2016 ASHRAE Winter Conference

Radiant cooling and heating systems can potentially provide significant energy savings, possible peak load reduction, and better thermal comfort for the occupants. Such systems primarily depend on the radiant mode, instead of convective mode, for the transfer of heat within a space. These systems are often required to supply ventilation air to maintain certain indoor air quality and humidity in the space. The air flow patterns of the supply air and resulting buoyant airflows in the space can affect the performance of radiant systems. The flow rate and temperature of the supply air; location and type of supply air diffusers; and strength and location of interior sensible heat loads can affect the relative share of radiative and convective heat transfer in the space. This paper with the help of Computational Fluid Dynamics (CFD) analysis will evaluate the impact of various parameters of the supply air including the supply airflow rate, supply air temperature, and location and type of diffusers on the operation of radiant systems.

Between 1990 - 2006 the energy use of the ventilation systems in Swedish schools has doubled. This is mainly due to an increase of cooling demand which results in higher air-flow rates. In recent years many schools changed from displacement ventilation (DV) to mixing ventilation (MV), because MV causes fewer problems with thermal discomfort, although DV has higher ventilation efficiency. Studies show that 87% of Swedish schools use constant air volume (CAV) and it’s estimated that a change to variable air volume (VAV) could save 0.12-0.33 TWh per year. Therefore the aim of this study is to investigate whether it’s possible to replace DV with MV to create a comfortable indoor climate in a typical classroom and at the same time decrease the energy use by using VAV and Low Pressure Drop Ceiling Supply Device (LPDCSD).

This paper introduces a new model to simulate the energy performance of VRF systems in heat pump operation mode (either cooling or heating is provided but not simultaneously). The main features of the new model are the introduction of separate curves for capacities and power inputs of indoor and outdoor units instead of overall curves for the entire system, the allowing of variable evaporating and condensing temperatures in the indoor and outdoor units, and variable fan speed based on the temperature and zone load in the indoor unit. These features enhance the accuracy of the estimation of VRF system performance in both heating and cooling modes, especially during low part load operations.

The variable refrigerant flow (VRF) technology provides multi-split ductless configurations that typically use one outdoor unit (ODU) and multiple indoor units (IDU). VRF systems offer many advantages, such as elimination of duct loss of air distribution, design and installation flexibility, compactness, integrated controls, quiet operation and reduced maintenance cost. Also, multi-functional VRF (MFVRF) systems have achieved remarkable development, offering flexible operation of individualized zoning control, i.e. making possible simultaneous heating and cooling in addition to cooling or heating only modes. Meanwhile, such flexibility further complicates the operation and control due to diversified system configurations and highly variable operational loads. This paper proposes extremum seeking control (ESC) schemes for MFVRF system under different operational modes, in order to maximize the efficiency provided the satisfaction of thermal comfort.

Building energy systems often consume in excess of 20% more electrical energy than was the design intent largely because of equipment performance degradation (e.g. filter or heat exchanger fouling), equipment failures, or detrimental interactions among subsystems such as cooling and then reheating of conditioned air. Identifying the root causes of efficiency losses is challenging because a gradual erosion of performance can be difficult to detect.  This paper is focusing on the faults that are implemented using OpenStudio measures. These measures are created in OpenStudio Application or the Parametric Analysis Tool, which are written in Ruby scripts. These faults related measures act like add-on marco to make changes to the energy model to reflect faults.