A new electric power train design to power an open-wheel, single seat electric race car to compete in the 2014 SAE Formula Electric competition is presented. This research project is part of the clean technology initiative for sustainable energy sources for automotive application proposed by the Santa Clara University Formula Electric (SCUFE) team. In this paper, a new battery pack design and integration is evaluated. This includes analysis of battery cell connectivity, cell enclosure, safety system, and battery control system. The cell connectivity proposed will maximize electrical conduction through surface area contact of the cell tabs while allowing for quick removal of individual cells in the case of cell failure. The structure of the pack enclosure will consist of polycarbonate panels. The connection panels will be mounted between the wall enclosure lid and wall in order to prevent the pack from being opened when the contactors are engaged, preventing the risk of exposure to high voltage. The proposed design allows the pack to be completely removed from the vehicle for easier disassembly and transportation. The exterior connections will consist of one large plug for the power cables as well as a smaller plug assembly for the battery management system (BMS) and other necessary sensors and connections. Cell voltage states will be monitored through the BMS. In addition, maximum voltage capacity and cell storage capacity will be analyzed and discussed. Recommendations on future designs will also be presented in light of preliminary results on the efficiency versus performance.

Local airflow distribution in data center environments has historically been accomplished through ventilation tiles distributed over a raised floor air distribution plenum. The tiles are initially configured upon the commissioning of the facility and, as IT equipment configuration changes with time, the tiles are adjusted accordingly. However, tile adjustment is a manual process that is error-prone and often non-intuitive. Tile flow rates are a strong function of under floor plenum pressure distribution which is subject to change as tile layouts are reconfigured. Thermal models are often developed to assist with layout changes, but these models can be time-consuming to generate and require skilled users to achieve accurate results. This paper presents an adaptive vent tile (AVT) for use in raised floor data centers that can adapt to the needs of nearby IT equipment. We present a multi-input-multi-output (MIMO) AVT controller that automatically and dynamically adjusts a multiplicity of AVT openings in coordination such that thermal management requirements are met with minimum use of airflow. We describe the development of dynamic models and algorithm design of the MIMO controller. The controller was evaluated with a set of AVT units in a production data center environment. Results show that the controller can optimize local airflow distribution, provide fine-grained rack intake temperature control and respond to disturbances in a manner that is not achievable through static distribution of tiles.

In data centers with raised floor architecture, the floor tiles are typically perforated, delivering the cold air from the plenum to the inlets of equipment located in racks. The environment of these data centers is dynamic in that the workload and power dissipation fluctuate considerably over both short-term and long-term time scales. As such, airflow requirements vary continuously. However, due to labor costs and lack of expertise, the tiles are adjusted infrequently, and many data centers are grossly over provisioned for airflow in general and/or lack sufficient airflow delivery in certain local areas. This wastes energy and reduces data center thermal capacity. We have previously introduced Kratos, an Adaptive Vent Tile (AVT) technology that addresses this problem by automatically adjusting mechanical louvers mounted to the tiles in response to the needs of nearby IT equipment. Our initial results were limited to a 3-tile test bed that allowed us to prove concept but did not provide for scalability. This paper extends the previous work by expanding the size of the test bed to 28 tiles and 29 racks located in multiple thermal zones. We present experimental modeling results on the MIMO (Multi-Input Multi-Output) system and provide insights on the external behavior of the system through CFD (Computational Fluid Dynamic) analysis. We develop an MPC (Model-based Predictive Control) controller to maintain the temperatures of racks below the thresholds through vent tile tuning. Experimental results show that the controller can maintain the temperature below the thresholds while reducing overall cooling air requirements.

A steady-state model for a centralized cooling system is developed to enable proper provisioning of cooling resources in capital intensive projects such as data centers. The model resolves the energy equations for a cooling tower and a centralized water-cooled chiller simultaneously. It requires inputs that are readily available to the design engineer. The user inputs are the ambient conditions, the cooling tower air flow rate, the condenser water flow rate, the evaporator water flow rate, the superheat and subcooling associated with the refrigeration cycle and the full load design conditions. The model utilizes an empirical relationship for the compressor power as a function of load and temperature and gives the user an option to select a constant speed chiller or a variable speed chiller. The outputs include the chiller coefficient of performance, compressor input power and compressor isentropic efficiency. The model results are validated with a manufacturer performance data (8) and compared to an experimental data collected at Hewlett- Packard Laboratories site for a 2110 kW (600 Ton) variable speed chiller and a 2286 kW (650 Ton) constant speed chiller. The model results are found to match the experimental data to within an acceptable deviation. For the constant speed chiller, the chiller efficiency increases with increasing heat load and peaks at full load. For the variable speed chiller, the chiller efficiency peaks between 50% and 70% loading depending on the ambient conditions.

The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive "always-on" data center infrastructure, and reducing the recurring cost of power. A need exists, therefore for an end to end physical model that can be used to design and manage dense data centers and determine the cost of operating a data center. The chip core to the cooling tower model must capture the power levels and thermo-fluids behavior of chips, systems, aggregation of systems in racks, rows of racks, room flow distribution, air conditioning equipment, hydronics, vapor compression systems, pumps and heat exchangers. Earlier work has outlined the foundation for creation of a "smart" data center through use of flexible cooling resources and a distributed sensing and control system that can provision the cooling resources based on the need. This paper shows a common thermodynamic platform which serves as an evaluation and basis for policy based control engine for such a "smart" data center with much broader reach - from chip core to the cooling tower. Computational Fluid Dynamics modeling is performed to determine the computer room air conditioning utilization for a given distribution of heat load and cooling resources in a production data center. Coefficient of performance (COP) of the computer room air conditioning units, based on the level of utilization, is used with COP of other cooling resources in the stack to determine the COP of the ensemble. The ensemble COP represents an overall measure of the performance of the heat removal stack in a data center.

Data centers will be the computational hub of the next generation. Hosting business and mission-critical applications demand a high degree of reliability and flexibility. Deployment of large number of high powered computer systems in very dense configurations in racks in a data centers will result in very high power densities at room level. Managing high power levels in a data center with cost effective reliable cooling solutions is essential for reliability and uptime. Energy consumption of data centers can also be severely increased by over-designed air handling systems and rack layouts that allow the hot and cold air streams to mix.In this paper, we present experimental results from a design of experiments conducted at a production data center facility. The "smart" data center includes a pervasive monitoring layer that enables controlled deployment of air conditioning resources based on demand. Temperature distribution across the data center is measured by a large distribution of sensors at different computer room air conditioning (CRAC) unit air flow and supply temperature configurations. Dimensionless parameters in the form of Supply Heat Indices (SHI) are calculated, based on rack inlet, outlet and CRAC unit supply temperatures, at rack level for validation purposes. Experimental data show that SHI provides a simple and powerful tool to understand the convective heat transfer and fluid flow in diverse regions inside the data center. Analysis based on supply heat index is carried out to understand the optimization of relative air flow distribution among heterogeneous rack heat loads. The index is also used to investigate CRAC unit influences and rack air flow.Results show that these parameters not only provide an invaluable tool to understand convective heat transfer in large data centers but also suggest means to improve energy efficiency in data centers.

The data center of tomorrow is characterized as one containing a dense aggregation of commodity computing, networking and storage hardware mounted in industry standard racks. In fact, the data center is a computer. The walls of the data center are akin to the walls of the chassis in today's computer system. The new slim rack mounted systems and blade servers enable reduction in the footprint of today's data center by 66%. While maximizing computing per unit area, this compaction leads to extremely high power density and high cost associated with removal of the dissipated heat. Today's Approach of cooling the entire data center to a constant temperature sampled at a single location, irrespective of the distributed utilization, is too energy inefficient. We propose a smart cooling system that provides localized cooling when and where needed and works in conjunction with a compute workload allocator to distribute compute workloads in the most energy efficient state. This paper shows a vision and construction of this intelligent data center that uses a combination of modeling, metrology and control to provision the air conditioning resources and workload distribution. A variable cooling system comprising variable capacity computer room air conditioning units, variable air moving devices, adjustable vents, etc. are used to dynamically allocate air conditioning resources where and when needed. A distributed metrology layer is used to sense environment variables like temperature and pressure, and power. The data center energy manager redistributes the compute workloads based on the most energy efficient availability of cooling resources and vice versa. The distributed control layer is no longer associated with any single localized temperature measurement but based on parameters calculated from an aggregation of sensors. The compute resources not in use are put on "standby" thereby providing added savings.

The industry is in the midst of a transformation to lower the cost of ownership through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive "always-on" data center infrastructure, and reducing recurring cost of power. A need exists, therefore for an end to end physical model that can be used to design and manage dense data centers and determine the cost of operating a data center. The chip core to the cooling tower model must capture the power levels and thermo-fluids behavior of chips, systems, aggregation of systems in racks, rows of racks, room flow distribution, air conditioning equipment, hydronics, vapor compression systems, pumps and cooling towers or heat exchangers. As a first step in data center consolidation, the ensemble model must be able to characterize a given data center and its level of capacity utilization, controllability, and room for expansion. Secondly, the continuous operation of the data center management system demands that the ensemble model be programmable to create new "set points" for power and cooling based on current customer cost and performance needs. The overall data center management system, when bundled as a product, must result in a simple payback of 1 year by increasing data center utilization to 80% of rated capacity and through savings in recurring cost of power. Therefore, economic ramifications drive a business need for the creation of an information technology management tool that can maximize the utilization of critical data center resources and minimize the power consumption. The creation of such an end to end management system that can sense and control a complex heat transfer stack requires a thermodynamics based evaluation model. Earlier work has outlined the foundation for creation of a "smart" data center through use of flexible cooling resources and a distributed sensing system that can provision the cooling resources based on the need. This paper shows a common thermodynamic platform which serves as an evaluation and basis for a policy based control engine for such a "smart" data center with much broader reach - from chip core to the cooling tower.