Validation of Gpu Lighting Simulation in Naturally And Artificially Lit Spaces

Jones, Nathaniel; Reinhart, Christoph

doi:10.26868/25222708.2015.2461

Cited by 6 publications

(5 citation statements)

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“…Ambient bounces were then increased to -ab2 and -ab3 to investigate differences in glare predictions and prediction speed. Jones and Reinhart's study, which compared daylighting and glare modelling with measurements taken in physical spaces, suggested that at 3 ambient bounces RADIANCE simulations converge to contrast ratio values close to those computed via HDR photographs [44]. Therefore, examining ambient bounces above -ab3 was not considered necessary since the tested view positions were close to the facade.…”

Section: Ambient Bouncesmentioning

confidence: 99%

Daylighting Performance Simulation: Prediction Accuracy/Processing Speed Trade-Off

Abdelwahab

Rutherford

Mayhoub

2023

Journal of Green Building

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In daylighting performance simulations of façade systems, a trade-off is often required between processing speed and prediction accuracy. This is particularly relevant at design onset, where plausible simulation outcomes are essential to drive decisions between several alternative façade configurations. To help address this trade-off, this paper presents a sensitivity analysis evaluating the influence of key input parameter settings, namely ambient bounces and grid size, on the convergence of performance outcomes and on simulation run times. The results provide statistical evidence that, although lower precision settings mostly accelerate calculations, they decrease the accuracy of prediction estimates, particularly for complex façades. Conversely, the relative increased accuracy resulting from higher precision simulations might reach a point where differences have a negligible practical impact. The paper concludes with a range of recommendations to support the early-stage selection of parameter settings and contributes to more robust simulation outcomes towards reducing the gap between simulated and measured data.

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Section: Ambient Bouncesmentioning

confidence: 99%

Daylighting Performance Simulation: Prediction Accuracy/Processing Speed Trade-Off

Abdelwahab

Rutherford

Mayhoub

2023

Journal of Green Building

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“…Simulation of radiation often only considers direct solar radiation [ JGP12 , MPLFC13 , AYG*20 ] and employs simplified models and calculation, e.g., polygon clipping or pixel counting [ HD17 , dOM17 , dRO*19 ]. Only a few works use GPU acceleration [ JR14b , JR14a , JR15 , JR17 ] or consider indirect radiation reflected by surrounding buildings or the environment and simulate thermal radiation [ AGNA*20 , CSF*22 ].…”

Section: Related Workmentioning

confidence: 99%

Precomputed Radiative Heat Transport for Efficient Thermal Simulation

Freude,

Hahn,

Rist

et al. 2023

Computer Graphics Forum

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Architectural design and urban planning are complex design tasks. Predicting the thermal impact of design choices at interactive rates enhances the ability of designers to improve energy efficiency and avoid problematic heat islands while maintaining design quality. We show how to use and adapt methods from computer graphics to efficiently simulate heat transfer via thermal radiation, thereby improving user guidance in the early design phase of large‐scale construction projects and helping to increase energy efficiency and outdoor comfort. Our method combines a hardware‐accelerated photon tracing approach with a carefully selected finite element discretization, inspired by precomputed radiance transfer. This combination allows us to precompute a radiative transport operator, which we then use to rapidly solve either steady‐state or transient heat transport throughout the entire scene. Our formulation integrates time‐dependent solar irradiation data without requiring changes in the transport operator, allowing us to quickly analyze many different scenarios such as common weather patterns, monthly or yearly averages, or transient simulations spanning multiple days or weeks. We show how our approach can be used for interactive design workflows such as city planning via fast feedback in the early design phase.

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“…Jones and Reinhart (2015) [29] proposed a graphical processing unit (GPU)-based tool, Accelerad, which translated Radiance's algorithms for use by the GPU. They compared daylighting simulation results from Radiance and Accelerad with measured data.…”

Section: Climate-based Daylight Modelling (Cbdm)mentioning

confidence: 99%

Prediction of Annual Daylighting Performance Using Inverse Models

Haberl

2023

Sustainability

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This paper presents the results of a study that developed improved inverse models to accurately predict the annual daylighting performance (sDA and lighting energy use) of various window configurations. This inverse model is an improvement over previous inverse models because it can be applied to variable room geometries at different weather locations in the US. The room geometries can be varied from 3 m × 3 m × 2.5 m to 15 m × 15 m × 10 m (length × width × height). The other variables used in the model include orientation (N, E, S, W), window-to-floor ratio, window location in the exterior wall, glazing visible transmittance, ceiling visible reflectance, wall visible reflectance, shade type (overhangs, fins), shade visible reflectance, lighting power density (LPD) (W/m2), and lighting dimming setpoint (lux). Such models can quickly advise architects during the preliminary design phase about which daylighting design options provide useful daylighting, while minimizing the annual auxiliary lighting energy use. The inverse models tested and developed were multi-linear regression (MLR) models, which were trained and tested against Radiance-based annual daylighting simulation results. In the analysis, 482 cases with different model conditions were simulated, to develop and validate the inverse models. This study used 75% of the data to train the model and 25% of the data to validate the model. The results showed that the new inverse models had a high accuracy in the annual daylighting performance predictions, with an R2 of 0.99 and an CV(RMSE) of 15.19% (RMSE of 58.91) for the lighting energy (LE) prediction, and an R2 of 0.95 and an CV(RMSE) of 14.38% (RMSE of 8.02) for the sDA prediction. In addition, the validation results showed that the LE MLR model and sDA MLR model had an R2 of 0.96 and 0.85, and RASE of 121.89 and 8.54, respectively, which indicate that the inverse models could accurately predict daylighting results for sDA and lighting energy use.

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Validation of Gpu Lighting Simulation in Naturally And Artificially Lit Spaces

Cited by 6 publications

References 0 publications

Daylighting Performance Simulation: Prediction Accuracy/Processing Speed Trade-Off

Daylighting Performance Simulation: Prediction Accuracy/Processing Speed Trade-Off

Precomputed Radiative Heat Transport for Efficient Thermal Simulation

Prediction of Annual Daylighting Performance Using Inverse Models

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