Reduced-order modeling framework for improving spatial resolution of data center transient air temperatures

Author(s):  
R. Ghosh ◽  
Y. Joshi ◽  
L. Klein ◽  
H. Hamann
Author(s):  
Rajat Ghosh ◽  
Yogendra Joshi

We developed a Proper Orthogonal Decomposition (POD) based dynamic reduced order model that can predict transient temperature field in an air-cooled data center. A typical data center is modeled as a turbulent convective thermal system with multiple length scales. A representative case study is presented to validate the developed methodology. The model is observed to be capable of predicting the transient air temperature field accurately and rapidly. Comparing with the computational fluid mechanics/heat transfer (CFD/HT) based model, it is revealed that our model is 100x faster without compromising solution accuracy. The developed modeling framework is potentially useful for designing a control system that can regulate flow parameters in a transient data center.


2019 ◽  
Vol 65 (2) ◽  
pp. 451-473 ◽  
Author(s):  
Hasini Garikapati ◽  
Sergio Zlotnik ◽  
Pedro Díez ◽  
Clemens V. Verhoosel ◽  
E. Harald van Brummelen

Abstract Understanding the failure of brittle heterogeneous materials is essential in many applications. Heterogeneities in material properties are frequently modeled through random fields, which typically induces the need to solve finite element problems for a large number of realizations. In this context, we make use of reduced order modeling to solve these problems at an affordable computational cost. This paper proposes a reduced order modeling framework to predict crack propagation in brittle materials with random heterogeneities. The framework is based on a combination of the Proper Generalized Decomposition (PGD) method with Griffith’s global energy criterion. The PGD framework provides an explicit parametric solution for the physical response of the system. We illustrate that a non-intrusive sampling-based technique can be applied as a post-processing operation on the explicit solution provided by PGD. We first validate the framework using a global energy approach on a deterministic two-dimensional linear elastic fracture mechanics benchmark. Subsequently, we apply the reduced order modeling approach to a stochastic fracture propagation problem.


2019 ◽  
Vol 100 (5) ◽  
Author(s):  
Sk. M. Rahman ◽  
S. Pawar ◽  
O. San ◽  
A. Rasheed ◽  
T. Iliescu

AIAA Journal ◽  
2020 ◽  
Vol 58 (2) ◽  
pp. 618-632 ◽  
Author(s):  
Jiayang Xu ◽  
Cheng Huang ◽  
Karthik Duraisamy

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