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, 07:24:42 CEST

 
 
Session Overview
Session
Session T1.2: Ensuring high quality building simulations
Time:
Thursday, 02/Sept/2021:
8:30 - 10:00

Session Chair: Thierry Duforestel, EDF R&D
Session Chair: Filip Jorissen, KU Leuven
Location: Cityhall (Belfry) - Room 2

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Presentations
8:30 - 8:48

Spatial distribution of thermal comfort: a case study in Paris' station

Edouard Walther1, Mateusz Bogdan1, Fanny Peyre2, Christian Inard2

1AREP L'hypercube, France; 2LASIE Université de La Rochelle, France

Aim and Approach

(max 200 words)

In Building Energy Simulation (BES), the environmental variables for thermal comfort such as air velocity, radiant temperature, dry bulb temperature and relative humidity are nodal, meaning a single scalar value is computed for each thermal zone, regardless of their size. This long-used approach is valid for most of the built environment, however it may prove to be inappropriate in specific cases, such as widely open and/or glazed buildings. Radiation and convection being major contributors to the heat balance of the human body (Pickup and De Dear, 2000), the existence of indoor solar fluxes and of significant variations in the air velocity field cannot be overlooked for a proper evaluation of thermal comfort.

Commercial tools lack the spatial distribution of radiant temperature and air velocity fields for the computation of comfort levels in semi-open places. The aim of the present work is hence to provide a detailed distribution of thermal comfort based on an "enriched” nodal approach, with hourly solar distribution and air velocity fields. A proof of concept is proposed under the form of a case study, using the state-of-the-art, open source softwares: energyplus, coupled with Radiance and openFOAM.

Scientific Innovation and Relevance

(max 200 words)

The method presented in (Walther et al. 2019) is chosen for the computation of natural ventilation, using CFD to obtain the detailed pressure boundary conditions. As a by-product, this approach yields a simulation set-up allowing to compute indoor velocity fields within open buildings with a moderate additional effort.

The indoor isothermal velocity field is obtained by transposing the approach of (Delpech et al. 2005) to the indoor environment: the distribution of air velocities is solved with an isothermal k-epsilon model for 12 incident wind directions, with their median meteorological velocity as inlet. Hourly velocities are reconstructed by field interpolation, selecting the closest precomputed fields. The amplitude is determined by means of scaling by the hourly weather data (Delpech et al. 2005).

The long wave contribution to radiant temperature is obtained directly from the nodal value of the BES. The shortwave contribution is calculated the daylight coefficients method, also known as “the Two-Phase" method (Subramaniam 2017).

The PET index (Walther & Goestschel, 2018) is chosen as comfort metric. In order to preserve both temporal and spatial variation of comfort, the Outdoor Thermal Comfort Autonomy (Nazarian et al. 2019) was used for the qualification of comfortable spaces.

Preliminary Results and Conclusions

(max 200 words)

The presented approach allows for the computation of a spatial distribution of comfort indexes in indoor spaces. It provides a better understanding of the causes of local thermal (dis-)comfort, as each of the underlying phenomena (e.g. solar radiation, wind distribution) has been calculated.

The study of the comfort patterns obtained is interesting for architects as it becomes possible to differentiate “sub-zones” of occupation within a given volume and provides information at a higher level of detail than previously available to space designers.

Interestingly, the computation of the OTCA using different thermal comfort indexes yields rather different results.

As a perspective, one could argue that zonal models are a cheaper alternative to computational fluid dynamics. The coupling of such models with energyplus is however numerically challenging. The actual longwave heat transfer may significantly impact thermal comfort depending on the indoor environment. The detailed radiation computation with view factors on idealised individuals is currently explored.

Main References

(max 200 words)

Delpech P., et al. 2005. Pedestrian wind comfort assessment criteria: A comparative Study. EACWE4. Prague: J. Naprestek & C. Fischer.

Nazarian, N., Acero, J. A., & Norford, L. (2019). Outdoor thermal comfort autonomy: Performance metrics for climate-conscious urban design. Building and Environment, 155, 145-160.

Pickup, Janelle, and Richard de Dear. "An outdoor thermal comfort index (OUT_SET*)-part I-the model and its assumptions." Biometeorology and urban climatology at the turn of the millenium. Selected papers from the Conference ICB-ICUC. Vol. 99. 2000.

Subramaniam S. (2017) Daylighting simulations with Radiance using matrix-besed methods, Berkeley Lab, (https://www.radiance-online.org/learning/tutorials/matrix-based-methods)

Edouard Walther, Antoine Hubert, Alexis Sauvageon, Mateusz Bogdan, Improving the Prediction of Natural Ventilation by Coupling CFD and BES : A Case Study in Strasbourg Train Station, IBPSA 2019.

Walther E, Goestchel Q. The PET comfort index: Questioning the model. Building and Environment. 2018 Jun 1;137:1-0.



8:48 - 9:06

Spectral and biological simulation methods for the design of healthy circadian lighting

J. Alstan Jakubiec, Athina Alight

University of Toronto, Canada

Aim and Approach

(max 200 words)

Medical and architectural research has identified that light has significant impacts on alertness, sleep quality, and productivity [1-4]. This has broadly been referred to as circadian lighting, and its effects are dependent on the time of exposure and are sensitive to blue, short wavelength light. Existing calculation methods and standards assess the impact of light on health outcomes [5-7]; however, they have a variety of weaknesses: lacking spectral information, estimating scaling factors to account for spectral power distributions, or relying on instantaneous calculations although light timing and exposure history are crucial in assessing the effects of light on health. No existing methodology calculates spectrally- and temporally-varying light exposure and maps its impact on biological and health-related outcomes. This paper will communicate such a simulation method using time-varying 81-channel spectrally specific lighting simulations [8] and apply the results to existing dynamic photobiology models from medical literature [9]. We predict reaction time, sleepiness, task performance, sleep time and melatonin levels in the bloodstream entirely as a factor of timed light exposure history. This paper shares how these calculation models are implemented, demonstrates them under some week-long scenarios, and proposes new metrics and communication methods to assess the impact of light on health.

Scientific Innovation and Relevance

(max 200 words)

We will present a new scheme for calculating spectral daylight simulations on an annual basis combined with electric lighting which can be dimmed, controlled, and color-shifted on a schedule. Innovations over existing spectral calculation methods include: a physics-based simulation process for the estimation of spatially resolved sky color and spectrum, the inclusion of spectral window and opaque surface materials at a high resolution of 81-channels (5 nm steps) [8], and the calculation of modern non-image forming light quantities following CIE standard S 026 [10]. Furthermore, we present an implementation of a dynamic model of human arousal dynamics [9] which can predict the impact of light on sleep time, subjective sleepiness, task performance, reaction time, and blood plasma melatonin levels. The model is responsive to the time, color, and quantity of light exposure in addition to light history. Two cases are presented using seven days of light history for a modelled occupant. These new calculation methods and metrics will illustrate the actual benefits of lighting design for circadian health, which has not been accomplished using simulation before.

Preliminary Results and Conclusions

(max 200 words)

A new simulation method for time-varying lighting spectra from daylight and controlled electric lighting is presented as well as its application to modelling human circadian biology. Detailed information will be presented on how light interacts with homeostatic and circadian bodily rhythms to impact health and alertness. We calculate the impact of light on sleep time, subjective sleepiness, task performance, reaction time, and blood plasma melatonin levels and present novel methods of evaluating these measures in architectural lighting design. By applying these new models, we present the means to answer longstanding questions regarding the specific health impacts of light exposure in the built environment. Two sample light exposure profiles are calculated over a period of 1 week to illustrate these effects.

In addition to this outcome, a second paper is being prepared to apply our method to a variety of parametric design variants and compare the outcomes to existing methods of evaluating circadian lighting.

Main References

(max 200 words)

1. Rea, M. and M. Figueiro, Light as a circadian stimulus for architectural lighting. Lighting Research & Technology, 2018. 50(4): p. 497-510.

2. Figueiro, M.G., K. Gonzales, and D. Pedler, Designing with circadian stimulus. LD+A: The Magazine of the Illuminating Engineering Society, 2016. 8: p. 30-34.

3. Dijk, D.-J. and S.N. Archer, Light, sleep, and circadian rhythms: together again. PLoS Biol, 2009. 7(6): p. e1000145.

4. Lucas, R.J., et al., Measuring and using light in the melanopsin age. Trends in Neurosciences, 2014. 37(1): p. 1-9.

5. Konis, K., A novel circadian daylight metric for building design and evaluation. Building and Environment, 2017. 113: p. 22-38.

6. Inanici, M., M. Brennan, and E. Clark, Spectral daylighting simulations: Computing circadian light, in Building Simulation. 2015: Hyderabad, India.

7. IWBI, The WELL Building Standard. Washington, DC, Delos Living LLC, 2016.

8. Solemma LLC, ALFA – Adaptive Lighting for Alertness. 2020.

9. Postnova, S., S.W. Lockley, and P.A. Robinson, Prediction of cognitive performance and subjective sleepiness using a model of arousal dynamics. Journal of biological rhythms, 2018. 33(2): p. 203-218.

10. CIE, CIE S 026/E:2018 CIE System for Metrology of Optical Radiation for ipRGC-Influenced Responses to Light. 2018, Commission internationale de l’eclairage.



9:06 - 9:24

An optimization of the switching temperature for an energy system with a double-source heat pump

Sara Bordignon, Giuseppe Emmi, Marco Rossi, Michele De Carli, Angelo Zarrella

Department of Industrial Engineering - Applied Physics Section, University of Padova, Italy

Aim and Approach

(max 200 words)

Enhancing the efficiency of the energy systems for the air-conditioning and domestic hot water production in buildings and reducing the related primary energy consumption through the exploitation of renewable energy sources have become of great interest in the late years.

This paper presents a dynamic analysis of a double-source energy system, aiming at optimally combine the air and the soil as heat source/sink for a heat pump.

In the field of multi-source energy systems, the dual-source heat pump represents a valuable solution to increase the system’s efficiency, compared to a more common air-source heat pump and, at the same time, to reduce the size of the borehole heat exchangers field that should be coupled to a ground source heat pump [1]. These solutions can, therefore, decrease the initial investment and, depending on the plant configurations and on the boundary conditions (i.e. climatic area) [2], an optimisation of the energy system should be carried out to avoid unnecessary oversizing of the components and to further increase the efficiency of the heat pump [3].

Scientific Innovation and Relevance

(max 200 words)

The dynamic energy model is developed in TRNSYS [4] environment, where a novel Type for the modelling of the heat pump is adopted. The purpose of the analysis is to investigate the performances of the system varying the switching temperature, that is the parameter which establishes the heat source to be used by the heat pump. The heat pump Type is a flexible model and can be used as a double-source heat pump. The outputs are based on the polynomials of the compressors, which can be modified by the user, while the fluid properties in the different points of the thermodynamic cycles can be obtained thanks to an internal link to REFPROP [5]. The heat pump is connected to two thermal energy storages from which heat is withdrawn for the air-conditioning and domestic hot water production respectively. Through this approach, it is possible to investigate and predict in detail the thermal and electrical behaviour of double source heat pump systems, which can be used in the context of both retrofitted and new buildings, under different weather conditions.

Preliminary Results and Conclusions

(max 200 words)

The study consisted in the dynamic energy modelling of a double source system where a heat pump exploits both the air and the soil as heat source/sink, for heating, cooling and domestic hot water production of a building. It allows testing a novel TRNSYS Type for the heat pump, which can be used for different simulation applications. The analysis identifies an optimal switching temperature for the investigated case study, which allows increasing the efficiency of the system, reducing the size of the borehole heat exchangers field and decreasing the thermal drift of the ground temperature, due to unbalanced operating condition. The simulations are carried out for different climatic conditions, to make it possible a comparison of the optimal switching temperature for the different locations.

Main References

(max 200 words)

[1] I. Grossi, M. Dongellini, A. Piazzi, and G. L. Morini, “Dynamic modelling and energy performance analysis of an innovative dual-source heat pump system,” Appl. Therm. Eng., vol. 142, pp. 745–759, Sep. 2018.

[2] T. Catalina, J. Virgone, and E. Blanco, “Multi-source energy systems analysis using a multi-criteria decision aid methodology,” Renew. Energy, vol. 36, no. 8, pp. 2245–2252, 2011.

[3] G. Hou, H. Taherian, L. Li, J. Fuse, and L. Moradi, “System performance analysis of a hybrid ground source heat pump with optimal control strategies based on numerical simulations,” Geothermics, vol. 86, p. 101849, Jul. 2020.

[4] University of Wisconsin--Madison. Solar Energy Laboratory. “TRNSYS, a Transient Simulation Program.” Madison, Wis. :The Laboratory, 1975.

[5] M. Lemmon, E.W., Bell, I.H., Huber, M.L., “M.O. NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties-REFPROP, Version 10.0.” National Institute of Standards and Technology, Standard Reference Data Program, Gaithersburg, 2018.



9:24 - 9:42

Wind-driven air flow modelling in Modelica: verification and implementation in the IDEAS library

Klaas De Jonge1,3, Filip Jorissen2, Lieve Helsen2,4, Jelle Laverge1

1Ghent University, Department of Architecture and Urban Planning , Belgium; 2University of Leuven (KU Leuven), Department of Mechanical Engineering, Belgium; 3FWO, Research Foundation Flanders; 4EnergyVille, Thor Park, Waterschei, Belgium

Aim and Approach

(max 200 words)

In building simulations, pressure driven airflow and heat flow modelling is often done in separate specialised programs. To include the mutual dependence of indoor temperatures and pressure driven airflow, simulations often require co-simulation using two distinct programs, where CONTAM is often used to compute the system flow rates and pressures. This requires the modeller to be skilled in multiple software packages, and co-simulation. Moreover, the resulting co-simulation framework is hard to extend and customize and an inefficient coupling can cause model inaccuracies or long computation times.

This paper presents a new implementation for pressure driven airflow in the open-source Modelica library IDEAS. It allows the modeller to model the heat transfer and interzonal, pressure driven airflow simultaneously, thereby using one software. This new implementation is validated dynamically by comparing the obtained results with a duplicate of the multi-zone model in the specialised software CONTAM.

Scientific Innovation and Relevance

(max 200 words)

The new implementation will allow modellers to model the mutual dependence of indoor temperatures and pressure driven airflow simultaneously and thus faster. The default implementation does not require extensive additional input which will make it easier for modellers, not familiar with pressure driven airflow, to include this factor. Due to the extensible nature of Modelica, the models can be coupled to detailed HVAC simulations and other system models.

Towards an energy neutral building stock, understanding the different mechanisms at play is key. Combined heating/cooling and ventilation simulations are important to gain a better understanding of the interaction between these depending factors and their impact on energy use and indoor air quality, respectively.

Preliminary Results and Conclusions

(max 200 words)

Results show almost perfect agreement between Modelica IDEAS and CONTAM when zone temperatures are comparable. This shows that the pressure driven airflow implementation is state-of-the-art and may be used as an alternative to co-simulations with CONTAM.

It is important to note that the default integration in IDEAS is limited to predefined airflow elements and relations. This can be a limiting factor for modelling specific airflow paths through a building (e.g. elevator shafts). However, the default implementation can be extended with custom airflow elements to model the additional airflow paths.

Main References

(max 200 words)

[1] F. Jorissen, G. Reynders, R. Baetens, D. Picard, D. Saelens, and L. Helsen, “Implementation and verification of the IDEAS building energy simulation library,” Journal of Building Performance Simulation, vol. 11, no. 6, pp. 669–688, Nov. 2018, doi: 10.1080/19401493.2018.1428361.

[2] W. S. Dols and B. J. Polidoro, “CONTAM User Guide and Program Documentation Version 3.2,” National Institute of Standards and Technology, NIST TN 1887, Sep. 2015. doi: 10.6028/NIST.TN.1887.

[3] M. Wetter, “Multizone Airflow Model in Modelica,” presented at the Modelica 2006, Vienna, Austria, Sep. 2006.



9:42 - 10:00

Evaluation of energy and indoor environmental performance of mechanical ventilation systems under different climatic conditions

Dragos-Ioan Bogatu1, Ongun Berk Kazanci1, Charalampos Angelopoulos2, Daniel Coakley2, Bjarne W. Olesen1

1International Centre for Indoor Environment and Energy, Department of Civil Engineering, Technical 6 University of Denmark, Nils Koppels Allé, Building 402, 2800 Kgs. Lyngby, Denmark; 2Mitsubishi Electric R&D Centre Europe BV, 17 Firth Road (AC3), Houstoun Industrial Estate, Livingston, West Lothian EH54 5DJ

Aim and Approach

(max 200 words)

One of the most significant global development challenges for the built environment is the mitigation of the impact of energy-intensive mechanical heating, ventilation and cooling systems (HVACS) in buildings. Growing population around the world and the fact that people tend to spend almost 90% of their time indoors [1], makes indoor environmental quality an important parameter to account for in new and existing buildings. Nowadays, more people rely on mechanical systems to maintain acceptable levels of Indoor Environmental Quality (IEQ). Mixed-mode buildings combine the use of mechanical and passive techniques to maintain thermally comfortable indoor environments. However, there are no clear guidelines on how to operate such mechanical ventilation systems in order to maintain acceptable levels of IEQ with the minimum possible energy consumption. The aim of this research was to perform dynamic thermal modelling simulations to evaluate the energy and indoor environmental performance of mechanical ventilation systems under different climatic conditions and building types. To examine this, the following objective was used: to identify the optimum operation mode of the mechanical system (bypass, heat recovery or enthalpy recovery) to achieve the energy savings whilst maintaining acceptable levels of IEQ.

Scientific Innovation and Relevance

(max 200 words)

The evaluation of the performance of mechanical systems is often compared to the level of energy use in a building. For the energy use there is one KPI, kWh/m2 per year primary energy, which includes all buildings systems and energy carrier. However, this approach excludes the analysis of parameters for indoor environmental quality such as relative humidity, CO2 concentration, and operative temperature. This paper uses dynamic computer simulations for a residential and an office application to examine the performance of the mechanical ventilation systems under different outdoor conditions and under different indoor heating and cooling loads. The following cases will be analyzed for the climatic conditions of Edinburgh, Scotland; Copenhagen, Denmark; Zurich, Switzerland; and Palermo, Italy: i) Operate ventilation systems in heat recovery mode; ii) Operate ventilation systems in enthalpy recovery mode; and iii) Operate ventilation systems in bypass mode and increased ventilation for cooling (day- and nighttime). In all cases the following KPIs will be used: i) Yearly Room temperature and humidity distribution; ii) Yearly CO2 concentration distribution as an indicator of IEQ; and iii) Energy use of the mechanical systems in the different cases.

Preliminary Results and Conclusions

(max 200 words)

It is expected that the enthalpy recovery mode will have the most effect in very humid and very dry climates. It is predicted that the combination of the passive modes that achieves higher energy savings will depend on the climate, period of the year and building type. It is expected that the enthalpy recovery mode will reach higher energy savings than the heat recovery mode since it allows extracting latent heat from humid air in addition to the recovery of sensible heat. Therefore, it is likely that the outdoor relative humidity and air temperature will influence the amount of energy savings obtained from the enthalpy recovery mode, compared to the heat recovery mode. The bypass mode will probably lead to higher energy savings in periods and locations where the outdoor temperature is lower than the indoor air temperature and there is a need for cooling (e.g. during nighttime in a hot summer period). Finally, it is expected that all the passive strategies analyzed in this study will reach acceptable CO2 concentrations during occupied hours.

Main References

(max 200 words)

[1] N. E. Klepeis, "The National Human Activity Pattern Survey (NHAPS) - A Resource for Assessing Exposure to Environmental Pollutants," Lawrence Berkeley National Laboratory, 2001.



 
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