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School of Architecture

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College of Design

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Publication Search Results

Now showing 1 - 10 of 102

Seeing architectural photographs: space and time in the works of Julius Shulman and Ezra Stoller

(Georgia Institute of Technology, 2016-11-15) Hyun, Myung Seok

This dissertation is about seeing architectural photographs. It begins by addressing a paradoxical aspect of some architectural photographs: they acquire a status as works of photographic art, yet are able to do so while ostensibly serving a documentary purpose – in fact, they take on their significance by virtue of presenting architectural content. This raises questions about the nature of architectural experience. In particular, what do we see of architecture, exactly, when we see an architectural photograph? I propose that what we see in some architectural photographs involves our visual construct of space and time, and bears upon our cognition of essential architectural qualities. To demonstrate this, I offer case studies of architectural photographs from mid-century America, the works by Julius Shulman and Ezra Stoller. The studies show how the photographers’ careful manipulation of technical variables and selective inclusion of secondary subject matter bring forth distinctive exemplificational architectural qualities from what appears to be objective presentation. In Shulman’s photographs of Richard Neutra’s houses, what is exemplified is the quality of a lived space, modulated by subtle depictive moves. In Stoller’s case, the secondary or peripheral subjects trigger various durations of seeing, against which the relative permanence of the building is made manifest. Ironically, these photographs offer the kind of seeing in question by obscuring key descriptive details of the photographed building, and letting seemingly incidental details acquire visual salience. They succeed by bringing forth the properties of the medium that exemplify those of architecture. The study thus offers telling insights into why visual representation matters to our experience of architecture.
Integrated performance based design of communities and distributed generation

(Georgia Institute of Technology, 2016-08-26) Street, Michael

The vertically integrated utility market within the U.S. is undergoing rapid changes due to the rise of small-scale distributed power generation known as microgrids, which are local networks of power generation and distribution typically serving a demand less than 40 MW. Primary drivers for microgrid investment are the performance benefits these systems return to their owners, which include increased reliability, reduced emissions and reduced operating costs. We define a novel modeling methodology to represent the microgrid as an integrated system of the demand and supply. Previous work to develop an integrated system model does not adequately model the building thermal demand, incorporate a modeler’s knowledge of the grid’s availability or allow for a user to model their tolerance for unmet demand. To address these modeling issues, we first demonstrate a technique for representing a building stock as a reduced order hourly demand model. Next, as demand side measures are typically defined at the building level as discrete options, we demonstrate a technique for converting a large discrete optimization problem into a simplified continuous variable optimization problem through the use of Pareto efficient cost functions. The reduced problem specification results in 90% fewer function evaluations for a benchmark optimization task. Then, we incorporate two new features into the Distributed Energy Resource Customer Adoption Model (DER-CAM) developed by Lawrence Berkeley National Laboratory (LBNL) that allow users to define grid outage scenarios and their limit of expected energy demand not served. Applying the integrated model to a microgrid design scenario return solutions that exhibit on average an 8% total annual cost reduction and 18% reduction in CO2 emissions versus a Supply Only case. Similarly, the results on average reduce total annual cost by 5% and annual emissions by 17% for a Demand First case. In summary, we present a modeling methodology with application to joint decision making that involve renewable power supply, building systems and passive building design measures and recommend this model for performance based microgrid design.
The impact of occupant modeling on energy outcomes of building energy simulation

(Georgia Institute of Technology, 2016-08-02) Kim, Ji Hyun

The reported performance gap between predicted and real building energy consumption has drawn keen attention from the building simulation community and related stakeholders. Alongside other research efforts to identify, quantify, and close this gap, the most recent attempt is the development of occupant behavior models that generate more “realistic” occupant inputs in the building energy simulation used for prediction. These new occupant models are typically realized by stochastic methods. To date, the newly developed models focus on mimicking real life variability. In spite of that, they have not led to more accurate consumption predictions than previous methods. Rather than adding yet another occupant behavior modeling approach, this thesis emphasizes the need to understand the impact of occupant models on building energy outcomes in real life applications. To accomplish this, we investigate two distinctive approaches to occupant modeling: top-down and bottom-up. We build the argument in the thesis that the top-down approach is suitable in highly variable situations where relatively little information about actual occupant variables can be known. This is usually the case in residential applications. By introducing a so-called “Life Style Factor,” we conclude that the use of this factor is promising to capture the variability of occupant-related parameters in residential buildings. For commercial buildings, a fundamental analysis is conducted to identify the impact of occupant-related inputs on the performance gap while explicitly considering the level of modelers’ knowledge about occupants’ presence and actions at the time of prediction. The results of a sensitivity analysis reveal that even in the case where the modelers’ ignorance of actual occupancy is significant and hence occupant parameters become important contributors to the performance gap, the resulting disparity could be fairly well quantified without introducing complex occupant behavior models. It is also found that the randomness of occupant behavior with respect to actions has no significant role in the performance gap at least in typical building simulation practice. This finding is significant as it advises us to rethink our pursuit of accuracy by developing new occupant behavior models, such as the ones that explicitly model the human reasoning, perception and action related to the opening of windows.
Accuracy, validity and relevance of probabilistic building energy models

(Georgia Institute of Technology, 2016-08-02) Wang, Qinpeng

Residential and commercial buildings consume 41% of total U.S. energy consumption. Since improving energy efficiency is still the most cost efficient energy saving option in the U.S., it is not surprising that many new buildings represent a push towards ultra-efficiency. Many studies argue that this calls for the use of high fidelity prediction models that by necessity will be probabilistic in nature due to many sources of uncertainty that affect the translation of a design specification into the actual reality of a constructed and operated facility. To inspect the fidelity of these probabilistic models against traditional deterministic models, we pose questions that address three major aspects of this new generation of building energy models: • Accuracy: do these models give more “correct” answers? • Validity: do these models lead to “better” design/retrofit decisions? • Relevance: does a profession that deploys these models provide “higher” value to the industry? This dissertation addresses the first question by identifying gaps in our understanding and quantifying various sources of model uncertainty reported in recent literature. Insufficiently understood and not well-quantified sources are further studied and resolved. The results of the above are analyzed in a sensitivity analysis that ranks input parameters alongside with model form uncertainties. Next, we adapt proven methods to conduct verification of probabilistic building energy models. Probabilistic calibration, marginal calibration and a continuous rank probability score are used to evaluate the “correctness” of the new generation of models. We illustrate the challenges of delivering validity proofs in a case study where outcomes of uncertainty analysis are translated into (monetary) risks and their influence is analyzed in a decision-making scenario involving energy performance contracts. Lastly, the study introduces a speculative approach to proving relevance by quantifying the overall societal benefit of a transparent risk framework that has the potential to unlock currently stagnating capital flow into large-scale building retrofits.
Downtown Atlanta 2041: Autonomous Vehicles and A-Street Grids

(Georgia Institute of Technology, 2016-08) Dunham-Jones, Ellen ; Blakeley, Meredith ; Bonn, Sarah Jane ; Goldstein, Eric ; Huang, Shijia ; McMullen, Meghan ; Pang, Lu ; Payson, Mikhail ; Reeves, Blake ; Scott, Stacy ; Shrestha, Animesh

Downtown Atlanta 2041 is a speculative look 25 years into the future at the opportunities available to build on parking lots and create a walkable network of Class A streets and distinctive neighborhoods around Downtown’s many assets. The design proposals are based on conversations with stakeholders, analysis of current conditions as well as bold assumptions about the future impact of autonomous vehicles. The work was produced by graduate students at Georgia Tech in the Master of Science in Urban Design, (MSUD) spring 2016 studio, under the direction of Professor Ellen Dunham-Jones in the School of Architecture in the College of Design.
Application of inverse modeling to performance-based architectural design in the early stage

(Georgia Institute of Technology, 2016-05-31) Rezaee, Roya

The architecture, engineering, and construction community is taking action to reduce energy consumption. Fulfilling energy performance requirements entails complex decision-making at the architectural design stage, when a large number of parameters are undecided and the level of uncertainty is high. The early stage of design, in particular, is characterized by its iterative nature of divergent phases in which design alternatives are generated and convergent phases in which alternatives are assessed and selected. It is during or at the end of these phases that decision-making occurs under considerable uncertainty. Therefore, the methods and tools applied during these phases should account for the iterative, complex, and uncertain characteristics of the design process. At present, the building industry lacks a consistent approach to decision making during the phrases of the early stage of design: The divergent phase, when concepts are generated, consists of no practical framework within which designers generate more promising alternatives regarding energy performance, and the convergent phase, when concepts are evaluated and selected, includes no algorithm within it that designers can use to validate their decisions and provide confidence in their decisions. These deficiencies necessitate a clear step-wise approach that supports the proper design exploration by generation and evaluation of design alternatives, highlights significant parameters regarding energy performance for a variety of design scenarios, allows for coupled decisions under uncertainty, and align with the iterative nature of design process. This research hypothesizes that (1) a new systematic method based on linear inverse modeling (LIM) can generate plausible ranges for design parameters given a preferred thermal energy performance at the early stage of architectural design; and (2) the application of the proposed approach can lead to a higher probability of achieving energy efficient buildings (increase the chances of developing promising concepts), which is the main objective of performance-based design; and finally (3) in comparison to the current prescriptive approach, the proposed performance-based method help designers with the design process by providing more design freedom and guidance. Such an approach also accounts for the iterative nature of an architectural design and promotes a step-by-step procedure for making a decision and updating information as each new decision is made. In contrast to the conventional “forward modeling” in building performance analysis in which the design parameters are considered input and the energy performance are output, the “inverse modeling” deals with the performance objective as input and the design parameters are inferred as the output of the analysis. The study practices the proposed inverse modeling approach for making decisions regarding energy performance at the early design stages in four case studies, representing two different types of buildings in four climate zones. Such practices show the capability of the proposed inverse modeling to help designers in design space exploration, sequential decision-making, and trade-off study at the early stage of design. This method is proven to be a validate candidate for fulfilling desired energy performance and provide guidance and freedom in building design process. This thesis research contributes to the body of knowledge pertaining to building energy modeling and decision making at the early design stage, and its framework can be used by all groups of designers, the energy analysis experts as well as non-energy-expert architects, for a more informed decision-making regarding energy.
Solar shift: A perspective on building energy performance under haze pollutions in China

(Georgia Institute of Technology, 2016-05-31) Jia, Yiyuan

The severe haze pollution in China has arisen concerns among the public and government officials, due to its impacts on pubic health, visibility, climate and agriculture. To augment these findings of the negative impacts of haze pollution, this study investigates the “solar shift” effect due to haze pollutions and the potential (unreported) impacts on buildings’ energy performance in China. This study takes the aerosol optical depth (AOD) as a measure of the solar blocking effect of haze pollutions. By plugging in the measured and projected AOD data in solar models, three weather files for Beijing are developed that represent different haze pollution for the following scenarios: the 2014 situation, the optimistic projection of 2050 (2050A) and the pessimistic projection of 2050 (2050B). Together with the TMY, these weather files serve as the boundary conditions in building energy modeling practices. The results indicate the district heating energy consumption under the 2014 aerosol emission levels would increase 5 % compared to the current practice using TMY weather file. In the pessimistic scenario where we assume to keep the current pace of aerosol emissions, the district heating energy would increase 10 %. The current ASHRAE design day sizing method would assure the heating load being met under all possible scenarios investigated in this study.
Assessing the measures of street connectivity: A comparative study of the largest American cities

(Georgia Institute of Technology, 2016-05-27) Haynie, Stephanie Dawn

The thesis offers a systematic characterization of the morphology of street networks in metropolitan areas in the United States in order to help assess current conditions. Measures of street connectivity, particularly those of road segment length, block area, metric reach and directional distance, are measured for all road segments within each of the 24 largest Metropolitan Statistical Areas in the U.S. In order to characterize local areas within each MSA, these measures are also studied for a stratified sample of more than 4,500 local areas, each two miles in diameter, chosen at varying distances from the metropolitan center. As a point of comparison, a smaller sample of ninety-six local areas is chosen to illustrate distinctive street network types identified and discussed in the existing literature on urban morphology and street connectivity. This smaller typological sample provides benchmarks then for the characterization of the much larger random sample of local areas. The thesis profiles the variation both between and within the local areas of these metropolitan areas. In the face of the prevailing variability on street network types, the thesis also examines which measures of street connectivity best capture significant differences and, by implication, might be most effective in assessing street connectivity in the context of policy development and planning for possible urban development. The thesis concludes that the measure of 'metric reach,' which captures the street network length accessible within a specified network distance from any given point, used on its own, is a more sensitive descriptor of street connectivity than some of the measures more traditionally used, such as the distance between, or the density of, intersections. Metric reach also provides the most consistent characterization of trends such as the reduction of street network density within increasing distances from the historic city center, or the identification of polynucleated centers at varying distances from that center, whether these emerge as recently grown edge cities, or survive as centers of old towns absorbed into the expanding urban fabric.
Performance measures for residential PV structural response to wind effects

(Georgia Institute of Technology, 2016-05-27) Goodman, Joseph Neal

This thesis applies structural reliability measures for the performance based design of residential PV system structures. These measures are intended to support designers in delivering systems with quantified and consistent reliability. Existing codified practices prescribe global factors (allowable stress design) and partial factors (load and resistance factor design) intended to provide an acceptable level of reliability as defined by historical practice. When applied to residential PV systems this prescriptive approach has two flaws, (1) calibration efforts needed to ensure consistency across structural system types have not kept up with the commercially available system types and (2) the actual expected reliability is not quantified and available to support decisions. The proposed reliability measures include probability of failure conditioned to wind speed in a fragility curve and the reliability index β, both of which are commonly used in performance based design. The approach is demonstrated through the application of the reliability measures to code compliant designs. Diverse system types are utilized to demonstrate how the existing code prescribed approach may lead to non-uniform structural performance. For each of the system types on which the reliability measures are demonstrated, a code compliant design is developed for three roof slopes, wind tunnel testing is conducted to provide an experimental measure of wind pressure coefficients, system specific fragility curves are generated to quantify the probability of failure conditioned to a set of wind speeds, and then, a site specific wind model is applied to produce a probability of failure and reliability index β. Through the performance based approach proposed in this thesis, two key outputs show non-uniform and unanticipated structural performance of PV systems designed according to the prescriptive code method. The two key outputs which illustrate this finding are fragility curves which illustrate the probability of failure over a range of wind speeds and reliability index, β values which couple the structural and wind distributions for a single measure of reliability.
Meta-modeling design expertise

(Georgia Institute of Technology, 2016-05-23) Bernal Verdejo, Marcelo

The general problem that this research addresses is that despite the efforts of cognitive studies to describe and document the behavior of designers in action and the evolution of computer-aided design from conceptual design to fabrication, efforts to provide computational support for high-level actions that designers execute during the creation of their work have made minimal progress. In this regard this study seeks answers to the following questions: What is the nature of design expertise? How do we capture the knowledge that expert designers embed in their patterns of organization for creating a coherent arrangement of parts? And how do we use this knowledge to develop computational methods and techniques that capture and reuse such expertise to augment the capability of designers to explore alternatives? The challenge is that such an expertise is largely based on experience, assumptions, and heuristics, and requires a process of elucidation and interpretation before any implementation into computational environments. This research adopts the meta-modeling process from the model-based systems engineering field (MBSE), understood as the creation of models of attributes and relationships among objects of a domain. Meta-modeling can contribute to elucidating, structuring, capturing, representing, and creatively manipulating knowledge embedded in design patterns. The meta-modeling process relies on abstractions that allow the integration of myriad physical and abstract entities independent from the complexity of the geometric models; mapping mechanisms that facilitate the interfacing of a repository of parts, functions, and even other systems; and computer-interpretable and human-readable meta-models that enable the generation and the assessment of both configuration specifications and geometric representations. For validation purposes three case studies from the domain of customs façade systems have been deeply studied using techniques of verbal analysis, complemented with digital documentation, for distilling the design knowledge that have been captured into the meta-models for reutilization in the generation of design alternatives. The results of this research include a framework for capturing and reusing design expertise, parametric modeling guidelines for reutilization, methods for multiplicity of external geometric representations, and the augmentation of the design space of exploration. The framework is the result of generalizing verbal analyses of the three case studies that allow the identification of the mechanics behind the application of a pattern of organization over physical components. The guidelines for reutilization are the outcome of the iterative process of automatically generating well-formed parametric models out of existing parts. The capability of producing multiple geometric representations is the product of identifying ae generic operation for interpreting abstract configuration specifications. The amplification of the design space is derived from the flexibility of the process to specify and represent alternatives. In summary, the adoption of the meta-modeling process fosters the integration of abstract constructs developed in the design cognition field that facilitate the manipulation of knowledge embedded in the underlying patterns of design organization. Meta-modeling is a mental and computational process based on abstraction and generalization that enable reutilization.