An artificial neural network model for predicting frictional pressure drop in micro-pin fin heat sink

Lee, Haeun; Kang, Min-Soo; Jung, Kwonil; Kharangate, Chirag R.; Sael, Lee; Iyengar, Madhusudan; Malone, Chris; Asheghi, Mehdi; Goodson, Kenneth E.; Lee, Hyoungsoon

doi:10.1016/j.applthermaleng.2021.117012

Cited by 39 publications

(3 citation statements)

References 53 publications

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“…In the area of micro finned heat sink optimisation, models have been developed to predict the pressure drop behaviour, (Lee et al, 2021), develop an artificial neural network to predict the thermal and hydrodynamic performance of finned micro heat sinks used in high heat flux electronic devices. They use a wide variety of geometric, operating and hydraulic performance conditions to train the artificial neural network as accurately as possible.…”

Section: Journal Of Technological Engineeringmentioning

confidence: 99%

Numerical study of the influence of the micro pin fin arrangement on the thermal and hydraulic performance of a micro heat sink

ZUÑIGA-CERROBLANCO¹,

MONTECILLO-SILLERO²,

HORTELANO-CAPETILLO³

et al. 2022

JTEN

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In this project, a numerical study of the thermal and hydraulic performance in a micro pin fin heat sink is carried out. The micro heat sinks pin fins are used for cooling of electronic, due that every day a more powerful and smaller chips and electronic components are required. The geometric parameters to the build of the heat sink with micro pin fins are chosen in base of the geometric needs of the electronic chip, a circular geometry is used for the micro pin fin and inline and staggered arrangement for the micro pin fins distribution is used, different velocities in the inlet of the heat sink are used for development of the study. In the result section, the temperature contours and the thermal resistance of the different cases analyzed are reported, as well as the pressures distributions and the total pressure drop along the heat sink micro pin fin are reported too. Taken account the results, the optimal geometry for thermal and hydraulic performance of the heat sink micro pin fin is obtained.

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Section: Journal Of Technological Engineeringmentioning

confidence: 99%

Numerical study of the influence of the micro pin fin arrangement on the thermal and hydraulic performance of a micro heat sink

ZUÑIGA-CERROBLANCO¹,

MONTECILLO-SILLERO²,

HORTELANO-CAPETILLO³

et al. 2022

JTEN

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“…Most of the methods for enhancing heat transfer in existing heat exchanger systems are inclined towards better fluid mixing, thereby improving heat transfer efficiency in different types of applications like conversion of liquid to vapour [1,2] and low droplet impact cooling [3][4][5]. The various passive heat transfer enhancement methods are ribs and impingement [6,7], vortex generators [8], the usage of numerous microchannels [9,10], small pin fins [11] and conventional twisted tapes [12]. These techniques cause rapid fluid mixing between cold and hot regions in the flow sections, further causing higher heat transfer.…”

Section: Introductionmentioning

confidence: 99%

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

Deshmukh,

Lahane,

Sumant

et al. 2023

Aerospace

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Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor R3 of 1.5 to 2.65 to the equivalent Reynolds number Rec value of 20,000. It is observed that most of the curved rib configurations show a performance ratio R3 maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement Nua/Nus, average heat transfer enhancement fa/fs, and performance ratio R3, i.e., Nua/Nuc.

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“…This alternative is the use of neural networks-based predictive models that could work as a source of data when a reasonable accuracy has been reached. A wide range of these kinds of models can be found in the literature with application in different fields of science [52][53][54][55][56][57][58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

et al. 2022

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The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

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An artificial neural network model for predicting frictional pressure drop in micro-pin fin heat sink

Cited by 39 publications

References 53 publications

Numerical study of the influence of the micro pin fin arrangement on the thermal and hydraulic performance of a micro heat sink

Numerical study of the influence of the micro pin fin arrangement on the thermal and hydraulic performance of a micro heat sink

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

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