Jeff Borgaard (VT)
Satish Narayanan (UTRC)
Deep domain knowledge
 Blend in with existing practice
 Improve Energy Efficiency & productivity
Data Reduction,
 ROM for risk analysis
 Reduce visualization data (leverage data fusion)
Put in hands of all users
 Multi-resolution models
Simplify to put in opt. cycle
 Shape functions (response surface)
Dynamics & Control,
Physics
 Especially dynamics
 Control not adequate for energy. Ok for comfort.
July 8-9, 2010
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
Scalable toolset
Input BIM – model – solver network simulation (Robust)
Mixed integer programming, couple feedback with CFD
Leverage DOE technologies
For example: Embedded optimization w/uncertainty
UQ (mean, EC) Risk analysis (ASCR interest)
Bridge long range algorithm develop with application
Algorithms that perform well when cycle limited
Analysis tools couple w/sensor data
Experience 1st person in building
Building studio  (cloud-based)
July 8-9, 2010
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
Look at Building Simulation Journal for current state in Building Simualtions
Leverage DOE
 Develop for users
 Get input from multiscale/multiphysics CSE community
 Couple to ROM (workforce development)
Current practice (rule of thumb)
 Communication between all parties
HPC should be used by small/med bus.
(OSTP project in modeling & Simulation)
 Links to come
Buildings are different!
HPC + math challenges
 Occupants
 Interaction w/grid
 Design for worst case
 Hierarchy of models
July 8-9, 2010
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Problem
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Bridging gap from short long time scales
Identify key dynamic behaviors & model that affect end performance
Fluid flow network coupling with (thermal) indoor environment and related
controls (switching….)
Visualization for design/decision making
Identify relevant dynamics & failure [forensics  control design] modes
Optimal control design from analysis/model, ID (dynamical invariants)
Coupled structure – thermal simulations
Develop model-based estimates, optimal sensor placement strategies.
Propagation across multiple scales for UQ, sensitivity analysis (compute “chains
of influence”) (how to do this in high-fidelity simulation environment?)
Compute stability regions for modes/behaviors
“integration of UQ capability into multi-scale code” [research question]
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Specifics R&D Investments
Multiscale Modeling & Simulation
Dynamics
Deliverables
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Model dynamic behaviors important to energy performance.
Visualization for practitioners
o Fault tolerance
Simulation to support opt. control
Robust placement of sensors
Uncertainty, sensitivity, adjoint propagation across hierarchy of models new
methodologies
Simulation code w/sensitivity adjoint, uncertain quantification capabilities
embedded within them.
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Need (mid-term)
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Macro–  micro–building climate…
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Analysis tools for decision making
Methodology tools for doing analysis (of energy impact) (decision making) from
“design models”
Approach
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Large-scale coupled structural – thermal simulations
Separation of concerns via modeling
Optimization tools for massing (lumped analysis)
Benefit
 Early decisions on siting/orientation/shell]
Have large impact on energy efficiency
 Effectiveness of modeling & anal. (turnaround time for model building &
analysis)
Need (Long term)
Need/Problem
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Controls design (Models) / Robust Building operation
UP & UQ within modeling toolchain
Multi-scale model extraction from simulations
System decomposition to separate time scales & enable modeling (ROMs)
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
Timeline:
July 8-9, 2010
(examples of large & small comp. benefits from HPC)
Model markup
(Structural, Thermal, climate, orientation)
Through hierarchy
Coarse thermal
Select scenarios by: cost, time, energy
Optimization
Incorporate operational aspects HVAC choices
Risk assessment for high performance designs
Jeff Borgaard (VT)
Satish Narayanan (UTRC)
July 8-9, 2010
Long-Term Multiscale/Multiplysics
Modeling
Dynamic effectiveness of controls
Better integration w/data
Robust control
Uncertainty propagation
Level of abstraction when writing the models
Discrete events
Gradient-based opt.
System decomposition by graph analysis
Embedded vs Sampling, UQ in algorithm design
Coupling models
-CS - what data is sufficient
-math - coupling conditions
Coupling the hierarchy
Multi objective optimization: energy cost, comfort
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Discontinuous right hand sides in the ROM
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Multiscale optimization, integer programming
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---------------Energy/Comfort
vs
$
New Workforce
Need – Benefit Trade-off
Implementation in embedded systems
On – off line
Algorithms for next generation hardware; GPU clusters
Climate (regional) forecasts
Time & space
For wind-farms (dual use)
Leverage existing weather models
Pedestrian - wind tunnels