Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Design needs multi-resolution models & solvers (CPU dependent, taking advantage of
current architectures, GPUs, etc.)
Better input to models
meshing, parameter specification,
Coupling.
Current tools are not “compatible”
Models are developed independently for different tasks (not holistic)
Parameter reduction for design (what parameters are important?)
Reduced models (couple, UQ, different physics)
Design to the worst case
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Buildings are stochastic
Designed using one-point (ideal or peak conditions)
Performance changes over time (requirements)
Software does not adequately model fine scales in components.
What are the important parameters?
Research  tool  industry (tools that bridge the gap between modern industrial
practice and the current research)
Mathematics for uncertainty analysis (apply to existing simulation tools or
develop new tools)
100s of parameters
High fidelity  reduced models
Take advantage of passive resources
Tools needed to explore new mechanisms in control of buildings (explore “what if”
scenarios)
Actual/design does not account for climate, tuning, etc.
QC should be introduced in build Simulation codes
Design times < 2 months to put in practice
Tools are constrained to keep this design cycle time
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Construction/Commissioning
Tool or building blocks (research  practice)
Financial incentives IPD (all stakeholders involved could identify inefficiencies)
Simulation:
design & compliance
(who is responsible if a contractor makes changes?)
Metric:
see Comnet (Michael Holtz), Occupant use
Engaging occupants in M&V
Affect occupant behavior
Fault detection & management
Computational priorities
 Fault detection identification of performance problems - tuning
 Simulation of climate adaptive performance
 Unifying model – design, construction, operation
Functional testing vs performance at commissioning model calibration – data collection
(1 year  )
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
Operation
How do we make best use of all measurements?
model predictive control
Model validation is needed (should match modes)
Math and/or engineering challenges
Communication between scientists & building managers is required
July 8-9, 2010
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Design | Construct | Control
Open Discussion
J Glimm: will performance = real world
ROM  full order with validation
Data + validation. V&V
Nick Long, NREL:
data gathering
-
ground temp
Slab temp
Weather
Translation of math/architect models to Simulation Models.
BIM (mega BIM keeps simulation output information storage)
Standards analysis 
scale up to city level systems
Understand/model occupants
Arch to SIM & human factors > not in ASCR
Geometric modeling
ASCR – math of petascale data. Existing program.
Includes data assimilation/ROM
Store data preserving design intent
Hans: Metrics (cost)
SIM – UQ – optimization (cost) will lead to adoption
What is the potential gain from optimization?
What components of models do we trust?
Michael (LBNL):
Tools:
-data
-phys.
-compilers
BIM & control/HVAC in the simulation
Thermal – Air – Elect -- Grid
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HVAC system sizing & cont analysis
DAE & discrete models
Optimization with mixed integer programming & stochastic simulation.
Usable by design engineers
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Simulation of multiscale systems.
Soil (days)  control (sec)
Component models coupled/exchanged through lifecycle.
Opt with large numbers of parameters under high uncertainty
In operation   control under uncertainty (act state or sensor inform. Model
(material properties))
Robust design, estimation and control for large scale systems.
Design for better control (management of uncertainty) for better
construction/control
Assess passive control vs active control design trade-offs
The large amount of data in buildings provide an opportunity for validating
models not usually seen in other fields.
John ALG: optimization – multi obj. mixed int.
Control and efficiency can be competing objectives
Uncertainty in f, in parameters, discrete uncertainty
Simulation under occupancy
Data reconciliation with uncertain model
Software Engineering: input parameters
Architecture  Construction  HVAC
GUI for practitioners (non expert)
Computing platforms – algorithms on
All req. diff approaches
Rapid prototyping – interface
HPC (a variety of platforms)
PC
Cloud
Grid
ROM with varying parameters (climate, materials, etc)
Scott: 2006 complex systems report areas (link on wiki)
Application driver for opt of Computational Science 2011
This white paper should: FOCUS on fundamental research issues – WP
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
And may be seen as complementary to the energy hubs
J. Grosh: “user driven’ applied math + CS program.
CS challenges – model red from “LARGE” sims.
Babel
Boundary cond. modeling
Software engineering
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Complexity of physics/models vs computational effort (e.g. cycles)
Modeling sensors/real-time
Accelerating a policy obj. through math + CS
What is unique to buildings?
Path from research to application
Marcello: building industry is resistant to change in general.
Need faster tools/cost efficiency
Cost of changes design
 At construction (cheap)
 At operation (expensive)
Owners are applying pressure to architects, builders, etc.
Feedback in buildings usually comes from failures (not from underperformance)
Learn from field data (while building is in use)
Todd: industry & math communication metrics (when do we declare success?)
Validation & verification, state estimation, calibration
Optimization
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Sensor placement semi-definite programming
Time delays in system (distributed vs centralized control)
Design for retrofit
CS – visualization, I/O, scientific computing, Workflows metadata
Behavioral economics (might model occupancy)
(401k opt-out)
Greg, HOK: optimization of a static bldg. of the design conditions.
Fitness testing in geometric relations.
Existing buildings: modeling inference  analysis
Point clouds – extracting information from existing buildings
Tools. CS standards
Problems
Deliverables
Timeline