Luca Cecchinato

An Assessment of Heat Transfer through Fins in a Fin-and-Tube Gas Cooler for Transcritical Carbon Dioxide Cycles

In CO2 transcritical refrigeration cycles, fin-and-tube coils are still considered possible gas cooling devices due to their lower cost when compared with recent aluminium minichannel heat exchangers. In spite of the very high working pressures, an off-the-shelf coil with four ranks of 3/8 in. (9.52 mm) copper tube and louvered fins was found suitable to work with high R-744 pressures and has been studied as a gas cooler in a test rig built for testing carbon dioxide (CO2) equipment operating with air as a secondary fluid. The test rig consists of two closed-loop air circuits acting as heat sink and heat source for the gas cooler and evaporator, respectively. The tested refrigerating circuit consists of two tube-and-fin heat exchangers as the gas cooler and the evaporator, a back-pressure valve as the throttling device, a double-stage compound compressor equipped with an oil separator, and an intercooler. A full set of thermocouples, pressure transducers, and flowmeters allows measurement and recording of all the main parameters of the CO2 cycle, enabling heat balance to be performed for both air side and refrigerant side. Tests focused on two different gas coolers, with continuous and cut fins, and on two different circuit arrangements. Tests on each heat exchanger were run at three different inlet conditions, for both CO2 and air. A simulation model was developed for this type of heat exchanger and three models (Dang and Hihara 2004; Gnielinski 1976; Pitla et al. 2002) proposed for the CO2 supercritical cooling heat transfer coefficients were implemented and compared in the code. The model results are compared with the experimental data for the finned coil; emphasis is given to the effect of heat conduction through fins between adjacent tube ranks on system efficiency. In the paper, the experimental results for transcritical CO2 entering the gas cooler at 87.0°C (7.911 MPa), 97.6°C (8.599 MPa), and 107.8°C (9.102 MPa) with air inlet temperatures of 20.3°C, 21.5°C, and 23.0°C, respectively, are presented. By using a coil with fins modified to reduce the heat conduction, a 3.7% to 5.6% heat flux improvement was gained. This improvement can be clearly translated in terms of coefficient of performance (COP), since a low value of the CO2 temperature at its outlet increases the cooling capacity. Considering a reference cycle with the same operating conditions, a 5.7% to 6.6% increase of COP can be obtained.

Optimisation of the throttling system in a CO2 refrigerating machine

This paper is an answer to the need of finding the optimal solution for the throttling system in refrigerating machines using CO2 as working fluid; such a solution must combine reliability, low installation cost and high energy efficiency. To this purpose, different expansion systems are compared by means of a simulation programme, including a new one, operating with a differential valve, a liquid receiver and a thermostatic valve. The typical compression refrigerating cycle performed by CO2 involves transcritical operations and therefore the upper pressure needs to be adjusted to the optimal value, that, unlike the traditional cycle, is not determined by heat transfer. The innovative system here proposed shows an intrinsic self-adjusting capability that leads to COP values quite close to the maximum ones when a fixed suitable value of the differential pressure is chosen, even if the temperature of the secondary fluid varies to a large extent.

A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

Abstract Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the occupants, energy wastage, unreliability and shorter equipment life. Such faults need to be detected early to prevent further escalation and energy losses. Commonly, data regarding unforeseen phenomena and abnormalities are rare or are not available at the moment for HVAC installations: for this reason in this paper an unsupervised One-Class SVM classifier employed as a novelty detection system to identify unknown status and possible faults is presented. The approach, that exploits Principal Component Analysis to accent novelties w.r.t. normal operations variability, has been tested on a HVAC literature dataset.

VAVAC systems modeling and simulation for FDD applications

Faulty operation of HVAC systems can lead to discomfort for the occupants, energy wastage, unreliability and shorter equipment life. Cost-effective Fault Detection and Diagnosis (FDD) methods can therefore ensure an increase in the system uptime, reliability, and overall efficiency. In this paper, in order to evaluate and compare FDD methods for HVAC systems, we resort to a particular case of Variable Air Volume models, that are capable of describing the response of the control system in both normal and fault operating conditions. The system is tested by performing extensive simulations using Matlab/Simulink™tools in order to allow the description of the most common and relevant fault affecting this kind of systems.

Load forecasting for the efficient energy management of HVAC systems

In this paper, Artificial Neural Networks (ANNs) are used to achieve cooling load forecasting in HVAC (Heating, Ventilating, and Air Conditioning) systems. Load forecasting is crucial in plant configurations making use of thermal storage technologies, where, during the nighttime, part or most of the energy required during daytime is produced at lower cost by cooling or icing water. Load forecasting is then needed to quantify the energy to be stored for the following daytime and to set up strategies for its release during daytime. Although many algorithms have been presented in the literature for load forecasting, they often need as input a large data set, that is not always available in practical situations. In this paper, we present an algorithm based on ANNs that allows to obtain sufficiently accurate load predictions by exploiting a limited data set, obtained by measuring quantities that are typically available in standard HVAC installations. Furthermore, knowledge of the current thermal load (which is needed to setup the data set for ANN training) can be obtained by using a load estimation algorithm previously proposed by some of the authors, that only need basic knowledge of the system hydronics. Another distinctive feature of the algorithm is the use of the AHU schedule as a means for inferring information on the internal loads, which is in general not available in practice. Simulation results for both CAV and VAV HVAC systems confirm the viability of the approach.

Thermal and comfort control for radiant heating/cooling systems

Water based, surface embedded heating and cooling systems, also referred to as radiant heating cooling systems (RHCS), are growing in popularity, due to their advantages in terms of low-noise, uniform temperature distribution, and energy saving potential. However, it is in general recognized that traditional control systems may deteriorate the energy performance of radiant systems, so that it is important to devise ad hoc strategies for such kind of systems. In this paper, a model-based approach is used to design an efficient control architecture for radiant heating/cooling systems coupled with fan-coil units with the main objective of increasing both thermal comfort for building occupants and energy saving. A building lumped parameter model for hygrothermal analysis coupled with a 2D discretization scheme for radiant heating/cooling systems is development in the Matlab/Simulink environment. The model simulation tool, together with a simple load forecasting strategy, allows to design a suitable controller, that we name comfortstat, which is based on the regulation of the Predicted Mean Vote (PMV) thermal comfort index. In this way, thermoigrometric conditions are kept within a range of acceptable comfort values, under performance constraints for reducing energy consumption and preventing floor surface condensation. The results show that the proposed thermal comfort control algorithm gives better satisfaction for the occupants and superior performance with respect to standard approaches.

Oil-free centrifugal chiller optimal operation

Centrifugal chillers, using variable-speed turbo compressors with magnetic bearings, are becoming very common in Heating, Ventilation and Air Conditioning (HVAC) systems. Such solution guarantees superior energy efficiency, mostly under part load conditions, compared with traditional equipments, and it provides additional advantages such as light weight and a compact package. On the other hand, turbo machinery adds its own complexity to the whole HVAC system and its efficient management is a non-trivial task. In this paper a hybrid optimisation technique is employed to determine optimal operation, under various working conditions, for air-condensed water centrifugal chillers. The proposed method provides optimal solutions using a combination of two algorithms: A random population-based optimiser, the Gravitational Search Algorithm (GSA), followed by the deterministic Levenberg-Marquardt (LM) algorithm. The hybrid method effectively overcomes the problem of high sensitivity to initial conditions of LM technique and a shortcoming of GSA which reduces its searching efficiency when close to the optimum. The hybrid method has been tested in a Matlab®-based simulation environment where the performance of an air-condensed centrifugal chiller is adequately evaluated. Simulation results guarantee high energy efficiency in a wide range of chiller working conditions.

Model-based fault detection and diagnosis for centrifugal chillers

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. In this paper a model-based approach is used in order to detect important chiller systems faults. First, a linear dynamic black-box model is identified for each of the relevant characteristic features of the system during the normal functioning of the chiller. Then, an on-line correlogram method verifies the whiteness property of the residuals in order to distinguish anomalies from normal operations. A decision table, that matches the influence of anomalies with the characteristic features, allows to identify chiller faults. The proposed fault detection and diagnosis approach is assessed by using real chiller data provided by the ASHRAE research project RP-1043.

A Simulation Environment for the Design of Advanced Chiller Control Systems

In this paper we address the problem of designing advanced control systems for increasing the performances of one of the key elements of an HVAC system, the chiller unit. In particular, we present a simulation environment based on Matlab/Simulink that has been validated on a state-of-the-art experimental facility and used to design an adaptive controller for single scroll compressor, packaged air-cooled water chillers, that allows to substantially increase the energy performance of the system, as well as to achieve excellent regulation performances in process applications.

Process history-based Fault Detection and Diagnosis for VAVAC systems

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) systems can lead to discomfort for the occupants, energy wastage, unreliability and shorter equipment life. Cost-effective Fault Detection and Diagnosis (FDD) methods can therefore ensure an increase in the system uptime, reliability, and overall efficiency. In this paper, a simulation environment based on Matlab/Simulink® is used in order to evaluate the performance of a FDD method using Support Vector Machines (SVMs). In detail, the proposed method is evaluated by performing extensive simulations to allow the investigation of the most common and relevant faults affecting this kind of systems.