Numerical Prediction of Electronic Component Operational Temperature: A Perspective

Eveloy, Valérie; Rodgers, Peter; Hashmi, M.S.J.

doi:10.1109/tcapt.2004.828583

Cited by 26 publications

(6 citation statements)

References 27 publications

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“…In addition, CFD-based parametric analysis of a wide range of heat sink geometries and Rayleigh numbers could be computationally prohibitive. Furthermore, parametric analysis may neither be quantitatively, nor qualitatively accurate (i.e., in terms of trends) over the complete design space [28,29]. This may be compounded by convergence difficulties due to thermal plume instabilities in free convection flows, resulting in transitional flow regime [30].…”

Section: Computational Fluid Dynamics Modelingmentioning

confidence: 99%

Air cooled heat sink design optimization in free convection

Rodgers¹,

Eveloy²

2013

29th IEEE Semiconductor Thermal Measurement and Management Symposium

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The optimization of heat sink performance is becoming ever more critical in passively air-cooled applications. In this paper, an overview of analytical, semi-empirical and Computational Fluid Dynamics (CFD)-based methodologies for air-cooled heat sink design optimization in free convection is presented. The advantages and potential limitations of these design techniques are summarized. A multi-faceted heat sink design strategy is advocated, which combines the analysis techniques presented with experimentation. The need to incorporate sustainability and formal optimization in the design process is highlighted.

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Section: Computational Fluid Dynamics Modelingmentioning

confidence: 99%

Air cooled heat sink design optimization in free convection

Rodgers¹,

Eveloy²

2013

29th IEEE Semiconductor Thermal Measurement and Management Symposium

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“…The working fluid is air, while the PCM is n-Eicosane. The n-Eicosane is organic paraffin, and it is been selected because its melting temperature is around the typical operating temperature of most of silicon chips [22]. Table I shows the thermophysical properties of the n-Eicosane.…”

Section: Problem Description and Numerical Approachmentioning

confidence: 99%

Thermal Management of Blocks in a Channel Using Phase Change Material

Alawadhi

2009

IEEE Trans. Comp. Packag. Technol.

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The application of the phase change material (PCM) in thermal management of an electronic device is presented in this paper. The considered geometry consists of a horizontal channel with three volumetrically heated blocks mounted on the bottom wall of the channel in a forced convection domain. The heated blocks simulate electronic chips. A PCM layer is attached to the bottom wall of the channel under the blocks. The objective of incorporating PCM into the system is to utilize its low melting temperature and high latent heat of fusion to absorb heat from the blocks during high-power periods, and to maintain their temperature at an acceptable level. The performance of the proposed PCM system is evaluated by comparing thermal characteristics of the system to a system without PCM. The peak temperature of the blocks can be reduced by 13.7%-26.8% for substantial amount of time, depending on the blocks' location in the channel, amount of the PCM utilized, and Reynolds number. Flow streamlines, temperature contours, blocks' peak temperature, and average Nusselt temperature are presented.Index Terms-Chip cooling, electronic cooling, flow over blocks, phase change material.

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“…Laminar flow is assumed in the CFD analysis. The conjugate CFD-thermal analysis model developed is validated using experimental results [21], as illustrated in Fig. 6.…”

Section: Thermal Model Validationmentioning

confidence: 99%

Multidisciplinary Placement Optimization of Heat Generating Electronic Components on a Printed Circuit Board in an Enclosure

Suwa

Hadim

2007

IEEE Trans. Comp. Packag. Technol.

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A multidisciplinary placement optimization methodology for heat generating electronic components on a printed circuit board (PCB) subjected to forced convection in an enclosure is presented. In this methodology, thermal, electrical, and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology based on channel flow forced convection boundary conditions is developed. The methodology consists of a combination of artificial neural networks (ANNs) and a superposition method that is able to predict PCB surface and component junction temperatures in a much shorter calculation time than the existing numerical methods. Three ANNs are used for predicting temperature rise at the PCB surface caused by a single heat source at an arbitrary location on the board, while temperature rise due to multiple heat sources is calculated using a superposition method. Compact thermal models are used for the electronic components thermal modeling. Using this optimization methodology, large calculation time reduction is achieved without losing accuracy. To demonstrate its capabilities, the present methodology is applied to a test case involving multiple heat generating component placement optimization on a PCB.Index Terms-Artificial neural networks (ANNs), electronic component placement optimization, genetic algorithm, printed circuit board (PCB) thermal analysis.

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Numerical Prediction of Electronic Component Operational Temperature: A Perspective

Cited by 26 publications

References 27 publications

Air cooled heat sink design optimization in free convection

Air cooled heat sink design optimization in free convection

Thermal Management of Blocks in a Channel Using Phase Change Material

Multidisciplinary Placement Optimization of Heat Generating Electronic Components on a Printed Circuit Board in an Enclosure

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