Tutorial 9: Use in practice
Q1. Describe three principal issues faced by engineers when deploying detailed
energy simulation tools in practice.
How to manage the application of simulation (who does what, when and where).
Implementation of a performance assessment method whereby each step in the
process is clearly demarcated and controlled (model definition, calibration,
simulation commissioning, results analysis, mapping to design decisions etc.).
How to quality assure models and the results they produce.
Q2. In relation to integrated building performance simulation, identify three
progressively detailed input levels and the performance assessments then made possible.
Select a relevant example from each section ‘Early’, ‘Intermediate’ and ‘Detailed’:
Cumulative model description
Early:
pre-existing database
+ geometry
+ constructional attribution
+ operational attribution
+ boundary conditions
thermal
Intermediate:
+ special material
+ control system
+ flow network
+ HVAC network
Detailed:
+ CFD domain
+ electrical network
+ enhanced resolution
+ moisture network
Typical performance assessment enabled
implied performance indicators (e.g. material hygrothermal and
embodied energy data etc)
visualisation, photomontage, shading, insolation etc
embodied energy, material quantities, time constants etc
casual gains, electricity demands etc
photo-realistic imaging, illuminance distribution, no-systems
and visual comfort levels etc
selectricity generation (photovoltaics), daylight levels
(switchable glazings) etc
daylight utilisation, energy performance, system response etc
natural/mechanical ventilation, heat recovery etc
psychrometric analysis, component sizing etc
indoor air quality
renewable energy integration, load control etc
thermal bridging
local condensation, mould growth and health.
Q3. In relation to energy systems simulation, select five principal program input parameters, state the
nature of a related uncertainty and suggest what action might be taken to reduce this uncertainty.
Any 5 from:
Climate: A stochastic system that is inherently uncertain. Micro-climate phenomena - wind hollows,
urban canyons, vegetation related cooling etc - can have significant impact. Models may be subjected
to a range of boundary conditions in order to test the robustness of a given design hypothesis.
Lighting: Clouds and pollution, will impact on the sky luminance distribution. Indoor surface finishes,
photocell response characteristics and lamp emissions have an associated uncertainty deriving from the
manufacturing process and calibration/maintenance considerations. It is possible to assume optimistic
and pessimistic scenarios to ensure an adequate lighting provision.
Glazing: The thermo-optical properties of glazing may exhibit a significant variation both within a
given sample (e.g. from centre to edge) and between samples (e.g. uncertainty in convective heat
exchange, particularly with solar shading devices). Context factors - such as frame conduction, urban
air pollution, window maintenance, shading device robustness etc - can have a significant effect on
performance related properties. Where this is likely, a higher modelling resolution could be adopted.
Form and fabric: The translation from design intent to on-site realisation gives rise to many
uncertainties in relation to final dimensions, construction composition and tightness. Each of these
factors can impact significantly on the final performance of the design. Contemporary concepts, such
as breathable facades, PV-integrated facades, transparent insulation, switchable glazings etc., will
increase the level of uncertainty inherent in the problem definition process and associated with the
selection of the thermo-physical properties of materials.
Q3. Continued
Ventilation: The quantification of a building's leakage distribution is an inherently uncertain task
and there exists scant guidance on the likely ranges to be found in practice. Likewise, the
determination of surface pressure distribution is dependent on the ameliorative effect of local
wind shelter phenomena that are the subject of considerable uncertainties. An otherwise
sophisticated program may well provide results that are inaccurate and inapplicable. A
sensitivity analysis, giving the variation of the results when input data are changed, is usually
required.
Occupant interactions: The physiological and psychological processes that give rise to particular
occupant responses to their environment are not well understood and few models exists for use in
predicting how people interact with ventilation, lighting and heating/cooling systems. The levels
of heat and moisture production can vary significantly, both between individuals and as a
function of the context.
Electrical systems: All electrical systems are characterised by fundamental parameters
(impedance, capacitance, inductance etc) that are affected by variations in temperature, moisture
and demand. As a stochastic process, demand, in particular, gives rise to a significant
uncertainty. Many electrical systems - such as PV components, power electronics and rotary
generators - are characterized by parameters that relate to standard test conditions (STC). As
conditions depart from STC so the applicability of the parameter values, and hence the
uncertainty, will grow.
Q4. Describe the steps involved in undertaking a performance assessment using an integrated energy
simulation program. For each step give an example of the required action and the related knowledge.
Establish a computer representation (e.g. a multi-component plant model with explicit representation of fluid flow
and control system) corresponding to a base case design (e.g. regulation compliant or with conservative component
sizes).
Calibrate this model (e.g. compare predictions to some reference case) using reliable techniques (e.g. inter-model
comparison).
Locate representative boundary conditions (e.g. of temperature, wind and solar irradiance) of appropriate severity
(e.g. typical or extreme).
Undertake integrated simulations (e.g. covering energy, mass and momentum balance) using suitable programs.
Express multi-variate performance (e.g. peak load, energy demand, environmental emissions, component
temperatures etc.) in terms of suitable criteria (e.g. PPD for thermal comfort, JPPD for visual comfort, reverberation
time for acoustics, NPI for energy).
Identify problem areas (e.g. overheating) as a function of criteria acceptability (e.g. PPD less than 10%).
Analyse simulation results (e.g. examine flow-path magnitudes) to identify cause of problems (e.g. excessive solar
gain).
Postulate remedies (e.g. change control system) by associating problem causes with appropriate design options.
For each postulate, establish a reference model (e.g. base case model with a change applied to a justifiable level of
resolution.
Iterate from step 4 until the overall performance is satisfactory (e.g. thermal comfort is attained for acceptable
energy consumption).
Q5. Identify five performance entities that might be displayed in an ‘Integrated
Performance View’ and describe how together they might be used to refine the
overall performance of a design.
1.
2.
3.
4.
5.
seasonal fuel and power consumption;
environmental emissions;
thermal comfort;
visual comfort; and
installed plant capacity.
Taken together, these performance entities allow a balance to be struck between
comfort requirements, energy use and environmental impact.