A gray-box model for real-time transient temperature predictions in data centers

Asgari, Sahar; MirhoseiniNejad, SeyedMorteza; Moazamigoodarzi, Hosein; Gupta, Ravi; Zheng, Rong; Puri, Ishwar K.

doi:10.1016/j.applthermaleng.2020.116319

Cited by 44 publications

(5 citation statements)

References 34 publications

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“…Persistent lack of physical comprehension continuously stymies preferable prediction performance of the key parameters in multiphase flow and reactor systems, although scientists have made systematic contributions to experimentally formulated correlations throughout the past decades. − The correlations of the key parameters in multiphase units are commonly expressed by gas/liquid/solid phase properties, operating conditions (e.g., phase concentration, velocity, and temperature), devices configurations (e.g., height and diameter), or a combination of them in dimensionless forms such as Archimedes, Froude, Nusselt, Reynolds, Sherwood, and Weber numbers. However, the prediction discrepancies between the existing empirical correlations of key parameters such as the particle entrainment and minimum fluidization velocity in gas-particle riser flows can reach several orders of magnitude. , Fortunately, the advanced research and development of flexible ML tools have the potential to complement the incomplete knowledge to boost the prediction ability of key multiphase field parameters such as mass flow rate/flux, − minimum fluidization velocity, , mixing rate/index, , overall/local hold-up, − pressure/pressure drop, − velocity, ,− temperature, − and other parameters − in multiphase/particulate flows and reactors. Note that interested readers may be referred to a relatively comprehensive list of the existing literature summarized in Table S4.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

118

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

118

View full text Add to dashboard Cite

show abstract

“…However, for more input variables and complex relationships among the variable and features, more complicated algorithms are required. 3,4 Recently, a lot of research has been going on applying different ML algorithms in different fields such as robotics, automobile, and the production industry. Different researchers are working on implementing ML techniques in the fields of heat transfer and fluid mechanics.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional curve fitting techniques may fit the model for datasets which having limited input variables and limited datasets. However, for more input variables and complex relationships among the variable and features, more complicated algorithms are required 3,4 …”

Section: Introductionmentioning

confidence: 99%

Machine learning approach to predict the thermal performance of closed‐loop thermosyphon

Birajdar

Sewatkar

2023

Heat Trans

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This paper presents the machine learning (ML) algorithm to predict the thermal performance of closed-loop thermosyphon (CLT). The experimentation is carried out on the acetone-charged CLT at different test conditions such as heat inputs, filling ratios, and adiabatic lengths. The test data is used to calculate the performance parameters such as thermal resistance, heat transfer coefficient, and effectiveness of the system. Based on the experimental dataset, the ML algorithms are developed to predict the performance parameters of the CLT system. The ML algorithms such as linear regression, decision tree (DT), random forest (RF), and lasso regression are used for the development of the prediction model. The hyperparameters are well-tuned and optimized. The prediction measuring parameters (mean absolute error, R 2 ) are analyzed carefully. It is noticed that the DT model outperformed the prediction of the other used models.The R 2 score of the DT model was 98.504; whereas, the R 2 scores of the RF model and linear regression model were about 94.76 and 92.17, respectively. This study will become a roadmap to the ML approach in the thermosyphon system.

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“…The SVM predictions were found to be better in comparison to the other methods. Asgari et al [3] developed a gray box model by combining an ANN model with thermo-fluid transport equations to predict transient temperatures in server CPUs. The ANN mainly predicts the pressure, which can be used as input to the thermo-fluid transport equations from which the temperature field can be predicted.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts

Panda¹,

B.²,

Patil³

et al. 2022

Preprint

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This article presents the application of machine learning (ML) algorithms in modeling of the heat transfer correlations (e.g. Nusselt number and friction factor) for a heat exchanger with twisted tape inserts. The experimental data for the heat exchanger at different Reynolds numbers and twist ratios were used for the correlation modeling. Three machine learning algorithms: Polynomial Regression (PR), Random Forest (RF), and Artificial Neural Network (ANN) were used in the data-driven surrogate modeling. The hyperparameters of the ML models are carefully optimized to ensure generalizability. The performance parameters (e. g. R 2 and M SE) of different ML algorithms are analyzed. It was observed that the ANN predictions of heat transfer coefficients outperform the predictions of PR and RF across different test datasets. Based on our analysis we make recommendations for future data-driven modeling efforts of heat transfer correlations and similar studies.

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A gray-box model for real-time transient temperature predictions in data centers

Cited by 44 publications

References 34 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Machine learning approach to predict the thermal performance of closed‐loop thermosyphon

Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts

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