{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:10:57Z","timestamp":1750219857754,"version":"3.41.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation, China","doi-asserted-by":"crossref","award":["62262026"],"award-info":[{"award-number":["62262026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangxi Education Department","award":["GJJ211111"],"award-info":[{"award-number":["GJJ211111"]}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Energy Research Testbed and Industry Partnership Funding Initiative"},{"name":"Energy Grid (EG) 2.0 programme and its Central Gap Fund","award":["NRF2020NRF-CG001-027"],"award-info":[{"award-number":["NRF2020NRF-CG001-027"]}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education, Singapore","doi-asserted-by":"crossref","award":["RT14\/22, RG96\/20"],"award-info":[{"award-number":["RT14\/22, RG96\/20"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Nanyang Technologi- cal University, Singapore","award":["NTU\u2013ACE2020-01"],"award-info":[{"award-number":["NTU\u2013ACE2020-01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for the online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration approaches apply heuristics to search the model configurations. However, each search step requires a long-lasting process of repeatedly solving the CFD model, rendering them impractical, especially for complex CFD models. This article presents\n            <jats:italic>Kalibre<\/jats:italic>\n            , a knowledge-based neural surrogate approach that calibrates a CFD model by iterating four steps of (i) training a neural surrogate model, (ii) finding the optimal parameters through neural surrogate retraining, (iii) configuring the found parameters back to the CFD model, and (iv) validating the CFD model using sensor-measured data. Thus, the parameter search is offloaded to the lightweight neural surrogate. To speed up Kalibre\u2019s convergence, we incorporate prior knowledge in training data initialization and surrogate architecture design. With about ten hours of computation on a 64-core processor, Kalibre achieves mean absolute errors (MAEs) of 0.57\u00b0C and 0.88\u00b0C in calibrating the CFD models of two production data halls hosting thousands of servers. To accelerate CFD-based simulation, we further propose\n            <jats:italic>Kalibreduce<\/jats:italic>\n            that incorporates the energy balance principle to reduce the order of the calibrated CFD model. Evaluation shows the model reduction only introduces 0.1\u00b0C to 0.27\u00b0C extra errors while accelerating the CFD-based simulations by thousand times.\n          <\/jats:p>","DOI":"10.1145\/3604283","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T10:10:29Z","timestamp":1686391829000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8827-9373","authenticated-orcid":false,"given":"Ruihang","family":"Wang","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7758-9875","authenticated-orcid":false,"given":"Deneng","family":"Xia","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0341-8213","authenticated-orcid":false,"given":"Zhiwei","family":"Cao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-5114","authenticated-orcid":false,"given":"Yonggang","family":"Wen","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-9973","authenticated-orcid":false,"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5405-9890","authenticated-orcid":false,"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Jiangxi Science and Technology Normal University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"[n.d.]. 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