{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:57:21Z","timestamp":1771268241247,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Cold chain temperature management is crucial for preserving product quality and safety across various industries. While Computational Fluid Dynamics (CFD) provides detailed insights into thermal analysis and fluid dynamics, its computational intensity limits practical applications. This study presents a novel hybrid approach combining CFD and machine learning to enhance both computational efficiency and prediction accuracy in cold storage temperature management. A validated 3D CFD model was developed to analyze temperature distribution and airflow patterns in a refrigerated container with multiple storage boxes. Taking advantage of the cold room\u2019s symmetrical design along its longitudinal axis significantly reduced computational requirements while maintaining model accuracy. Over 200 cases were simulated by varying key process parameters to generate training data for machine learning models. Random Forest (RF) and Neural Network (NN) models were developed and compared, with RF demonstrating consistently superior performance across all storage locations. Feature importance analysis revealed cold air temperature as the dominant control variable, while SHAP analysis identified optimal operational ranges for air velocity and heat transfer coefficients that balance product quality with energy efficiency. This research work also revealed distinct patterns in the influence of process parameters, with cold air and ambient temperatures showing hierarchical impacts on system performance. The hybrid methodology successfully addresses the computational limitations of traditional CFD approaches while maintaining high prediction accuracy, offering a practical solution for sustainable temperature management in cold storage applications. Finally, this research provides valuable insights for optimizing cold chain operations and demonstrates the potential of hybrid modeling approaches in thermal management systems.<\/jats:p>","DOI":"10.3390\/sym17040539","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T05:43:33Z","timestamp":1743572613000},"page":"539","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Symmetry-Based Hybrid Model of Computational Fluid Dynamics and Machine Learning for Cold Storage Temperature Management"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5087-8133","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}]},{"given":"Lanting","family":"Guo","sequence":"additional","affiliation":[{"name":"The Department of Food Science and Human Nutrition, University of Illinois Urbana-Champaign, Champaign, IL 61801, USA"}]},{"given":"Xiaoyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030, USA"}]},{"given":"Mengjie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5121\/ijmvsc.2015.6205","article-title":"Issues and Challenges in the Supply Chain of Fruits & Vegetables Sector in India: A Review","volume":"6","author":"Negi","year":"2015","journal-title":"Int. J. Manag. Value Supply Chain."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1111\/1541-4337.12269","article-title":"Time\u2013Temperature Management Along the Food Cold Chain: A Review of Recent Developments","volume":"16","author":"Mercier","year":"2017","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3920\/JCNS2008.x089","article-title":"Generic model for the prediction of remaining shelf life in support of cold chain management in pork and poultry supply chains","volume":"8","author":"Raab","year":"2008","journal-title":"J. Chain Netw. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Derens, E., Palagos, B., and Guilpart, J. (2006, January 17\u201321). The cold chain of chilled products under supervision in France. Proceedings of the 13th World Congress of Food Science & Technology 2006, Nantes, France.","DOI":"10.1051\/IUFoST:20060823"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.tifs.2006.05.004","article-title":"Computational fluid dynamics (CFD)\u2014An effective and efficient design and analysis tool for the food industry: A review","volume":"17","author":"Norton","year":"2006","journal-title":"Trends Food Sci. Technol."},{"key":"ref_6","first-page":"257","article-title":"The use of CFD to simulate temperature distribution in refrigerated containers","volume":"8","author":"Umeno","year":"2015","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Daniel, L., Jakhar, S., and Dasgupta, M.S. (2024). Optimizing cold storage for uniform airflow and temperature distribution in apple preservation using CFD simulation. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-76385-y"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109359","DOI":"10.1016\/j.enbuild.2019.109359","article-title":"A review of advanced air distribution methods\u2013theory, practice, limitations and solutions","volume":"202","author":"Yang","year":"2019","journal-title":"Energy Build."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103097","DOI":"10.1016\/j.est.2021.103097","article-title":"Thermal performance investigation of door opening and closing processes in a refrigerated truck equipped with different phase change materials","volume":"42","author":"Kousksou","year":"2021","journal-title":"J. Energy Storage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.applthermaleng.2016.11.046","article-title":"3D and transient numerical modelling of door opening and closing processes and its influence on thermal performance of cold rooms","volume":"113","author":"Carneiro","year":"2017","journal-title":"Appl. Therm. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1098\/rsta.2002.0990","article-title":"High-performance computing in computational fluid dynamics: Progress and challenges","volume":"360","author":"Catlow","year":"2002","journal-title":"Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Eze, J., Duan, Y., Eze, E., Ramanathan, R., and Ajmal, T. (2024). Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-70638-6"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111156","DOI":"10.1016\/j.jfoodeng.2022.111156","article-title":"Machine learning for temperature prediction in food pallet along a cold chain: Comparison between synthetic and experimental training dataset","volume":"335","author":"Loisel","year":"2022","journal-title":"J. Food Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"153935","DOI":"10.1109\/ACCESS.2024.3482572","article-title":"Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities","volume":"12","author":"Alkhulaifi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, T.H., Kim, J.H., Kim, J.Y., and Oh, S.E. (2022). Egg Freshness Prediction Model Using Real-Time Cold Chain Storage Condition Based on Transfer Learning. Foods, 11.","DOI":"10.3390\/foods11193082"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105749","DOI":"10.1016\/j.icheatmasstransfer.2021.105749","article-title":"A study on computational fluid dynamics modeling of a refrigerated container for COVID-19 vaccine distribution with experimental validation","volume":"130","author":"Zhang","year":"2022","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s10652-018-9637-1","article-title":"A comparison of standard k\u2013\u03b5 and realizable k\u2013\u03b5 turbulence models in curved and confluent channels","volume":"19","author":"Shaheed","year":"2019","journal-title":"Environ. Fluid Mech."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.ijrefrig.2006.03.019","article-title":"A review of numerical models of airflow in refrigerated food applications","volume":"29","author":"Smale","year":"2006","journal-title":"Int. J. Refrig."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1142\/S0129065704001899","article-title":"Gaussian processes for machine learning","volume":"14","author":"Seeger","year":"2004","journal-title":"Int. J. Neural Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jfoodeng.2015.12.006","article-title":"Effective thermal conductivity prediction of foods using composition and temperature data","volume":"175","author":"Carson","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.egypro.2019.02.086","article-title":"Vacuum insulation in cold chain equipment: A review","volume":"161","author":"Verma","year":"2019","journal-title":"Energy Procedia"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ferrandez-Garcia, C., Ferr\u00e1ndez-Garc\u00eda, A., Ferr\u00e1ndez-Villena, M., Hidalgo-Cordero, J.F., Garc\u00eda-Ortu\u00f1o, T., and Ferr\u00e1ndez-Garc\u00eda, M.-T. (2018). Physical and Mechanical Properties of Particleboard Made from Palm Tree Prunings. Forests, 9.","DOI":"10.3390\/f9120755"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/21642583.2014.956265","article-title":"Random forests: From early developments to recent advancements","volume":"2","author":"Fawagreh","year":"2014","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"118808","DOI":"10.1016\/j.enconman.2024.118808","article-title":"Machine learning development to predict the electrical efficiency of photovoltaic-thermal (PVT) collector systems","volume":"315","author":"Gharaee","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102286","DOI":"10.1016\/j.artmed.2022.102286","article-title":"Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review","volume":"128","author":"Comito","year":"2022","journal-title":"Artif. Intell. Med."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/539\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:07:55Z","timestamp":1760029675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040539"],"URL":"https:\/\/doi.org\/10.3390\/sym17040539","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,1]]}}}