Isiklik müük

http://www.ester.ee/record=b1053328*es

Performance of Heat Recovery Ventilation System with Ground Source Brine Heat Exchanger Pre-Heating System in the Context of nZEB

The paper analyzes effectiveness of the ventilation unit pre-heating system with a ground source brine heat exchanger in a nearly zero energy building in Estonia. Focus is on the analysis of measured energy usage and possible effects on the energy usage of alternative solutions of the ventilation pre-heating system in terms of nearly zero energy building. The studied building was planned and realized according to the international passive house concept. To further lower the energy demand, the building was equipped with a solar thermal system and a photovoltaic solar system to cover the total final energy demand of the building, making it nearly zero energy building. The ventilation system is equipped with temperature and relative humidity sensors to measure supply, extract, exhaust air parameters and air parameters before and after the pre-heating system. Energy usage to pre-heat the ventilation airflow with a ground source brine heat exchanger was also measured. Our results show that annual energy used for pre-heating the ventilation airflow is around 420 kWh, which makes about 3% of the building’s total energy usage. The efficiency of the ventilation unit heat exchanger was over 80 % in the winter season due to the pre-heating system

Performance evaluation of Monte Carlo simulation: Case study of Monte Carlo approximation vs. analytical solution for a chi-squared distribution

The guide to the expression of uncertainty in measurement (GUM) describes the law of propagation of uncertainty for linear models based on the first-order Taylor series approximation of Y = f(X1, X2, …, XN). However, for non-linear models this framework leads to unreliable results while estimating the combined standard uncertainty of the model output [u(y)]. In such instances, it is possible to implement the method(s) described in Supplement 1 to GUM – Propagation of distributions using a Monte Carlo Method. As such, a numerical solution is essential to overcome the complexity of the analytical approach to derive the probability density functions of the output. In this paper, Monte Carlo simulations are performed with the aim of providing an insight into the analytical transformation of the probability density function (PDF) for Y = X2 where X is normally distributed and a detailed comparison of analytical and Monte Carlo approach results are provided. This paper displays how the used approach enables to find PDF of Y = X2 without the use of special functions. In addition, the singularity of the PDF and the nonsymmetric coverage interval are also discussed