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

Isotopic composition of water vapor near the air-water interface

Evaporation is a key process in water cycle that links liquid water to the atmosphere. In the last fifty years stable isotopes of hydrogen and oxygen have been intensively used to describe climate processes related to evaporation and precipitation, ranging in different spatial and temporal scales. Evaporation introduces large isotopic effects in the phases involved. The well known Craig-Gordon model (Craig & Gordon, 1965) describes those isotopic effects involving several steps and different processes, moving from the air-water interface to the free atmosphere. However, very few works in literature have tested the vertical behavior of the Craig-Gordon model in natural conditions on both fresh and marine waters. In this work we present the results from four field experiments aimed to describe the vertical variability of δ18O and δD in the first few meters over a large water body (the coastal lagoon of Venice, northern Italy) and to test the Craig-Gordon model in such conditions. Each experiment involved cryotrapping of water vapor at different height over the water surface (0.1m, 2m and 4m) and the sampling of the liquid water at two depth (surface and 0.5m). During the experiments, water vapor was also sampled in the nearest mainland (~2.5 km from gradient measurements) to determine the isotopic composition of background water vapor. Liquid samples were then analyzed with a Picarro L1102-i and Thermo-Fisher Delta Plus Advantage for water vapor and lagoon water, respectively. The last two experiments have also involved simultaneous measurements of relative humidity using commercially-available humidity probes at each height. This approach was used to determine a reference scale in order to compare observations to modeled estimates. Despite the coarse time resolution due to cryotrapping method (measurements are averaged over 1.5 hours), preliminary results show measurable differences in the isotopic composition of water vapor along the vertical gradient and good agreement between observations and predicted values from the model. Even if this work is an exploratory phase it shows an interesting potential to grow our understanding of the processes involved as well as a useful implementation for future studies focused on fractionation of water isotopes due to evaporation in natural conditions

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.Comment: 20 pages, 13 figure

Efficient Management of HVAC Systems

In HVAC (Heating, Ventilation and Air Conditioning) plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other in order to provide standby capacity, operational exibility, and less disruption maintenance. However, the problem of an eciently managing of multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore signicant energy savings can be achieved by optimizing the chiller operations of HVAC systems. In this Thesis an unied method for Multi-Chiller Management optimization is presented, that deals simultaneously with the Optimal Chiller Loading and Optimal Chiller Sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an ecient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment, developed in Matlab and Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of both energy performance and load prole tracking.Negli impianti HVAC di capacità frigorifera medio-grande vengono spesso impiegati sistemi con più refrigeratori di liquido (chiller) in parallelo. Il problema della gestione eciente di tali sistemi è complesso sotto diversi punti di vista. In particolare, il consumo di energia elettrica dell'impianto aumenta notevolmente allorché i refrigeratori siano gestiti scorrettamente. In questa Tesi viene presentato un metodo unicato per l'ottimizzazione della gestione di chiller in parallelo che risolve simultaneamente i problemi del carico ottimo e della sequenza ottima di accensioni/spegnimenti relativi ai refrigeratori. L'obiettivo principale è quello ridurre il consumo energetico ed abbassare i costi di esercizio. L'approccio si basa su un algoritmo di stima del carico frigorifero richiesto e l'ottimizzazione è realizzata attraverso l'impiego di un algoritmo genetico multi-fase; quest'ultimo fornisce un approccio eciente per risolvere questo genere di problema di ottimo multi-obiettivo. Le prestazioni dell'algoritmo sono valutate ricorrendo ad un ambiente di simulazione dinamico, sviluppato in Matlab e Simulink, dove le dinamiche del sistema sono accuratamente descritte. Si evince che l'algoritmo proposto fornisce prestazioni superiori, rispetto agli approcci standard, sia in termini di soddisfacimento del carico che di prestazione energetica

Automatic Regulation of Anesthesia via Ultra-Local Model Control

As a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays