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Parametric Spatial Models for Energy Analysis in the Early Design Stages


Abstract and Figures

Much of the research into integrating performance analysis in the design process has focused on the use of Building Information Modeling (BIM) as input for analysis engines. The main disadvantage of this approach is that BIM models are resource intensive and thus are usually developed in the later stages of design. BIM models are also not necessarily compatible with energy analysis engines and thus a conversion and export process is needed. This can lead to data loss, calculation errors, and failures. Starting with the premise that energy analysis is more compatible with earlier design stages where simpler schematic models are the norm, this paper presents a software system that integrates non-manifold spatial topology, a parametric design environment and an energy analysis engine for a more seamless generate-test cycle in the early design stages. The paper includes a description of the system architecture, initial results, and an outline of future work.
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SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
Parametric Spatial Models for Energy Analysis in the Early
Design Stages
Wassim Jabi
Welsh School of Architecture
Cardiff University
Cardiff, United Kingdom, CF10 3NB
Keywords: Spatial Models, Energy Analysis, Parametric
Design, Thermal Simulation, Non-manifold Geometry.
Much of the research into integrating performance
analysis in the design process has focused on the use of
building information models (BIM) as input for analysis
engines. The main disadvantage of this approach is that
BIM models are resource intensive and thus are usually
developed in the later stages of design. BIM models are also
not necessarily compatible with energy analysis engines and
thus a conversion and export process is needed. This can
lead to data loss, calculation errors, and failures.
Starting with the premise that energy analysis is more
compatible with earlier design stages where simpler
schematic models are the norm, this paper presents a
software system that integrates non-manifold spatial
topology, a parametric design environment and an energy
analysis engine for a more seamless generate-test cycle in
the early design stages. The paper includes a description of
the system architecture, initial results, and an outline of
future work.
The noted architect Steven Holl has written “While
artists work from the real to the abstract, architects must
work from the abstract to the real” (Holl 2013).
Underpinning this quote is the notion that architects
invariably begin their design process with representations
that are not merely simpler models of what they would
construct in later stages, but ones that are abstracted to
encompass only the ideas, concepts, and information
necessary to move the design process forward. As the
design process unfolds, architects create models that more
closely correspond to the real project. They also then start to
analyse the implications of their design decisions and
simulate performance aspects of their project.
Unfortunately, most of these analyses happen later in the
design process where design modification may not be
feasible or very costly. In many cases, the analysis (e.g.
thermal and daylight analysis) is conducted to measure
compliance with external regulations or to satisfy the client
regarding the performance of the project rather than to
fundamentally reflect on the parameters of the design
(Mhadavi et al. 2003). Many researchers, architects, and
clients wish to have that analysis done earlier in the design
process so that they discover and avoid problems earlier and
create a more considered design solution (Brahme et al.
2001). Unsurprisingly, the difficulty with conducting
performance analysis in the early design stage stems mainly
from the lack of appropriate representations and
information. Current performance analysis software such as
daylighting requires detailed inputs of materials and
constructions that are simply not available during the early
design stages.
While architects create several types of analogue and
digital representations and conduct several types of
analyses, the focus in this paper is on spatial digital
representations and models of their work and the role of
energy analysis in the early stages of design. The aim of this
research project is to more closely harmonise the outputs of
parametric digital spatial representations with the input
requirements for building performance simulation. The goal
is to better integrate energy analysis in the design process
and thus improve the overall outcome of design.
DSOS is software that was developed by the author to
analyse the energy use of a parametrically designed building
using Autodesk DesignScript and the U.S. Department of
Energy (DOE) EnergyPlus software through the use of the
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
OpenStudio Software Development Kit (SDK). EnergyPlus
is a whole building energy simulation program that allows
building professionals and researchers to better understand
the performance of the simulated building and optimise its
design to use less energy and resources (Crawley et al
2000). It was chosen over other tools such as ECOTECT
due to the fact that it is an industry standard and offered a
robust API that was easy to work with. Additionally,
EnergyPlus is being incorporated in cloud-based solutions
that promise to vastly increase processing speed. However,
additional analysis engines should be considered in the
future. OpenStudio is a collection of software tools
developed by the U.S. National Renewable Energy
Laboratory (NREL) to support whole building energy
modelling using EnergyPlus and advanced daylight analysis
using Radiance (Guglielmetti et al. 2011). OpenStudio is an
open source project to facilitate community development,
extension, and private sector adoption. OpenStudio includes
graphical interfaces along with an SDK. The DSOS plugin
exposes many of these services in scripts, handling most of
the process of sorting, labelling, and otherwise abstracting a
model into OpenStudio‟s specifications. To generate an
analysis, a scriptwriter simply needs to specify the
building‟s location through a weather file, specify what days
and conditions to simulate, its general program
(architectural use), and a spatial representation of the form.
Once the simulation is complete, the results are displayed in
the parametric design environment. This workflow allows
the users to iterate parametrically through multiple design
alternatives and even display a matrix of alternatives side-
by-side for comparative analysis without the need to export
to different software packages and use file formats that may
lose information in the process.
While daylight simulations are most accurate when
models closely represent reality in detail and material (Ibara
and Reinhold 2009), energy analysis software such as
EnergyPlus require input models to consist of
infinitesimally thin surfaces that represent walls or partitions
between thermal zones. This has presented a problem for the
simulation of models built within building information
modeling (BIM) environments. BIM models strive to
include all details needed to construct and manage a
building (Figure 1). Yet, energy simulation requires a
simplified and abstracted version of that model. As
Jackubiec and Reinhart report, this has divided the building
simulation community because while BIM strives for a
single model, there are advantages for using hybrid models
that combine several models for several types of analyses
(Jackubeic and Reinhart 2011).
Figure 1. A typical mature BIM model from industry.
Interestingly, architects and designers generally use
these types of „sketch models‟ in the early stages of design
(Granadeiro 2012). Architects frequently think of their
design projects to be made out of spaces that are enclosed
by boundaries that have yet to acquire thickness and
materiality (Figure 2). They place these spaces intuitively
and visually in proportion to each other without much
attention to tectonic detail (Jabi 1998). This conceptual or
idealised approach allows designers to: “build the lightest
possible model using the least effort that gives the most
accurate feedback about their design …” (Aish and Pratap
Figure 2. A typical architect‟s sketch from the early design stages. Image
courtesy of fc3 architecture+design.
If architects and designers sketch and build idealised
analogue spatial models that can be a good fit with energy
simulation, the question then becomes: do they do the same
when building digital models? Sadly, in most cases the
answer is negative. One example is the use of SketchUp as a
generator of models for energy analysis. SketchUp is one of
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
the most popular tools used in the early stages of design. Its
power stems from the fact that it allows users to quickly
build schematic massing models that can be easily modified.
Indeed, NREL‟s OpenStudio heavily depends on SketchUp
(through their own plugin) to create input geometry for
EnergyPlus analysis (Figure 3). Unfortunately, SketchUp
suffers from two main shortcomings in the context of energy
analysis. The first shortcoming is that SketchUp is not truly
a parametric system. Thus, changes need to be made
manually and are thus time consuming. The second
shortcoming is that models created in SketchUp are not
necessarily compatible with the zero-thickness requirements
for energy analysis. This means that manual work needs to
be undertaken to convert any existing geometry to a set of
conceptual masses that represent thermal zones. A more
ideal workflow in SketchUp is to create idealised conceptual
masses from the start so that the resulting models are
compatible with the input requirements of EnergyPlus. Even
then, modelling errors in SketchUp can lead to failures in
energy simulation. The main issue is that SketchUp cannot
guarantee that surfaces that belong to different spaces
exactly match and that the constructions on these surfaces
also match (i.e. are mirror reflections of each other) as
required by EnergyPlus.
Figure 3. An example of a shoebox model. Image courtesy of NREL.
These problems point to the fact that the problems
associated with input models for energy analysis have yet to
be solved. The current workaround has been to either
abstract more complex models as is the case with BIM
models that are far too detailed for energy analysis or to try
to automatically convert general massing models into
spatially and topologically inter-connected models that are
suited for energy simulation. The third prevalent alternative
is to build extremely simple shoebox models in order to
understand the effects of design decisions at the most basic
level. While these approaches are routinely used, they are
not ideal solutions. There is a need for a new way of
thinking about and building models in the early design
Another important aspect of work in the early design
stages is that of design space exploration. As the project
progresses, the design space of possible solutions is not
merely expanded and populated with more alternatives.
Previous analysis of artefacts produced during the early
design stages by individuals found that artefacts share two
dichotomous characteristics: divergence and convergence
(Jabi 1996). While providing divergence by broadening the
design space under exploration, the depiction of multiple
alternatives also leads to the elimination of undesirable
solutions and, through progressive refinement, to
convergence on satisfactory ones. Interesting artefacts were
drawn with more detail and level of craft while uninteresting
artefacts were quickly abandoned in an incomplete state.
One could almost retrace the design process by examining
and comparing the resulting artefacts. The work of
Woodbury and Burrow asserts that computers and
parametric design systems are particularly suited for this
exploration: “Design space exploration is the idea that
computers can be used to help designers by representing
many designs, arraying them in a network structure (the
space part), and by assisting designers to make new designs
and to move amongst previously discovered designs in the
network (the exploration part)” (Woodbury and Burrow
2006). The ability to explore the design space depends
heavily on the ability of the software to quickly vary the
parameters of the design (i.e. parametric design) and
generate potential design solution candidates (i.e. generative
design). Judging by the proliferation of papers in
conferences and seductive student work in progressive
schools of architecture, parametric and generative design
systems, such as Grasshopper, have become the most
popular digital tools for the exploration of design
alternatives (Figure 4). Despite the existence of plugins that
link Grasshopper to energy simulation (e.g. Gerilla and
Geco), the majority of these explorations remain in the
realm of geometric form finding where variations in the
design are not usually tested or compared from a
performance aspect.
The problem of reaching an optimal solution by
carefully conducting a multi-variate parametric analysis of
relevant parameters – as opposed to focusing exclusively on
aesthetic and formal parameters remains a vexing
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
problem. Some researchers have made progress by using
evolutionary heuristic methods to quickly evolve and search
the solution space for a set of potentially optimal candidates
(Figure 5). For example, Gerber and Lin have researched
and integrated computational strategy using genetic
algorithms to “expand the solution space of a design
problem as well as presort and qualify candidate designs.”
Their approach avoids the pitfall of attempting to find the
perfect solution: “… What is not afforded nor necessarily
expected is the solving for a single optimal result … the
experiment is still inclusive of designer-driven choice,
where the [chosen alternatives] exhibit some improved
scoring, they are not necessarily the aggregate best scores as
form and implicit architectural constraints come into play
and become major factors for consideration in the design
decision making.”
Figure 4. A typical exploration of architectural form using Grasshopper.
Image courtesy of Rodrigo Ruiz.
Figure 5. Multi-variate parametric design space exploration. Image
courtesy of Dr. David Gerber, USC School of Architecture.
Combining rapid design alternative generation with
evolutionary heuristic methods can enable complex
geometries to be better understood beyond their aesthetics
and significantly strengthen the relationship between
geometry and performance (Gerber and Lin 2012).
As stated earlier the aim of this research is to integrate
parametric systems and performance analysis engines. We
envisage a seamless generate-test cycle that will ultimately
be conducted in real-time and is invisible to the user (Figure
6). Such a cycle would avoid the drawbacks of file import
and export and data loss due to subtle incompatibilities. This
research project seeks to find out spatial representations and
parametric digital workflows that are suitable for early
design stages as well as energy simulation. The research
starts with two premises based on the work of (Aish and
Pratap 2013). The first premise is that a non-manifold
topology, as explained below, satisfies the requirement for
conceptual design as well as energy analysis. The second
premise is that integrating a generative/parametric scripting
environment with an energy simulation engine can help the
designer make better decisions in the early design stages.
Figure 6. The design-analyse cycle.
4.1. Manifold and Non-Manifold Topology
While it is beyond the scope of this paper to delve into
the mathematics of manifold topology, for the purposes of
this paper, one can think of three-dimensional manifold
geometries with boundaries to be polyhedral solids such as
cuboids. In manifold polyhedral geometries, each surface
separates the interior solid condition of the object from the
exterior world. Each edge is shared by exactly two surfaces
of the solid. All surfaces form the outer boundary of the
solid such that it is said to be watertight. These guaranteed
attributes allow 3D software to easily operate on such
geometry (e.g. calculating surface area, volume and
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
centroid, and intersecting the solid with other solids). Non-
manifold geometry is also made of surfaces, edges, and
vertices. However, edges can be shared by more than two
surfaces (Figure 7). Furthermore, surfaces can either be a
boundary between the solid interior of the object and the
exterior world or between two spatial cells within the object.
The implementation of non-manifold topology within
DesignScript allows the scriptwriter to create regular
polyhedral geometries at the start and then boolean them
with other geometries using a non-regular (i.e. non-
manifold) operation that maintains the interior surfaces that
would have been otherwise lost (Aish and Pratap 2013). The
implementation also allows the scriptwriter to slice a
manifold geometry using a series of planes in order to create
a non-manifold geometry with cells and surfaces. The
scriptwriter can then query the non-manifold geometry for
its vertices, its edges, its surfaces, and its cells. Methods
within the non-manifold class return useful information
such as the cells at each side of a surface or whether the
surface is a boundary between the inside and the outside of
the object.
Figure 7. (Left) A manifold polyhedral geometry; (Right) a non-
mainfold geometry.
4.2. The DSOS System Architecture
The DSOS software architecture is composed of a
dynamically linked library (.dll) written in C# and a set of
scripts using DesignScript which in turn runs as a plugin
within the AutoCAD environment (Figure 8). In order to use
the DSOS plugin, one must first install AutoCAD, the
DesignScript software, EnergyPlus, and the DSOS files.
DSOS provides the needed OpenStudio .dll files. The DSOS
plugin generates files that are fully compatible with
OpenStudio and EnergyPlus. Thus, a user can optionally
install the OpenStudio graphical user interface software to
further investigate the generated models or use other
software that can read EnergyPlus file formats.
The DSOS system architecture is composed of the
following files and parameters:
1) dsos.dll. This is a dynamically linked library that
provides the dsos.Utility object. This object
provides the main functionality (methods and
attributes) for specifying an OpenStudio model. For
example, the dsos.Utility object can create the main
abstract model, the building, the building storeys,
the spaces and thermal zones and save the model in
preparation for EnergyPlus analysis.
2) DSProtoGeometry.dll. This is a special version of
the DesignScript ProtoGeometry library. In order to
avoid naming conflicts with OpenStudio, this
library prefixes all DesignScript geometries with the
letters “DS” (e.g. DSCuboid instead of Cuboid).
This is a temporary workaround. Autodesk is
working to resolve this issue so that DSOS
scriptwriters can revert to using the regular
ProtoGeometry.dll file.
3) OpenStudio.dll. This is the NREL OpenStudio suite
of linked libraries.
4) EPW file. This is the standard EnergyPlus Weather
data file. This file describes the weather data for a
specific geographic location.
5) DDY file. This is the ASHRAE Design Conditions
Design Day Data file. These files specify the design
days and their conditions that EnergyPlus uses to
run its simulation.
6) OSM file. This is the NREL OpenStudio Template
File. This file provides a minimal template for a
particular building type. NREL provides several
templates. A template usually contains default
construction sets and schedule sets, but no
7) Initialise.ds. This script imports the required classes
for DSOS to function properly. It initialises certain
parameters with default values that can be changed
by the user.
8) Building.ds. This script can be named by the user
and is where the user constructs a parametric
building using a non-manifold topology.
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
9) Analyse.ds. This script loads the building from the
previous scripts, conducts the analysis and displays
the results.
10) Building.osm. This is the OpenStudio model
generated by the DSOS software. It serves as the
input to the analysis cycle.
11) eplusout.sql. This is the standard SQL database file
generated by EnergyPlus. This file gets read back
into the software and can be queried using the
standard SQL format.
Figure 8. The DSOS system architecture.
4.3. The DSOS Workflow
The DSOS Workflow is quite simple. The user
structures their design scripts by modifying default scripts
organised in three stages (e.g. Initialise.ds, Building.ds,
Analyse.ds). The initialisation script usually remains as is
with only a change to some path names depending on the
installation. The second file is where the user can generate
any parametric non-manifold geometry they wish. This
geometry can be derived from the parametric process at any
time. The only three requirements for that script are: 1)
Define a global variable called building to store the
resulting non-manifold geometry of the building. 2) Define
a global variable called numFloors with a value greater
than 1 that represents the number of floors (stories) in the
building. 3) Define a global variable called glazingRatio
that represents the ratio of glazing to exterior wall surfaces.
Within the analysis script a user can request that the
analysis is conducted and then construct an SQL query to
retrieve any results from the EnergyPlus analysis. These
results can then be visually displayed to the user or used to
further investigate and modify the design.
The usual steps to build a parametric model and
visualise analysis results consist of the following steps:
1) Import the Initialise.ds file:
2) Define the required parameters for the building. The
script assumes the dimensions of the building are in
meters and the temperatures in degrees Celsius. The
glazing ratio applies to each exterior wall surface
buildingWidth = 40;
buildingLength = 30;
buildingHeight = 15;
numFloors = 5;
glazingRatio = 0.5;
3) Generate a non-manifold geometry (to be called
building). Each cell in the non-manifold geometry
translates into a space in the OpenStudio model and
has its own thermal zone. Each horizontal slice of
the geometry will translate into an OpenStudio
Building Storey.
4) Create an Analyse.ds script (starting from the given
default file) that import the previous script:
5) Get the OpenStudio Model from the template:
osModel =
EPWPath, DDYPath);
6) Get the OpenStudio Building from the osModel:
osBuilding = dsos.getBuilding(osModel,
buildingName, buildingType, buildingHeight,
numFloors, spaceType);
7) Create a space for each cell in the non-manifold
numCells = Count(building.Cells);
list = 1..numCells;
names = "Space_" + list;
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
spaces = addSpace(building.Cells, names,
osModel, WCS.ZAxis);
8) Save the model and run the analysis:
saveCondition = dsos.saveModel(osModel,
result = getAnalysisResults(logPath,
outputPath, EPWPath, outDirPath);
totalSE = result.totalSiteEnergy();
Print("Total Site Energy use:
The scriptwriter can then experiment with retrieving and
displaying different energy simulation results. Further
information regarding NREL‟s sqlFile object specification
can be found at:
This paper reports on initial results from test runs of the
software where some analysis results such as peak
temperatures for cooling loads where translated into colours
and assigned to the respective spaces in the model (Figure
Figure 9. The DSOS graphical user interface.
The resulting geometry is overlaid with the original
parametric model. These results should not be considered
indicative of the full extent of the capabilities of the
software. For example, the parametric software can create
geometry of any degree of complexity and is not limited to
orthogonal structures (Figure 10). Multiple parametric
studies can also be conducted and presented to the user in a
matrix or browsed through. The results indicate that using
non-manifold topology is a powerful representation of both
parametric spatial constructs and input models for energy
analysis. The tight integration of parametric design system
and an energy analysis engine created a reasonably seamless
experience and, more importantly, avoided the pitfalls of
file export and import. By being mostly script-based, DSOS
can be considered an open platform for scriptwriters to
develop their own spatial parametric constructs and analysis
Figure 10. Parametric modeling and analysis of non-orthogonal building.
The DSOS software is under active development. One of
the main obstacles to overcome is the amount of time it
takes EnergyPlus to complete its analysis. A possible
solution is to integrate distributed high performance
computing (HPC) with DSOS. We are pursuing that
solution with our university‟s HPC centre as well as
investigating NREL‟s new initiative to offer OpenStudio as
server software on Amazon‟s Elastic Compute Cloud (EC2).
In doing so, we will thrive to maintain a seamless generate-
test cycle. Another option would be to use HPC to generate
a vast array of possible candidates; similar to (Gerber and
Lin 2012) and invent user-friendly ways to visualise,
explore and select optimal pre-analysed solutions. The
current implementation of non-manifold topology in
DesignScript does not allow for the specification of sub-
surfaces (e.g. glazing subsurfaces). In DSOS, we opted to
use a glazing ratio as a temporary substitute for a more
accurate representation of glazing surfaces. While it is not
impossible to add the ability to model glazing surfaces in
DSOS itself, it would be preferable if the non-manifold
topology itself were extended to handle subsurfaces. Finally,
we intend to offer DSOS on as many platforms as possible.
To that end, we are currently developing a version of it that
will run within Autodesk‟s 3ds Max since it too can create
and represent non-manifold geometries. We are also
planning support for Autodesk‟s Vasari and Revit if and
when Autodesk incorporates non-manifold geometry into
DesignScript and/or Dynamo for these platforms. Finally,
we are keenly interested in verifying the software against
SimAUD 2014
Symposium on Simulation for Architecture and Urban Design
Tampa, Florida, USA
industry standard case studies to find out if energy analysis
in the early design stages using idealised spatial models is
comparable in accuracy to analysis conducted using more
complex and mature BIM models. The comparison to real
world case studies is always of interest, but we are cognisant
that even the most sophisticated energy analysis results are
far from their real-world values. Because this software is
intended for the early stages of design, we are less interested
in exact measures and more interested in trend analysis
based on parametric variation.
This paper provided an alternative approach to the
integration of energy analysis in the design process. Rather
than rely on file export and import of BIM models and their
associated pitfalls, energy analysis can be conducted much
earlier in the design cycle using far simpler and more
idealised models. We presented the DSOS software that
integrated the use of non-manifold topology, a parametric
design system, and an energy analysis engine for use in the
early design stages. The results of this software can be
visualised using user-friendly methods to investigate trends
rather than focus on precise values. We believe this is an
appropriate approach for designers and architects in the
early design stages where complete building information is
not available and precise numeric information is not needed.
Since analysis data is present in the software as it is running,
it can be both visualised and used to affect the parameters of
the design. Although DSOS is in its initial development
stages, initial results indicate that energy analysis in the
early design stages is possible and can raise awareness of
the energy implications of early design decisions.
This research would not have been possible without the
contribution of Dr. Robert Aish who suggested I research
non-manifold geometries and helped me develop my ideas. I
am also grateful to the invaluable help of Daniel Macumber
and the whole OpenStudio team at NREL. I would also like
to thank Prof. Ian Knight, Simon Lannon, and Enrico Crobu
at the Welsh School of Architecture for their insights and
help developing the ideas behind this paper.
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JABI, W. 1996. An Outline of the Requirements for a Computer-Supported
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Supplementary resources (2)

... necessary aisles) or energy provision into account. Regarding the latter aspect, Jabi [32,33] developed the methodological framework as well as the software library and implementation for coupled parametric design and energy simulation which may serve as a starting point for integrating energetic components into GD of factory layouts. ...
... Since a criterion should reflect the impact of different parameter settings as precise as possible, simulation methods are generally conceivable as a target function for the GD model. The already indicated linking of parametric design and energy simulation by Jabi [32] provides base for automated design evaluation. However, it is not unlikely that GD algorithms operate with a population of 50 or more individuals and more than 1000 iterations. ...
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Planning and optimization of facility layouts have been investigated for decades and manifold approaches are applied for structuring and design of production layouts. However, results heavily depend on the experience and creativity of involved planning experts. Currently, complexity of planning processes constantly increases, e.g. due to further requirements of energy and media supply. Generative Design, hitherto mainly applied in component development, provides opportunities to cope with a much larger solution space and develop creative layout concepts. Thus, based on a structured overview on established planning methods, a concept and first results of factory layout planning with Generative Design are described.
... The glazing design system uses the Non-Manifold Topology (NMT) data structures and algorithms, which have been found to be suitable for the integration of energy analysis tasks in early building design stages [7]. Based on a review by the authors on existing literatures and software libraries [8], the NMT framework used in this paper comprises of the following elements. ...
... floors and ceilings), therefore satisfying constraints T7 and T8. Connection to OpenStudio is provided via the DSOS toolkit [7], which converts the Topologic cellcomplexrepresentation of a building into an OpenStudio model. The wall panels and glazing faces are respectively converted to OpenStudio's surfaces and subsurfaces. ...
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Geometry is an important architectural design issue in the assessment of a building model's energy performance using Building Performance Simulation tools. The commonly used window-to-wall ratio method, while providing a fast and automatic way of modelling glazing, is prone to geometrical inaccuracies, which can contribute to the energy performance gap between modelled and monitored buildings. To alleviate these challenges, this paper presents a mechanism for creating a bespoke glazing design on curved surfaces based on the concept of UV-mapping. The glazing can be designed on a 2D planar vector drawing as a set of interconnected curves, which are then mapped unto the UV space of the subdivided and planarised input wall as the glazing. It is hypothesised that a building model with a bespoke glazing design, while more time consuming, allows a more aesthetically representative and geometrically accurate glazing design, thus minimising the energy performance gap.
... The freedom of adapting to the particular needs of the user is offered through algorithmic modeling. A reasonable connection between design intents and responses is defined, established, and clarified by parameters and objective functions [47]. Parameters, which are linked to geometry, are selected by designers and modified gradually through trial and error in order to improve the design [48]. ...
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Free-form architecture is a prominent trend in contemporary architecture where streamlined geometric buildings are constructed. The main problem in free-form architecture is rationalization, which involves realizing free-form surfaces at a reasonable cost while meeting design requirements. Balancing the design intents and construction costs simultaneously is essential for successful rationalization. This study proposes parametric and generative program flows to balance both requirements efficiently. The suggested parametric program flow, which is based on a mathematical algorithm, classifies a free-form surface into multiple areas, which are favorable to flat, single-curved, and double-curved panels. The proposed generative program flow optimizes the double-curved panels’ area through the integration of Multi-Objective Optimization and Pareto optimality. Designers can select the best design option that fits their design objectives through trade-offs using the results of the program flows. Eventually, more efficient and mindful decisions can be made in the early design process by using the results of this study for successful free-form architecture.
... Jabi [30] outlined that the majority of ES are achieved later in the design phases, when design modifications are difficult to integrate and very costly. Most of the time the ES is conducted to measure compliance with external regulations rather than to fundamentally change the design towards higher performance. ...
... But usually, BPS software do not allow to change the parameters of the model automatically and moreover within these simulation engines it is impossible to affect geometric features of the building after the model importation. With the creation of a link between an autonomous geometric model of the building and a BPS software, however, the early design stages can rely on an automated building performance simulation (ABPS) workflows to generate and evaluate several design alternatives, by means of parametric investigations [17,18]. Simulations of this kind can be very resource intensive and time consuming due to the large dimensions of design spaces, namely the existence of several design alternatives that have to be analysed. ...
Delaying the analysis of the building performance to the final stages of the design process can often cause the actual results to differ from the expected ones, with the issue of significant redesign costs. The aim of this work is to improve the designer’s control on thermal and geometric variables of the building in the first stages of the design process, to obtain an optimized building from different points of view. An innovative computational performance-driven design optimization workflow is proposed. The integration of a parametric algorithmic modelling tool in a genetic algorithm powered performance optimization procedure allows to minimize energy need and construction cost of the building, and to optimize the natural illumination levels, affecting several geometric variables. This approach is tested on the early design stages for the new construction of an educational building located in the centre of Italy (L’Aquila). The results are post-processed in order to provide an innovative graphical visualization of the outputs to improve the decision-making process. Useful insights on the influence of different design choices on three key performance indicators can be drawn for the case study building, by comparison of the Pareto solutions: optimization results highlight the strongest influence of window-to-wall ratio, among the other variables, on energy need and useful daylight illuminance.
... One of the advantages of the NMT technique is the topological clarity that it provides, which allows architects to better design, analyse, reason about, and produce their buildings. The potential of NMT in the early design stages is already acknowledged and research has been undertaken with regard to the advantages of NMT's application for energy analysis in the early design stages [22], [24]. NMT has already been applied together with parametric and associative scripting to model the spatial organization of a building [1]. ...
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The aim of this paper is to provide a review of the characteristics of 3D solid modeling software libraries – otherwise known as ’geometric modeling kernels’ in non-manifold applications. ’Non-manifold’ is a geometric topology term that means ’to allow any combination of vertices, edges, surfaces and volumes to exist in a single logical body’. In computational architectural design, the use of non-manifold topology can enhance the representation of space as it provides topological clarity, allowing architects to better design, analyze and reason about buildings. The review is performed in two parts. The review is performed in two parts. The first part includes a comparison of the topological entities’ terminology and hierarchy as used within commercial applications, kernels, and within published academic research. The second part proposes an evaluation framework to explore the kernels’ support for non-manifold topology, including their capability to represent a non-manifold structure, and in performing non-regular Boolean operations, which are suitable for non-manifold modeling.
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Computational methods of design optimization offer great potential in solving the challenges posed by the design of high-performing buildings. However, numerical optimization in building design industry is not sufficiently exploited and has not yet emerged as part of the design process. This reveals the need to develop optimization methods to support the identification of well performing designs while overcoming the long computational time of performance evaluation due to the multi-dimensionality of real-world problems. In response, this thesis aims to evaluate a sequential approach in the multi-objective design optimization of building geometry, fabric, HVAC system and its controls. An algorithm based on individual optimization steps conducted in sequence while concurrently considering building design elements is developed to ensure a low computational burden and a whole-building holistic approach. Three metrics are developed to evaluate the sequential design optimization: the performance of the sequential search, the reliability (robustness) to variations of the same problem formulation with respect to uncertain boundary conditions (weather and internal loads) and scale (four scales with increasing number of solutions), and the efficiency of the search as compared performance with a NSGA-II algorithm. Six criteria pertaining to three categories are applied to the three metrics for the evaluation: search effectiveness in finding solutions with respect to the search and global optima, computational performance of the search with respect to the computational load and computational savings compared to a full factorial search, and solution optimality with respect to the objective and variable space. 24 different configurations of the search derived from 4 initial starting points, 3 categories of grouping of the building design elements and 2 runs of the algorithm are investigated. The results are presented for the landscape of configurations of the sequential search and benchmarked against the global set of optimal solutions derived from a full factorial search. The obtained results displayed different magnitudes of performance, reliability, and efficiency depending on the configurations of the search. Therefore, according to the purpose of the optimization, different configurations of the sequential search could be preferred for different stages of the building design process. Among them, the iterative run of the search in a 2-stage sequential process mapping the architectural and engineering disciplines (field grouped) configured as (building geometry + fabric) and (HVAC system + controls) matched the results obtained from a full factorial search in terms of effectiveness in finding solutions and solution optimality, with a computational saving of function evaluations of 91.2% compared to the full factorial. Additionally, the sequential search proved to be reliable to variations with respect to uncertain boundary conditions and scale of the problem formulation. Finally, for an equal number of function evaluations, the sequential search outperformed the NSGA-II in terms of effectiveness in finding solutions and solution optimality.
Buildings consume around 70% of all domestic electricity (US EIA, How much energy is consumed in U.S. residential and commercial buildings? – FAQ – U.S. Energy Information Administration (EIA), F.A.Q.s, 2018), which is mostly generated by fossil fuel combustion in electric utility power plants. This causes buildings to have major contributions to the total greenhouse gas (GHG) emissions (REN21, Renewbles in cities – 2019 global status report, 2019). Building photovoltaics (PV) systems can provide a clean electricity source to reduce the buildings’ environmental impact. This fact and decreasing prices, market availability, and supportive policies are contributing factors for the increased interest in implementing PV systems. However, with the complexity of the built environment and the existence of different factors and several measures, the impact of PV systems may have contradicting effects on the performance of the built environment. For instance, while integrating PV systems can reduce the buildings’ GHG emissions and the energy cost for end-users, they can also affect the buildings’ energy balance and performance, the buildings’ interaction with the grid, in terms of grid stability, and the architectural aesthetic of the buildings. Thus, the impact of the PV systems integration on buildings needs to be carefully evaluated and optimized, in the design process, to account for all factors. Optimizing the PV systems’ design improves the performance of the built environment and ensures the reduction of its environmental impact. To this end, the aim of this chapter is to review the current practices for the design and implementation of PV systems in buildings and discuss their impact on the built environment’s performance. Also, the use of performance simulation, generative design, and optimization processes are reviewed to assess their potential in improving the design process of PV systems.KeywordsDistributed solar photovoltaicsUrban scaleBuilding performance simulation and optimizationEnergy supply and demand
The building industry’s high-energy usage and greenhouse gas emissions have given rise to the improvement of more easy methods to accomplish low-carbon structures. Nowadays, the use of the building information model in the engineering, architecture, operation and construction industries is constantly increasing and it takes this relationship into account when analyzing the energy of buildings. Thus, this paper aims to investigate the feasibility of using Building Information Modeling to simulate and manage the energy of a health clinic in a cold region, and to compare it to a customized natural Andezit stone sandwich wall structure from Kayseri city and a green wall module for energy performance evaluation of constructions. Type 2 was designed by adding a green wall layer to the outermost layer of the wall, which was planned as Type 1. Among the existing building external wall types, the lowest energy use intensities determined as 110,738 kWh at Type 1. In this wall type, the largest and least energy use intensity values obtained as 76,809 (space heat) and 12,529 (misc. equipment) kWh, respectively. A health clinic Building Information Modeling was improved in Autodesk-Revit displaying its benefit when used as a presence for organizing and storing energy-based information. The effects of the add-on green wall settlement on energy performance for the health clinic, which located in a cold zone, were examined in Autodesk Green Building Studio using energy data from Building Energy Modelling and Building Information Modeling.KeywordsAutodesk revitEnergy analysisRevitGreen building studioEnergy efficiencyNatural stoneGreen wall
The use of Building Information Modeling (BIM) for building energy modeling (BEM) is a recent evolution in design practice. The success of BIM-BEM execution relies on considering two important aspects: process and technology. In this paper, a review of the literature using a systematic approach is proposed to highlight that these two aspects are rarely addressed concurrently. This review includes an overview of the BIM-BEM process and recent technological developments, while elaborating on the main research gap. In order to address the identified research gap, the creation of a framework is proposed that would embed the technological approaches within the whole design process by using a proper Level Of Development (LOD) and information requirements via Model View Definition (MVD).
Conference Paper
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This paper describes a highly optimized design workflow for integrating detailed daylighting analysis using Radiance / DAYSIM with thermal load simulations using EnergyPlus within DIVA, a sustainable analysis plugin for the Rhinoceros 3D Nurbs modeling program. First, the importance of using detailed daylighting models is discussed such that occupant comfort and behavior when operating blinds can be accurately considered. DIVA’'s method of this implementation is then described for a single-zone perimeter office model. Finally, schedules and energy results generated using EnergyPlus defaults are compared to those from detailed analysis in DIVA/DAYSIM. The merits of an integrated workflow are discussed, and it is shown that such an analysis has a significant impact on lighting, heating and cooling loads for a building.
Conference Paper
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High-performance buildings require an integrated design approach for all systems to work together optimally; systems integration needs to be incorporated in the earliest stages of design for efforts to be cost and energy-use effective. Building designers need a full-featured software framework to support rigorous, multidisciplinary building simulation. An open source framework - the OpenStudio Software Development Kit (SDK) - is being developed to address this need. In this paper, we discuss the needs that drive OpenStudio's system architecture and goals, provide a development status report (the SDK is currently in alpha release), and present a brief case study that illustrates its utility and flexibility.
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The daylight factor is usually one of the first daylight performance measures that simulation newcomers calculate. Apart from the intrinsic limitations of the daylight factor as a meaningful daylighting performance metric, little work has been done in the past as to how accurate one can actually expect simulation novices to simulate the daylight factor compared to an expert modeler. This paper presents the comparison of daylight factor predictions from a 'best practice' model of an L-shaped perimeter classroom to a total of 69 novice/student models. In all cases the models were prepared in ECOTECT and simulated in RADIANCE. The paper discusses common mistakes that simulation beginners make when carrying out a daylight simulation, how close their simulation results were compared to the best practice model, and how software developers and educators could potentially guide users to avoid making these mistakes. In addition, a comparison of simulation results obtained with ECOTECT's build- in split flux method as opposed to RADIANCE is carried out for the 69 models in order to quantify in how far using a less reliable simulation engine compromises the accuracy of a simulation.
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Computer-Aided Architectural Design (CAAD) systems have adequately satisfied several needs so far. They have dramatically improved the accuracy and consistency of working drawings, enabled designers to visualize their design ideas in three-dimensions, allowed the analysis of designs through data exchange and integrated databases, and even allowed the designers to evaluate (and in some cases generate) designs based on comparisons to previous cases and/or the formalization of specific rules and grammars. Yet, there is a general consensus that CAAD systems have not yet achieved their full potential. First, most systems employ a single-user approach to solving architectural problems which fails to grapple with the fact that most design work is done through teamwork. Second, current systems still cannot support early design stages which involve client briefing, data collection, building program formulation, and schematic design generation. Thus, if CAAD is to ultimately benefit the design process, it must (1) emulate and support the design team approach to architectural problem solving and (2) be deployed in the earliest possible stages of the design process. This paper seeks to study remedies to both of the afore-mentioned limitations through focusing on the interaction between a set of requirements (the building program) and the architectural solution that satisfies them (the schematic design). The core of this interaction forms the fundamental dialectic and collaborative nature of what is called designing: a concerned social activity that proceeds by creating architectural elements to address a set of requirements and their re-thinking as a result of architectural conjecture. To investigate this relationship, it is proposed to build a computer-supported collaborative design environment using the tools of conceptual modelling (based on the NIAM notation), object-oriented algorithms, and distributed agents. Based on a literature survey and earlier findings on the role of artifacts in collaborative design, this paper outlines the requirements for the above system and reports on initial experiments. Thus, it constitutes the first stage of a research project that will lead to a full implementation of a distributed collaborative computer environment addressing the above issues.
The envelope shape, the most salient design characteristic in a building, has significant influence on its energy performance. However, in the early design stages, when the envelope shape is defined, energy performance information is normally nonexistent, due to modeling for energy simulation being a time-consuming task, frequently overlooked at this phase. This paper presents a methodology to assist design decisions regarding the building envelope shape considering its implications on energy performance. Basically, this methodology involves a flexible design system, to generate alternative envelope shape designs, with integrated energy simulation, to calculate the energy demand of each design. Shape grammars are particularly suitable to encode architectural design systems, given their ability to encode compositional design principles. Their downside is the complexity in developing computer implementations. This methodology converts a grammar into a parametric design system and is illustrated with an application to the grammar for Frank Lloyd Wright's prairie houses.
Design space exploration is a long-standing focus in computational design research. Its three main threads are accounts of designer action, development of strategies for amplification of designer action in exploration, and discovery of computational structures to support exploration. Chief among such structures is the design space, which is the network structure of related designs that are visited in an exploration process. There is relatively little research on design spaces to date. This paper sketches a partial account of the structure of both design spaces and research to develop them. It focuses largely on the implications of designers acting as explorers.
Spatial Information Modeling of Buildings Using Non-Manifold Topology with ASM and DesignScript
  • Aish R.
  • Pratap A.
Complex Building Performance Analysis in Early Stages of Design
  • R Brahme
  • Al
BRAHME, R. ET AL. 2003. Complex Building Performance Analysis in Early Stages of Design. Seventh International IBPSA Conference, Rio de Janeiro, Brazil, August 13-15, 2001. 661-668.