scholarly journals Manufactured Homes Acquisition Program : Heat Loss Assumptions and Calculations, Heat Loss Coefficient Tables.

1992 ◽  
Author(s):  
Bob Davis ◽  
David Baylon
Keyword(s):  
Heat Loss ◽  
Loss Coefficient ◽  
Manufactured Homes

Construction and Experimental Study of an Elevation Linear Fresnel Reflector

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
J. D. Nixon ◽  
P. A. Davies
Keyword(s):  
Heat Loss ◽  
Thermal Efficiency ◽  
Theoretical Models ◽  
Loss Coefficient ◽  
Heat Transfer Fluid ◽  
Stagnation Temperature ◽  
Model Predictions ◽  
The Difference ◽  
Predicted Values ◽  
Linear Fresnel Reflector

This paper outlines a novel elevation linear Fresnel reflector (ELFR) and presents and validates theoretical models defining its thermal performance. To validate the models, a series of experiments were carried out for receiver temperatures in the range of 30–100 °C to measure the heat loss coefficient, gain in heat transfer fluid (HTF) temperature, thermal efficiency, and stagnation temperature. The heat loss coefficient was underestimated due to the model exclusion of collector end heat losses. The measured HTF temperature gains were found to have a good correlation to the model predictions—less than a 5% difference. In comparison to model predictions for the thermal efficiency and stagnation temperature, measured values had a difference of −39% to +31% and 22–38%, respectively. The difference between the measured and predicted values was attributed to the low-temperature region for the experiments. It was concluded that the theoretical models are suitable for examining linear Fresnel reflector (LFR) systems and can be adopted by other researchers.

Cavity receiver thermal performance analysis based on total heat loss coefficient and efficiency factor

2018 ◽  
Vol 42 (6) ◽  
pp. 2284-2289 ◽  
Author(s):  
Qiangqiang Zhang ◽  
Xin Li ◽  
Zhifeng Wang ◽  
Zhi Li ◽  
Hong Liu ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Heat Loss ◽  
Thermal Performance ◽  
Efficiency Factor ◽  
Loss Coefficient ◽  
Total Heat ◽  
Total Heat Loss

scholarly journals Towards the characterization of the heat loss coefficient via on-board monitoring: Physical interpretation of ARX model coefficients

2019 ◽  
Vol 195 ◽  
pp. 180-194 ◽  
Author(s):  
Marieline Senave ◽  
Glenn Reynders ◽  
Peder Bacher ◽  
Staf Roels ◽  
Stijn Verbeke ◽  
...  
Keyword(s):  
Heat Loss ◽  
Physical Interpretation ◽  
Loss Coefficient ◽  
Arx Model ◽  
Board Monitoring

scholarly journals Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3322 ◽  
Author(s):  
Marieline Senave ◽  
Staf Roels ◽  
Stijn Verbeke ◽  
Evi Lambie ◽  
Dirk Saelens
Keyword(s):  
Sensitivity Analysis ◽  
Steady State ◽  
Data Analysis ◽  
Heat Loss ◽  
Input Data ◽  
Loss Coefficient ◽  
Wide Range ◽  
Board Monitoring ◽  
The Impact

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

scholarly journals Obtaining the heat loss coefficient of a dwelling using its heating system (integrated coheating)

2016 ◽  
Vol 117 ◽  
pp. 1-10 ◽  
Author(s):  
David Farmer ◽  
David Johnston ◽  
Dominic Miles-Shenton
Keyword(s):  
Heat Loss ◽  
Heating System ◽  
Loss Coefficient
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