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4th International Conference On Building Energy, Environment

Efficient Daylight Factor Rendering in Sketch Up Based on High Performance Computing of Radiance

H. Chen1,2, B. Lin1,2, Z. Li 1,2, H. Zhou1,2

1Department of Building Science & Technology Tsinghua University, Beijing 100084, China

2Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University, Beijing, 100084, China

SUMMARY Daylight factor is one of the most important building performance indicators. Based on parallel computing of Radiance, an efficient daylight factor rendering method was proposed in this paper. The method is composed of four steps, 1) package Sketch Up geometry models and transfer data toserver; 2) generate self-adaptable girds; 3) divide the gridsinto N parts, call N Radiance processes simultaneously tocalculate daylight factor, and merge the results; 4) renderingdaylight factor results in Sketch Up, where N is the number ofparalleling unit. Through case study, the strengths andweaknesses are explained in details. With the “divide andconquer” idea, the method proposed is much more faster thanconventional approach, however, because of the timeconsumption of grid generation and division, and the timeconsumption for starting up N processes, the speed of daylightfactor calculation cannot reach N times faster.

INTRODUCTION With the development of design scheme, the cost of improving building performance become higher and higher, so decisions make in early design are very important to design energy efficient buildings (Kyle Konis 2016, U. Bogenstt�̈�ter 2000). Daylight factor is one of the most important building performance indicators, through rendering of daylight factor distribution, designers can not only better utilize natural light to reduce using of electronic lighting, but can also enhance occupant’s comfort.

Radiance is the most widely used daylight factor calculating program. However, due to the limitations of personal computer, the speed of daylight factor calculating is very slow, which cannot meet the real-time feedback requirement of early design stage. In the past years, many researchers about acceleration of Radiance computing speed were conducted. Especially, (Zuo, W. 2014) proposed a solution based on acceleration of matrix multiplication of portion of three-phase Radiance simulation method (McNeil, A. 2012), the method can utilize the latest parallel computing technology and make apparent acceleration, but its complexity made it hard to be applied in early design stage.

Besides, most early design platforms, e.g. Skecth Up and Rhino, only have basic geometry data, but lack the hierarchical building space topology information and calculating grids data, which are required for running Radiance simulation. (Ying Gao 2016) proposed that performance evaluation plugins for early design stage should be easy to use, it’s better not to let designers convert model and generate girds in this stage.

So fast rendering of daylight factor in early design stage had not implemented in the past, but with propose of automatic conversion algorithm and with the increasing parallel computing ability of cloud servers, it becomes realizable. This paper proposes an integrated method that can efficient calculate and render daylight factor for Skecth Up model by parallel computing of Radiance simulation. In the following sections, the method will be illustrated in detail, through case studies, its time efficiency and accuracy will be tested, and key points of parallel computing of Radiance will be discussed.

METHODS From reading Sketch Up geometry model, to render daylight factor in real-time, the proposed method is composed of four steps, 1) package Sketch Up geometry models and transfer data to server; 2) generate self-adaptable girds; 3) divide the grids into N parts, call N Radiance processes simultaneously to calculate daylight factor, and merge the results; 4) rendering daylight factor results in Sketch Up, where N is the number of paralleling unit, as shown in Figure 1.

Figure 1. Steps of the Proposed Method

Convert Model

Most early design platform adopt boundary representation model (Brep) to describe their geometries, so do Sketch Up, this kind of model only contain basic geometry elements including vertex, edge, face and volume, but lack hierarchical building space topology information, that is, how these building elements make up of a room or a zone is unknown. In the past, human do this work by remodelling in simulation software and manually combine building elements into room or zone. But this work can be replace by automatic conversion algorithm proposed by the authors (Hongzhong Chen 2017). With the conversion result, each room or zone of a building model is known, and the building elements of each room is also known, so floors and boundary surfaces of each room can be obtained. As shown in Figure 2, the recognized model has 1 zone including 1 floor, 1 roof, 11 vertical walls and 1 window. The converted model will then be sent to simulation engine ran on cloud severs, using this model, the engine can automatically generate daylight factor calculating grid points, and then the following work can be done.

ISBN: 978-0-646-98213-7 COBEE2018-Paper152 page 443


Figure 2. Converted Model (one zone with four type of building elements)

Generate Self Adaptable Grids

With the converted model, the engine will generate grids for every floor surfaces according to grid size value and grid height value, the default value for size is 1m and the default value for height is 0.8m which is the height of working panel, users are also allowed to change this two values. The proposed method adopts a rapid rasterization algorithm of vector polygon (Zhenjie Chen 2014), the algorithm can scan polygon boundary and change the polygon into square grids. As shown in Figure 3, the boundary polygon of floor has 11 edges, the rasterization algorithm scan the boundary from bottom to top, and from left to right, with one time scan, all grids will be generated.

Figure 3. Generated Grids using Rasterization Algorithm

Parallel Computing of Radiance

Once the grids were generated, the engine divides them into N parts with same number of girds from bottom to top, where N is the number of parallel computing unit, as shown in Figure 4, the girds were divided into 5 parts. The program will export N grids description file, the name of each file is grid_part_x.rad, where x is the number of grids part, and export 1 geometry file named geometry.rad, 1 material file named material.rad, 1 sky model file named sky.rad. Then, the program will launch N threads, each thread reads files including sky.rad, geometry.rad, material.rad and grid_part_x.rad , run Radiance separately to get daylight factor results. Generally, if the parallel number doesn’t exceed the total number of cloud servers of simulation engine, the acceleration can be achieved.

Figure 4. Divide Grids into 5 Parts from Bottom to Top

Rendering of Daylight Factors

After the simulation done, the results of N parallel parts will be read and combined into one result for each building, then the simulation engine send the result including grids coordinates and daylight factor values to Sketch Up. The plugin ran on Sketch Up generates grids with “Drawing Face” operation, assign each grid with different colour for different daylight factor value, and a legend will be generated to illustrate these values, as shown in Figure 5.

Figure 5 Daylight Factor Distribution Rendered in Sketch Up

RESULTS In this section, the proposed high performance computing method of Radiance in Sketch Up is tested from its accuracy and acceleration ability.

Accuracy

Only when the proposed method can get accurate result of daylight factor for each grid as real life Radiance did, the acceleration of Radiance make sense. The only factor influencing accuracy is the proposed method divides grids into multiple parts and calculates gird’s daylight factor value separately by running multiple Radiance program simultaneously. So, in the experiment, firstly, the proposed method is used to generate grids and calculate daylight factor values, secondly, using real life Radiance to calculate daylight factor values for the grids generated in first step, finally, 10 grids points were selected for compassion of daylight factor value calculated by the proposed method and real life Radiance, as shown in Figure5, under the same simulation settings as shown in Table 1. Through recording values of the 10 grid points, it’s found that their values are exactly same.



Figure 5 Selected Point for Daylight Factor Value Comparison

Table 1. Simulation Settings for Radiance

Parameter Value Unit

Grid Height 800 mm

Grid Size 1000 mm

Sky Model Overcast Sky 0.5

Reflection Time 3 -

Calculation Accuracy 0.25 -

Exterior Wall Reflectance Value 0.371 -

Interior Wall Reflectance Value 0.718 -

Ceiling Reflectance Value 0.9

Floor Reflectance Value 0.25

Time Efficiency

The case for time efficiency evaluation is shown in Figure 6, its long side’ length is 145m, its short side length is 31m, and the size of gird is set as 0.5m, so the total number of girds is 17980.

Figure 6 Case for Time Efficiency Evaluation

In the experiment, simulation time for different parallel number is recorded to illustrate the acceleration effect of paralleling computing of Radiance. 4 cloud servers are deployed to run simulation, each server has 32 CPU cores and 16G RAM, so the maximum parallel was set as 128. As shown in Table 2 and Figure 7, grids number of each parallel thread and maximum time for single thread is recorded for each parallel simulation experiment. It’s found in results that with the increasing of parallel number, the grid number of each thread decrease, and the time needed to finish simulation is also decrease. Especially, when the parallel number doubled, the time is nearly halved. And 16.92 seconds of simulation time can be achieved when parallel number is 16, which means many highly configurable personal computer can have fast Radiance simulation functions to get daylight factor feedbacks.

Table 2. Simulation Time for Different Parallel Number

Parallel Number

Grid Number for each Thread

Maximum Time for Single Thread (s)

1 17980 81.15

2 8990 48.82

4 4495 30.59

8 2248 21.02

16 1124 16.92

32 562 13.98

64 281 8.78

128 140 6.49

Figure 7. Simulation Time for Different Parallel Number

DISCUSSION The key points of this paper is parallel computing of Radiance, with the advance of computer technology, especially cloud computing and distributed parallel computing, this idea was realizable. As result shows, acceleration of Radiance simulation can be achieve by increasing parallel number, which make it can be applied to early design stage.

However, due to the cost of dividing grids, starting up parallel computing and combining results, the acceleration times cannot be in proportion to the number of parallel units.

CONCLUSIONS As above, this paper proposed an efficient daylight factor calculation method using parallel computing of Radiance ran on cloud servers, which can be applied in early design stage software Sketch Up, and provide fast feedbacks of natural light utilization.

ACKNOWLEDGEMENT This research is supported by China National 13th Five-year Science and Technology Support Project (2016YFC0700209) and “Total Performance of Low Carbon Buildings in China and the UK” project which is provided by the National Natural Science Foundation of China (NSFC, 51561135001).



REFERENCES Hongzhong Chen, Ziwei Li, Xiran Wang, Borong Lin. (2017).

A graph- and feature-based building space recognition algorithm for performance simulation in the early design stage. Building Simulation: An International Journal (Already Accepted).

Kyle Konis, Alejandro Gamas, Karen Kensek. Passive performance and building form: An optimization framework for early-stage design support. Solar Energy 125 (2016) 161–179

McNeil, A., & Lee, E. S. (2012). A validation of the Radiance three-phase simulation method for modelling annual daylight performance of optically complex fenestration systems. Journal of Building Performance Simulation, 6(1), 1-14.

U. Bogenstt�̈�ter, Prediction and optimization of life-cycle costsin early design, Building Research & Information 28 (5)(2000) 376–386

Ying Gao, Huanyu Hou, & Weidong Wu. (2016). Discussion on Application of Building Performance Simulation Software based on SketchUp. Architecture Technologies(7), 96-98.

Zhenjie Chen, Chen Zhou, Fiexue Li, Manchun Li, Yibin Ren. (2014). Rapid Parallelization Method for Sequential Rasterization Algorithms of Vector Polygons. Remote Sensing Information, 2014,29(05), 3-8+12.

Zuo, W., McNeil, A., Wetter, M., & Lee, E. S. (2014). Acceleration of the matrix multiplication of Radiance three phase daylighting simulations with parallel computing on heterogeneous hardware of personal computer. Journal of Building Performance Simulation, 7(2), 152-163.


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