Direct-Contact Air, Gravel, Ground Heat Exchanger in Air Treatment Systems for Cowshed Air Conditioning

This paper describes the analysis of the possibility of use of the direct-contact air, gravel, ground heat exchanger (acronym GAHE), patented at the Wroclaw University of Science and Technology, as a means of improving microclimate parameters in dairy cows’ barns. Different possibilities of introducing GAHE to the standard mechanical ventilation system of cowsheds have been proposed and investigated. Based on literature data, the required air parameters in the barns of dairy cows were determined and discussed. Computer simulations were carried out and the results obtained were compared to the baseline model. Year-round changes in microclimate parameters, especially air temperature, relative humidity, and THI index were investigated. The benefits of GAHE use were indicated. The possible increase in the minimum air volume of ventilation during the winter season and the decrease in the maximum values of this parameter in the summer were presented. Indications were made of the systems where the application of GAHE could be the most beneficial. A further research path has been proposed.

Thermodynamic Evaluation of a Direct Expansion Ground-Sourced Heat Pump with Horizontal Ground Heat Exchangers Using Advanced Exergy Analysis

This paper deals with the advanced exergetic analysis of a horizontal direct-expansion ground sourced CO2 heat pump operating in a transcritical cycle. The cycle is thermodynamically modeled in Engineering Equation Solver (EES) considering the pressure drops in both high and low temperature heat exchangers, and the system is to provide a fixed heating load. Conventional exergy analysis orderly suggests a compressor, expansion valve, gas cooler and ground heat exchanger to be considered for system improvement, while tracing exergy destruction of all components in detail demonstrates true improvement potential of each and all components and the system as a whole and offers a different order. Advanced exergy analysis points out that the compressor is directly and indirectly responsible for 56% of the overall exergy destruction generated in the cycle, confirming the detrimental role of this component in the system. The second influential component is recognized to be a ground heat exchanger accounting for 20% exergy destruction of the compressor as well as submitting 89% avoidability in its own exergy destruction, and expansion valve proves to be the last option for system improvement according to this analysis.

THERMAL STATE OF THE AIR - GROUND HEAT EXCHANGER PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK

The work aim is to predict the thermal state of the air-ground heat exchanger based on an artificial neural network. Training, testing and validation of the proposed model were made on experimental data obtained in the thermophysical laboratory of the Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine. A simple neural network is used in this work. The air temperature at the inlet to the heat exchanger, and its relative humidity are selected as input parameters for the neural network. The MATLAB (R2016a) and Levenberg-Markwatt model were used in this article's calculations. One hidden layer and 10 neurons were presented in the model. The array of analysed data was divided into ratios of 70%, 15%, 15% for neural network training, validation and testing, respectively. As a result, it is obtained that the forecasting takes place with acceptable accuracy in all models. The root mean square error for the whole data set for different models varies from 0.105 to 2.323°С. The maximum mean absolute percentage error was the largest for CFD model and was 11.2%. The minimum mean bias error of the predicted data from the experimentally measured ones was found in the model using temperature, humidity, and air temperature at the outlet of the air-ground heat exchanger for the previous hour and was 0.02%. The training and testing of the proposed models based on an artificial neural network are satisfactory enough to predict the temperature taking into account the influence of weather conditions. Artificial neural networks can be used to predict the thermal state of the air-ground heat exchanger. Data representing the description of a real system are required for forecasting the parameters based on the ANN.

Novel Methodology for Sizing a Single U-Tube Ground Heat Exchanger Useful at the Early Design Stage

Renewable energy system (RES) is an environmentally friendly source of energy. A suitable design of RES is crucial to implement an energy-efficient building such as a zero energy building (ZEB). The significance of appropriate decision-making for the successful implementation of energy-efficient buildings has been increasing. In addition, the identification of the sizing of RES is equally important for architects or HVAC engineers. In this study, a novel sizing method for a single U-tube ground heat exchanger (GHE) is proposed. A transient thermal analysis for a single GHE is performed by considering ground temperature recovery effect as well as other major design parameters. The results are used to design the proposed sizing method and were verified by transient simulations for different design cases. Additionally, it was observed that the coefficient of variation of root mean square error (CV(RMSE)) for all ten design cases was lower than 15% during the heating and cooling seasons. Thus, the proposed design method can be used for sizing a GHE in the early design stage.