Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 17th May 2022, 05:27:42 CEST

 
 
Session Overview
Session
Session T4.9 (Online Track): Ensuring high quality building simulations
Time:
Thursday, 02/Sept/2021:
15:00 - 16:30

Location: Virtual Meeting Room 3

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Presentations
15:00 - 15:18

ThRend: a ray tracing module for infrared rendering of urban scenes

Jose Pedro Aguerre, Eduardo Fernández

UdelaR, Uruguay

Aim and Approach

(max 200 words)

This paper presents ThRend, a ray tracing software that allows for accurate and physically-plausible infrared rendering of urban environments. ThRend is designed to rapidly generate simulated thermograms based on few input data. It can be considered as a post-processing tool that takes the output of thermal simulation software and simulates the behavior of long-wave radiation reaching a virtual infrared sensor. The software provides a simple interface that gives the user the possibility to try different emissivity and reflectivity configurations to render thermal images. The results can be used to compare thermal simulations against real measurements performed with thermal cameras. This approach enables the use of thermography as a more reliable tool to assess the energy efficiency and performance of buildings in their corresponding urban context. Combining computational simulation of classic thermal solvers with infrared rendering appears as an alternative to better understand the results captured in a measurement campaign.

Scientific Innovation and Relevance

(max 200 words)

Thermography is used for building diagnostics and energy efficiency evaluation [EGGA18]. It allows to obtain spatialized information about the thermal exchanges within the city [BG19]. Nevertheless, the temperature results captured in a thermography campaign are not always directly reliable [DICM05], mainly because the camera produces a temperature estimation based on the incoming radiative flux [VM17]. This radiative flux depends on many aspects, such as the emissivity of the materials, the reflectivity behavior of surfaces, the atmosphere between the camera and the lens, and the temperature of reflected objects. At the urban scale, such complex phenomena produces significant bias in the apparent surface temperature of the buildings [ANG*19].

The state-of-the-art thermal solvers used by the building simulation community (e.g. EnergyPlus, CitySim, SOLENE) produce temperature distribution results that need to be post-processed for performing a reliable validation with thermography measurements [HMLV12,WZCH15]. In this paper, a post-processing ray tracing module named ThRend is presented for this purpose. A Monte Carlo approach is used to estimate the incident radiation on a virtual thermal camera considering directional emissivity models [AG*20] and microfacet BRDF theory [WMLT07]. This approach enables dealing with the multiplicity of material that are characteristic of urban environments.

Preliminary Results and Conclusions

(max 200 words)

A preliminary version of ThRend is already available as a free software solution [Agu20]. It has already been validated with a real urban measurement campaign, using it as a post-processing tool for the results of a finite element urban thermal analysis. The software is able to read the output of the simulation in UCD format [AVS20], and the infrared properties are set through a very simple material definition txt file. The camera and image settings can be set through another txt file. Moreover, an interactive alternative GPU accelerated version [AW20] is available for real time inspection of the infrared scene.

The present article aims to present the flexibility of the tool by showing the results under different material configurations and several temperature conditions of the same urban scene. Careful attention is put on the algorithms that are used in the current implementation, as well as on the computational performance of the solution.

Main References

(max 200 words)

[EGGA18]EVANGELISTI et al.“Assessment of equivalent thermal properties of multilayer building walls coupling simulations and experimental measurements”. Building and Environment 127(2018)

[BG19]BECKERS and GARCIA-NEVADO.“Urban Planning Enriched by Its Representations, from Perspective to Thermography”. Sustainable Vernacular Architecture. Springer International Publishing(2019)

[DICM05]DATCU et al.“Improvement of building wall surface temperature measurements by infrared thermography”. Infrared physics & technology 46.6(2005)

[VM17]VOLLMER and MÖLLMANN. Infrared thermal imaging: fundamentals, research and applications. Wiley&Sons(2017)

[ANG*19]AGUERRE et al.“A street in perspective: Thermography simulated by the finite element method”. Building and Environment 148(2019)

[HMLV12]HÉNON et al.“An urban neighborhood temperature and energy study from the CAPITOUL experiment with the SOLENE model”. Theor.and app.climatology 110(2012)

[WZCH15]WU et al.“Real-time mid-wavelength infrared scene rendering with a feasible BRDF model”. Infrared Physics & Technology 68(2015)

[AG*20]AGUERRE et al.“Physically Based Simulation and Rendering of Urban Thermography”. Computer Graphics Forum 39-3(2020)

[WMLT07]WALTER et al.“Microfacet Models for Refraction through Rough Surfaces.” Proceedings of the 18th Eurographics conference on Rendering Techniques(2007).

[Agu20]AGUERRE.”ThRend: Infrared rendering for thermography simulation”. Acces. July2020. URL:https://github.com/jpaguerre/ThRend.

[AVS20]Advanced Visual Systems (AVS). “Unstructured Cell Data file format”. Acces. July2020. URL:https://people.sc.fsu.edu/~jburkardt/data/ucd/ucd.html.

[AW20]AGUERRE and WALD.”ThRend ported to GPU with Optix Wrapper Library”. Acces.July2020. URL:https://gitlab.com/ingowald/ThRend/-/tree/iw/owl



15:18 - 15:36

CityFFD/CityBEM: Fast and high accurate urban microclimate model

Mohammad Mortezazadeh1, Senwen Yang1, Jiwei Zou1, Ali Katal1, Sylvie Leroyer2, Leon Wang1

1Concordia University, Canada; 2Environment and Climate Change, Dorval, Canada

Aim and Approach

(max 200 words)

Urban microclimate and building energy models have been developed to address the increasing concerns over thermal and wind comfort, and building energy consumption due to rapid urbanization and building resilience as a result of climate change. Modeling urban scale problems is mostly computationally expensive. Currently, researchers need to consider some simplifications, such as uniform microclimate, for modeling building energy performance or simulate urban microclimate for small regions. Otherwise, using powerful hardware is unavoidable. In this study, we developed an integrated two-way platform by combining CityFFD (City Fast Fluid Dynamics), an urban-scale fast fluid dynamics model for microclimate modeling, and CityBEM (City Building Energy Model), a new urban building energy model with a library of 1700 building archetypes for facilitating urban model creation. Local aerodynamics and heat transfer information are exchanged between both models at each time step. Graphics processing unit computing is also applied to CityFFD for simulation speedup. This model has been validated by several numerical and experimental results.

Scientific Innovation and Relevance

(max 200 words)

CityFFD/CityBEM is an in-house model for modeling urban microclimate and building energy model. This model has been written based on CUDA-C++ and includes a group of novel numerical schemes to improve the accuracy and speed of the simulation. CityFFD is a 4th order numerical scheme that is suitable for modeling large scale problems.

Preliminary Results and Conclusions

(max 200 words)

In the present work, a group of urban regions in Canada have been modeled. Main focus of this research paper is to show the performance of the model for modeling various urban problems, including wind and thermal comfort, building energy performance, mitigation strategies, etc. Our research study shows significant impact of building energy consumption and urban microcliamte on local climate distribution.

Main References

(max 200 words)

Mortezazadeh, M. and Wang, L.L., 2017. A high‐order backward forward sweep interpolating algorithm for semi‐Lagrangian method. International Journal for Numerical Methods in Fluids, 84(10), pp.584-597.

Mortezazadeh, M. and Wang, L.L., 2019. SLAC–a semi-Lagrangian artificial compressibility solver for steady-state incompressible flows. International Journal of Numerical Methods for Heat & Fluid Flow.

Mortezazadeh, M. and Wang, L.L., 2020. Solving city and building microclimates by fast fluid dynamics with large timesteps and coarse meshes. Building and Environment, p.106955.

Katal, A., Mortezazadeh, M. and Wang, L.L., 2019. Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations. Applied Energy, 250, pp.1402-1417.



15:36 - 15:54

A parametric analysis of the impact of thermophysical, geometry and urban context features on the energy demand of a simplified building shoebox model

Federico Battini, Giovanni Pernigotto, Andrea Gasparella

Free University of Bozen-Bolzano, Italy

Aim and Approach

(max 200 words)

In Urban Building Energy Modeling, one of the main issues is to obtain reliable results with limited computational effort [1]. To achieve such goal, one of the options is to reduce the complexity of the modeling domain. In this respect, reducing the building into shoeboxes showed to be particularly effective [2]. Nevertheless, no extensive step of sensitivity analysis and uncertainty screening were carried out at urban scale so far [3] to identify the most relevant features affecting the shoebox thermal demand [4]. In this work, a parametric analysis was performed with the aim to improve the shoebox definition from the real building characteristics. Besides conventional thermophysical and geometry properties, also context-related features were considered. After selecting the most suitable sampling technique to perform a multi-variate sensitivity analysis, several configurations of shoeboxes were simulated in the climate of Denver, Colorado, USA, using EnergyPlus as building simulation code. As in the ANSI/ASHRAE 140, Denver is chosen as reference climate due to its cold winters, hot summers and large daily temperature variations. Annual energy needs and peak loads for space heating and cooling were then analyzed. Finally, in order to identify the most significant features, the Spearman correlation coefficient was calculated.

Scientific Innovation and Relevance

(max 200 words)

Other sensitivity analyses in the literature already focused on the impact of the urban context on the thermal load. However, the considered features mainly pertained the building and urban geometry [5] or focused on tropical cities [6]. Moreover, such sensitivity analyses did not fully consider the single building characteristics and the urban ones at the same time. Only characteristics describing the urban environment were considered, or just few geometric features of the building were implemented as well. To develop reliable and representative models, it is necessary to understand which are the most important variables and how they interact with each other, impacting on the building performance. Identifying the features influencing a shoebox’s thermal behavior at multiple spatial scales permits to draw some guidelines on how to set shoeboxes’ properties, while developing shoebox-simplified urban building energy models.

Preliminary Results and Conclusions

(max 200 words)

As a whole, the variables showing the highest Spearman correlation coefficient with the heating and cooling demand outputs were ventilation and cooling setpoint, respectively. Among the urban variables considered, the context ended up being the most correlated to the output with moderate correlation with the cooling demand and very weak correlation with the heating demand. Instead, the albedo of the context surfaces showed very low correlation to the output, differently to what obtained for tropical climates [6]. For what regards the remaining single building features, the obtained results were in agreement with previous studies performed on the same kind shoebox [4]. According to the most influencing variables determined in this study, it will be possible to consistently choose the features to simplify and represent more effectively a building into a shoebox in urban simulation.

Main References

(max 200 words)

[1] C. F. Reinhart and C. C. Davila, "Urban building energy modeling - A review of a nascent field", Building and Environment, vol. 97, pp. 196-202, 2 December 2015.

[2] T. Dogan and C. Reinhart, "Shoeboxer: An algorithm for abstracted rapid multi-zone building energy model generation and simulation", Energy and Buildings, vol. 140, pp. 140-153, 2017.

[3] C. Cerezo, J. Sokol, C. Reinhart and A. Al-Mumim, "Three methods for characterizing building archetypes in urban energy simulation - a case study in Kuwait city", 14th Conference of International Building Performance Simulation Association, Hyderabad, 2015.

[4] G. Pernigotto, A. Prada, A. Gasparella and J. L. M. Hensen, "Development Of Sets Of Simplified Building Models For Building Simulation", International High Performance Buildings Conference, Purdue, 2014.

[5] A. Vartholomaios, "A parametric sensitivity analysis of the influence of urban form on domestic energy consumption for heating and cooling in a Mediterranean city", Sustainable Cities and Society, vol. 28, pp. 135-145, 2017.

[6] T. A. d. L. Martins, L. Adolphe, L. E. G. Bastos and M. A. d. L. Martins, "Sensitivity analysis of urban morphology factors regarding solar energy potential of buildings in a Brazilian tropical context", Solar Energy, vol. 137, pp. 11-24, 2016.



15:54 - 16:12

Modeling district heating and cooling systems with URBANopt, GeoJSON to Modelica Translator, and the Modelica Buildings Library

Nicholas Long1, Antoine Gautier2, Hagar Elarga1, Amy Allen1, Ted Summer3, Lauren Klun1, Nathan Moore1, Michael Wetter2

1National Renewable Energy Laboratory, United States of America; 2Lawrence Berkeley National Laboratory, United States of America; 3Devetry

Aim and Approach

(max 200 words)

The URBANopt project has been successfully leveraging OpenStudio/EnergyPlus, REopt, OpenDSS, and Reference Network Models to bridge the gap between buildings, distributed energy resources, and electric grids; however, URBANopt has been lacking the ability to model district thermal energy systems until recently. Conventional simulation tools such as EnergyPlus, have difficulties modeling modern district heating and cooling (DHC) systems due to the lack of pressure-driven analysis and their limited capabilities to represent tailored control logics and coupling between plants, distribution network and substations. This paper will present the modeling infrastructure that was developed specifically for the analysis of district heating and cooling systems, and how it is integrated into the existing URBANopt framework.

Scientific Innovation and Relevance

(max 200 words)

The paper will discuss the development of a new extension to URBANopt to model various district energy system components. It will describe the new models that were added to the Modelica Buildings Library to represent the central plants, the distribution networks, the energy transfer stations (ETS), and ultimately the loads on the DES. The supported modeling approaches and system configurations will be discussed. For instance, the loads can be provided as time series (CSV), TEASER models, or Spawn of EnergyPlus models, and the ETS models can represent direct or indirect connections, or even distributed heat recovery chillers for ambient loop systems. URBANopt allows the user to switch between the various configurations. It uses a translator from an URBANopt GeoJSON file describing the various buildings and district systems to scaffold an entire Modelica system model ready for running analysis.

Preliminary Results and Conclusions

(max 200 words)

The paper will conclude with a comparative analysis between a 1st generation district system and a 4/5th generation district system using load profiles generated from OpenStudio/EnergyPlus.

Main References

(max 200 words)

URBANopt: https://www.nrel.gov/buildings/urbanopt.html

GMT: https://github.com/urbanopt/geojson-modelica-translator

MBL: https://github.com/lbl-srg/modelica-buildings

TEASER: https://github.com/RWTH-EBC/TEASER

Spawn: https://github.com/NREL/Spawn



16:12 - 16:30

Evolving co-heating tests to deliver additional performance metrics and support model calibration

Jon William Hand1, Lori Barbara McElroy1, Colin Sinclair2

1University of Strathclyde, United Kingdom; 2Building Research Establishment, United Kingdom

Aim and Approach

(max 200 words)

The paper compares using an innovative, short duration ‘Pulse Test’ with a standard ‘Co-heating Test’, which typically requires a number of weeks to establish an overall facade heat transfer metric. The method relies on monitoring the heating/cooling and temperatures in each room during a typical one week period in order to:

a) capture the response to a step-change in heating or cooling, to reach a 25-30oC delta T against ambient;

b) identify the energy inputs required to maintain the elevated/depressed conditions; and

c) profile the response of the building during its return to ambient.

This is used to calibrate a numerical model of the building, thus creating a digital twin of the monitored building and physical test. Having carried out both co-heating and pulse tests the paper compares and contrasts working procedures, experimental design, performance assessment model design, model calibration steps as well as the building performance appraisal facilities required.

Scientific Innovation and Relevance

(max 200 words)

The innovation is in making use of room-by-room data from short term testing and monitoring to calibrate a simulation model. This minimises disruption to occupants in the case of existing buildings and avoids unnecessary delay in occupation of new buildings while significantly improving model accuracy, thus closing the performance gap between the virtual and real worlds.

The three phases of the Pulse Test are key to capturing the impact of internal mass as well as local variances in façade performance within both the building and its digital twin. Although the method does not identify faults in specific portions of a rooms, it is at a higher spacial resolution than many other approaches. Combined with real and virtual twin blower door tests and thermographic surveys the resulting model is a better platform on which to explore broader performance assessments.

Preliminary Results and Conclusions

(max 200 words)

The application of the method in a number of major Government funded new build and retrofit projects in the UK has resulted in both an overall reduction in the time required to undertake the experimental setup and monitoring and the creation of numerical models which reflect the actual response characteristics of the building.

For example, in one project differences between the physical and virtual tests indicated that on-site floor build-ups differed from the construction details provided as well as highlighting the impact of façade air leakage faults when the tests were undertaken with and without formal pressure tests. In one case a second round of building and digital twin tests were carried out to confirm improvements after on-site corrections were carried out.

After calibration, the digital twin was used in performance appraisals with local weather data to establish longer term performance characteristics. Initial indications from performance patterns observed in the digital twin were also noted in POE.

From such well-calibrated models, further virtual experiments have been conducted, exploring issues such as matching heating system characteristics with lifestyle/ occupancy factors.

Main References

(max 200 words)

HIT2GAP - Highly Innovative Building Control Tools - Grant Agreement number: 680708 - H2020 ENERGY-EFFICIENT BUILDINGS -2016-2017 - www.hit2gap.eu

Wall-ACE – Novel Wall Insulation Systems - Grant Agreement number: 723574 - H2020 ENERGY-EFFICIENT BUILDINGS -2016-2017 – www.wall-ace.eu

ESP-r www.strath.ac.uk/research/energysystemsresearchunit/



 
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