination. This review shows that AI is increasingly becoming a key enabler of innovation and critical to solving engineering challenges. Continued evolution is expected to increase the impact of AI in engineering applications and enhance the creation of sustainable and efficient solutions in many sectors.

Silicon carbide (SiC) and alumina (Al₂O₃) reinforced hybrid aluminum metal matrix composites (HAMMCs) are a remarkable development in material engineering because of improved mechanical and thermal characteristics. This review includes a detailed discussion of their production methods, liquid-state processes such as stir casting and solid-state methods such as powder metallurgy. These techniques are discussed with respect to their effects on reinforcement distribution, bonding, and overall composite properties. The potential of the hybrid reinforcement strategy to promote both properties which increase strength, hardness, wear resistance and thermal stability as well as properties which are desirable for lightweight materials needed in many industries is investigated. The issues that arise in processing HAMMC and the importance of uniform reinforcement dispersion and avoiding agglomeration and weak interface bonding are also discussed, as well as recent advancements in technologies such as ultrasonic cast aluminum alloy and frictional stir processing of LM13 aluminum alloy. These developments are intended to overcome the shortcoming in typical fabrication techniques whilst providing enhanced mechanical characteristics and low cost. Uses of HAMMCs have been found in aerospace, automotive, and biomedical fields where the parts need to be light, strong, durable, and withstand wear and tear for use in structural elements or prosthetic limbs, respectively. The review also explores the fabrication and characterization methodologies to assess the mechanical and microstructural characteristics of HAMMCs including tensile tests, hardness test and wear test. Last but not the least, the document discusses future research directions that are more related to use of environmentally friendly reinforcements, modern techniques of manufacturing and sustainability. The escalating demand for high performance and light weight products makes HAMMCs suitable to meet the new challenges in engineering.

Recently, energy efficient solutions to conventional cooling methods were provided by the utilization of Phase Change Materials (PCMs). PCMs exploit the latent heat properties to absorb and release thermal energy during phase transitions, thus being good heat affecters in terms of temperature control in numerous applications. This paper investigates the possible integration of PCMs in cooling systems that have the potential to reduce energy consumption in buildings, electronic devices, and industrial process, as well as increase heat storage energy and reduce temperature fluctuation. A range of types of PCMs, including organic, inorganic, and eutectic, is explored with regard to their thermal properties, their advantages, and their challenges in applied systems. In this study, the principles of thermal management in regard to PCM, conduction, convection, and radiation mechanisms that determine the performance of PCM are elaborated. Discussion of the benefits of PCMs in active and passive cooling systems are provided, and attention is placed on reducing the reliance on mechanical cooling and on reducing carbon footprints. Low thermal conductivity, stability concerns and cost implications of a PCM are analyzed, an introduction to new solutions like nano enhanced PCMs and composite materials, is provided. Examples of PCM application in building cooling, battery thermal management and electronic cooling show huge efficiency improvement, especially in operations. Research directions for future are suggested to be advances in material science, machine learning based optimization, and the hybrid cooling technologies to achieve better performance of PCM. Proper addressing of existing limitations and utilizing innovative engineering strategies, PCMs can make a significant contribution to energy efficient cooling solutions in realizing global sustainability goals, and mitigation of climate change

In view of the escalating energy costs and global environmental concerns, it has become imperative to achieve energy efficiency in modern buildings. A large fraction of the energy consumed in buildings is for such things as heating, ventilation, and air conditioning. Fuelled by the reality of the need to improve the energy efficiency of heating, ventilation, and air conditioning systems while not sacrificing indoor environmental quality, this study employs computational fluid dynamics modeling to optimize heating, ventilation, and air conditioning system operation and design, although the design and operation of the system are most likely more complex. Through advanced simulation techniques, the research investigates airflow distribution, thermal comfort, and pollutant dispersion within air-conditioned spaces. The study shows how computational fluid dynamics can be used to predict temperature gradients, air circulation optimization, and the effect of some of the architectural features including window placement, insulating quality and air circulation strategies. Furthermore, computational fluid dynamics is combined with Building Energy Modeling to run real world operation result, thus enabling data driven optimization. By integrating the controller with common power saving and heating, ventilation, and air conditioning control strategies, such as time and daily charging frequency switching, the capability to develop heating, ventilation, and air conditioning systems that dynamically respond to fluctuating loads is enhanced. It also outlines how computational fluid dynamics can be used for evaluating new energy saving strategies such as demand controlled ventilation and smart thermostat integration as well as renewable energy coupling. In addition, computational fluid dynamics is used not only to consider the associated system inefficiencies that impact consuming energy and creating discomfort for occupants such as airflow structure and heat accumulation at localized sites, but also to identify alternative configurations that demonstrate gains in energy or water consumption along with movement comfort. Additionally, the potential of the digital twin technology and its ability for real-time heating, ventilation, and air conditioning performance monitoring and predictive adaptation based on the real-time data streams is discussed. A comprehensive computational base for developing next-generation, energy-efficient, environment-friendly heating, ventilation, and air conditioning systems that meet regulatory standards and improve occupant well-being is provided. As a piece of work to address rising energy consumption in buildings, especially in HVAC systems, and the lack of efficiency of traditional designing methods, this paper is articulated. The existence of affordable and simple models that are often utilized to predict airflow and temperature dynamics across HVAC systems is emphasized, but it points to the fact that these models often fail to provide sufficiently accurate insights; there is energy inefficiency, poor thermal comfort, and high operational costs. However, Computational Fluid Dynamics fills in this gap by providing much more accurate ways of simulating real-world HVAC performance for dynamic conditions. It also suggests a Computational Fluid Dynamics modeling in conjunction with the Building Energy Model to improve energy efficiency and comfort.