Modelling to Drive Design

Transcription

1 Ed May Modelling to Drive Design Honing the SU+RE House through Performance Simulations 72

2 Stevens Institute of Technology, Temperature and relative humidity percentage monitoring results, SU+RE House, Solar Decathlon, Irvine, California, 2015 Looking closely at the monitored performance for Monday 12 October 2015 to Thursday 15 October 2015 illustrates how well the home performed even as exterior temperatures climbed very high during the day. This granular level of performance data allowed the design team to optimise the home s configuration during the competition. 73

3 Digital simulation technologies are for the first time allowing designers to directly study and impact the flows of energy, water, air, heat and sound which affect a building s occupants. The SU+RE House is a prime example of this. Guest-Editor Ed May, one of the Stevens Institute of Technology faculty leaders for the project and also a partner in the Brooklyn-based design and consulting firm BLDGtyp, looks closely at how it demonstrated the possibilities of integrating data and environmental analysis techniques into the architectural design process, to produce truly sustainable and resilient buildings. BLDGtyp, 2D heat-flow simulation of window installation, Ozone Park Passive House, New York City, 2015 An example of how LBNL THERM is used to simulate interstitial temperatures on three versions of a window installation. The flow of thermal energy changes as the position of the window moves from interior to middle to exterior, which results in a modified internal surface temperature as well as a change to the PSI value (Btu/hr-ft-F) that is used to quantify the thermal bridging at the installation site. The architectural model has a long history within the profession. With the advent of digital models, their utility has expanded and now regularly includes not only highly photorealistic renderings and 3D models but fully immersive virtual-reality experiences. While digital 3D modelling tools are useful for developing and communicating the visual experience of a building, digital building performance modelling is now allowing for the design team to engage more fully with the non-visual aspects of their buildings. These latent aspects of building performance primarily include flows such as energy, heat, moisture, air, water, sound, movement and light. Using these tools, designers can quickly measure, document and manipulate these flows directly. The integration of performance simulation into the design process carries with it huge potential for increasing the comfort, durability and energy efficiency of our buildings through smarter decision making based on data. A digital simulation, like any form of architectural representation, requires the selective abstraction and inclusion/exclusion of certain elements of the building. This curation and model construction requires a great degree of experience and a clear view of what questions are being asked of the simulation. This question seeking can be conceived of in a similar manner to the programming phase in a traditional architectural design sequence. Through careful inquiry, the model maker works with the project stakeholders to identify the project goals and uncover areas of required investigation. Understanding what questions to ask is critical to the effective use of these tools and will change how and to what level of detail the model is built. The digital simulation process can be outlined as three basic steps: (1) the decision to pursue a simulation and the question seeking ; (2) the actual construction and operation of the model; and (3) the interpretation and analysis of the model results. While the model construction and operation typically receives the most attention from users and requires the greatest amount of technical training, the effective deployment of a simulation actually depends much more on strategically defining the question and accurate interpretation of the outputs. The interpretation of the model outputs is a particularly consistent stumbling block for users of all experience levels and requires the model builders to have both a grasp of the basic science but also the experience to understand which are the key results to focus on. Relying on specialist practitioners to aid with this analysis and interpretation stage is probably unavoidable for the near term in order to gain effective use of these tools within the design process. 74

4 Though seemingly unavoidable at present, this reliance on outside domain experts for the construction and interpretation of these models is unfortunate, as it increases project costs, stretches project timelines and most likely results in less than optimal design solutions. A more effective approach would be for designers to engage more directly and fully with these new tools within the context of their existing design process. This approach is not new and has been attempted in various ways since the advent of digital simulation tools. The primary mechanism for providing these new tools to designers, however, has been through the construction of so-called designer-friendly tools. These seek to reduce the model complexity and input variables enough to allow non-experts to deploy them without fear of erroneous results or significant up-front time commitments or training. Unfortunately, the experience to date has demonstrated that these simplified tools do not allow for designers to generate meaningful results. This is due both to the simplified inputs but also to a general lack of context for simulation results. For this reason, it seems clear that if they are to take advantage of the enormous potential of these new tools, design teams will need to educate themselves in the operation and interpretation of these models. While domain experts and specialist consultants will probably always have a role in certain projects with non-standard construction or spatial configurations, in order to truly deliver better buildings across a broad range of project types it will be the designers themselves who will need to be the ones to do this work and fold it into their individual design process. Multiple Models for Multiple Problems: The SU+RE House Example In 2015, Stevens Institute of Technology successfully competed in the US Department of Energy (DOE) Solar Decathlon in Irvine, California. This international student competition challenges students from interdisciplinary design teams to fully design, build, operate and test sustainable solar-powered homes. While the contest itself only takes place for 10 days, the lead-up to it is an intensive multi-year effort by students and faculty to develop the final built work. The student design process of the Stevens team s winning entry, SU+RE House, over the academic cycles offers a model of how a truly integrated design team can leverage the power of digital simulation to create innovative sustainable architecture which delivers outstanding occupant comfort, increased building durability and radically reduced energy consumption. 75

5 Stevens Institute of Technology, Overall yearly energy gains and losses from the digital model, SU+RE House, Hoboken, New Jersey, 2015 A whole-building energy model was used to evaluate the home s projected space-conditioning energy consumption over the winter period. A full accounting of modelling losses (left) and gains (right) is used to calculate the modelled yearly energy demand for heating (shown). A similar accounting is undertaken for the cooling energy over the summer period. By implementing these models early, the information drawn from the models was able to shape and inform the design. Stevens Institute of Technology, Evaluating shading depth impact on energy demand using digital simulation, SU+RE House, Hoboken, New Jersey, 2015 The horizontal overhangs of the shutters on the south facade are designed to block summer sun which is high in the sky, while allowing winter sun which is lower in the sky to warm the building. Spaceconditioning energy consumption (y-axis) is plotted against shutter overhang depth (x-axis). As the depth is increased, the cooling energy is reduced, but heating energy increases. These competing desires intersect at a balance point representing the lowest total space-conditioning energy consumption. 76

6 A whole-building energy model was executed by student team members using two primary tools: first a numerical steady-state model called the Passive House Planning Package (PHPP), and then a full dynamic model using transient systems simulation software package TRNSYS. Both of these models rely on detailed geometric inputs, material and climate data as well as occupancy profiles to calculate the yearly energy consumption. By implementing these models early, the information drawn from the models was able to shape and inform the design. Decisions about formfactor, glazing distribution, assembly and construction details, shading and building orientation were all informed by the results from the models. During the project s development, several tools were used to assess the interior daylighting, both for baseline illuminance levels but also for the assessment of critical comfort glare issues. The primary tool used for this analysis was the Ladybug expansion of the popular parametric 3D modelling environment Grasshopper for McNeel s Rhino. This tool requires a detailed 3D model of the interior spaces with critical surface material parameters such as reflectivity being input. Using historical weather data, a series of pointin-time analyses were undertaken to evaluate the interior performance levels. Critical viewpoints were evaluated for both minimum surface illuminance levels but also discomfort glare potential. The results of these analyses drove the selection of interior finishes as well as the deployment and geometry of exterior shading elements such as overhangs and louvres, and fed into decisions about the electric lighting design and specification. The results of these analyses drove the selection of interior finishes as well as the deployment and geometry of exterior shading elements Stevens Institute of Technology, Detailed predicted hourly heating and cooling energy use from a dynamic simulation tool, SU+RE House, Hoboken, New Jersey, 2015 The student team used the dynamic energy modelling platform TRNSYS to simulate the building s energy consumption and environment. Interior and exterior temperatures as well as heating, cooling and dehumidification energy demand are modelled on an hourly basis. 77

7 Stevens Institute of Technology, Shadow study using Rhino Grasshopper Ladybug, SU+RE House, Hoboken, New Jersey, 2015 In order to design the solar louvres of the home, careful solar analysis of the south facade glazing was undertaken in parallel with the whole building energy model. The targets from the energy model were used to guide the louvre spacing, shape and placement. 78

8 While daylighting and energy consumption were both foregrounded during the architectural design and specification phase, a parallel track of in-depth mechanical system simulations were undertaken by the student team as well, particularly in regards to the home s hot-water system. While most single-family home hot-water systems rely on simple boilers for heat generation, some sustainable projects may include a so-called solar thermal system which use solar energy to heat domestic water through exterior (usually rooftop) panels. The Stevens Institute of Technology team decided to pursue a fully electric solar hot-water system, using products which are not readily available and working with a hot-water tank manufacturer to create a custom solution. This system was integrated with the building s facade and solar shutters and was able to use electric power to directly feed the hot-water system. Clearly the creation and integration of a fully custom mechanical system is a large task, and the team relied on a detailed system model, executed in TRNSYS, to evaluate and design both the mechanical components but also the solar photovoltaic facade-integrated elements and controls. Through these simulations, a complete low-energy hot-water system was executed which was able to deliver a significant amount of free hot water to the building using only solar energy and without any fluid, pumps, heat exchangers or complicated user-controls. During the detailing phase, a fourth distinct simulation tool was deployed in order to evaluate the durability and energy efficiency of the proposed construction details. Students on the architectural detailing team used THERM, a program developed by the US s Lawrence Berkeley National Laboratory (LBNL), to iterate through design alternatives, and relied on the feedback from these simulations to drive material, fastening, sequencing and assembly decisions. The team focused on reducing the thermal bridging through all connection details such as building corners in order to reduce the building s overall heat loss. In addition, the students used the simulations to evaluate interior surface temperatures for minimum critical levels, to avoid condensation and mould risk. This phase was perhaps the clearest example of why digital simulation tools need to be integrated into the individual designer s toolkit. The designer executing the detailing must be able to simultaneously perform simulations if this tool is to be used effectively; the time lag between generating a detail and sending it off for subconsultant review and simulation is clearly prohibitive to effective use. Stevens Institute of Technology, Hot-water system monitored results, SU+RE House, Solar Decathlon, Irvine, California, 2015 The measured results from the final photovoltaic solar thermal hot-water system clearly show how well the final system functioned, with only minimal heat-pump energy consumption. The bulk of the home s hot-water energy comes directly from the solar photovoltaic system which dramatically reduces the overall energy consumption. 79

9 Stevens Institute of Technology, 2D heat-flux simulation of the typical roof-to-wall joint, SU+RE House, Hoboken, New Jersey, 2015 Heat flux (Btu/hr-ft 2 ) is simulated with LBNL THERM and mapped for the typical wall-to-roof junction. The areas of high heat flux (red) are locations of thermal bridging where thermal energy is able to bypass the insulation layer. By utilising a triple-layer insulation strategy with continuous exterior mineral wool insulation, these thermal bridges from framing can be significantly reduced. 80

10 Stevens Institute of Technology, Predicted energy consumption of the SU+RE House versus a typical New Jersey home, 2015 As a result of a focus on the building envelope and energy performance, the team was able to predict a 91 per cent reduction in energy consumption relative to an average New Jersey home. This dramatic reduction in energy is accomplished while still delivering a comfortable, durable and healthy building, thanks to careful design. The creation of more comfortable, durable and efficient buildings through these technologies is clearly possible, but it will require designers to be open and engaged with these exciting new methods. Lessons from the SU+RE House Process Clearly the student team here was able to integrate these tools into their process well. This was accomplished both due to good organisation and planning, but also thanks to the interdisciplinary nature of the team which included both architects and engineers sitting at the table together throughout the entire project development. Even in the context of this successful project, however, certain clear challenges to the adoption of these new tools were immediately apparent. First, the models themselves clearly need to be built in a manner which allows for iteration and testing while maintaining quality levels and speed. By allowing the design team to adjust critical parameters such as glazing areas, shading or assembly build-ups, optimised solutions can be found through iterative analysis. Creating a design process for this type of optimisation is both a tool problem and a process problem, but if designers are to be expected to take up these new programs then certain measures will need to be implemented to ensure that the models are built in a robust and task-appropriate manner. More importantly than concerns about the model construction, though, is the fundamental issue of interpreting the results appropriately. This is related to the very first step of problem seeking and making sure that the right questions are being asked. It is clear that both students and professionals alike can have unrealistic expectations of these tools, and it should be understood very clearly that these tools are only helpful if they are used to answer the right questions. They have immense capabilities for reducing building energy consumption, as well as increasing durability, but it is up to the designer to use them wisely and with clear intent. One potential solution to this issue is an increased use of external standards or certifications to establish clear project performance goals. Standards such as EnergyStar, Passive House, LEED, Living Building Challenge, WELL Building and others can provide critical assistance with the establishment of project goals and relevant threshold levels. Particularly for the inexperienced designers engaging with these tools for the first time, having an outside marker or target can prove to help put in place functional and conceptual boundaries which will aid in the evaluation of the simulation results and provide relevant scales for evaluation and decision making. Digital simulation tools are a clear evolution of traditional methods of evaluating the built object. These tools allow designers to now see how their design decisions will affect the flows of energy, light, heat, sound and more. But it is also becoming clear that while these tools have tremendous potential, for their power to be fully realised it will require designers to integrate these simulation technologies into their design process. The creation of more comfortable, durable and efficient buildings through these technologies is clearly possible, but it will require designers to be open and engaged with these exciting new methods. 1 Text 2018 John Wiley & Sons Ltd. Images Stevens Institute of Technology, SU+RE House Team 81