Importance-driven approach for reducing urban radiative exchange computations

Aguerre, José Pedro; Fernández, Eduardo; Beckers, Benoît

doi:10.1007/s12273-018-0482-4

Cited by 5 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of reflected rays was set to be

L = 100

, and the threshold was

t = 10^{- 5}

. Note that matrix

boldA

is an n

\times

n matrix of floating point numbers, with n

\approx

100 k. Following [AFB18], sparse representations were used to reduce the memory requirements of such matrix, leading to a total consumption of around 8 GB. Then, the non‐diffuse long‐wave radiation boundary conditions were implemented in Cast3m using the pre‐computed matrix

boldA

.…”

Section: Methodsmentioning

confidence: 99%

“…Extended form factors [SP89] are used to enable other kinds of reflections, such as the behaviour of window glasses (specular surfaces). This approach provides short computational times and negligible error when compared to classical ray‐based approaches, as shown in [AFB18]. The absorbed short‐wave fluxes are then considered as the nodal imposed flux

q_{s}

by averaging the values of all the adjacent elements of each node.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Physically Based Simulation and Rendering of Urban Thermography

Aguerre

García‐Nevado

Miño

et al. 2020

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

Urban thermography is a non‐invasive measurement technique commonly used for building diagnosis and energy efficiency evaluation. The physical interpretation of thermal images is a challenging task because they do not necessarily depict the real temperature of the surfaces, but one estimated from the measured incoming radiation. In this sense, the computational rendering of a thermal image can be useful to understand the results captured in a measurement campaign. The computer graphics community has proposed techniques for light rendering that are used for its thermal counterpart. In this work, a physically based simulation methodology based on a combination of the finite element method (FEM) and ray tracing is presented. The proposed methods were tested using a highly detailed urban geometry. Directional emissivity models, glossy reflectivity functions and importance sampling were used to render thermal images. The simulation results were compared with a set of measured thermograms, showing good agreement between them.

show abstract

“…The number of reflected rays was set to be

L = 100

, and the threshold was

t = 10^{- 5}

. Note that matrix

boldA

is an n

\times

n matrix of floating point numbers, with n

\approx

boldA

.…”

Section: Methodsmentioning

confidence: 99%

q_{s}

by averaging the values of all the adjacent elements of each node.…”

Section: Methodsmentioning

confidence: 99%

Physically Based Simulation and Rendering of Urban Thermography

Aguerre

García‐Nevado

Miño

et al. 2020

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…The radiosity method is commonly used in daylight simulation tools and micrometeorological models like ENVI-met (Bruse and Fleer 1998), SOLENE (Vinet 2000;Miguet and Groleau 2002;Musy et al 2015), LASER/F (Roupioz et al 2018), and PALM-4U (Resler et al 2017;Krč et al 2021). A major disadvantage of the radiosity method is that large matrices of view factors have to be stored in memory (Milliez 2006), especially for large model domains with many facets, and when considering specular reflections (Aguerre et al 2019). Krč et al (2021) present a method to reduce the number of view factors by excluding those facets which contribute only little to the irradiance or are too far from a specific facet.…”

Section: Obstacle-resolving Radiative Transfer Modelling In Urban Areasmentioning

confidence: 99%

Model of Spectral and Directional Radiative Transfer in Complex Urban Canopies with Participating Atmospheres

Caliot

Schoetter

Forest³

et al. 2022

Boundary-Layer Meteorol

View full text Add to dashboard Cite

Thermal heat transfers, including solar and infrared radiation in cities, are key processes for studying urban heat islands, outdoor human thermal comfort, energy consumption, and production. Thus, accurate radiative transfer models are required to compute the solar and infrared fluxes in complex urban geometry accounting for the spectral and directional properties of the atmosphere and city fabric materials. In addition, these reference models may be used to evaluate existing parametrization models of radiative heat transfer and to develop new ones. The present article introduces a new reference model for outdoor radiative exchange based on the backward Monte Carlo method. The integral formulations of the direct and scattered solar, and the terrestrial infrared radiative flux densities are presented. This model can take into account the ground (e.g., roads, grass), different types of buildings and vegetation (e.g., trees consisting of opaque leaves and trunks) with their spectral and directional (Lambertian and specular) reflectivity of materials. Numerical validations of the algorithm are presented against the results of a state-of-the-art model based on the radiosity method for the particular case of an infinitely long street canyon. In addition, the convergence of urban solar radiation budgets is studied for a selection of urban complex geometries including

show abstract

“…HLA). Other approaches for managing large scenes are based on limiting processing overheads in the case of radiosity calculations, for example, (1) via visibility analysis (Cohen-Or et al., 2003), (2) using an importance-based approach to speed-up radiosity calculation (Aguerre et al., 2019), (3) by superimposing a regular grid (Muñoz et al., 2018; Rodríguez et al., 2017). With the regular grid approach, each grid cell is simulated independently with some boundary overlap with surrounding cells.…”

Section: Related Workmentioning

confidence: 99%

Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation

Zakhary

Rosser

Siebers

et al. 2020

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

Microsimulation is a class of Urban Building Energy Modeling techniques in which energetic interactions between buildings are explicitly resolved. Examples include SUNtool and CitySim+, both of which employ a sophisticated radiosity-based algorithm to solve for radiation exchange. The computational cost of this algorithm increases in proportion to the square of the number of surfaces of which an urban scene is comprised. To simulate large scenes, of the order of 10,000 to 1,000,000 surfaces, it is desirable to divide the scene to distribute the simulation task. However, this partitioning is not trivial as the energy-related interactions create uneven inter-dependencies between computing nodes. To this end, we describe in this paper two approaches ( K-means and Greedy Community Detection algorithms) for partitioning urban scenes, and subsequently performing building energy microsimulation using CitySim+ on a distributed memory High-Performance Computing Cluster. To compare the performance of these partitioning techniques, we propose two measures evaluating the extent to which the obtained clusters exploit data locality. We show that our approach using Greedy Community Detection performs well in terms of exploiting data locality and reducing inter-dependencies among sub-scenes, but at the expense of a higher data preparation cost and algorithm run-time.

show abstract

Importance-driven approach for reducing urban radiative exchange computations

Cited by 5 publications

References 32 publications

Physically Based Simulation and Rendering of Urban Thermography

Physically Based Simulation and Rendering of Urban Thermography

Model of Spectral and Directional Radiative Transfer in Complex Urban Canopies with Participating Atmospheres

Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation

Contact Info

Product

Resources

About