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DATA ANALYSIS &
VISUALIZATION AT
LBNL Harinarayan(Hari) Krishnan, Computational
Research Division
SIMULATING URBAN
ENVIRONMENTS FOR ENERGY
ANALYSIS
G. H. Weber1,2, H. Johansen1, D. T. Graves1, and T. J. Ligocki1
1Computational Research Division, Berkeley Lab, USA 2Department of Computer Science, UC Davis, USA
Complexity of Urban Power Supply and
Demand Growing Rapidly • New technologies
• renewables (e.g., solar and wind energy)
• demand response
• electric vehicles
• combined heat/power systems
• battery storage
must coexist with existing generation capacity
• New strategies needed that do not
• exacerbate grid outages/single points of failure
• miss energy savings/investment opportunities
3
External Environment Drives Energy
Balance
• Factors driving energy balance include • air temperature
• air quality
• humidity
• precipitation
• incident sunlight
• shade and cloud cover
• Dominated by uncertainty and day-to-day variation
➜Static peak views of system (solar angle, average hours sunlight per day) miss opportunities for optimizing supply/demand and solar energy usage
4
Optimal Design Tool for Urban Energy
Planning Requires Regional Models • Use high-resolution regional weather/climate models in
optimal planning tool for urban energy usage
• Multi-scale simulation of key factors affecting incident solar
radiation (e.g., cloud cover)
• Incident solar energy calculation considering cloud cover,
topography, angle of the sun, shadows, etc.
• Per-building (per surface) time series of incident solar energy
• Use to evaluate potential for optimizing solar energy
generation and electricity demand
5
System Overview
• Represent geometry as cut cells
• Advect clouds in incompressible flow using Chombo AMR code
• Compute energy transfer through clouds and shadows via VisIt [Childs et al., 2012]
• Compute per building intensities as function of time; cluster based on energy profiles
• Model Generation
• San Francisco model
• 3D surface model of financial district
• Represented as embedded boundary (volume fractions) for simulation
6
Cloud Advection Using Chombo Code
• Steady flow, random distribution of Gaussian clouds over urban
region (not a full weather simulation)
• Evolve velocity field with incompressible flow solver
• Adaptive mesh: High-resolution embedded boundary for
buildings, coarser resolution for atmosphere
Goal: Dynamic cloud layer to test solar intensity algorithm;
extensible to use weather predictions (future)
7
Light Intensity Computation –
Assumptions • Region small All light rays parallel & solar angle
function of time (based on latitude and time of year)
• Absorption only model approximates light transfer into
buildings; diffuse light contributions (reflections within
cloud layer) negligible
• Reflected light from other buildings also negligible; casting
shadows only noticeable effect
• Cloud layer far above the buildings, no light absorption at
low altitudes (e.g., due to fog)
8
Intensity Modeling
• Plane between cloud layer and urban region
• 1st pass: Compute transmitted intensity fraction along parallel
rays (X ray query) and save in bitmap
• 2nd pass: Reconstruct building geometry, cast rays to plane
and read intensity information
9
Sunlight
direction
Ray perpendicular
to image plane
Sunlight
direction
Computing Shadows
• 1st pass:
• Label individual buildings, encode building id as color
• Render from view of light source resulting in “shadow buffer” [Williams,
1978] of building ids
• 2nd pass: Cast rays from reconstructed geometry, compare
visible building id to current building id
10
Computing Intensity Per Building
• Utilize intensity/shadow information in second simulation:
energy transfer into buildings
• Compute time curves for energy transfer into individual
buildings
• Cluster based on energy profiles
Results – Analysis
• Lighter color = more energy
• Arrows: Districts or building that
might be focus of policy or demand
initiatives 12
Incident
solar
energy
Energy
per area
Energy
per
building
Incident solar energy per unit area vs.
time
Regional Energy Modeling in the Future
13
Detailed Climate and Energy Available: –Electricity grid/meter data becomes pervasive, detailed
–Climate/weather prediction improved long-term accuracy
2025 Model: –Real-time weather data and energy usage data enables
simulation and control of regional energy distribution net-
works, and strategies to optimize efficiency, minimize risk
–Use of integrated data collection and simulation will
support detailed “what-if?” analyses and drive energy policy
decisions
Data Simulation Models
Policy
Data assimilation and model reduction
FROM URBAN PLANNING
TO X-RAYS
Characterization of Advanced
Cementitious Materials Using
X-Ray Synchrotron Radiation
Paulo J.M. Monteiro
Department of Civil and Environmental Engineering
University of California at Berkeley
World demand/year
Mehta & Monteiro, fourth edition, 2014
Concrete: 33 billion ton
Water: 2.7 billion ton
Aggregate: 27 billion ton
Cement: 3.7 billion ton
(wrong again!!)
Environmental Impact
Production 1 ton of cement
generates 0.87 ton of CO2
Cement industry generates
3 billion ton of CO2
China used more cement in three years
than the US did in a century
Deteriorating Infrastructure
—In the US: out of 614,387 bridges,
56,007 are structurally deficient;
—On average there were 188 million
trips across a structurally deficient
bridge each day
Long-term deformation
To be continued…
Not only doom and gloom…
—Development of new advanced
concrete: 3D printing, self-
consolidating concrete, self-healing
—Increased financial support for the
development of new cements
—Scientific advances that allow the
study of complex and messy systems
Digital fabrication
Asprone et al., special issue, CCR,
2018 Anna Szabo, ETHZ
Smart Dynamic Casting
Mesh mould
Concrete extrusion
Particle-bad 3D printing
Courtesy from Delphine Marchon
Potential Problems
Calcium silicate hydrate
the “glue” of concrete
Crystalline Amorphous
22
Scanning Transmission X-Ray Microscopy
Can we increase the mechanical
properties of C-S-H? High Ca/Si ratio?
Images Sources: http://my-lifestyle-mania.com/say-no-to-calcium-supplements/, Wikipedia, http://www.seasonsatihc.com/
High Al/Ca ratio?
Organic
Supplements?
63µm
Sample in the metal gasket
X-ray Detector
X-ray
How to measure the stiffness of CSH? 180µm
27
Soft X-ray Ptychographic Imaging
STXM Ptychography
Bae, S., Taylor, R., Shapiro, D., Denes, P., Joseph, J., Celestre, R., S. Marchesini, H. Padmore, T.
Tyliszczak, T. Warwick, D. Kilcoyne, P. Levitz . Monteiro, P. J. M. (2015). Soft X-ray Ptychographic
Imaging and Morphological Quantification of Calcium Silicate Hydrates (C-S-H). Journal of the American
Ceramic Society.
Overlap
and average
frames.
FFT
For each pixel
replace magnitude
with experimental
value
lFFT
Multiply
Object
with
Probes
Zone Plate Lens Ptychography
Frame Stack
X-ray
Beam
Scan
Direction
Diffraction Pattern Scanned Sample
CCD
Detector
Outputi
Iterationi
2
9
Iterative Reconstruction
Overlap
and average
frames.
FFT
For each pixel
replace magnitude
with experimental
value
lFFT
Multiply
Object
with
Probes
Zone Plate Lens Ptychography
Frame Stack
X-ray
Beam
Scan
Direction
Diffraction Pattern Scanned Sample
CCD
Detector
Outputj
Iterationj
3
0
Iterative Reconstruction
Overlap
and average
frames.
FFT
For each pixel
replace magnitude
with experimental
value
lFFT
Multiply
Object
with
Probes
Zone Plate Lens Ptychography
Frame Stack
X-ray
Beam
Scan
Direction
Diffraction Pattern Scanned Sample
CCD
Detector
Outputk
Iterationk
3
1
Iterative Reconstruction
Overlap
and average
frames.
FFT
For each pixel
replace magnitude
with experimental
value
lFFT
Multiply
Object
with
Probes
Zone Plate Lens Ptychography
Frame Stack
X-ray
Beam
Scan
Direction
Diffraction Pattern Scanned Sample
CCD
Detector
Final Output
Iterationn
3
2
Iterative Reconstruction
1 um
1. Ptychography image raw data collected at 800 eV
(much less beam damage);
2. Pixel resolution 5 nm. Real 2D resolution 10-15 nm.
32
New Challenge 3D imaging
Unaligned projections from -80° to
80°.
-80° -40° 0°
40° 80°
33
3D Printing with Stereolithography
Step size: 100 m 20 hours later…
Holding C-S-H
in your hand
Sept 6, 2018 in Genova, Italy
… back to long-term deformation
Development of texture under
deviatoric stresses
Nanotomography of the Roman concrete
Jackson et al. Journal of American Ceramics Society, AUG 2013.
Jackson et al., American Mineralogist,
2013
STXM P
ress
ure
(GP
a)
0
0.88 0.90 0.92 0.94 0.96 0.98 1.00 1.02
V/Vo
2
4
6
8
2nd Birch-Murnaghan equation of state
Experiments
Jackson et al. Journal of American
Ceramics Society, AUG 2013.
High-Pressure
— Thresholded residual in which the cracks appear very clearly. The small
residual values have been made transparent to render large values visible,
and hence to reveal the presence of a crack network
In-situ cracking propagation
Acknowledgements
• Eric Brugger (X-ray query help) & VisIt developers in
general
• Members of LBNL Applied Numerical Algorithms and
Visualization groups
• Members of SDAV
• Department of Energy (DOE), Office of Science,
Advanced Scientific Computing Research (ASCR), under
Contract No. DE-AC02-05CH11231
41
Acknowledgments
M.H Zhang P. Krishnan L. E. Yu P. A. Itty S. Yoon G. Geng C. Bae J. Li V. Rheinheimer R. Winarski M. Marcus M. Jackson
P. Levitz R. Meyers D. A. Kilcoyne M. J. A.Qomi D. Ushizima R. Maboudian D. Shapiro D. Marchon P. Pisher D. Attwood D. Ushizima K. Ku
Financial Support: NSF, SinBerBEST, SCG 55
Thanks!
43