Fundamentals of Building Performance Simulation

Ian Beausoleil-Morrison

• Publisher: Routledge
• Year: 2020
• eBook ISBN: 9781000169287
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Architecture General
• Index: Technology & Engineering

Fundamentals of Building Performance Simulation pares the theory and practice of a multi-disciplinary field to the essentials for classroom learning and real-world applications. Authored by a veteran educator and researcher, this textbook equips graduate students and emerging and established professionals in engineering and architecture to predict and optimize buildings' energy use. It employs an innovative pedagogical approach, introducing new concepts and skills through previously mastered ones and deepening understanding of familiar themes by means of new material. Covering topics from indoor airflow to the effects of the weather, the book's 19 chapters empower learners to:

• Understand the models and assumptions underlying popular BPS tools
• Compare models, simulations, and modelling tools and make appropriate selections
• Recognize the effects of modelling choices and input data on simulation predictions
• And more.

Each subject is introduced without reference to particular modelling tools, while practice problems at the end of each chapter provide hands-on experience with the tools of the reader's choice. Curated reading lists orient beginners in a vast, cross-disciplinary literature, and the critical thinking skills stressed throughout prepare them to make contributions of their own.

Fundamentals of Building Performance Simulation provides a much-needed resource for new and aspiring members of the building science community.

I
_______________

Prelude

CHAPTER 1

---

Introduction to BPS

---

THIS chapter provides an introduction to BPS. It briefly describes what it is, how it works, and who uses it. You will come to understand how to use this book, and you will apply your chosen BPS tool to represent a simple building called the Base Case that will form the basis for simulation exercises you conduct in subsequent chapters.

Chapter learning objectives

1. Understand the key characteristics of BPS and the role it can play in the design, analysis, and operation of buildings.

2. Appreciate how history has influenced the choice of models utilized in current BPS tools.

3. Become aware of the methods used to validate BPS models and tools.

4. Understand the learning approaches employed in this book.

5. Become familiar with operating your chosen BPS tool and develop skills at translating the description of a simple building into appropriate input data.

1.1 WHAT IS BPS?

BPS employs a large number of mathematical models to simulate a building’s performance under a given set of boundary conditions. Many aspects of performance might be appraised by BPS, including energy consumption, ventilation effectiveness, thermal comfort, lighting quality, etc. The objective is to represent the significant physical processes so that the simulation provides an accurate— or at least a useful—representation of reality.

The mathematical models employed in BPS are simplified descriptions of complex systems and processes. They necessarily make approximations to reduce the complexity to a manageable level for both the computer and the user. The necessity of these approximations can be appreciated by focusing on the building illustrated in Figure 1.1.

Figure 1.1: The Urbandale Centre for Home Energy Research (a research facility at Carleton University)

Consider all of the heat and mass transfer processes occurring between this building and its surrounding environment. Heat is being transferred from the warm indoor surfaces of the building envelope to the cold exterior surfaces by means of conduction through solid materials such as the gypsum interior wall board, the wood studs forming the wall’s structure, and the cedar cladding. And heat is being transferred by convection, radiation, and conduction (solid and gaseous) through the wall’s fibreglass batt and foam insula-tions. All of these processes are three-dimensional and transient in time.

There is convective heat transfer from the exterior wall surfaces to the outdoor air, which is dependent upon wind velocity (speed and direction). The wind velocity patterns in the vicinity of the building are in turn affected by the house’s shape and size, and by surrounding buildings and objects. There is radiation heat transfer in the infrared spectrum from the exterior wall surfaces to water molecules in the earth’s atmosphere, to deep space, and to the surfaces of the surrounding ground and objects.

Solar radiation—some of which is scattered by the earth’s atmosphere—is incident upon exterior wall and window surfaces. Some of this radiation will be transmitted through window glazing layers, while some will be absorbed and reflected by the individual glazing layers, all of which depends upon the solar radiation’s angle of incidence. And some solar radiation is reflected by the ground towards the building, further increasing solar transmission to the interior, but the amount of reflection is dependent upon the composition of the snow cover, which is influenced by moisture content, temperature, and time.

The solar collectors on the roof of the building are partially covered from a fresh snowfall, which influences their ability to capture solar gains, and therefore impacts the thermal storage and auxiliary heating systems. The positioning of the blinds is another complication, as they affect the previously mentioned infrared and solar radiation processes, and may be controlled by occupant behaviour. Air can infiltrate past the house’s air barrier due to imperfections in sealing, and these flows will depend upon local indoor–outdoor pressure differences in the vicinity of these unintentional openings.

This is only a partial inventory of the significant heat and mass transfer processes occurring in reality. Mathematical models must be constructed to represent each significant physical process and these must be discretized in numerical form, and then solved collectively. This solution approach is necessary because all of these processes are interconnected so they cannot be solved in isolation. For example, the solar radiation absorbed on the exterior glazing surface influences its temperature which, in turn, influences the infrared radiation emitted by that glazing to the atmosphere.

It is easy to understand why simplifications are necessary to reduce this complexity to a manageable level of detail for solution purposes. But this necessity to simplify is also driven by user considerations. Can you imagine how a user might describe when blinds would be retracted or deployed? Or whether snow will build-up on the solar collectors, and how quickly it might melt?

Due to the complexity of the reality—and the necessity of simplifications—BPS inherently operates with significant uncertainty, and this must be recognized and acknowledged by users. How accurately can the solar reflectivity of that snow be estimated, and what impact does this have upon simulation predictions? What is the thermal conductivity of the wood studs within the wall assemblies (did the construction crew use fir or spruce?), and what impact does this uncertainty have upon predictions?

Another characteristic of BPS is that it operates with transient (time-varying) boundary conditions. We rarely wish to predict the performance of buildings at a single snapshot in time. Rather, we typically use BPS to march through time (maybe a week, a month, a year, or multiple years) to predict performance subject to time-varying boundary conditions. These time-varying boundary conditions include occupant presence and behaviour (e.g. window openings, appliance operation) and changing weather, all of which must be prescribed by the user or predicted with models.

1.2 HOW DOES IT WORK?

The user must provide considerable input data to the BPS tool to exercise all of these models. The level of detail required depends upon the modelling approaches employed by a particular tool or selected by the user. Table 1.1 broadly categorizes the types of input data that may be required.

Table 1.1: Types of input data required from user

Category	Inputs
Geometry	Building plan and elevation Internal space layout Window sizes, locations, and shades Shading by neighbouring buildings and objects
Materials	Properties of structural and insulating materials Radiative properties of glazings
HVAC	Energy conversion and di...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Nomenclature
• Part I Prelude
• Part II Building interior
• Part III Exterior environment
• Part IV Building envelope
• Part V HVAC
• Part VI Finale
• References
• Index