Enough hot air: the role of immersion cooling

Abstract Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks’ power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended

Mining Sequential Patterns for Appliance Usage Prediction

Reducing the energy consumption in buildings and homes can be achieved by predicting how energy-consuming appliances are used, and by discovering their patterns. To mine these patterns, a smart-metering architecture needs to be in place complemented by appropriate data analysis mechanisms. Once the usage patterns are obtained, they can be employed to optimize the way energy from renewable installations, home batteries, and even micro grids is managed. We present an approach and related experiments for mining sequential patterns in appliance usage. In particular, we mine patterns that allow us to perform device usage prediction, energy usage prediction, and device usage prediction with failed sensors. The focus of this work is on the sequential relationships between the state of distinct devices. We use data sets from three existing buildings, of which two are households and one is an office building. The data is used to train our modified Support-Pruned Markov Models which use a relative support threshold. Our experiments show the viability of the approach, as we achieve an overall accuracy of 87% in device usage predictions, and up to 99% accuracy for devices that have the strongest sequential relationships. For these devices, the energy usage predictions have an accuracy of around 90%. Predicting device usage with failed sensors is feasible, assuming there is a strong sequential relationship for the devices

Planning meets activity recognition: Service coordination for intelligent buildings

Building managers need effective tools to improve occupants' experiences considering constraints of energy efficiency. Current building management systems are limited to coordinating device services in simple and prefixed situations. Think of an office with lights offering services, such as turn on a light, which are invoked by the system to automatically control the lights. In spite of the evident potential for energy saving, the office occupants often end up in the dark, they have too much light when working with computers, or unnecessary lights are turned on. The office is thus not aware of the occupants' presence nor anticipates their activities. Our proposal is to coordinate services while anticipating occupant activities with sufficient accuracy. Finding and composing services that will support occupant activities is however a complex problem. The high number of services, the continuous transformation of buildings, and the various building standards imply a search through a vast number of possible contextual situations every time occupants perform activities. Our solution to this building coordination problem is based on Hierarchical Task Network (HTN) planning in combination with activity recognition. While HTN planning provides powerful means for composing services automatically, activity recognition is needed to identify occupant activities as soon as they occur. The output of this combination is a sequence of services that needs to be executed under the uncertainty of building environments. Our solution supports continuous context changes and service failures by using an advanced orchestration strategy. We design, implement and deploy a system in two cases, namely offices and a restaurant, in our own office building at the University of Groningen. We show energy savings in the order of 80% when compared to manual control in both cases, and 60% when compared to using only movement sensors. Moreover, we show that one can save a figure of 600 annually for the electricity costs of the restaurant. We use a survey to evaluate the experience of restaurant occupants. The majority of them are satisfied with the solution and find it useful. Finally, the technical evaluation provides insights into the efficiency of our system

Magnetic field orientation of liquid crystalline epoxy thermosets

The effect of magnetic fields on the orientation and properties of 4,4′-bis(2,3-epoxypropoxy)-α-methylstilbene cured with sulfanilamide has been studied. This epoxy system is initially isotropic and forms a smectic A phase upon curing. A magnetic field was applied during the cure reaction, resulting in alignment of the molecules along the direction of the applied field. Measurement of the orientation parameter of the fully cured material by wide-angle X-ray scattering (WAXS) showed that orientation improved with an increase in field strength. The orientation parameters of the smectic layer normals calculated from the inner reflection of the WAXS pattern attained a maximum level of approximately 0.8 at a field strength of approximately 12 T. The orientation parameters calculated from the outer reflection of the WAXS pattern were considerably lower, possibly due to the presence of amorphous regions associated with domain boundaries or the loss of molecular alignment within the smectic layers due to topological restrictions of the cross-linking sites. Orientation resulted in an anisotropic linear thermal expansion coefficient after curing, although the overall volumetric expansion was constant. The elastic tensile modulus increased with the square of the orientation parameter, attaining a maximum value of 8.1 GPa, compared to 3.1 GPa for the unoriented material. The change in modulus with orientation could be fit with a simple model for the modulus of anisotropic materials

Akt1, Akt2 and Akt3 mRNA relative expression in the organ of Corti (OC), the spiral ganglion (SG) and the stria vascularis (SV) of 5-day-old C57/B6 mice.

<p>Expression was measured by quantitative real-time PCR with GAPDH as an endogenous control. Akt1, Akt2 and Akt3 expression levels are presented relative to expression in the brain. Each bar represents mean ± standard deviation. Each experiment was repeated three times, with different biological replicates in triplicate. For each experiment mRNA of 20 ears were pooled.</p