MODELING & SIMULATION · 2016-09-13 · Modelling methods for energy in buildings. John Wiley &...

MODELING & SIMULATION

Week 2

7LYM30Building performance and energy systems simulation

7LYM30 - Building performance and energy systems simulation2

Course outline

2. Modeling and simulation

7LYM30 - Building performance and energy systems simulation

» Modeling and simulation» Calibration, verification, validation» Complexity vs. accuracy» Building models» Main concepts in building energy simulation» Quality assurance

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Outline


Simulation vs. experiment

HypothesisExperiments

Observations

Simulated data

Simulation

Scientific method

Scientific method extended

Representationof reality

Via physicalmodels

Via virtual models


Why do we use models?


Why do we use models?

Understand the world around us

Predict the future

Manage risks

Make better-informed decisions


» System: subject or thing that will be investigated using M & S.– Physical: something that already exists– Notional: a plan or concept for something physical that does not

or not yet exist

» Model: physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process. A model is not meant to represent all aspects of the system being studied, but only those that are relevant for the objective of the study.

» Simulation: an applied methodology that can describe the behavior of that system using either a mathematical model or a symbolic model. It can be the imitation of the operation of a real-world process or system over a period of time.

Modeling and simulation

Sokolowski, J. A., & Banks, C. M. (2010). Modeling and simulation fundamentals: theoretical underpinnings and practical domains. John Wiley & Sons.


Modeling and simulation

Sokolowski, J. A., & Banks, C. M. (2010). Modeling and simulation fundamentals: theoretical underpinnings and practical domains. John Wiley & Sons.


Virtual experiment

Augenbroe, G. (2011). The role of simulation in performance based building. Building performance simulation for design and operation, 15-36.


Modeling and simulationcycle» Incremental and iterative

» Communication is important

» Different model versions

» VV&T is a continuous process

Balci, Osman. "Verification validation and accreditation of simulation models." In 29th conference on Winter simulation, pp. 135-141. IEEE Computer Society, 1997.


» Verification is concerned with determining whether the conceptual simulation model (model assumptions) has been correctly translated into a computer “program”, i.e., debugging the simulation computer program.

» Validation is the process of determining whether a simulation model is an accurate representation of the system, for the particular objectives of the study. If a model is “valid,” then it can be used to make decisions about the system similar to those that would be made if it were feasible and cost-effective to experiment with the system itself.

» Note: model is constructed and validated with certain questions and experiments in mind

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Verification and validation


SINSFIT (Simulation is not a substitute for intelligent thinking)» Failure to have a well-defined set of objectives at the beginning of the

study» Misunderstanding of simulation by management» Failure to communicate with the decision-maker on a regular basis» Failure to collect good system data» Too little information about the expected performance » Inappropriate level of model detail – this is one of the

most common errors, particularly among new analysts» Lack of knowledge of simulation methodology

and also probability and statistics» …

Banks, J., & Chwif, L. (2011). Warnings about simulation. Journal of simulation, 5(4), 279-291.


» Scope: How much of the real world is represented– Building, HVAC, renewables– Thermal, daylight, airflow,

acoustics, …– Room, floor, building,

district– Day … year … decades

» Resolution: The number of variables in the model and theirprecision or granularity– Spatial– Temporal

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On simulation model complexity

Model complexity

Developmentalcomplexity

Scope Resolution

Computational complexity

Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000).Theory of modeling and simulation:. Academic press.


Error vs. uncertainty

Trčka, M., & Hensen, J. L. (2010). Overview of HVAC system simulation. Automation in Construction, 19(2), 93-99.


» Error is a recognizable inaccuracy in any phase or activity of modeling and simulation that is not due to lack of knowledge

» Aleatory uncertainty is the inherent variation associated with the physical system or the environment under consideration

» Epistemic uncertainty is a potential inaccuracy in any phase or activity of the modeling process that is due to lack of knowledge

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Error vs. uncertainty

Trčka, M., & Hensen, J. L. (2010). Overview of HVAC system simulation. Automation in Construction, 19(2), 93-99.

Sum

Predictive uncertainty 1

Predictive uncertainty 2

Decreasing uncertainty in input parameters


» Simple models can be developed faster» Simple models are more flexible» Simple models require less data» Simple models run faster» The results of simple models are easier to interpret since the

structure of the model is better understood

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Fit-for-purpose

Kotiadis, K. Robinson, S. (2008) Conceptual modeling: knowledge acquisition and model abstraction. Proceedings of the 2008 Winter Simulation Conference

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Building models

7LYM30 - Building performance and energy systems simulation21 [http://www.fosterandpartners.com/projects/30-st-mary-axe/]

Many models are made in the design phase of a complex building

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Main concepts in building energy simulation

7LYM30 - Building performance and energy systems simulation25 [Clarke, J. A. (2001). Energy simulation in building design. Routledge.]


Everything is in balance and influences other things

TIME HISTORYSOLAR MODELS

INTERNAL SOURCES MODELS

BUILDING FABRIC MODELS

OVERALL AIR HEAT BALANCE MODEL

MATERIALS PROPERTIES

WEATHER DATA

HEAT INPUT

WEATHERDATA

SHADING GEOMETRY SOLAR ENERGY INPUTS

GLAZING MODELS

SOLAR ENERGY INPUTS

MATERIALS PROPERTIES

WEATHER DATA

HEATINPUT

HEAT INPUT

HVAC SYSTEMS

HEAT INPUT

TIME HISTORY


Transient heat conduction

Underwood, C., & Yik, F. (2008). Modelling methods for energy in buildings. John Wiley & Sons.


Transient conduction, thermal inertia

[http://www.nudura.com]


» Steady-state

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Building energy modeling techniques

» No accurate inclusion of many effects

» No dynamic response of buildings

» Limited use in design stage (accuracy!)


» Steady-state» Simple dynamic

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» Based on regression techniques

» Original results from more powerful modelling systems

» Results in tabular, graphical format


» Steady-state» Simple dynamic» Numerical

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» Approximation of partial differential equations

» Spatial/temporal integrity

» Handle complex flow paths

» Time varying parameters

» Handle systems with different time constants.


Example: convective heat transfer coefficient

– Time-dependent– Function of wind speed, wind

direction and turbulence regime

– Influenced by urban context– Spatial distribution– Depends on surface

roughness

200

160

120

80

40

0 Flow

CHTC (W/m2 K )

Flow

150

120

90

60

30

0 Flow

REALITY

[Montazeri H, Blocken B, Derome D, Carmeliet J, Hensen JLM. 2015. CFD analysis of forced convective heat transfer coefficients at windward building facades: influence of building geometry. Journal of Wind Engineering and Industrial Aerodynamics]


CHTC implementation in IES VE


Example: convective heat transfer coefficient

REALITY TYPICAL BES MODEL

– Time-dependent– Function of wind speed, wind

direction and turbulence regime

– Influenced by urban context– Spatial distribution– Depends on surface

roughness

– Time-dependent or fixed– Function of wind speed and

direction– Isolated building– Surface averaged


Assumptions: example

Liu, J., Heidarinejad, M., Gracik, S., & Srebric, J. (2015). The impact of exterior surface convective heat transfer coefficients on the building energy consumption in urban neighborhoods with different plan area densities. Energy and Buildings, 86, 449-463.


Impact of assumptions

Mirsadeghi, M., Cóstola, D., Blocken, B., & Hensen, J. L. M. (2013). Review of external convective heat transfer coefficient models in building energy simulation programs: implementation and uncertainty. Applied Thermal Engineering, 56(1), 134-151.

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Quality assurance


» QA is all about developing confidence in the predictions of simulation tools

» We need to be confident that our models are providing an “accurate” representation of how a building or system will behave in reality

» This is important as we are basing design decisions on the results from simulations

» One of the main mechanisms to improve the quality of a simulation tool is to undertake rigorous validation

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Quality assurance


» The validation of building simulation programs is a challenging field that has existed almost as long as building simulation itself

» Extensive validation efforts have been conducted under the auspices of:– the International Energy Agency (IEA)– the American Society for Heating Refrigeration and Air-

Conditioning Engineers (ASHRAE)– the European Committee for Standardization (CEN)

» The aim of these efforts was to create methodologies, tests, and standards to verify the accuracy and reliability of building simulation programs

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Validation tests

R. Judkoff, D. Wortman, B. O’Doherty, and J. Burch. A methodology for validating building energy analysis simulations. Technical Report TR-254-1508, Solar Energy Research Institute, Golden USA, 1983.


» Validation is a means to diagnose internal sources of error in simulation codes

» Judkoff et al. [1983] classify these errors in three groups:– differences between the actual thermal transfer mechanisms

taking place in reality and the simplified model of those physical processes;

– errors or inaccuracies in the mathematical solution of the models; – coding errors

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Types of errors

R. Judkoff, D. Wortman, B. O’Doherty, and J. Burch. A methodology for validating building energy analysis simulations. Technical Report TR-254-1508, Solar Energy Research Institute, Golden USA, 1983.


» Judkoff and Neymark [1995] proposed three primary validation approaches to check for internal errors: – analytical verification; – empirical validation; – comparative testing

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Validation types


» In analytical verification, the program output is compared to a well known analytical solution for a problem that isolates a single heat transfer mechanism

» typically this necessitates very simple boundary conditions» although analytical verification is limited to simple cases for which

analytic solutions are known, it provides an exact standard for comparison

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Analytical verification

Xiao, D., Spitler, J. D., Rees, S. J., & Dougherty, R. L. (2005). Transient conduction analytical solutions for testing of building energy simulation programs.Building Services Engineering Research and Technology, 26(3), 229-247.


» Real buildings, controlled test cells, or in a laboratory» Obtaining high-quality data sets is complex and expensive, thus

restricting this approach to a limited number of cases» the characterization of some of the more complex physical

processes treated by building simulation programs (such as heat transfer with the ground, infiltration, indoor air motion, and convection) is often excluded due to measurement difficulties and uncertainty

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Empirical validation


Empirical validation

IEA EBC Annex 58SolarBEAT. www.seac.cc


» A program is compared to itself or other programs during comparative testing

» This approach enables inexpensive comparisons at many levels of complexity

» However, in practice the difficulties in equivalencing program inputs and outputs can lead to significant uncertainty in performing inter-model comparisons

» Comparative testing also provides no absolute measurement of program accuracy; while different programs may make similar predictions, all of these predictions may be incorrect

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Inter-model comparison


BESTEST – diagnostic tests


Quality assurance


External sources of error

» Experiment with 12 practitioners» Typical information

– Architectural drawings– Mechanical plans + schedule– Lighting plans + schedule– Location

Berkeley, P., Haves, P., & Kolderup, E. (2014). Impact of modeler decisions on simulation results. in Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, Atlanta, GA.


» Incomplete information» Different interpretations» Mistakes» Default assumptions» Difference between building information and simulation input

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External sources of error

Berkeley, P., Haves, P., & Kolderup, E. (2014). Impact of modeler decisions on simulation results. in Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, Atlanta, GA.


» Each student (individually!) reviews a weekly bundle of 3 assignments via PEACH

1. Rank the assignments from high to low2. Give at least three remarks per assignment

– Choose: +++, ++-, +--, ---

» Submit the remarks as *.pdf» Follow instructions on website and template document; the PEACH

system will ensure that both assignment and review are anonymized.

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Peer review


» Q&A sessions, every Friday: MF08

» Submit Concept Scripts as pdf!» How strict is the 3-page limit?

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To conclude


Questions?

MODELING & SIMULATION · 2016-09-13 · Modelling methods for energy in buildings. John Wiley &...

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