Towards external coupling of BES and HAM envelope programs for whole building HAM simulation

This paper presents preliminary results of on-going research on the integration of building energy simulation (BES) and building envelope heat, air and moisture transfer (HAM) programs. The paper contrasts the capabilities of two BES and HAM programs, and presents the theory and preliminary results of one-way coupling between them

Review of external convective heat transfer coefficient models in building energy simulation programs : implementation and uncertainty

Convective heat transfer coefficients for external building surfaces (hc,ext) are essential in building energy simulation (BES) to calculate convective heat gains and losses from building facades and roofs to the environment. These coefficients are complex functions of, among other factors, building geometry, building surroundings, building facade roughness, local air flow patterns and temperature differences. Previous research on hc,ext has led to a number of empirical models, many of which are implemented in BES programs. This paper first provides an extensive overview of such models for hc,ext calculation implemented in BES programs together with the corresponding assumptions. Next, the factors taken into account by each model are listed, in order to clarify model capabilities and deficiencies. Finally, the uncertainty related to the use of these models is discussed by means of a case study, where the use of different models shows deviations up to ±30% in the yearly cooling energy demand (in relation to the average result) and ±14% in the hourly peak cooling energy demand of an isolated, well-insulated building, while deviations in yearly heating energy demand are around ±6%. The paper concludes that each model has a specific range of application, which is identified in this review paper. It also concludes that there is considerable uncertainty in the prediction of hc,ext, which can be transferred to the BES results. This large uncertainty highlights the importance of using an appropriate convection model for simulations of a specific building, certainly for calculating cooling demands and related important performance indicators such as indoor temperatures, indoor relatively humidity, thermal comfort, etc

In vitro antibacterial property assessment of silver nanoparticles synthesized by Falcaria vulgaris aqueous extract against MDR bacteria

Silver nanoparticles (AgNPs) were fabricated in the presence of Falcaria vulgaris aqueous extract as a biosynthesis method without utilizing any surfactant or template. AgNPs were prepared under different synthesis conditions such as silver ion concentration and the amount of plant used for the extraction, reaction duration and temperature for the extraction. The effect of these variables on the size of resulted AgNPs was examined, and operation conditions were optimized statistically with analysis of variance (ANOVA) to describe the role of these variables in tuning the size of AgNPs. The results of ANOVA displayed the optimum conditions for the synthesis procedure that resulted in AgNPs with the average size of 28 ± 8 nm. Furthermore, the growth of AgNPs was monitored by UV-Vis spectroscopy, and they were characterized using TEM, SEM, X-ray diffraction, and FT-IR spectroscopy. Finally, in vitro antibacterial activity of the AgNPs showed the maximum inhibition zone alongside Staphylococcus aureus (ATCC 25923) and lowermost inhibition zone touching E. coli (MDR). The minimum inhibitory concentration (MIC) for the AgNP-Fv was in a range between 0.535 and 0.001 µg/ml. According to the results, the ATCC bacteria were more sensitive to AgNP-Fv compared to multiple-drug resistance bacteria, except for Pseudomonas aeruginosa (MDR). Figure not available: see fulltext.. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged-Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15-minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures