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.

Evaluation of a Data Center Recirculation Non-Uniformity Metric Using Computational Fluid Dynamics

2011 ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Simple Model ◽  
Temperature Rise ◽  
Data Center ◽  
Large Temperature ◽  
Inlet Temperature ◽  
Temperature Pattern ◽  
Cfd Analysis ◽  
Tile Flow

The work presented in this paper is an extension of the companion work by the authors on a simplified thermodynamic model for data center optimization, in which a recirculation non-uniformity metric, θ, was introduced and used in a parametric analysis to highlight the deleterious effect of recirculation non-uniformity at the inlet of racks on the data center cooling infrastructure power consumption. In this work, several studies are done using a commercial computational fluid dynamics (CFD) package to verify many of the assumptions necessary in the development of the simplified model and to understand the degree of recirculation non-uniformity present in typical data center configurations. A number of CFD simulations are used to quantify the ability of the simple model at predicting θ. The results show that the simple model provides a fairly accurate estimate of θ, with a standard deviation in the prediction error of ∼10–15%. The CFD analysis are also to understand the effect of row length and server temperature rise (ΔTs) temperature non-uniformity. The simulations show that reasonable values of θ range from 2–6 for open aisle data centers depending on operating strategy and data center layout. As a means to understand the effect of buoyancy, a data center Archimedes number (Ar), the ratio of buoyancy to inertia forces, is introduced as a function of tile flow rate and server temperature rise. For servers with modest temperature rise (∼ 10.0°C), Ar is ∼0.1; however, for racks with large temperature rise (∼20°C), Ar > 1.0, meaning buoyancy needs to be considered important. Through CFD analysis the significant effect buoyancy has on the inlet rack temperature patterns is highlighted. The Capture Index (ψ), the ratio of cold air ingested by the racks to the required rack flow, is used to investigate its relationship to the ratio of server flow to tile flow (Y), as the inlet rack temperature patterns are changed by increased Ar. The results show that although the rack inlet temperature patterns are extremely different, ψ does not change significantly as a function of Ar. Lastly, the effect of buoyancy on the assumption of linearity of the temperature field is considered for a range of Ar. The results show the emergence of a stratified temperature pattern at the inlet of the racks as Ar increases and buoyancy becomes more important. It is concluded that under these conditions, a δT change in tile temperature does not produce a δT change in temperature everywhere in the field.

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

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.

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Yao Kouassi Benjamin ◽  
Emmanuel Assidjo Nogbou ◽  
Gossan Ado ◽  
Catherine Azzaro-Pantel ◽  
André Davin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Molar Ratio ◽  
Ann Model ◽  
Sequential Method ◽  
Modeling And Optimization ◽  
Artificial Neural ◽  
Better Than

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ? X1 ? 0.77; 1.773 g ? X2 ? 1.86 g; 289.74 °C ? X3 ? 291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67% and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting.

scholarly journals Application of Computational Fluid Dynamics (CFD) in the Deposition Process and Printability Assessment of 3D Printing Using Rice Paste

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Timilehin Martins Oyinloye ◽  
Won Byong Yoon
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
3D Printing ◽  
Deposition Process ◽  
Operating Conditions ◽  
Nozzle Diameter ◽  
Cfd Analysis ◽  
Flow Properties ◽  
Swell Ratio ◽  
Computational Fluid Dynamics Cfd

Computational fluid dynamics (CFD) was utilized to investigate the deposition process and printability of rice paste. The rheological and preliminary printing studies showed that paste formed from rice to water ratio (100:80) is suitable for 3D printing (3DP). Controlling the ambient temperature at C also contributed to improving the printed sample’s structural stability. The viscoelastic simulation indicated that the nozzle diameter influenced the flow properties of the printed material. As the nozzle diameter decreased (1.2 mm to 0.8 mm), the die swell ratio increased (13.7 to 15.15%). The rise in the swell ratio was a result of the increasing pressure gradient at the nozzle exit (5.48 × 106 Pa to 1.53 × 107 Pa). The additive simulation showed that the nozzle diameter affected both the residual stress and overall deformation of the sample. CFD analysis, therefore, demonstrates a significant advantage in optimizing the operating conditions for printing rice paste.

Management of an Industrial Facility Using an Artificial Neural Network Monitoring System

2007 ◽  
Author(s):  
Rory Hynes ◽  
Babak Seyedan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Model ◽  
Direct Method ◽  
Load Capacity ◽  
Operating Conditions ◽  
Good Time ◽  
Ann Model ◽  
Computer Technique ◽  
Artificial Neural

The objective of this paper is to assess the optimum heat load capacity of a real CHP plant and provide recommendations to improve heat utilization and reduce costs using a computerized system. A simulation model based on component actual behaviour has been developed. The simulation model is capable of plant optimization that could lead to significant economic and energy consumption improvements. The general modular structural of the plant component is described together with a discussion of the results and cost analysis. In the second part, feasibility of the Artificial Neural Network (ANN) approach is evaluated. The data from the simulation model of the plant is used to train such an ANN model. Results from the conventional computer technique are compared with that of the direct method based ANN approach. The results indicate it is feasible to use ANN to predict plant-operating conditions. The ANN gives a good time response and performance prediction capability with change of boundary conditions. Significantly shorter computation time is obtained with the ANN compared to the physical model. The accuracy of the ANN output and its suitability for on-line monitoring of a CHP plant are discussed.

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