Multi-Objective Optimization of a Data Center Modeling Using Response Surface

2016 ◽  
Author(s):  
Long Phan ◽  
Cheng-Xian Lin
Keyword(s):  
Radial Basis Function ◽  
Response Surface ◽  
Thermal Management ◽  
Basis Function ◽  
Data Center ◽  
Data Centers ◽  
Maximum Temperature ◽  
Design Parameters ◽  
Running Time ◽  
Radial Basis

Energy consumption and thermal management have become key challenges in the design of large-scale data centers, where perforated tiles are used together with cold and hot aisles configuration to improve thermal management. Although full-field simulations using computational fluid dynamics and heat transfer (CFD/HT) tools can be applied to predict the flow and temperature fields inside data centers, their running time remain the biggest challenge to most modelers. In this paper, response surface methodology based on radial basis function is used to significantly reduce the running time for generating a large set of generations during a two-objective minimization process which uses the genetic algorithm as its main engine. Three design parameters including mass flow inlet, inlet temperature, and server heat load are investigated for a two-objective optimization. The goal is to minimize both the temperature difference and the maximum temperature inside the data center and search for a range of design parameters that satisfy both of these objectives. Numerous radial basis function models are studied and compared. Discussion on a more preferred scheme for the response surface construction is provided. Finally, a graph of Pareto font is generated showing the set of optimal designs in the objective space, and Pareto design validation is also performed.

Data Center Modeling Using Response Surface With Multi-Parameters Approach

2015 ◽  
Author(s):  
Long Phan ◽  
Cheng-Xian Lin ◽  
Mackenson Telusma
Keyword(s):  
Response Surface ◽  
Thermal Management ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Temperature Fields ◽  
Design Parameters ◽  
Full Field ◽  
Running Time ◽  
Training Error

Energy consumption and thermal management have become key challenges in the design of large-scale data centers, where perforated tiles are used together with cold and hot aisles configuration to improve thermal management. Although full-field simulations using computational fluid dynamics and heat transfer (CFD/HT) tools can be applied to predict the flow and temperature fields inside data centers, their running time remains the biggest challenge to most modelers. In this paper, response surface methodology based on radial basis function is used to drastically reduce the running time while preserving the accuracy of the model. Response surface method with data interpolation allows the study of many design parameters of data center model more feasible and economical in terms of modeling time. Three scenarios of response surface construction are investigated (5%, 10%, and 20%). The method shows very good agreement with the simulation results obtained from CFD/HT model as in the case of 20% of the original CFD data points used for response surface training. Error analysis is carried out to quantify the error associated with each scenario. Case 20% shows superb accuracy as compared to others. With only 2.12 × 104 in mean relative error and R2 = 0.970, the case can capture most of the aspects of the original CFD model.

scholarly journals Implementasi HSV dan GLCM untuk Deteksi Kesegaran Ikan Bawal menggunakan Radial Basis Function Berbasis Android

2019 ◽  
Vol 8 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Muhammad Sarimin ◽  
Nurul Hayaty ◽  
Martaleli Bettiza ◽  
Sapta Nugraha
Keyword(s):  
Radial Basis Function ◽  
Image Recognition ◽  
Basis Function ◽  
Data Center ◽  
Data Centers ◽  
Training Data ◽  
Recognition Method ◽  
Fish Products ◽  
Fresh Fish ◽  
Radial Basis

Tanjungpinang is one of the fish producing cities. fish with a good level of freshness are needed to produce quality fish products. In this case, a system is needed that can recognize fresh and non-fresh fish. In this study using the HSV and GLCM methods as a feature then image recognition is carried out using the Radial Basis Function (RBF). In the RBF recognition method it is necessary to have a central point that becomes the data center. Data center retrieval uses the K-Means method, where this method greatly determines the success of the RBF's introduction. By determining the best number of data centers in the best data center, it is at number 7 with MAD of 0.98. At the time of image acquisition did not pay attention to lighting so as to produce training data with low quality. How in the introduction process using this RBF gets a low level of accuracy, which is equal to 50%

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

scholarly journals Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network

2018 ◽  
Vol 225 ◽  
pp. 02023
Author(s):  
Marwah N. Mohammed ◽  
Kamal Bin Yusoh ◽  
Jun Haslinda Binti Haji Shariffuddin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Response Surface Methodology ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Conversion Rate ◽  
Maximum Effect ◽  
Thermoresponsive Polymers ◽  
Radial Basis

A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data.

Multi-Objective Optimization of a Disc Brake System by Using SPEA2 and RBFN

2013 ◽  
Author(s):  
Kaveh Amouzgar ◽  
Asim Rashid ◽  
Niclas Stromberg
Keyword(s):  
Finite Element ◽  
Radial Basis Function ◽  
Basis Function ◽  
Brake System ◽  
Maximum Temperature ◽  
Disc Brake ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Radial Basis

Many engineering design optimization problems involve multiple conflicting objectives, which today often are obtained by computational expensive finite element simulations. Evolutionary multi-objective optimization (EMO) methods based on surrogate modeling is one approach of solving this class of problems. In this paper, multi-objective optimization of a disc brake system to a heavy truck by using EMO and radial basis function networks (RBFN) is presented. Three conflicting objectives are considered. These are: 1) minimizing the maximum temperature of the disc brake, 2) maximizing the brake energy of the system and 3) minimizing the mass of the back plate of the brake pad. An iterative Latin hypercube sampling method is used to construct the design of experiments (DoE) for the design variables. Next, thermo-mechanical finite element analysis of the disc brake, including frictional heating between the pad and the disc, is performed in order to determine the values of the first two objectives for the DoE. Surrogate models for the maximum temperature and the brake energy are created using RBFN with polynomial biases. Different radial basis functions are compared using statistical errors and cross validation errors (PRESS) to evaluate the accuracy of the surrogate models and to select the most accurate radial basis function. The multi-objective optimization problem is then solved by employing EMO using the strength Pareto evolutionary algorithm (SPEA2). Finally, the Pareto fronts generated by the proposed methodology are presented and discussed.

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