Enhancing Heat Exchangers with Graphene as Al2O3 Nanofluid Coatings - A Review Study
Keywords:
Hair Pin Heat Exchanger, Graphene, Nanofluid, Thermal Analysis.Abstract
A well-designed heat exchanger improves the effectiveness of the heat exchanger. A hairpin heat exchanger resembles a hairpin when a single-pass shell-and-tube heat exchanger unit is folded in half, which can be used where space is a constraint. Design, CFD analysis and evaluation of various parameters of hairpin heat exchanger with graphene layer and its comparison with hairpin heat exchanger without graphene layer is the main aim of this paper. The heat exchanger is modified with the addition of a graphene layer on both the side (inside and outside) of the tube of the heat exchanger. Graphene is an allotropic form of carbon having a single layer of atoms distributed in a 2-D honeycomb lattice. The thermal conductivity of graphene is very high as compared to other materials. In addition to it, nanofluid Al2O3 is introduced as cold fluid. Nanofluids are colloidal suspensions made out of nanoparticles in some base fluid. ANSYS FLUENT 2020 has been used to model the geometry and to perform numerical simulation. Turbulent flow conditions were used to analyse the heat exchanger. CFD analysis has been done on hairpin heat exchangers using graphene layer. The results indicate that the high thermal conductivity of graphene increases heat transfer and the numerical value of convective heat transfer coefficient is also high.
References
- Abiodun, K., Ogbuonyalu, U. O., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2023). Exploring Cross-Border Digital Assets Flows and Central Bank Digital Currency Risks to Capital Markets Financial Stability. International Journal of Scientific Research and Modern Technology, 2(11), 32–45. https://doi.org/10.38124/ijsrmt.v2i11.447
- Alcaraz, C., & Lopez, J. (2022). Digital twin: A comprehensive survey of security threats. IEEE Communications Surveys & Tutorials, 24(3), 1475-1503.
- Alsmadi, I., & Xu, D. (2020). Security of cyber-physical systems using zero trust architecture. Computers & Security, 92, 101751. https://doi.org/10.1016/j.cose.2020.101751
- Alzubaidi, A., & Kalutarage, H. K. (2021). Policy-based access control for Zero Trust architecture in smart industrial systems. Computers & Security, 106, 102286. https://doi.org/10.1016/j.cose.2021.102286
- Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C., & Dijmărescu, I. (2021). Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability, 13(10), 5495.
- Atalor, S. I. (2019). Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy ICONIC RESEARCH AND ENGINEERING JOURNALS JUN 2019 | IRE Journals | Volume 2 Issue 12 | ISSN: 2456-8880
- Atalor, S. I., Ijiga, O. M., & Enyejo, J. O. (2023). Harnessing Quantum Molecular Simulation for Accelerated Cancer Drug Screening. International Journal of Scientific Research and Modern Technology, 2(1), 1–18. https://doi.org/10.38124/ijsrmt.v2i1.502
- Atalor, S. I., Raphael, F. O. & Enyejo, J. O. (2023). Wearable Biosensor Integration for Remote Chemotherapy Monitoring in Decentralized Cancer Care Models. International Journal of Scientific Research in Science and Technology Volume 10, Issue 3 (www.ijsrst.com) doi : https://doi.org/10.32628/IJSRST23113269
- Balta, E. C., Pease, M., Moyne, J., Barton, K., & Tilbury, D. M. (2023). Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems. IEEE Transactions on Automation Science and Engineering, 21(2), 1695-1712.
- Balta, E. C., Pease, M., Moyne, J., Barton, K., & Tilbury, D. M. (2023). Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems. IEEE Transactions on Automation Science and Engineering, 21(2), 1695-1712.
- Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
- Cunningham, C., & Pollard, J. (2017). The eight business and security benefits of zero trust. Forrester Reseach November.
- Federici, F., Martintoni, D., & Senni, V. (2023). A zero-trust architecture for remote access in industrial IoT infrastructures. Electronics, 12(3), 566.
- Fernández-Caramés, T. M., & Fraga-Lamas, P. (2020). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201–45218. https://doi.org/10.1109/ACCESS.2019.2908780
- Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
- Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1–14. https://doi.org/10.2514/6.2012-1818
- Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
- He, Y., Xu, X., & Wang, L. (2021). Cybersecurity challenges for industrial cyber–physical systems: A comprehensive review. Robotics and Computer-Integrated Manufacturing, 68, 102114. https://doi.org/10.1016/j.rcim.2020.102114
- Ihimoyan, M. K., Enyejo, J. O. & Ali, E. O. (2022). Monetary Policy and Inflation Dynamics in Nigeria, Evaluating the Role of Interest Rates and Fiscal Coordination for Economic Stability. International Journal of Scientific Research in Science and Technology. Online ISSN: 2395-602X. Volume 9, Issue 6. doi : https://doi.org/10.32628/IJSRST2215454
- Imoh, P. O. (2023). Impact of Gut Microbiota Modulation on Autism Related Behavioral Outcomes via Metabolomic and Microbiome-Targeted Therapies International Journal of Scientific Research and Modern Technology (IJSRMT) Volume 2, Issue 8, 2023 DOI: https://doi.org/10.38124/ijsrmt.v2i8.494
- Koulamas, C., & Kalogeras, A. P. (2018). Cyber-Physical Systems and Digital Twins in the Industrial Internet of Things: A Review. Computer Science Review, 30, 100–112. https://doi.org/10.1016/j.cosrev.2018.10.002
- Lee, J., Bagheri, B., & Jin, C. (2016). Cyber-physical systems for predictive production systems. CIRP Annals, 65(1), 715–728. https://doi.org/10.1016/j.cirp.2016.06.001
- Leng, J., Liu, Q., Ye, S., Jing, J., Wang, Y., & Zhang, H. (2021). Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of Manufacturing Systems, 58, 52–64. https://doi.org/10.1016/j.jmsy.2020.06.017
- Leng, J., Wang, D., Shen, W., Li, X., & Liu, Q. (2022). Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of Manufacturing Systems, 62, 731–749. https://doi.org/10.1016/j.jmsy.2022.01.007
- Li, X., Peng, J., Niu, J., Wu, F., Liao, J., & Choo, K. K. R. (2017). A robust and energy efficient authentication protocol for industrial internet of things. IEEE Internet of Things Journal, 5(3), 1606-1615.
- Li, Y., Tao, Z., Wang, L., Du, B., Guo, J., & Pang, S. (2023). Digital twin-based job shop anomaly detection and dynamic scheduling. Robotics and Computer-Integrated Manufacturing, 79, 102443.
- Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., ... & Chen, X. (2021). Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. Journal of Manufacturing Systems, 58, 52-64.
- McLaughlin, S., Konstantinou, C., Wang, X., Davi, L., Sadeghi, A. R., Maniatakos, M., & Karri, R. (2016). The cybersecurity landscape in industrial control systems. Proceedings of the IEEE, 104(5), 1039-1057.
- Mutahi, D. (2023). Navigating The Delicate Balance: Privacy and Data Security In Computing, https://www.linkedin.com/pulse/navigating-delicate-balance-privacy-data-security-computing-mutahi
- Ononiwu, M., Azonuche, T. I., & Enyejo, J. O. (2023). Exploring Influencer Marketing Among Women Entrepreneurs using Encrypted CRM Analytics and Adaptive Progressive Web App Development. International Journal of Scientific Research and Modern Technology, 2(6), 1–13. https://doi.org/10.38124/ijsrmt.v2i6.562
- Ononiwu, M., Azonuche, T. I., Imoh, P. O. & Enyejo, J. O. (2023). Exploring SAFe Framework Adoption for Autism-Centered Remote Engineering with Secure CI/CD and Containerized Microservices Deployment International Journal of Scientific Research in Science and Technology Volume 10, Issue 6 doi : https://doi.org/10.32628/IJSRST
- Ononiwu, M., Azonuche, T. I., Okoh, O. F., & Enyejo, J. O. (2023). AI-Driven Predictive Analytics for Customer Retention in E-Commerce Platforms using Real-Time Behavioral Tracking. International Journal of Scientific Research and Modern Technology, 2(8), 17–31. https://doi.org/10.38124/ijsrmt.v2i8.561
- Ononiwu, M., Azonuche, T. I., Okoh, O. F.. & Enyejo, J. O. (2023). Machine Learning Approaches for Fraud Detection and Risk Assessment in Mobile Banking Applications and Fintech Solutions International Journal of Scientific Research in Science, Engineering and Technology Volume 10, Issue 4 doi : https://doi.org/10.32628/IJSRSET
- Paul, B., & Rao, M. (2022). Zero-trust model for smart manufacturing industry. Applied Sciences, 13(1), 221.
- Paul, B., & Rao, M. (2022). Zero-trust model for smart manufacturing industry. Applied Sciences, 13(1), 221.
- Qi, Q., Tao, F., Zuo, Y., Zhao, D., & Lin, Y. (2021). Digital Twin Service Towards Smart Manufacturing. Journal of Manufacturing Systems, 58, 185–195. https://doi.org/10.1016/j.jmsy.2020.06.017
- Qi, Q., Zhao, D., Liao, T. W., & Tao, F. (2018). Modeling of cyber-physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing. In International manufacturing science and engineering conference (Vol. 51357, p. V001T05A018). American Society of Mechanical Engineers.
- Ray, P. P. (2023). Web3: A comprehensive review on background, technologies, applications, zero-trust architectures, challenges and future directions. Internet of Things and Cyber-Physical Systems, 3, 213-248.
- Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture. NIST Special Publication 800-207. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207
- Terca, K. (2022). Digital twin: Manufacturing case studies, smart factories and predictive maintenance, https://blog.3ds.com/brands/netvibes/digital-twin-manufacturing-case-studies-smart-factories-and-predictive-maintenance/
- Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., ... & Karkazis, P. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, 20(19), 5480.
- Vigna, R. (2023). The Tangible Benefits of Digital Twins: A New Era of Efficiency and Optimization https://www.linkedin.com/pulse/tangible-benefits-digital-twins-new-era-efficiency-rodrigo-vigna-x0zkc
- Wang, P., Xu, N., Zhang, H., Sun, W., & Benslimane, A. (2021). Dynamic access control and trust management for blockchain-empowered IoT. IEEE Internet of Things Journal, 9(15), 12997-13009.
- Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023). A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet of Things Journal, 10(17), 14965-1498
- Patil, P., Kataria, B., Redkar, V., Banait, A., Shilpa, C., Patil, & Khetani, V. (2024). Automated Detection of Tuberculosis Using Deep Learning Algorithms on Chest X-rays. Frontiers in Health Informatics, 13, 218–229.
- Kataria B, H B. Jethva, “Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning”, Int J Sci Res Sci Eng Technol, vol. 11, no. 5, pp. 342–352, Sep. 2024, doi: 10.32628/IJSRSET24105474.
- Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep Learning-Based Image Classification of Lung Radiography for Detecting COVID-19 Using a Deep CNN and ResNet 50. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 241–250. Retrieved from http://dx.doi.org/10.2139/ssrn.4951255
- Kataria, B., Jethva, H.B., Shinde, P.V., Banait, S.S., Shaikh, F., Ajani, S. (2023). SLDEB: Design of a Secure and Lightweight Dynamic Encryption Bio-Inspired Model for IoT Networks. International Journal of Safety and Security Engineering, Vol. 13, No. 2, pp. 325-331. https://doi.org/10.18280/ijsse.130214
- Shivadekar, S., Kataria, B., Limkar, S., et al. (2023). Design of an Efficient Multimodal Engine for Preemption and Post-Treatment Recommendations for Skin Diseases via a Deep Learning-Based Hybrid Bio-Inspired Process. Soft Computing. https://doi.org/10.1007/s00500-023-08709-5
- Wen, J., Gabrys, B., & Musial, K. (2022). Toward digital twin oriented modeling of complex networked systems and their dynamics: A comprehensive survey. Ieee Access, 10, 66886-66923.
- Xie, J., Ma, H., Yu, L., & Fan, X. (2021). Real-time data-driven digital twin framework for complex equipment operation and maintenance in smart manufacturing. Advanced Engineering Informatics, 47, 101230. https://doi.org/10.1016/j.aei.2020.101230
- Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023). A survey on digital twin for industrial internet of things: Applications, technologies and tools. IEEE Communications Surveys & Tutorials, 25(4), 2569-2598.
- Xu, L. D., & Duan, L. (2020). Big Data for Cyber-Physical Systems in Industry 4.0: A Survey. Enterprise Information Systems, 14(2), 148–169. https://doi.org/10.1080/17517575.2019.1652321
- Yan, Z., Zhang, P., & Wang, Y. (2020). Dynamic trust management and adaptive access control for Zero Trust in smart industrial environments. Future Generation Computer Systems, 108, 1232–1244. https://doi.org/10.1016/j.future.2020.03.010
- Zheng, Y., Yang, S., Cheng, H., & Wang, T. (2021). Intelligent predictive maintenance strategy with digital twin for sustainable manufacturing systems. Journal of Cleaner Production, 316, 128321. https://doi.org/10.1016/j.jclepro.2021.128321
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