DATA ANALYSIS & VISUALIZATION AT LBNL Visualization...•Compute energy transfer through clouds...

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!

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