Notes on Computational Science White Paper
July 11, 2010
Summary
The intent is to deliver a white paper that presents the opportunities for deploying computational
resources to the area of buildings. The intent is to highlight where computation can be brought to
bear on buildings to increase energy efficiency and to address bottlenecks in the delivery
process.
The audience is intended to be DOE with the prime audience being ASCR. The near term use of
computation should be highlighted and should be of interest to a number of agencies concerned
with economics, security and standards including EERE, DOD, OSTP and NIST.
The working title is
Increasing Energy Efficiency in Buildings Through Computation: Addressing Design and
Operations To Increase Competitiveness Through Developing and Deploying Enabling
Technology
The white paper will include background on energy efficiency and why buildings are the best
target for decreasing energy use in the US. The white paper will include technology findings for
multiscale modeling and simulation, dynamics & control, uncertainty analysis and software
infrastructure that are all investable areas with short and long term payback.
The recommendations are: priority at DOE, DOD, NIST for buildings, targeted programs for
computation (not components and moved to systems); national workshops to examine
consequences on economics and to assemble roadmaps and investable areas for computation.
Outline
1. Executive summary
a. Meeting summary: address computational science needs in building design &
operation. When meeting was held, who attended, key findings and
recommendations.
b. Situation. Buildings matter for energy consumption (40% total consumption, 70%
electrical demand); delivery of buildings have bottlenecks in design & operation
where the potential for energy savings is lost.
c. Problem.
i. Speed & quality of decision making across the entire building lifecycle are
not where they need to be.
Notes White Paper Outline
Computational Science for Building
Energy Efficiency
Page 1
Version 1
July 11, 2010
ii. Examples of speed – ability to trade off multiple choices early in the
design process, ability to pinpoint options in value engineering that
contribute most to energy savings.
iii. Examples of quality – must be able to have predictive capability early in
design process, must be able to pinpoint in operations equipment or
subsystems that are contributing to energy consumption above targets.
iv. Talent. Small set of highly skilled practitioners (PhD level) are required
for design, are required for handoffs through the delivery process and must
assist the facility managers to pinpoint operational problems beyond
simple alarms.
v. Broad emerging issues of complex systems applicable across multiple
industries (automotive, aerospace, and buildings). System level issues
throughout – heterogeneity, interactions, uncertainty – are characteristics
of the problems
d. Implications. US Competitiveness (jobs for green technology in design and
operations), emissions for climate change, security.
e. Need. Target opportunities for use of computation. Can use short term resources.
Can build fundamental program targeted to buildings as application driver for
mathematics.
2. Building Energy Consumption, proof points and systems
a. Proof points exist. Artisan approaches that require high skill, long time and
dedicated support to make buildings functional.
b. Transsolar, NREL examples. Focus on systems: system level metrics (WBCSD)
and system interactions (handling through passive design and supervisory
controls).
c. Identification of design & operation bottlenecks. Graphic showing problems in
delivery process.
3. Investable Opportunities.
a. Highlight opportunities (scalability, risk, productivity) and key metrics (speed,
quality) that must emerge from computation.
b. Couch as “T-charts” showing short and long term investments. State problems,
deliverables, Outline need, approach and benefits.
i. Multiscale modeling and simulation.
ii. Dynamics & control.
iii. Uncertainty.
iv. Software.
Notes White Paper Outline
Computational Science for Building
Energy Efficiency
Page 2
Version 1
July 11, 2010