Optimization for cooling

1. A computer implemented method for designing a data center optimized for cooling of components in the data center, the method comprising:

generating, by a processor, a small geometric length scale model of the data center, wherein the small geometric length scale model includes geometrical specifications of a small component of the data center, the small component comprising at least one of a processor chip and vent-tile;

ascertaining, by the processor, for the small geometric length scale model, outlet values of at least one flow parameter associated with a cooling medium, based on input inlet values of the at least one flow parameter and a fluid dynamics simulation of circulation of the cooling medium in the small geometric length scale model;

determining, by the processor, an empirical model indicative of fluid behaviour of the cooling medium in the small geometric length scale model using a data-based modeling framework, based on the input inlet values and the ascertained outlet values of the at least one flow parameter;

generating, by the processor, a full-scale physics-based model based on a full-scale model of the data center, the full-scale physics based model being indicative of geometry of a full-scale of the components of the data center without the geometrical specifications of the small component;

integrating, by the processor, the empirical model with the full-scale physics-based model to obtain an integrated physics-based model for simulating fluid flow behaviour of the cooling medium in the data center;

determining, by the processor, full-scale outlet values of the at least one flow parameter of the cooling medium for the data center, based on inlet values of the at least one flow parameter in the data center and the integrated physics-based model, for optimizing the data center for cooling of the components; and

wherein integrating the empirical model further comprises validating the empirical model comprising:

selecting the at least one correlation;

providing test data as input to the at least one correlation, wherein the test data comprises inlet values of the at least one flow parameter;

determining test outlet values of the at least one flow parameter, based on the at least one correlation and the test data; and

comparing the test outlet values with ascertained outlet values of the at least one flow parameter to validate the empirical model.

2. The method as claimed in claim 1 , wherein the ascertaining the outlet values comprises simulating fluid flow behaviour in the small geometric length scale model, using a physics-based model.

3. The method as claimed in claim 2 , wherein the physics-based model is a computational fluid dynamics (CFD) based model.

4. The method as claimed in claim 1 , wherein the determining the empirical model comprises ascertaining at least one correlation and one or more coefficients of the at least one correlation, based on the inlet values and the outlet values of the at least one flow parameter.

5. The method as claimed in claim 1 , wherein the data-based modeling framework is an artificial neural network (ANN)-based model.

6. A design optimization system for designing a data center, the design optimization system comprising:

a processor; and

a memory coupled to the processor, the memory comprising,

a determination module configured to,

generate a small geometric length scale model of the data center, wherein the small geometric length scale model includes geometrical specifications of a small component of the data center, the small component comprising at least one of a processor chip and vent-tile;

ascertain, for the small geometric length scale model, outlet values of at least one flow parameter associated with a cooling medium, based on input inlet values of the at least one flow parameter and a fluid dynamics simulation of circulation of the cooling medium in the small geometric length scale model; a modeling module configured to:

determine an empirical model indicative of fluid behaviour of the cooling medium in the small geometric length scale model using a data-based modeling framework, based on the input inlet values and the ascertained outlet values of the at least one flow parameter;

generate a full-scale physics-based model based on a full-scale model of the data center, the full-scale physics based model being indicative of geometry of a full-scale of the components of the data center without the geometrical specifications of the small component;

integrate the empirical model with the full-scale physics-based model to obtain an integrated physics-based model for simulating fluid flow behaviour of the cooling medium in the data center;

determine full-scale outlet values of the at least one flow parameter of the cooling medium for the data center, based on inlet values of the at least one flow parameter in the data center and the integrated physics-based model, for optimizing the data center for cooling of the component;

select a primary flow parameter from the at least one flow parameter; and

determine the empirical model based on the selected primary flow parameter using the data-based modeling framework.

7. The design optimization system as claimed in claim 6 , wherein the determination module is configured to:

obtain the inlet values of the at least one flow parameter in the small geometric length scale model of a data center; and

compute the outlet values of the at least one flow parameter in the small geometric length scale model of a data center based on a computational fluid dynamics (CFD) based model.

8. The design optimization system as claimed in claim 6 , further comprising a validation module configured to validate an accuracy of the empirical model determined by the modeling module, wherein the validation module is configured to:

determine test outlet values of the at least one flow parameter, based on test data and the empirical model;

compare the test outlet values with available outlet values of the at least one flow parameter computed by the modeling module; and

validate the accuracy of the empirical model based on the comparison.

9. A non-transitory computer-readable medium having a set of computer readable instructions that, when executed, perform acts comprising:

generating a small geometric length scale model of a data center, wherein the small geometric length scale model includes geometrical specifications of a small component of the data center, the small component comprising at least one of a processor chip and vent-tile;

ascertaining for the small geometric length scale model, outlet values of at least one flow parameter associated with a cooling medium, based on input inlet values of the at least one flow parameter and a fluid dynamics simulation of circulation of the cooling medium in the small geometric length scale model;

determining an empirical model indicative of fluid behaviour of the cooling medium in the small geometric length scale model using a data-based modeling framework, based on the input inlet values and the ascertained outlet values of the at least one flow parameter;

generating a full-scale physics-based model based on a full-scale model of the data center, the full-scale physics based model being indicative of geometry of a full-scale of the components of the data center without the geometrical specifications of the small component;

integrating the empirical model with the full-scale physics-based model to obtain an integrated physics-based model for simulating fluid flow behaviour of the cooling medium in the data center;

determining full-scale outlet values of the at least one flow parameter of the cooling medium for the data center, based on inlet values of the at least one flow parameter in the data center and the integrated physics-based model, for optimizing the data center for cooling of the component; and

wherein integrating the empirical model further comprises validating the empirical model comprising:

selecting the at least one correlation;

providing test data as input to the at least one correlation, wherein the test data comprises inlet values of the at least one flow parameter;

determining test outlet values of the at least one flow parameter, based on the at least one correlation and the test data; and

comparing the test outlet values with ascertained outlet values of the at least one flow parameter to validate the empirical model.