Material Based Urban Modeling: An Approach to ... - Science Direct

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ScienceDirect Energy Procedia 105 (2017) 3765 – 3771

ICAE2016-8th International Conference on Applied Energy

Material Based Urban Modeling: An Approach to Integrate Smart Materials in a Near-Zero Community Design Perry Pei-Ju Yanga,b, Annette Wiedenbackc, Michael Tobeya,b, Yihan Wu a,b, Steven Jige Quana,b, Yogendra Chauhanc, Jiang Wua a

b

Sino-U.S. Eco Urban Lab, College of Architecture and Urban Planning, Tongji University, Shanghai, China Eco Urban Lab, School of City and Regional Planning and School of Architecture, Georgia Institute of Technology, Atlanta, USA c Covestro Polymers (China) Co., Ltd

Abstract

This article outlines a research agenda to incorporate smart materials into district-scale urban building energy modeling frameworks. This research agenda aims to design eco-communities driven by the nearzero emission objective. Two strategic materials, polyurethane and polycarbonate, were selected for testing. Tests were conducted to evaluate to what extent these smart materials, when embedded in multiscale systems from construction components, to buildings, to a community, would enhance total urban energy performance. A conceptual framework of holarchies is proposed to articulate subsystems of an urban system. This framework explores the multiple-scale and cross-sectorial relationship of material properties and their building spatial configurations. An experimental test case uses Building Information Modeling (BIM) to construct databases from the various levels of the urban system from M (material), to P (products and building component) and B (building systems). It is then connected to N (neighborhood) by incorporating Urban Energy Building Modeling (UBEM). This connection considers contextual factors such microclimate and landscape features. After conducting the M-P-B-N multi-scale modeling experiment, emerging system properties will be derived and turned into design principles for informing future design decision-making. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. Keywords: Material Based Modeling, Energy Performance Modeling, Smart Materials, BIM, UBEM

1. Introduction Material based urban modeling is a systematic approach to understanding how materials and their construction assembly configurations affect overall energy performance in the urban system, from the building to neighborhood level. Existing research of this kind has often focused on a singular building or small segment of the urban environment. This limitation in existing research is potentially the result of * Corresponding author. Tel.: +1-404- 894-2076; fax: +1-404-894-1628. E-mail address: [email protected]

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.1052

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several issues. First, the material dimension has been seldom addressed in urban-scale performance modeling. Much of the existing literature focuses on product scale modeling or on marco materials, like insulation. Material research has been important in architecture and building research, however, is not often seen in literature of urban design and planning. Second, research that examines green building design views materials as a part of closed system in buildings, or as accounting for the baseline embodied energy. Third, the added complexity that is inherent in scaling up from a building to a district-level model possesses a significant challenge to the modeling process. New properties emerge from the interactions among buildings and their urban contexts. This complexity of the urban environment at the community level is comprised of elements that are complete fields of studies in and of themselves An urban system is composed of not just its own system properties that need to be modeled, but also of lower level subsystems such as materials, building components and building systems that are part of the greater urban-scale system. 1.1. Holons and Holarchy The complex nature of the urban system, as a system comprised of multiple subsystems, lends itself well to being described by the concept of Holons arranged into a Holarchy. The concept of Holarchies or Holons, as applied to the field of sociology, is a term first coined by Arthur Koestler in his book “The Ghost in the Machine” [1]. Holons are a way of modeling complexity in a simplified fashion and the understanding that a complex system is formed out of other complex systems. In this way one holon has three key aspects: one, that it controls the lower level order of holons that comprise it, two, that it is controlled or affected by higher level aspects, and three, that it is a complete system that could be investigated on its own. This type of relationship is often referred to as a “Janus Face”, as described by Pichler and Linz [2]. It was then developed further in industrial ecology to describe complex systems that are comprised of smaller or more simplistic systems and their structural relationship [3]. Based upon this concept of Holons, or complete systems of themselves, a preliminary relationship can be drawn between the differing levels of the systems at work in the urban environment. The concept of Holarchies appears analogous to that of a “Matryoshka Doll”, in that systems at different levels maintain their own coherent structure and system properties while being connected to a multiple-scale complex structure. This analogy is often used to describe complex and intelligent relationships that range from molecules/materials/components (such as polycarbonates window glazing and polyurethane foam) to products/integrated systems (such as electric vehicles and zero emission buildings) that can be scaled up further to a community-level complex system (such as smart transport infrastructure or eco districts). Each level of “Matryoshka Doll” diagram shows a set of holons, in a holarchy for different aspects of net-zero urban systems. Three different multi-scale structures, transportation, building, and industrial production sectors, are represented respectively in this diagram. The horizontal green box represents a cross-sectorial urban system that contains the three sectors that are to be connected in a complex eco urban community-scale system. The community-level system presents a focal scale territory, in which problems at this level are designable or manageable through interactive and collaborative processes among stakeholders of a community. The vertical red box focuses on the building systems that range from materials and products to blocks and eco districts, which is a focus of this paper (Fig 1).

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Fig. 1: Holarchies of a net-zero urban system

1.2. M-P-B-N Scale Holarchy The Holarchies concept is redrawn to inform the material-based urban modeling projects that move from M (materials), P (products), B (buildings) and N (neighborhoods) (Fig 2). Based upon this methodology, urban material modeling is proposed to address multiple-scale problems, goal setting, performance metrics and design. In order to go from one Holon to another, multi-level of study and modeling are needed and empirical projects are to be designed for validation. A research design is needed, ranging from database of constructing material and products component, to modeling of building and district scale systems that focuses on energy performance. By breaking up the problem into stages, performance modeling is to be connected to prototyping design through an iterative design process to layout a blueprint of a near to net zero eco district, in which smart materials are essential building blocks for constructing such a complex system. Fig. 2 M (Material), P (Product), B (Building) and N (Neighborhood) multi-scale modeling system

2. M-P-B-N Research Design

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2.1. Defining Smart Materials The term smart materials in this research is defined as materials during their production, fabrication and use in the built environment that meet the following criteria: 1. A lower environmental impact when compared with other materials; 2. Better performance than the existing materials in built environment that are to be replaced; 3. Energy savings associated with the materials; 4. Better quality than other competing materials, e.g. quality of materials in healthy, recyclable and aesthetically appealing dimensions; 5. Ease of integration and use with other materials in building systems used on the market. This definition was informed by multiple interactions with scientists and researchers in a multiple fields of study. Through the help of material scientists, architects and urban planners, a consensus of the key aspects of what comprises a smart material was derived. 2.2. Research Question and Research Design The M-P-B-N scale of material based urban modeling requires a multi-layered research design to address a governing research question: how does changing a smart material result in different energy performance and lifecycle cost of buildings and neighborhoods, and what are their tradeoffs? A four-stage process of research design is proposed to explore how materials are embedded in a multi-layered urban system, and how interactions across different scales generate new complex properties. They are outlined here to study this complex system of systems. The first stage examines how materials form and interact as products. This stage is being examined jointly by urban building modelers and material scientists who understand the material properties and to a certain extent, their assembly and fabrication into products. The database of materials and their connections to products provide a basis of the bottom-up approach of the material based urban modeling. Stage two involves products and how they are incorporated into built assemblies, which constitute a structural element of a building such as facades, roofs, floors, foundations or building cores. These assemblies are constructed of individual products, which can be modified and changed to alter the performance outcome of the building. Stage three investigates parameters of varying built configurations over a range of different building typologies. It is concerned with the configuration and calibration of the building models, and the platform of BIM is used for incorporation. The final stage examines the obstructing relationship among buildings as individual objects and their interactions with contextual factors such as microclimate and landscape conditions, in which the complex properties would allow us to scale up the system boundary from buildings to street blocks. UBEM is employed to study the relationships at this scale. This four-stage design investigates the relationship across scales of building sector. In the future, crosssectorial factors such as building-transportation-water-energy nexus systems should be integrated to define a near to net zero energy district (nZED), including future expansion from modeling and design. 3. Defining Building Typology Cities are composed of a myriad of buildings with various uses/functions and shapes/forms that define their uniqueness from building to building that situates in individual site contexts of the urban

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environment. The M-P-B-N system faces the difficult problem of tackling an increasing number of objects in systems when scaling up from the material-product to the building-neighborhood level, which is inherently complex. The concept of typologies is applied to classify buildings in the real world that contains differing configurations. Each of these, although unique in nature, shares common elements among certain types and categories. Key parameters of fundamental building elements that govern the majority of the building are used to define or describe a building typology: including Land Use, Height, Footprint, and Age of the building. These classifications vary in different social, cultural and regulatory contexts such as zoning ordinances. For this reason, the fine-grained approach or higher-resolution data are not appropriate for problems of this kind, instead coarse-grained parameters and lower-resolution data such as land use and building use are taken. Due to the limit in data availability in cities like Shanghai, the reference building database of the Department of Energy (DOE) were used as a base framework, and further expanded based upon contextual knowledge of Shanghai’s land use categories [4][5]. The Land Use is related to building functions. It is to be combined with other key parameters to construct a building typology. The Building Height acting as another key parameter, when combined with land use, will influence the structure, window to wall ratio, and envelope characteristics of a building. The Building Footprint, or building cover ratio, is another parameter that is often tied together with Building Height. It is normally constrained by zoning ordinance through FAR (Floor Area Ratio), a build density limit. Given the same FAR, the height and footprint would jointly determine a variety of building shapes. When combining building shape with land use, they will jointly influence the structure of the building, window to wall ratio and the envelope. In building energy modeling, they affect the number of thermal zones and the amount of internal structure that has to be modeled. The following diagram categorizes five building heights, and uses the first three to describe building typologies in details [6][7][8]: • • • • •

Low Rise Mid Rise High Rise Super High Rise Mega High Rise

0 – 5 Stories 6 – [14-28-40] Stories [14-28-40] – 84 Stories 84 – 167 Stories 167+ Stories

/ / / / /

0 – 18m 18m – [50m-100m-150m] [50m-100m-150m] – 300m 300m – 600m 600+m

(L) (M) (H) (SH) (MH)

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These categories simplify the actual built environment, in which the classification of buildings is affected by local contexts. Over 100 different types of buildings are classified using ten differing land uses, three building heights, and three building footprint sizes that are commonly observed in Shanghai’s urban context. Each of these individual classifications then has a corresponding parametric model to generate a near-infinite number of combinations, in which their building performance would have to be tested. The factor of Building Age has not yet been considered at this stage, and will have to be incorporated for problems of urban renovation or regeneration. 4. Material Based Urban Modeling – A Preliminary Simulation Experiment To limit the number of test cases to a manageable amount of controllable variables, and account for the lack of age data in the Shanghai context, three buildings types were selected: (A) High-Rise Office, (B) Mid-Rise Residential Apartments, and (C) Low-Rise Worker Village, as these three represent typical block typologies in the City of Shanghai. The building typology is determined by building functions and corresponding geometries, which provides a spatial framework for understanding how a building that governs the form generates parametric models and creates varying degrees of impact on energy performance. The construction of building types was based upon the examination and study of real floor plans of existing buildings in Shanghai. From these the parameters for the models were determined and parametric models, in Rhino Grasshopper, were developed. These three served as the test case for the first round of energy performance modeling that was performed. Fig. 5 shows the three simulated experiment side by side.

Fig. 5 Energy Performance Modeling of (A) Office, (B) Apartment, and (C) Worker Village

These three simulation experiments were run by using the previous four-step process. Step One selects materials, e.g. polycarbonate window glazing and polyurethane foam as changeable variables in construction materials. Step Two applies the selected material to products and integrates products into construction assemblies. Each building has different construction properties like using different wall thickness and assembly constructions. Step Three applies these elements to the parametric model based on the three selected building typologies. Step Four simulates building energy use to determine performance characteristics, which can be connected to UBEM by considering contextual factors. Further validation will require empirical data of system properties from actual building operations that are to be obtained in future, due to lack of data in Shanghai at this stage. Future research will apply on-site measures of building properties and energy performance, as well as other 3D high-resolution data that can be acquired from sources such as LIDAR.


5. Conclusion This research explores a BIM to GIS integrated modeling method to incorporate smart materials in a community-scale urban system. The term smart materials, is defined as materials during their production, fabrication and uses in the built environment that meet the criteria such as low environmental impact, better performance, better quality and ease for integration. Two strategic materials, polyurethane and polycarbonate, are selected for testing to what extent those smart materials would enhance the total urban energy performance. A multi-scale material-based urban modeling is proposed for designing an eco community that is driven by the near-zero emission objective. It shows the relationship from M (materials), P (products) to B (buildings) by modeling energy performance of windows and façades to buildings using BIM system, and then is scaled up to N (neighborhoods) through UBEM by considering the contextual and exterior factors such as microclimate and landscape factors when accounting for materials. The research is still in its early stage. Future work will incorporate validation between modeling and empirical data. The system properties derived from the modeling process will be turned to guidelines for informing better decisions in community design for meeting near zero emission objectives. Acknowledgements The research is jointly supported by Covestro through the Bayer Chair at the UNEP- Tongji Institute of Environment for Sustainable Development, as well as the Eco Urban Lab at the College of Design of Georgia Institute of Technology and the College of Architecture and Urban Planning of Tongji University. References [1] Koestler, A. (1968). The Ghost in the Machine. New York: Macmillan. [2] Pichler, F., & Linz, A. (n.d.). Searching for Arthur Koestler’s Holons – a system theoretical perspective. Retrieved March 28, 2016, from http://www.dse.ec.unipi.it/persone/docenti/Luzzati/italiano/didattica/holonspichler.pdf [3] Graedel T E, Allenby B R, 2010, Industrial Ecology and Sustainable Engineering, Prentice Hall. [4] Commercial Reference Buildings. (n.d.). Retrieved February 29, 2016, from http://energy.gov/eere/buildings/commercialreference-buildings [5] NYC Open Data. (n.d.). Retrieved March 29, 2016, from https://nycopendata.socrata.com/ [6] Council on Tall Buildings and Urban Habitat. (n.d.). Retrieved May 29, 2016, from http://www.ctbuh.org/ [7] IBC Construction Types. (n.d.). Retrieved June 15, 2016, from http://codes.iccsafe.org/app/book/toc/2015/I-Codes/2015 IBC HTML/ [8] NYC Fire Code. (n.d.). Retrieved June 12, 2016, from http://www1.nyc.gov/site/fdny/about/resources/code-and-rules/nycfire-code.page

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