Thermal Management of a Data Center White Space: A numerical study using computational fluid dynamics, flow & temperature predictions using neural networks, and white space design optimization using a genetic algorithm.

The total electrical energy consumption by all the operational data centers (located all over the world) is enormous (approx. 1% of the global electricity demand 20000TWh). This electrical energy is required 24x7 to operate and cool all the IT equipment present in the data center. The electrical energy required to cool all the servers present in the white space can range from as low as 10% to as high as 40% of the total data center electrical consumption. The server's inlet temperature has to be within the ASHRAE recommended range (18o C – 27o C), so that they can function correctly. A simplified design of a raised floor white space with hot aisle / cold aisle configuration is considered. The tile flow rate through the floor tiles influences the server's inlet temperature. To control the tile flow rate, 11 design variables of the data center white space are identified. These are the position of the 4 perforated plates, the amount of perforations of each perforated plates, the floor tiles perforation, the raised floor height, and the CRAH distance to the cabinet. 600 design samples of the white space are generated by applying the Latin Hypercube Sampling (LHS) technique on these 11 design variables. A well-validated CFD software 6SigmaRoom by Future Facilities is used to generate the CFD results. The standard k-ε model is used to model the turbulence in the CFD simulations, in a steady-state condition. A database of 600 samples is generated by recording the cabinet's inlet temperature, flow rate of floor tiles from the CFD simulations, and the corresponding changeable design parameters generated by the LHS technique. The error due to CFD simulation is estimated at less than 4% for the tile flow rate and 1.7o C for the server inlet temperature. Four Artificial Neural Networks (ANN) are trained on the data from the database to predict the floor tile's tile flow rate and the cabinet's inlet temperature, respectively. The average R2 prediction (testing) accuracy is 0.97 for the tile flow rate predictions and 0.92 for the cabinet's inlet temperature predictions. Their average prediction error is less than 5% for the tile flow rate and less than 20 C for the cabinet's inlet temperature. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a variant of the genetic algorithm, is used to find the optimum values of the 11 design parameters of the white space. These optimum values are going to ensure the server's inlet temperature to be within the ASHRAE recommended range. The genetic algorithm optimizes the design variables based on the predictions made by the neural network predicting the tile flow rate. The values of the optimized design parameters are verified using the 6SigmaRoom software by comparing the server's (mean) inlet temperature of the optimized case with the non-optimized case. More number of servers have their (mean) inlet temperature below 270 C in the optimized case as compared to the non-optimized case. The electrical power required by the CRAHs to cool the white space is reduced by 10% in the optimized case as compared to the power which is required by the CRAHs in the non-optimized case. In this study, a CFD simulation of the white space took 40 minutes. The neural networks took less than a minute to make the predictions, and the NSGA-II algorithm took less than 10 minutes to find the optimized design parameters of the white space. Thus, in this thesis, it is shown that using an artificial neural network and a genetic algorithm, in combination with computational fluid dynamics gives satisfying results in optimizing the white space design, required to keep the server's inlet temperature within the ASHRAE recommended range. The computational time required to find the optimum white space design is also reduced by using a neural network and a genetic algorithm. The prediction by the neural network and the optimization performed by the genetic algorithm can be improved further with the availability of more training data and in-depth knowledge of applying these techniques (the neural network and the genetic algorithm) in predicting and optimizing the solutions respectively.