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Fig. 18. Nature View Factor, construction. Lines of sight intercepted by the tree canopies footprints (blue) vs those caught by buildings (red). (For interpreta- tion of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Figure 18 Nature View Factor, construction. Lines of sight intercepted by the tree canopies footprints (blue) vs those caught by buildings (red). (For interpreta- tion of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 2. A schematic of the workflow and Key Performance Indicators (KPI). Fig. 1. The regenerative design workflow proposed in this paper.
Fig. 2. A schematic of the workflow and Key Performance Indicators (KPI). Fig. 1. The regenerative design workflow proposed in this paper.
Fig. 4. The UTCI calculation workflow here diagrammed into more details. Fig. 3. A schematic of the workflow used plugins and definition of inputs and outputs.
Fig. 4. The UTCI calculation workflow here diagrammed into more details. Fig. 3. A schematic of the workflow used plugins and definition of inputs and outputs.
Fig. 5. Perspective view of the site, La Luz in Malaga (google coordinates: 36.696504, -4.455054).
Fig. 5. Perspective view of the site, La Luz in Malaga (google coordinates: 36.696504, -4.455054).
Fig. 6. The 3D image of the simplification process for performance evaluation.
Fig. 6. The 3D image of the simplification process for performance evaluation.
Fig. 7. Wind map for the four main directions selected for the outdoor microclimate analysis. For the Building Energy Performance Simulation (Energy Use Intensity and Energy Generation), the depth of the perimeter zone was set to 6m, corresponding to twice the height of the zone [48]. The geometrical model considers a window to wall ratio of 40%. Inputs parameters for the energy simulation were defined based on the Spanish energy code [73] for climatic zone 3A (Appendix E). The ra- diation analysis drove the selection of the envelope surfaces for the BIPV energy yield calculations. The following viability thresholds were assumed for annual irradiance rates: 800kWh/m2 for facades and 1000 kWh/m2 for rooftops. Floors subdivided the facade surfaces, and these thresholds were tested against the average radiation levels for each storey (Fig. 8). The selected surfaces were then used for the PV Wind simulations were done for the four prevailing trends in Malaga, according to local meteorological data. Previous studies have shown that the error in UTCI calculation using the four prevailing wind
Fig. 7. Wind map for the four main directions selected for the outdoor microclimate analysis. For the Building Energy Performance Simulation (Energy Use Intensity and Energy Generation), the depth of the perimeter zone was set to 6m, corresponding to twice the height of the zone [48]. The geometrical model considers a window to wall ratio of 40%. Inputs parameters for the energy simulation were defined based on the Spanish energy code [73] for climatic zone 3A (Appendix E). The ra- diation analysis drove the selection of the envelope surfaces for the BIPV energy yield calculations. The following viability thresholds were assumed for annual irradiance rates: 800kWh/m2 for facades and 1000 kWh/m2 for rooftops. Floors subdivided the facade surfaces, and these thresholds were tested against the average radiation levels for each storey (Fig. 8). The selected surfaces were then used for the PV Wind simulations were done for the four prevailing trends in Malaga, according to local meteorological data. Previous studies have shown that the error in UTCI calculation using the four prevailing wind
Fig. 9. The Energy and Daylighting model, including the surrounding buildings that cast shadows or reflect light. Fig. 8. Annual radiation analysis for potential photovoltaic studies. The roofs have more potential than facades, as expected.
Fig. 9. The Energy and Daylighting model, including the surrounding buildings that cast shadows or reflect light. Fig. 8. Annual radiation analysis for potential photovoltaic studies. The roofs have more potential than facades, as expected.
Fig. 10. Representation of the local yearly temperatures measured at Malaga Airport, a rural location [top left], and the modified urban weather time series, at the urban project site [lower left] obtained from Urban Weather Generator (UWG). The chart at right zooms into a 24-h cycle. The sDA (300 Lux/50%) ranges vary from 40% to 80% on the outer shell of the district, while this range drops to between 13% and 50% in
Fig. 10. Representation of the local yearly temperatures measured at Malaga Airport, a rural location [top left], and the modified urban weather time series, at the urban project site [lower left] obtained from Urban Weather Generator (UWG). The chart at right zooms into a 24-h cycle. The sDA (300 Lux/50%) ranges vary from 40% to 80% on the outer shell of the district, while this range drops to between 13% and 50% in
Fig. 12. A visualisation of the Cooling energy demand of each building. Fig. 11. A schematic overview of the calculation of the Universal Thermal Climate Index (UTCD.
Fig. 12. A visualisation of the Cooling energy demand of each building. Fig. 11. A schematic overview of the calculation of the Universal Thermal Climate Index (UTCD.
Fig. 13. The monthly breakdown of energy demand across all buildings by end-user type. Fig. 17 shows that the smallest road sections in the study area feature a low sky-view factor of less than 30% while the central square has a factor higher than 80%: as a reference, Times Square in New York features a sky-view factor equal to 37%. Trees are currently not con- sidered in the analysis of sky view. In the proposed case-study, on average 25% of the circular visual field is occupied by trees. This may Furthermore, the ability to effectively and automatically generate thermal zones out of the district's geometry through Grasshopper can make outdoor comfort, energy demand and daylighting evaluation far more integrated into the design process. Adding energy production considerations can be achieved just as seamlessly, and lead to the cri- tical energy balance evaluation between supply and demand. For bio- philia, an easy-to-calculate metric quantifying the visual prominence of vegetation in outdoor urban areas offers an objective and numerical scale to determine whether the presence of trees and green areas is
Fig. 13. The monthly breakdown of energy demand across all buildings by end-user type. Fig. 17 shows that the smallest road sections in the study area feature a low sky-view factor of less than 30% while the central square has a factor higher than 80%: as a reference, Times Square in New York features a sky-view factor equal to 37%. Trees are currently not con- sidered in the analysis of sky view. In the proposed case-study, on average 25% of the circular visual field is occupied by trees. This may Furthermore, the ability to effectively and automatically generate thermal zones out of the district's geometry through Grasshopper can make outdoor comfort, energy demand and daylighting evaluation far more integrated into the design process. Adding energy production considerations can be achieved just as seamlessly, and lead to the cri- tical energy balance evaluation between supply and demand. For bio- philia, an easy-to-calculate metric quantifying the visual prominence of vegetation in outdoor urban areas offers an objective and numerical scale to determine whether the presence of trees and green areas is
Fig. 14. Hourly energy demand curves for a cold (left) and hot (right) days in the historical, 2050, and 2080 typical files. The energy requirements for the hot day are larger for future files than for historical ones, while the energy requirement for a cold day decreases into the future.
Fig. 14. Hourly energy demand curves for a cold (left) and hot (right) days in the historical, 2050, and 2080 typical files. The energy requirements for the hot day are larger for future files than for historical ones, while the energy requirement for a cold day decreases into the future.
Fig. 15. Monthly energy demand and supply for the three different climatic periods. Substantial energy use intensity level was recorded in summer, com- pared to negligible changes in PV production.
Fig. 15. Monthly energy demand and supply for the three different climatic periods. Substantial energy use intensity level was recorded in summer, com- pared to negligible changes in PV production.
Fig. 16. Continuous daylight autonomy and spatial daylight autonomy representations. The incorporation of uncertainty into our model is rudimentary for now because climate change integration is limited to the inclusion of ‘typical’ (median) future files. This has the potential to miss many features of concern, such as heat waves and extreme urban heat stress. While the synthetic weather generator can generate any number of plausible future year samples, including years with extreme events, the computational cost of urban calculations makes the use of many files prohibitively expensive. The same limitation applies to the character- isation of UHI, where computational resources and time represent a limit. This impacts the number of wind directions and the resolution
Fig. 16. Continuous daylight autonomy and spatial daylight autonomy representations. The incorporation of uncertainty into our model is rudimentary for now because climate change integration is limited to the inclusion of ‘typical’ (median) future files. This has the potential to miss many features of concern, such as heat waves and extreme urban heat stress. While the synthetic weather generator can generate any number of plausible future year samples, including years with extreme events, the computational cost of urban calculations makes the use of many files prohibitively expensive. The same limitation applies to the character- isation of UHI, where computational resources and time represent a limit. This impacts the number of wind directions and the resolution
Fig. 17. Sky View Factor. Sky view factor on a 1 m raster grid, computed in the plan (left) and axonometric (right) view
Fig. 17. Sky View Factor. Sky view factor on a 1 m raster grid, computed in the plan (left) and axonometric (right) view
Fig. 19. Nature View Factor. The angular ratio between the number of lines of sight intercepted by tree canopies footprints and the total number of lines of sight at each viewpoint (left). Minimal distance from the closest visible tree to each viewpoint (right). The advantage of using parametric modelling means that it is pos- sible to create design variations or climatic variations based on rule- based generation. The main benefit of this approach is the enormous time savings compared to modelling in a non-parametric way and thus the possibility to explore more design variants, which may lead to a reduction of design costs and an increase in the spatial design qualities. The performance-based computational design supports regenerative analysis, synthesis and evaluation that mainly focuses on the The need for target-oriented, regenerative and efficient urban planning and design in cities is urgent due to relentless urbanisation. While regenerative approaches are attracting increasing interest among
Fig. 19. Nature View Factor. The angular ratio between the number of lines of sight intercepted by tree canopies footprints and the total number of lines of sight at each viewpoint (left). Minimal distance from the closest visible tree to each viewpoint (right). The advantage of using parametric modelling means that it is pos- sible to create design variations or climatic variations based on rule- based generation. The main benefit of this approach is the enormous time savings compared to modelling in a non-parametric way and thus the possibility to explore more design variants, which may lead to a reduction of design costs and an increase in the spatial design qualities. The performance-based computational design supports regenerative analysis, synthesis and evaluation that mainly focuses on the The need for target-oriented, regenerative and efficient urban planning and design in cities is urgent due to relentless urbanisation. While regenerative approaches are attracting increasing interest among
Fig. 20. A schematic view of the presentation of results overlaid on the geometrical models.
Fig. 20. A schematic view of the presentation of results overlaid on the geometrical models.
Appendix B. Weather file generation input values
Appendix B. Weather file generation input values
Appendix D. CFD settings
Appendix D. CFD settings
3 Representative Concentration Pathways have replaced the emissions ‘scenarios’ used in previous IPCC reports from Assessment Report 5 onward The setup of the CFD simulations followed recognised best practices for outdoor wind simulations for all relevant simulation parameters [74]. The wind tunnel generated was 2.5km long, 1 km wide, and 200m tall. The model used was the realisable KE, a RANS variant that has shown outstanding performance and convergence for outdoor studies. Simulations were run until the convergence criteria for all relevant parameters were satisfied. A summary of all relevant input parameters can be found in Appendix F. All four simulations were created with Butterfly, which allows for a rapid and accurate CFD case generation and runs with OpenFOAM engine.
3 Representative Concentration Pathways have replaced the emissions ‘scenarios’ used in previous IPCC reports from Assessment Report 5 onward The setup of the CFD simulations followed recognised best practices for outdoor wind simulations for all relevant simulation parameters [74]. The wind tunnel generated was 2.5km long, 1 km wide, and 200m tall. The model used was the realisable KE, a RANS variant that has shown outstanding performance and convergence for outdoor studies. Simulations were run until the convergence criteria for all relevant parameters were satisfied. A summary of all relevant input parameters can be found in Appendix F. All four simulations were created with Butterfly, which allows for a rapid and accurate CFD case generation and runs with OpenFOAM engine.
Related topics:
EngineeringClimate ChangeRenewable EnergyRegenerative DesignParametric design architectureoutdoor microclimate
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