Rajat Ghosh

Designing oscillating cilia that capture or release microscopic particles.

We use computational modeling to capture the three-dimensional interactions between oscillating, synthetic cilia and a microscopic particle in a fluid-filled microchannel. The synthetic cilia are elastic filaments that are tethered to a substrate and are actuated by a sinusoidal force, which is applied to their free ends. The cilia are arranged in a square pattern, and a neutrally buoyant particle is initially located between these filaments. Our computational studies reveal that, depending on frequency of the beating cilia, the particle can be either driven downward toward the substrate or driven upward and expelled into the fluid above the cilial layer. This behavior mimics the performance of biological cilia used by certain marine animals to extract suspended food particles. The findings uncover a new route for controlling the deposition of microscopic particles in microfluidic devices.

Effect of rack server population on temperatures in data centers

To reduce energy wastage during equipment upgrades that require changing the number of servers inside a rack, this paper investigates the effect of the server population of a rack on air temperatures in a data center facility. A rack-level containment system was implemented and air temperatures in cold and hot aisles with different numbers of servers were measured. A computational fluid dynamics (CFD)-based numerical model is used to study the air flow field. In addition, this paper includes the investigation of the effect of void locations on CPU temperatures. The study reveals that the server population inside a rack has a significant impact on air temperatures.

Rapid Temperature Predictions in Data Centers using Multi-Parameter Proper Orthogonal Decomposition

A proper orthogonal decomposition (POD)-based multi-parameter, reduced-order modeling framework that rapidly predicts air temperatures in an air-cooled data center is developed. The modeling parameters are heat load and time. The framework is applied on initial temperature snapshots acquired near a server simulator rack by measurements at discrete time instants and selected rack heat loads. To estimate the accuracy of the modeling framework, the predicted temperature data are compared with corresponding experimental observations. The proposed algorithm is demonstrated to be effective and efficient for full-factorial parametric temperature characterization.

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

A proper orthogonal decomposition (POD)-based modeling framework is developed for improving the spatial resolution of transient rack air temperature data collected in a heterogeneous data center (DC) facility. Blocking cooling air inflow into racks periodically, three sets of transient temperature data are collected at the outlets of electronic equipment residing in three different racks. Using various combinations of initial discrete data as ensembles, the capability of the proposed POD/ interpolation framework for predicting new temperature data is demonstrated. The accuracy of POD-based temperature predictions is validated by comparing it to corresponding experimental data. The root mean square deviations between experimental data and POD-based predictions are found to be on the order of 5%.

Proper Orthogonal Decomposition-Based Modeling Framework for Improving Spatial Resolution of Measured Temperature Data

This paper presents a proper orthogonal decomposition (POD)-based reduced-order modeling framework to improve spatial resolution of measured temperature data in an air-cooled data center. This data-driven approach is applied on transient air temperature data, acquired at the exhaust of a server simulator rack. Temperature data is collected by a distributed thermocouple network at 1 Hz sampling frequency following a step impulse in the rack heat load. The input data are organized in a 2-D array, comprising transient temperature signals measured at various spatial locations. Because its computational time scales logarithmically with the input size, the proposed POD-based approach is potentially useful as an efficient tool for handling large transient data sets. With spatial location being the parameter for the input data matrix, the proposed approach is suitable for rapid synthesis of transient temperature data at new spatial locations. The comparison between POD-based local air temperature predictions and corresponding data indicates a maximum prediction uncertainty of 3.2%, and root mean square prediction uncertainty of 1.9%.

DYNAMIC REDUCED ORDER THERMAL MODELING OF DATA CENTER AIR TEMPERATURES

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.Copyright

Using Actuated Cilia to Regulate Motion of Microscopic Particles

Marine animals use microscopic elastic filaments, or cilia, to capture food particles that are suspended in the surrounding solution [1, 2]. In the respiratory tract, active cilial layers facilitate the transport of particulates such as dust or mucous. These motile cilia experience the surrounding fluid as a highly viscous, low Reynolds number environment, where the effects of inertia are negligible [2]. Nevertheless, by oscillating in a periodic, time-irreversible manner, the elastic cilia can generate net currents within the fluid and thereby, effectively transport and direct microscopic particles. The behavior of these biological cilia provides a useful design concept for creating microfluidic devices where actuated “synthetic cilia” would regulate the movement of micrometer-sized particles, such as biological cells and polymeric microcapsules.Copyright

Thermal simulations in support of multi-scale co-design of energy efficient information technology systems

Purpose – Information technology (IT) systems are already ubiquitous, and their future growth is expected to drive the global economy for the next several decades. However, energy consumption by these systems is growing rapidly, and their sustained growth requires curbing the energy consumption, and the associated heat removal requirements. Currently, 20-50 percent of the incoming electrical power is used to meet the cooling demands of IT facilities. Careful co-optimization of electrical power and thermal management is essential for reducing energy consumption requirements of IT equipment. Such modeling based co-optimization is complicated by the presence of several decades of spatial and temporal scales. The purpose of this paper is to review recent approaches for handling these challenges. Design/methodology/approach – In this paper, the authors illustrate the challenges and possible modeling approaches by considering three examples. The multi-scale modeling of chip level transient heating using a combi...