modelling and control of a free cooling system for data centers

Abstract Data centers are facilities hosting a large number of servers dedicated to data storage and management. In recent years, their power consumption has increased significantly due to the power density of the IT equipment. In particular, cooling represents approximately one third of the total electricity consumption, therefore efficiently cooling data centers has become a challenging problem and it represents an opportunity to reduce both IT energy costs and emissions environmental impact. The efficiency of computers room air conditioning (CRAC) systems can be increased using both advanced control techniques and new free cooling technologies, such as the indirect adiabatic cooling (IAC), that is the humidification of air under adiabatic conditions. Water sprinkled by spray nozzles humidifies and cools down the air taken from the outside, which then cools down the computers room air by means of a crossflow heat exchanger. In this way, the process air temperature is economically reduced and the cooling process is effective even when the outside temperature is warmer than that desired in the computers room. Beside the traditional approach, that improves energy efficiency of CRAC systems through advanced hardware design, nowadays advanced control systems offer the opportunity to improve both efficiency and performance by mostly acting on software components. In particular, a model-based paradigm can result very useful in the design of the controller. This approach involves three main steps: plant modelling, controller design, and simulations. In this paper, First-Principle Data-Driven (FPDD) techniques have been considered in the modelling phase, in order to obtain a model as simple as possible but accurate enough. All the main components of the plant, such as fans, spray nozzles, heat exchanger, and the computers room have been taken into account and they have been calibrated exploiting real data. The dynamics of the computers room variables (e.g. temperature) are slower than those of the components of the cooling system, due to higher thermal inertias of the computers room. Therefore, fans, heat exchanger, and spray nozzles are described by static models, whereas the computers room is described by a LTI dynamic model. Once obtained a model of the plant, a simulation environment based on Matlab/Simulink is designed accordingly. The developed control system is hierarchical: a supervisor determines the best combination of CRAC water and process air flows which minimizes the total power consumption, while satisfying the cooling demand. This system energy management problem is formulated as a non-linear optimization problem, subject to internal air condition requirements and system operating constraints. The optimization problem is repeatedly solved at each supervision period by using a population based stochastic optimization technique (Particle Swarm Optimization). Results of simulations show that the proposed control system is effective and minimizes the input electric power while satisfying both the data center thermal load and system operating constraints

Energy and Exergy Analysis of Data Center Economizer Systems

Electrical consumption for data centers is on the rise as more and more of them are being built. Data center owners and operators are looking for methods to reduce energy consumption and electrical costs. One method of reducing facility costs for a chilled water plant is by adding an economizer. Most studies concerning economizer systems are conducted largely by looking at energy alone since the primary focus is reducing electrical costs. Understanding how much exergy is destroyed, where it is destroyed, and why it is destroyed provides a more complete view on how environmental impacts can be minimized while reducing energy usage. The purpose of this study is to develop energy and exergy-based models of the most common economizer systems. A normal chiller plant without an economizer and a chiller plant with an indirect wet-side economizer (the most common type of economizer system) are compared. Results show outdoor conditions influence facility energy consumption and exergy destruction. For a chiller plant operating with an economizer, the CRAH is found to be the largest source for exergy destruction. For a chiller plant without an economizer, the chiller is the largest source for exergy destruction

Economic Feasibility of Utilizing Waste-Water Heat from Coal-Fired Electrical Generating Plants in Commercial Greenhouses in North Dakota

This study provides information on the economic feasibility of establishing commercial greenhouses utilizing waste-water heat in North Dakota.Production Economics, Resource /Energy Economics and Policy,

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

All season heat pipe system.

Our energy choices impact the earth’s natural systems and climate. As this becomes increasingly important, the need for decreasing our energy usage is essential. Conventional passive solar systems can significantly reduce the heating load. Similarly, passive ambient energy systems, such as ventilation and sky radiation, can reduce cooling loads. However, the integration of passive heating and cooling systems in the same building and the benefits of actively controlling these otherwise passive systems to maximize annual energy savings has largely been unexplored. This study first evaluates the building cooling capacity of sky radiation, which was previously identified to have the greatest cooling potential among common ambient sources for climates across the U.S., and the design parameters of the system. Next, the study develops and varies the control strategies of a passive heating and cooling system with the objective of maximizing annual energy and cost savings. The systems were simulated with thermal networks using Matrix Laboratory (MATLAB), a computer software package. Nodal temperatures were simultaneously solved as functions of time using Typical Meteorological Year (TMY3) weather data. Auxiliary heating and cooling were added as needed to limit room temperature to a maximum of 23.9 ˚C and minimum of 18.3 ˚C. Results were compared to a Louisville baseline with LRR = 10 W/m2K, horizontal radiator and one cover, which provided an annual sky fraction (fraction of cooling load provided by sky radiation) of 0.855. A decrease to 0.852 was found for an increase in radiator slope to 20˚, and a drop to 0.832 for 53˚ slope (latitude + 15˚, a typical slope for solar heating). These drops were associated with increases in average radiator temperature by 0.73˚C for 20˚ and 1.99˚C for 53˚. A 30% decrease in storage capacity caused a decrease in sky fraction to 0.843. LRR and thermal storage capacity had strong effects on performance. Radiator slope had a surprisingly small impact, considering that the view factor to the sky at 53˚ tilt is less than 0.5. Chapter 3 expanded on and analyzed the design of the windscreen for the sky radiator used for cooling as well as the effects of implementing the heat pipe augmented sky radiator to varying climates. When applying a windscreen of polyethylene, which is mostly transparent to long-wave radiation, a drawback of polyethylene is its susceptibility to degradations of the optical properties. Sky fractions of 100% were possible in cities with small cooling loads (Rock Springs, Seattle, San Diego and Denver). Sky fractions of over 50% were achieved in New Orleans and Houston and over 40% in Miami. A second study examined the degradation of polyethylene cover material. Louisville and two challenging climates (Miami and New Orleans) were simulated. In the Louisville, Miami and New Orleans climate, performance was reduced by 2.7%, 14.1% and 9.0% respectively, due to degradation of the cover’s material. Chapter 4 explores the combination of a solar heating heat pipe system and sky radiation heat pipe cooling system. Two configurations were modeled in the Louisville, KY climate. The first system configuration, called a Separate System (SS), consists of a sky radiator and thermal mass that are separate from a solar heat pipe system and its thermal mass. The second system configuration, called a Combined System (CS), utilizes a shared thermal mass between the solar absorber and sky radiator. The control strategies simulated included: Seasonal, Ambient, Room and Matrix. The highest fraction of energy supplied by ambient sources for the SS was 0.707 with Matrix control, while for the CS, the highest fraction (0.704) was with Matrix temperature control with switchable attributes for heating and cooling. In Chapter 5, the two configurations in Chapter 4 were simulated with additional active control approaches. The four control strategies in Chapter 5 included variables: ambient temperature (current and forecasted), indoor air temperature, calculated auxiliary load and heating/cooling (current and forecasted) load. The highest ambient energy fractions (fraction of the total annual load served by the system) of the configurations using a SS for Louisville were 0.710, 0.708, 0.715 and 0.712 respectively. With an estimated cost savings of $49-$54/m2 USD for the Louisville baseline climate using a SS. The ambient energy fraction only decreased by 1% for the CS (AUX-24HR ambient energy fraction of 0.709)

Internet of Things and Neural Network Based Energy Optimization and Predictive Maintenance Techniques in Heterogeneous Data Centers

Rapid growth of cloud-based systems is accelerating growth of data centers. Private and public cloud service providers are increasingly deploying data centers all around the world. The need for edge locations by cloud computing providers has created large demand for leasing space and power from midsize data centers in smaller cities. Midsize data centers are typically modular and heterogeneous demanding 100% availability along with high service level agreements. Data centers are recognized as an increasingly troublesome percentage of electricity consumption. Growing energy costs and environmental responsibility have placed the data center industry, particularly midsize data centers under increasing pressure to improve its operational efficiency. The power consumption is mainly due to servers and networking devices on computing side and cooling systems on the facility side. The facility side systems have complex interactions with each other. The static control logic and high number of configuration and nonlinear interdependency create challenges in understanding and optimizing energy efficiency. Doing analytical or experimental approach to determine optimum configuration is very challenging however, a learning based approach has proven to be effective for optimizing complex operations. Machine learning methodologies have proven to be effective for optimizing complex systems. In this thesis, we utilize a learning engine that learns from operationally collected data to accurately predict Power Usage Effectiveness (PUE) and creation of intelligent method to validate and test results. We explore new techniques on how to design and implement Internet of Things (IoT) platform to collect, store and analyze data. First, we study using machine learning framework to predictively detect issues in facility side systems in a modular midsize data center. We propose ways to recognize gaps between optimal values and operational values to identify potential issues. Second, we study using machine learning techniques to optimize power usage in facility side systems in a modular midsize data center. We have experimented with neural network controllers to further optimize the data suite cooling system energy consumption in real time. We designed, implemented, and deployed an Internet of Things framework to collect relevant information from facility side infrastructure. We designed flexible configuration controllers to connect all facility side infrastructure within data center ecosystem. We addressed resiliency by creating reductant controls network and mission critical alerting via edge device. The data collected was also used to enhance service processes that improved operational service level metrics. We observed high impact on service metrics with faster response time (increased 77%) and first time resolution went up by 32%. Further, our experimental results show that we can predictively identify issues in the cooling systems. And, the anomalies in the systems can be identified 30 days to 60 days ahead. We also see the potential to optimize power usage efficiency in the range of 3% to 6%. In the future, more samples of issues and corrective actions can be analyzed to create practical implementation of neural network based controller for real-time optimization.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136074/1/Final Dissertation Vishal Singh.pdfDescription of Final Dissertation Vishal Singh.pdf : Dissertatio

MODEL PREDICTIVE CONTROL OF ENERGY SYSTEMS FOR HEAT AND POWER APPLICATIONS

Building and transportation sectors together account for two-thirds of the total energy consumption in the US. There is a need to make these energy systems (i.e., buildings and vehicles) more energy efficient. One way to make grid-connected buildings more energy efficient is to integrate the heating, ventilation and air conditioning (HVAC) system of the building with a micro-scale concentrated solar power (MicroCSP) sys- tem. Additionally, one way to make vehicles driven by internal combustion engine (ICE) more energy efficient is by integrating the ICE with a waste heat recovery (WHR) system. But, both the resulting energy systems need a smart supervisory controller, such as a model predictive controller (MPC), to optimally satisfy the en- ergy demand. Consequently, this dissertation centers on development of models and design of MPCs to optimally control the combined (i) building HVAC system and the MicroCSP system, and (ii) ICE system and the WHR system. In this PhD dissertation, MPCs are designed based on the (i) First Law of Thermo- dynamics (FLT), and (ii) Second Law of Thermodynamics (SLT) for each of the two energy systems. Maximizing the FLT efficiency of an energy system will minimise energy consumption of the system. MPC designed based on FLT efficiency are de- noted as energy based MPC (EMPC). Furthermore, maximizing the SLT efficiency of the energy system will maximise the available energy for a given energy input and a given surroundings. MPC designed based on SLT efficiency are denoted as exergy based MPC (XMPC). Optimal EMPC and XMPC are designed and applied to the combined building HVAC and MicroCSP system. In order to evaluate the designed EMPC and XMPC, a com- mon rule based controller (RBC) was designed and applied to the combined building HVAC and MicroCSP system. The results show that the building energy consump- tion reduces by 38% when EMPC is applied to the combined MicroCSP and building HVAC system instead of using the RBC. XMPC applied to the combined MicroCSP and building HVAC system reduces the building energy consumption by 45%, com- pared to when RBC is applied. Optimal EMPC and XMPC are designed and applied to the combined ICE and WHR system. The results show that the fuel consumption of the ICE reduces by 4% when WHR system is added to the ICE and when RBC is applied to both ICE and WHR systems. EMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 6.2%, compared to when RBC is applied to ICE without WHR system. XMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 7.2%, compared to when RBC is applied to ICE without WHR system