Reduced Order Design Optimization of Liquid Cooled Heat Sinks

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Aniket Ajay Lad ◽  
Kai A. James ◽  
William P. King ◽  
Nenad Miljkovic
Keyword(s):  
Design Optimization ◽  
Power Density ◽  
Building Blocks ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Heat Sinks ◽  
Geometrical Parameters ◽  
Power Module ◽  
Reduced Order

Abstract The recent growth in electronics power density has created a significant need for effective thermal management solutions. Liquid-cooled heat sinks or cold plates are typically used to achieve high volumetric power density cooling. A natural tradeoff exists between the thermal and hydraulic performance of a cold plate, creating an opportunity for design optimization. Current design optimization methods rely on computationally expensive and time consuming computational fluid dynamics (CFD) simulations. Here, we develop a rapid design optimization tool for liquid cooled heat sinks based on reduced-order models for the thermal-hydraulic behavior. Flow layout is expressed as a combination of simple building blocks on a divided coarse grid. The flow layout and geometrical parameters are incorporated to optimize designs that can effectively address heterogeneous cooling requirements within electronics packages. We demonstrate that the use of population-based searches for optimal layout selection, while not ensuring a global optimum solution, can provide optimal or near-optimal results for most of the test cases studied. The approach is shown to generate optimal designs within a timescale of 60–120 s. A case study based on cooling of a commercial silicon carbide (SiC) electronics power module is used to demonstrate the application of the developed tool and is shown to improve the performance as compared to an aggressive state-of-the-art single-phase liquid cooling solution by reducing the SiC junction-to-coolant thermal resistance by 25% for the same pressure drop.

Development of a Common Platform for Testing Metamodel Based Design Optimization Methods

2013 ◽  
Author(s):  
Adel A. Younis ◽  
George H. Cheng ◽  
G. Gary Wang ◽  
Zuomin Dong
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Optimization Methods ◽  
Global Optimum ◽  
Test Problems ◽  
Test Bed ◽  
Test Procedures ◽  
Local Optima

Metamodel based design optimization (MBDO) algorithms have attracted considerable interests in recent years due to their special capability in dealing with complex optimization problems with computationally expensive objective and constraint functions and local optima. Conventional unimodal-based optimization algorithms and stochastic global optimization algorithms either miss the global optimum frequently or require unacceptable computation time. In this work, a generic testbed/platform for evaluating various MBDO algorithms has been introduced. The purpose of the platform is to facilitate quantitative comparison of different MBDO algorithms using standard test problems, test procedures, and test outputs, as well as to improve the efficiency of new algorithm testing and improvement. The platform consists of a comprehensive test function database that contains about 100 benchmark functions and engineering problems. The testbed accepts any optimization algorithm to be tested, and only requires minor modifications to meet the test-bed requirements. The testbed is useful in comparing the performance of competing algorithms through execution of same problems. It allows researchers and practitioners to test and choose the most suitable optimization tool for their specific needs. It also helps to increase confidence and reliability of the newly developed MBDO tools. Many new MBDO algorithms, including Mode Pursuing Sampling (MPS), Pareto Set Pursuing (PSP), and Space Exploration and Unimodal Region Elimination (SEUMRE), were tested in this work to demonstrate its functionality and benefits.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

scholarly journals Nonlocal Modeling and Swarm-Based Design of Heat Sinks

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
David Geb ◽  
Ivan Catton
Keyword(s):  
Heat Transfer ◽  
Heat Sink ◽  
Electronics Cooling ◽  
Optimization Methods ◽  
Volume Averaging ◽  
Population Based ◽  
Design Tool ◽  
Heat Sinks ◽  
Optimal Designs ◽  
Traditional Approaches

Cooling electronic chips to satisfy the ever-increasing heat transfer demands of the electronics industry is a perpetual challenge. One approach to addressing this is through improving the heat rejection ability of air-cooled heat sinks, and nonlocal thermal-fluid-solid modeling based on volume averaging theory (VAT) has allowed for significant strides in this effort. A number of optimization methods for heat sink designers who model heat sinks with VAT can be envisioned due to VAT's singular ability to rapidly provide solutions, when compared to computational fluid dynamics (CFD) approaches. The particle swarm optimization (PSO) method appears to be an attractive multiparameter heat transfer device optimization tool; however, it has received very little attention in this field compared to its older population-based optimizer cousin, the genetic algorithm (GA). The PSO method is employed here to optimize smooth and scale-roughened straight-fin heat sinks modeled with VAT by minimizing heat sink thermal resistance for a specified pumping power. A new numerical design tool incorporates the PSO method with a VAT-based heat sink solver. Optimal designs are obtained with this new tool for both types of heat sinks, the performances of the heat sink types are compared, the performance of the PSO method is discussed with reference to the GA method, and it is observed that this new method yields optimal designs much quicker than traditional approaches. This study demonstrates, for the first time, the effectiveness of combining a VAT-based nonlocal thermal-fluid-solid model with population-based optimization methods, such as PSO, to design heat sinks for electronics cooling applications. The VAT-based nonlocal modeling method provides heat sink design capabilities, in terms of solution speed and model rigor, that existing modeling methods do not match.

An Adaptive Location-Aware Swarm Intelligence Optimization Algorithm

2021 ◽  
Vol 29 (02) ◽  
pp. 249-279
Author(s):  
Shenghao Jiang ◽  
Saeed Mashdoor ◽  
Hamid Parvin ◽  
Bui Anh Tuan ◽  
Kim-Hung Pho
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Mathematical Programming Problem ◽  
Initial Population ◽  
Instinctive Behavior ◽  
Conventional Optimization

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds’ classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds’ sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles’ speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles’ merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems.

ANALYSIS OF GEOMETRICAL PARAMETERS IN HEAT SINKS UNDER FREE CONVECTION USING DOE

2018 ◽  
Author(s):  
Bruno de Campos Salles Anselmo ◽  
Sandro Metrevelle Marcondes de Lima e Silva
Keyword(s):  
Free Convection ◽  
Heat Sinks ◽  
Geometrical Parameters

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

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