Prediction of Hot Aisle Partition Airflow Boundary Conditions

2013 ◽  
Author(s):  
Zhihang Song ◽  
Bruce T. Murray ◽  
Bahgat Sammakia
Keyword(s):  
Boundary Conditions ◽  
Data Center ◽  
Computation Time ◽  
Design Tool ◽  
Operating Conditions ◽  
Ann Model ◽  
Flow Rates ◽  
Level Model ◽  
Optimization Methodology ◽  
Artificial Neural Network Ann

The integration of a simulation-based Artificial Neural Network (ANN) with a Genetic Algorithm (GA) has been explored as a real-time design tool for data center thermal management. The computation time for the ANN-GA approach is significantly smaller compared to a fully CFD-based optimization methodology for predicting data center operating conditions. However, difficulties remain when applying the ANN model for predicting operating conditions for configurations outside of the geometry used for the training set. One potential remedy is to partition the room layout into a finite number of characteristic zones, for which the ANN-GA model readily applies. Here, a multiple hot aisle/cold aisle data center configuration was analyzed using the commercial software FloTHERM. The CFD results are used to characterize the flow rates at the inter-zonal partitions. Based on specific reduced subsets of desired treatment quantities from the CFD results, such as CRAC and server rack air flow rates, the approach was applied for two different CRAC configurations and various levels of CRAC and server rack flow rates. Utilizing the compact inter-zonal boundary conditions, good agreement for the airflow and temperature distributions is achieved between predictions from the CFD computations for the entire room configuration and the reduced order zone-level model for different operating conditions and room layouts.

Supply Air Temperature Prediction in an Air-Handling Unit Using Artificial Neural Network

2018 ◽  
Author(s):  
Nikita Sukthankar ◽  
Abhishek Walekar ◽  
Dereje Agonafer
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Data Center ◽  
Operating Conditions ◽  
Cfd Analysis ◽  
Ann Model ◽  
Air Handling Unit

Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

scholarly journals Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2216 ◽  
Author(s):  
Ravi Kishore ◽  
Roop Mahajan ◽  
Shashank Priya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Ann Model ◽  
Predicting Performance ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

scholarly journals Development of ANN Model for the Prediction of VIV Fatigue Damage of Top-tensioned Riser

2018 ◽  
Vol 203 ◽  
pp. 01013 ◽  
Author(s):  
Eileen Wee Chin Wong ◽  
Han Suk Choi ◽  
Do Kyun Kim ◽  
Fakhruldin Mohd Hashim
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fatigue Damage ◽  
Computation Time ◽  
Outer Diameter ◽  
Ann Model ◽  
Damage Prediction ◽  
Simplified Approach ◽  
Input Parameters ◽  
Artificial Neural Network Ann

Marine riser experiences vortex-induced vibration (VIV) caused by current, leading to fatigue damage if VIV is not considered in design of riser. Estimation of VIV fatigue damage is essential in designing feasible and operable riser. A simplified approach for predicting fatigue damage is required to reduce the computation time to analyze the fatigue damage. This study aims to explore the applicability of artificial neural network (ANN) approach in developing top-tensioned riser fatigue damage prediction model. A total of 2100 riser model is generated with different combination of four main input parameters: riser outer diameter, wall thickness, top tension and uniform current velocity. The modal analysis is performed using OrcaFlex and VIV fatigue damage of the riser is computed using SHEAR7. The four input parameters and corresponding fatigue damage results make up the database for training a 2-layer neural network. Weight and bias values acquired from the training of ANN are used to develop the VIV fatigue damage prediction model of the riser. The results show ANN approach is suitable for prediction of the riser fatigue damage due to VIV. The proposed approach requires further refinements and extension to more input features to improve the accuracy and usefulness of the developed prediction model.

scholarly journals MODELING, OPTIMIZATION AND CONTROL OF A FCC UNIT USING NEURAL NETWORKS AND EVOLUTIONARY METHODS

Engevista ◽  
2013 ◽  
Vol 16 (1) ◽  
pp. 70 ◽  
Author(s):  
Vitor Diego da Silva Bispo ◽  
Elina Sandra Ramos de Lira e Silva ◽  
Luiz Augusto Da Cruz Meleiro
Keyword(s):  
Optimization Problems ◽  
Neural Model ◽  
Nonlinear Process ◽  
Operating Conditions ◽  
Strong Interactions ◽  
Ann Model ◽  
Evolutionary Methods ◽  
Operational Variables ◽  
Regenerator System ◽  
Artificial Neural Network Ann

This paper presents a simulation study of the use of an artificial neural network (ANN) model for control and optimization of a Fluidized-Bed Catalytic Cracking reactor-regenerator system (FCC). This case study, whose phenomenological model was validated with industrial data, is a multivariable and nonlinear process with strong interactions among the operational variables. In order to obtain a dynamic model of the FCC system, a feedforward ANN model was identified. Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) evolutionary methods were used to set optimal operating conditions for the FCC, and both algorithms presented good and consistent results for typical FCC optimization problems. The neural model was also used in the design of a Model-Based Predictive Control (MPC) for the FCC process. It was showed that the ANN-based MPC was able to reject the imposed disturbance as well as to track the proposed trajectory, while considering operational constraints of the plant.

Physical Models in Data Center Airflow Simulations

2003 ◽  
Author(s):  
Jeffrey D. Rambo ◽  
Yogendra K. Joshi
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Current Data ◽  
Design Tool ◽  
Unit Cell ◽  
Operating Conditions ◽  
Physical Models ◽  
Total Power ◽  
Modeling Framework ◽  
Length Scales

The trend of increasing functionality of electronics with a reduction in size has caused a rapid increase in the volumetric heat generation of today’s equipment. This problem is compounded by the vertical stacking of such components in tall enclosures, called racks. The organization of these racks into large infrastructure facilities, or data centers, generates enough heat to require a room-level cooling strategy. The total power dissipated in current data centers can be as large as several MW. Since all the heat generated must be removed, a systematic thermal management methodology is required to ensure efficient, reliable and safe operating conditions. The mathematical description of the airflow and heat transfer characteristics involves a range of length scales, all of which are infeasible to approach simultaneously. In this study, a modeling framework for data centers is investigated, with emphasis on the physical models employed in numerical simulations. Computational fluid dynamics (CFD) models are presented to develop a unit cell, or a minimum sized model which is representative of these facilities. A unit cell architecture is a useful design tool for the evaluation of tomorrow’s cooling strategies. A premium on floor space may result in oddly shaped facilities, so a need exists for a common basis of comparison. The flow patterns inside a data center typically fall into the regime of turbulent mixed convection. The choice of turbulence model employed in a Reynolds-averaged Navier-Stokes (RANS) type simulation is examined using a commercial code. Comparisons are made with indoor airflow simulations of office spaces and auditoria because of the similarity of office spaces and auditoria because of the similarity in velocities and length scales. Results shows up to 20% variation in temperature predictions can occur between various commercially-implemented turbulence models.

Multivariate Prediction of Airflow and Temperature Distributions Using Artificial Neural Networks

2011 ◽  
Author(s):  
Zhihang Song ◽  
Bruce T. Murray ◽  
Bahgat Sammakia
Keyword(s):  
Real Time ◽  
Thermal Management ◽  
Data Centers ◽  
Design Tool ◽  
Operating Conditions ◽  
Ann Model ◽  
Predictive Tool ◽  
Temperature Distributions ◽  
Design Variables ◽  
Artificial Neural

Thermal Management optimization for data centers, including prediction of airflow and temperature distributions, is generally an extremely time-consuming process using full-scale CFD analysis. Reduced order models are necessary in order to provide real-time assessment of cooling requirements for data centers. The use of a simulation-based Artificial Neural Network (ANN) is being investigated as a predictive tool. A model for a basic hot aisle/cold aisle data center configuration was built and analyzed using the commercial software FloTHERM. The flow field and temperature distributions were obtained for 100 representative sets of operating conditions using the CFD package. The Latin Hypercube Sampling technique was employed to select values for three design variables: plenum height, percentage open area of the perforated tiles and air leakage fraction. The FloTHERM results were used to generate a database for the ANN training. The CFD results from 85 cases were used for training and 16 cases were used for validation. A multivariate mapping between the input design variables and output variables (individual tile flow rates and maximum rack temperatures) was obtained. Good agreement (0.5% average relative error) was obtained between the ANN model predictions and the CFD results. These preliminary results are promising and show that an ANN based model may yield an effective real-time thermal management design tool for data centers.

scholarly journals Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2932
Author(s):  
Hung-Ta Wen ◽  
Jau-Huai Lu ◽  
Mai-Xuan Phuc
Keyword(s):  
Artificial Intelligence ◽  
Fixed Bed ◽  
Operating Conditions ◽  
Gradient Boosting ◽  
Ann Model ◽  
Rice Husks ◽  
Ensemble Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Combustible Gases

The purpose of this study is to utilize two artificial intelligence (AI) models to predict the syngas composition of a fixed bed updraft gasifier for the gasification of rice husks. Air and steam-air mixtures are the gasifying agents. In the present work, the feeding rate of rice husks is kept constant, while the air and steam flow rates vary in each case. The consideration of various operating conditions provides a clear comparison between air and steam-air gasification. The effects of the reactor temperature, steam-air flow rate, and the ratio of steam to biomass are investigated here. The concentrations of combustible gases such as hydrogen, carbon monoxide, and methane in syngas are increased when using the steam-air mixture. Two AI models, namely artificial neural network (ANN) and gradient boosting regression (GBR), are applied to predict the syngas compositions using the experimental data. A total of 74 sets of data are analyzed. The compositions of five gases (CO, CO2, H2, CH4, and N2) are predicted by the ANN and GBR models. The coefficients of determination (R2) range from 0.80 to 0.89 for the ANN model, while the value of R2 ranges from 0.81 to 0.93 for GBR model. In this study, the GBR model outperforms the ANNs model based on its ensemble technique that uses multiple weak learners. As a result, the GBR model is more convincing in the prediction of syngas composition than the ANN model considered in this research.

scholarly journals Load balancing method for KDN-based data center using neural network

2021 ◽  
pp. 87-97
Author(s):  
Alex M. R. Ruelas ◽  
Christian E. Rothenberg
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Data Center ◽  
Network Performance ◽  
Data Centers ◽  
Ann Model ◽  
Network Analytics ◽  
Cloud Application ◽  
Artificial Neural Network Ann ◽  
Knowledge Plane

The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved.

Nitrogen Removal from Anaerobically-Treated Leachate

1984 ◽  
Vol 19 (1) ◽  
pp. 87-100
Author(s):  
D. Prasad ◽  
J.G. Henry ◽  
P. Elefsiniotis
Keyword(s):  
Nitrogen Removal ◽  
Air Flow ◽  
Operating Conditions ◽  
Air Flow Rate ◽  
Flow Rates ◽  
Anaerobic Filter ◽  
Ph Values ◽  
Wide Range ◽  
Ammonia Desorption ◽  
Effects Of Ph

Abstract Laboratory studies were conducted to demonstrate the effectiveness of diffused aeration for the removal of ammonia from the effluent of an anaerobic filter treating leachate. The effects of pH, temperature and air flow on the process were studied. The coefficient of desorption of ammonia, KD for the anaerobic filter effluent (TKN 75 mg/L with NH3-N 88%) was determined at pH values of 9, 10 and 11, temperatures of 10, 15, 20, 30 and 35°C, and air flow rates of 50, 120, and 190 cm3/sec/L. Results indicated that nitrogen removal from the effluent of anaerobic filters by ammonia desorption was feasible. Removals exceeding 90% were obtained with 8 hours aeration at pH of 10, a temperature of 20°C, and an air flow rate of 190 cm3/sec/L. Ammonia desorption coefficients, KD, determined at other temperatures and air flow rates can be used to predict ammonia removals under a wide range of operating conditions.

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