Early Design Decisions Research Articles

Vaporization in Liquid-Core Nuclear Thermal Rockets

This work provides an overview of vaporization phenomena in liquid-core nuclear thermal rockets to aid in unifying modeling assumptions by characterizing the impact of vaporization. Historical variations in vapor modeling have led to reported specific impulses ranging from 1000 to 2000s following model refinements in the 1960s and the omission of vaporization consideration post 1990. The only preexisting vapor transport study to not rely on the Reynold’s analogy was performed for the radiator design. This paper performs the first such analysis for the bubbler design, given its renewed interest. Consideration is given to potential centrifugal species separation, proposed fuel mixtures, interplay between chamber pressure and vaporization, impacts on propellant thermoproperties, evaporative cooling, power requirements for vapor separation, and the complexity of hydrocarbon interactions under the proposed chamber conditions. The bubbler design is shown to be well approximated as fully saturated, whereas the radiator and droplet designs may be configured to reduce vaporization to 20% saturation. Characterization of vaporization within liquid cores is essential to early design decisions, such as overall archetype, defining the operating point, and liquid media composition. For a threshold temperature corresponding to around 10% of the propellant being vapor by mass, further increases in temperature begin reducing the specific impulse. Below this threshold temperature, the impact of vaporization on thermal modeling with the chamber is likely negligible. Above this threshold temperature, higher-fidelity thermal modeling will be required, and the work required to separate the vapor begins exceeding 100 kW/(kgH/s). Nozzle kinetics are applied to liquid cores for the first time, showing that specific impulses increase with increasing chamber pressure contrary to historical findings. Historically, modeled uranium-zirconium-carbon mixtures are predicted to reach a specific impulse up to 1260 s. A uranium-tungsten-carbon fuel mixture yielded the best theoretical specific impulse of slightly under 1400 s; however, no nucleonic or thermal analysis has been conducted with this fuel composition.

A Systems Engineering Approach to Incorporate ESG Risks and Opportunities in Early-Stage Mine Design and Planning

This study examines how ESG risks and opportunities can be systematically identified, assessed, and incorporated into the early-stage design and planning of natural resources projects. The focus for this study will be on the mining activities required to source the resources for the global decarbonization effort. The need for a framework to incorporate ESG risks and opportunities into the strategic mine planning process was first identified in the de Beers Sustainability Valuation Approach. The Social Value Capital Decision Model advanced by BHP represents an advance on the de Beers model. This is the first example of a structured methodology for systematically considering stakeholder values and incorporating these into the capital decision framework. To test the applicability of a new approach to mine design by using Quality Function Deployment (QFD), a case study involving a copper mine located in South America was developed. This case study demonstrates how QFD can provide clear line-of-sight to connect design decisions with priority stakeholder concerns. The framework provides a communications tool for aligning the ESG design process across functional silos within complex organizations. The development of appropriate software tools could assist in managing the inherent complexity associated with integrating stakeholder value concerns into early stage design decisions.

Multi-objective optimization of daylighting performance and energy consumption of educational buildings in different climatic zones of China

With the implementation of China's “Healthy Building” and “Dual Carbon” strategies, daylight health and energy-saving carbon reduction in buildings have become key research areas. In educational buildings, early design decisions significantly impact students' eye health and energy consumption reduction, making it crucial to balance these concerns. Studies indicate that sufficient and continuous daylight can significantly improve students' eye health, mood and sleep. To maximize the UDI level at the eye level (seated) and minimize EUI and DGP, this study used the multi-objective optimization tool Wallacei based on NSGA-II (Non-dominated Sorting Genetic Algorithm II) for genetic optimization. This study uses the Shiji Building of Central South University in Changsha, Hunan, as a reference model to provide optimal classroom designs for China's five major thermal design zones: severe cold, cold, hot summer and warm winter, hot summer and cold winter, and mild. The results indicate that optimizing the classroom's deflection angle, length, width, height, and WWR significantly improves energy efficiency and daylighting performance. Specific data shows that an orientation 5° east or 5° west of south helps balance energy consumption and daylighting performance in both large and small classrooms. The length and width of the classrooms vary greatly, but the height generally converges to 3.2 m. Increasing the WWR to 0.5–0.7 demonstrates good energy efficiency and daylighting performance in both large and small classrooms in hot climate zones. In all climate zones, compared to the initial scheme, the Knee solutions reduced the EUI of both types of classrooms by up to 19.0 %, reduced DGP by up to 56.4 %, and increased UDI by up to 19.0 %.

THE IMPACT OF DESIGN VARIABLES ON THE HEAT DEMAND OF A RESIDENTIAL BUILDING IN THE COLD REGION

This study is about the impact of early design decisions on the heat demand of a small residential building. Heat demand is a significant part of the energy use of residential buildings in the Cold regions. Reducing this demand will reduce the strain on national energy resources and even allow buildings to become energy neutral or independent with the addition of energy supply and storage systems. Based on a case study building of apartments, the simulation study explores the impact of several individual design aspects: insulation, orientation of glass facades and building shape. Furthermore, the balcony facade of the case study building is compared to a plain facade and a sunspace (balconies with a glass facade) in terms of heat demand and comfortable use (operative temperature). Based on these studies a final set of design guidelines is developed for designers interested in designing small residential buildings with low heat demand.

Does architectural design require single-objective or multi-objective optimisation? A critical choice with a comparative study between model-based algorithms and genetic algorithms

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.

Characterizing three-dimensional general arrangements and distributed system configurations utilizing an architecturally normalized current representation

Designing ships involves intricate layouts and multifaceted systems—ranging from mechanical to operational—that must be interdependent and thus precisely arranged. Traditional automated tools, though effective, are often too resource-intensive to be feasibly employed during the critical early stages of design. This paper builds on prior work that introduced an innovative solution: a network-based, architecturally normalized current representation, which offers a computational method to predict system arrangements in two dimensions without generating detailed vessel models. Our method’s advantage lies in its ability to guide early stage design decisions, thereby optimizing the use of subsequent, more resource-intensive design tools. This study extends the method to a three-dimensional framework, capturing more nuanced system-to-system interactions and yielding more realistic ship arrangements. A methodology was proposed to support this three-dimensional extension and demonstrate its applicability through a case study focused on the conceptual design of a naval frigate.

End-user research into understanding perceptions of and reactions to a microarray patch (MAP) for contraception among women in Ghana, Kenya and Uganda.

Many organizations are developing new contraceptive products and approaches that promote self-care including a microarray patch (MAP) that has the potential for self-administration with appropriate training. We studied women's perceptions of the MAP technology with the primary goal of providing feedback on product attributes to inform early technical design decisions regarding various MAP contraceptive products in development by MAP developers. Our study consisted of a qualitative phase with in-person In-Depth Interviews (IDIs) with a total of 60 women of reproductive age (WRA) and quantitative surveys, via face-to-face computer-assisted interviews of a total of 927 women in Ghana, Kenya and Uganda. Women's perceptions on 12 attributes of the MAP were assessed through written descriptions, a profile, and visual stimuli such as graphics and images. Overall, the most widely preferred attribute set included: a hand-applied MAP, utilizing one circular patch, with a sticky backing, no larger than 2 cm diameter in size, applied by self, to the arm, offering sensory feedback (clicking sound and/or color change signals) to confirm enough pressure, successful application and removal, lasting 6 months with up to 12 months return to natural state of fertility. There is space to allow for variation in MAP designs (including the use of an applicator or provider administered MAP) if the design promotes and reflects the needs and expectations of users and providers. The contraceptive MAP had a high and broad level of appeal amongst all groups of women who participated in the study and has a strong value proposition around important contraceptive needs such as ease of use, convenience, and discretion.

Improved single‐layer powder core inductor design procedure for DC‐DC converters

AbstractOne of the early design decisions for DC‐DC converters is the choice of an appropriate magnetic core material. Powder cores in particular are suitable due to their saturation flux density of up to 1.5 T. However, high DC currents constitutes a significant problem since the winding cross‐section rises with the current, which consequently increases AC losses due to skin effects. Another challenge is the reduction of winding layers to decrease proximity losses. This paper presents an improved single‐layer winding design procedure for DC‐DC converter powder cores. The procedure determines the optimum number of parallel cores and stacks. This way, the procedure enables a predefined maximum wire cross‐section for copper loss optimisation and single‐layer winding. The introduced approach defines the optimum switching frequency as a trade‐off between core loss and core volume. Finally, experimental tests with two setups validate 27 kW DC‐DC converters and hence the design procedure using Si‐IGBTs and SiC‐MOSFETs. The results demonstrate that SiC‐MOSFETs reduce core volume by a factor of 3 compared to Si‐IGBTs. Further, the SiC setup reaches high efficiencies of up to 98.53% compared to 98.17% for the Si‐based converter.

Integrated climate-responsive thermal load ML model and cost/embodied energy estimate from a preliminary building design

This paper proposes a framework applicable to preliminary building design that cohesively integrates material cost estimation, embodied energy assessment, and the prediction of the thermal demand of a building. At its core is a climate-responsive machine learning model specifically developed using readily accessible early-stage design data. This model forecasts the thermal demand both in the present and in the face of likely climatic shifts. To strike a balance in energy performance goals, material cost estimation is seamlessly incorporated. Building on the gradient boosting machine, the proposed approach showed better performance than other algorithms, predicting thermal demand 25 to 37.5 times faster than conventional energy software without compromising accuracy. The key findings of the presented case studies highlight the significant influence of the Window-to-Wall ratio and floor thermal mass on thermal demand—especially in mitigating cooling demands posed by potential climate change scenarios. By enhancing interior floor types, heating demand can be reduced by nearly 49%. The study further suggests that incorporating climate change considerations into life cycle energy designs is projected to amplify thermal and cooling loads by 32% and 94%, respectively, highlighting the crucial role of early design decisions in shaping sustainable and efficient buildings.

Building upcycling or building reconstruction? The ‘Global Benefit’ perspective to support investment decisions for sustainable cities

Investment decisions on demolition and reconstruction vs. refurbishment of the existing building stock can extend beyond financial and economic criteria. However, they must involve energy savings, environmental preservation, material consumption, and waste management for sustainable cities. The regulatory framework used in the past decades and the correlated research seem more unbalanced toward the containment of building energy consumption than toward embodied energy (EE) management in production processes and environmental impact management. Foreshadowing the perspective of a more restrictive regulatory framework on EE, such as prohibiting the displacement of materials with residual energy potential, such as waste in landfills, some challenging frontier issues are involved when facing the limits of the economic evaluation methodologies for transformation projects. Thus, this study aimed to propose a reasoning and an operative modality to support urban governance policies and investment decisions involving private and public subjects in the construction sector. Circular economy and life cycle thinking principles, through life cycle costing (LCC) and life cycle assessment (LCA), are assumed and harmonized with the discounted cash-flow analysis (DCFA): (1) monetizing and modeling into the DCFA the EE and the embodied carbon (EC); (2) internalizing the Global Cost and the new ‘Global Benefit’ into the net present value (NPV) calculation; and (3) focusing on the residual end-of-life value calculation from the early design and investment decision stages. The reasoning can be extended to single buildings, the urban scale, or even entire portions of existing buildings in urban areas concerning typological sub-segments. The operative modality is yet to be explored in a concrete application for orienting urban governance policies and sustainable public–private partnerships, including environmental and, thus, social externalities even in the private real estate investment decision process, in the scope of evolving regulations.

Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design decisions

Postponing performance analysis of buildings until the final stage of the design process often leads to unexpected results, introducing errors and disrupting the architect's workflow. The literature review identifies prior research limitations, motivating this study to develop a high-rise design optimisation workflow, automate design preparation, explore alternative airflow simulation methods, and compare Optimisation (Opt) algorithms with Genetic Algorithms (GAs) for broader design considerations. We thus propose an integrated computational workflow for sustainable building design optimisation with four objectives. This study applied it in the early design phase of a practical energy efficiency-oriented redevelopment project involving a cluster of seven high-rise office buildings in Nanjing. The project aimed to decrease annual energy use, increase solar and wind energy potential, and reduce structural displacement. Our methodology primarily utilises the surrogate-model algorithm Radial Basis Function Muti-objective Optimisation (RBFMOpt) algorithm, enabling quick identification of self- and new- energy demands and cost-effective structural solutions for tall buildings while optimising energy performance. Different from traditional genetic algorithm-driven optimisation procedures or with a separated post-optimisation, our workflow follows a pre-design approach. Test results are automatically optimised in a loop without post-processing, aligning with the architect's research objectives and improving the decision-making process. Our study yielded a 12% increase in energy use efficiency and a 23% improvement in structural stability. However, achieving these gains resulted in a 32% decrease in solar energy potential. Furthermore, we provide a comparative study between RBFMOpt and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), offering critical guidance for designers when selecting optimisation algorithms to address efficiency and precision challenges in energy-related design. These findings contribute to an integrated research-for-design workflow, providing data-driven evidence to support early design decisions.

Informing Early Design Decisions Through Functional Analysis of Maintenance Drivers

Operational expenditures dominate the cost of long field-life systems, with maintenance comprising a significant proportion for many systems. However, engineers lack the tools to assess maintenance during conceptual design. Familiar systems mitigate this problem by providing historical maintenance data from which empirical models can be derived. Emerging technologies, like marine renewables, lack operational maintenance data. As a result, engineers must make decisions with no historical data and minimal, if any, operational experience. The high operations cost incurred from basic maintenance tasks, and the loss of energy production during maintenance, further highlight maintenance as a critical cost driver. This paper develops a data-driven model to estimate maintenance intervals of long field-life systems during conceptual design. The model links the elementary functions of a component to maintenance requirements. Relative maintenance considerations were determined by mining function and maintenance data from manuals of long field-life systems. Machine learning was applied to generate a function-maintenance model from the maintenance data. The model consisted of functions grouped into buckets of increasing maintenance demand. The machine learning model was applied to an exemplary long field-life system, a wave energy converter, to explore possible redesigns to reduce maintenance costs. This paper shows that maintenance costs, actions and intervals can be confidently accounted. The function-maintenance model offers two beneficial impacts: it reduces life cycle cost uncertainty, and allows engineers to make informed decisions during conceptual design when redesign costs are at their lowest.

Iterative design development of a reusable micro-launcher within the RRTB project

The Recovery and Return-To-Base European Reusable Micro-Launcher project, part of the European Commission’s H2020 programme, intends to develop a novel reusable micro launcher system to bring independent and cost-effective access-to-space for the European small satellite market. The multi-disciplinary project focuses on the optimization of aerospike tail-first atmospheric re-entry of the MESO vehicle, together with the design and manufacturing of reusable cryogenic tanks, and the effort is divided into two design loops, where each design loop starts from establishing early design decisions and assumptions so that dedicated subsystem work may progress immediately. The multiple design loop approach allows for unsuitable design decisions, or risky and speculative assumptions to be revised or excluded at the project half-way point, thereby acting as a form of risk management. Throughout the work performed in the first design loop several conclusions were reached. The design presented both novel design features, but also critical issues which became better understood as the fidelity of the subsystem designs and mission analysis increased. These issues ultimately lead to the overall system design and CONOPS to be updated for the second loop, with several of the early design choices being modified, especially through the removal of the EDF landing system, based on the results of the first loop. For the second design loop, the design is updated to alleviate the most critical problem drivers, and to improve the system’s ability to fulfil the top-level requirements and objectives. The updated design uses a Mid-Air Catch approach, and leverages on the heritage of the previous design loop, implementing tailored changes based on the outcome of the trade-offs performed at mission and system levels.

Carbon assessment of building shell options for eco self-build community housing through the integration of building energy modelling and life cycle analysis tools

To achieve the UK's net-zero carbon transition by 2050, new homes need to be designed to high energy efficiency standards and minimise life cycle carbon emissions. This study develops a method that integrates building energy modelling (BEM) and life cycle assessment (LCA) tools, IES Virtual Environment (IES VE) and One Click LCA, to conduct a life cycle assessment of the operational and embodied carbon impacts of a typical terraced building shell in an eco self-build community housing (ESBC) project, using Water Lilies as a case study. It evaluates and compares the 60-year life cycle carbon impacts of six design options that apply different insulation materials to the case study building shell. The results are based on grid-connected Future Energy Scenarios (FES) that have different carbon intensities predicted over time and the microgrid-connected Water Lilies Community Energy (WLCE) scenario that is operationally net-zero carbon. Depending on the FES, embodied carbon accounted between 59 and 80% of total emissions compared to 20–41% from operational carbon across design options. The results show that operational carbon is highly sensitive to the percentage of renewables in the energy mix of the grid, which is difficult to predict, and can significantly influence the design choice. As such, future researchers should account for predicted changes to the carbon intensity of the grid by applying a similar method to that proposed. Overall, the integration of IES VE and One Click LCA provided a more streamlined life cycle assessment to help inform early design decisions by enabling energy consumption simulation in BEM to assess operational carbon emissions and the automated transfer of building materials from the BEM model to the LCA tool to assess embodied carbon emissions. The limitations included the unavailability of material-specific data in early design stage plans, the inability to automatically transfer the timber frame structure from IES VE to One Click LCA, and the uncertainty of future energy trends.

Towards the prediction of carbon consequences in early design decisions

Manufacturing processes and associated supply chains are a recognised source of carbon emissions. In part these emissions are related to the design of the product itself. For example, decisions surrounding the selection of materials have a high impact on emissions and hence the embedded carbon in manufactured products. While there are tools that support the analysis of designs from environmental perspectives, they tend to require near complete design descriptions that are only available later in the design and development process when time and scope for change is limited, e.g., by scheduling and cost constraints. Further, there are often trade-offs to be made. For example, decisions related to the selection of materials impacts the embedded carbon of resources needed to manufacture a product but may also have a knock-on effect on transport and associated carbon emissions.This paper reports early work exploring the feasibility of establishing engineering design tools that are suitable for use early in the design process when the design definition is incomplete and there is scope for exploring a wider range of design options. An illustrative case study was used to consider the quantification of carbon costs associated with material selection and associated transportation. Discrete event simulation models were derived from early design descriptions coupled with a range of alternative material and supply chain scenarios. Early results are promising in that they can be used to compare carbon consequences of alternative design directions.

Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Abstract Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.

An Evaluation Process for IoT Platforms in Time-Sensitive Domains

Determining the temporal behavior of an IoT platform is of utmost importance as IoT systems are time-sensitive. IoT platforms play a central role in the operation of an IoT system, impacting the overall performance. As a result, initiating an IoT project without the exhaustive knowledge of such a core software piece may lead to a failed project if the finished systems do not meet the needed temporal response and scalability levels. Despite this fact, existing works on IoT software systems focus on the design and implementation of a particular system, providing a final evaluation as the validation. This is a risky approach as an incorrect decision on the core IoT platform may involve great monetary loss if the final evaluation proves that the system does not meet the expected validation criteria. To overcome this, we provide an evaluation process to determine the temporal behavior of IoT platforms to support early design decisions with respect to the appropriateness of the particular platform in its application as an IoT project. The process defines the steps towards the early evaluation of IoT platforms, ranging from the identification of the potential software items and the determination of the validation criteria to running the experiments and obtaining results. The process is exemplified on an exhaustive evaluation of a particular mainstream IoT platform for the case of a medical system for patient monitoring. In this time-sensitive scenario, results report the temporal behavior of the platform regarding the validation parameters expressed at the initial steps.

Suggestions for solution space exploration in the early stage of architectural design based on a literature review

Early design decisions have higher potential to influence building performance compared with the decisions made at later design stages. Performance simulation and optimization algorithms have been integrated to assist early design in reducing carbon emissions, improving indoor thermal comfort, etc. However, early decision making within a limited time frame is still challenging due to the large number of design options, the lack of decision-making guidance, and the trade-offs among various requirements. Selecting appropriate methods to explore design space is the key to find an ideal solution. This paper reviewed the challenges and identified the key questions to access the ability of existing decision-making methods to cope with different challenges. It is concluded that the interactive exploration of design space could be more effective and efficient by (1) combining the surrogate models and the automated optimization algorithms to improve the efficiency of the building performance calculation and the optimal design space position; and by (2) extending the optimal design space to increase the solution diversity, and (3) filtering the near optimal design space with consideration of the stakeholders’ preferences and values. Further integration of tools for building performance simulation, diversity description and decision-making guidance is needed to support the decision -making process.

Investigating effects of group model building on sustainable design decision-making

This research seeks to investigate the effects of a Group Model Building approach on the sustainability knowledge base prior to the weighting of design requirements. Current practice shows that the knowledge about sustainability impacts and implications on other design objectives often is limited and dispersed among decision-makers. Including sustainability criteria in needs and requirements, therefore, tend to rely on limited knowledge and decision-support, leading to decision-makers often prioritizing design objectives that they are more experienced with. The aim of this research was to capture and discuss effects on team alignment of contextual sustainability understanding from co-modeling impacts and implications of sustainability on other design objectives. The research question guiding the study is ‘what are the effects of a group model building approach on early sustainability design decisions?’. A mixed-methods research, structured according to four assumptions, was designed to analyze effects quantitatively and qualitatively. The assumptions were that the proposed approach helps design teams foster an improved contextual sustainability understanding, align the view of the relative importance of sustainability, identify suitable sustainability improvement, and increase the knowledge maturity of their design decisions. The results triangulation showed that the participants showed signs of improved sustainability understanding of their design projects and that improvement actions were identified from applying the approach. A statistically relevant converging effect could be found on the team alignment of the weighting of sustainability criteria but not on the knowledge maturity. These findings are discussed to conclude on main contributions, limitations, and areas for future work.

Evaluation of the Natural Lighting Performance of Rooftop Daylight Installations for Multi-purpose Sports Hall in Seoul

Purpose: The demand for natural light to maintain studetns’ the physical and psychological health is increasing As indoor activities are required in multi-purpose sports halls owing to the outbreak of Covid-19 and the worsening of outdoor fine dust pollution. Therefore, this study aimed to provide design information for architects and all stakeholders to rely on in the initial design stage. Method: This study analyzed useful daylight index and daylight glare probabilty for side-lit windows, rooftop lightwells, and hybrid types. This study examined daylight performance with the Radiance engine and compared the numerical results and patterns for cases with different window sizes to find optimized instances. Result: The side-lit clerestory and eye-level types showed the best daylight performance at window-wall-ratio of 20% and 15%, respectively. The sawtooth and the cone lightwell type proved superior performance among the rooftop types; they showed excellent useful daylight index performance in cases with window-floor-ratio of 10% or higher. The daylight glare probability results showed that in all subjective cases disturbing glare was not found; in particular, the rooftop application demonstrated well-controlled glare-free environments. Moreover, the hybrid type demonstrated significantly improved daylight performance compared with the basic separate applications. The best daylight performance was the hybrid cases of sawtooth and cone types with side-lit windows of window-wall-ratio 10%. This paper provides helpful information in the early design decision stages, and detailed design research is required in the future.