Analysis Software_BNCA_2020

ANALYSIS SOFTWARES Anahita Ghatage | DA1905 F.Y. DA | BNCA | Semester 02 | 2019-20 | 2019 DA 206 Tutors : Kaushik Sardesai | Vinita Wagh | Supriya Dhamale

Department of Digital Architecture Semester 02_2019-20

Anahita Ghatage | DA1905 F.Y. DA | BNCA | Semester 02 | 2019-20 | 2019 DA 206 Tutors : Kaushik Sardesai | Vinita Wagh | Supriya Dhamale 2

01 Urban Mapping Analysis 04 Network Analysis 1.1 Elk Maps

4.1 Space Syntax Theory

1.2 Elk Topography - Terrain Generation

4.2 Space Syntax

02 Environmental Analysis

contents

2.1 Sunpath Analysis 2.2 Shade and Shadow Analysis

4.3 Integration 4.4 Betweeness / Choice 4.5 Centrality

2.3 Sunlight Hours

05 Agent Based Modelling

2.4 Solar Radiation Analysis

5.1 Physarealm

2.5 Radiation Rose

5.2 Quelea

2.6 View Rose 2.7 Wind Rose

06 Galapagos

2.8 Psychometric Chart

6.1 Galapagos Volume Optimisation

2.9 Annual Bar Chart (3D Charts)

6.2 Galapagos FAR Optimisation

03 Slope Analysis

07 Octopus

3.1 Slope Elevation

7.1 Courtyard Optimisation

3.2 Slope Degree

7.2 Volumes

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URBAN MAPPING ANALYSIS

CHAPTER 01 Plugin : ELK Use : Urban Mapping Analysis Data Input Requirements : OSM (Open Street Maps) & SRTM (Shuttle Radar Topography Mission)

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1.1 ELK MAPS

Location : London

A : Street Network

B : Buildings

C : Open Spaces

Hierarchy in roads is observed

Densely populated Neighborhoods and clusters

Evenly distributed open spaces amongst the clusters.

D : Waterway

E : Amenity Spaces

F : 3d Building Extrusions

The Thames River : 60 ft wide

Evenly distributed amenity spaces within this zone.

Allows for extraction of heights of buildings.

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OSM Data Component : Extracts Data for creating layers in Rhino Cluster : Extracts Points from Elk, the latitude domain and curves to create contours.

Location (.osm File)

3d Buildings from

SRTM Topo:

Open Street Maps

Elk

Data File

For Terrain Generation

Steps to Run Elk - Step 01 : Load .osm File > Step 02 : .osm File Data Output > Step 03 : Extract Polylines and Bake > Step 04 : Load .srtm topo file 6

1.2 ELK TOPOGRAPHY : TERRAIN GENERATION

Contour Generation in Plan

Achieved Terrain through ELK

Legend : Major Contours Minor Contours

Frequency of Contours : Contour Interval: 1 meter Major Contours : 10 meters

Cluster for Major and Minor Contours Steps to Run Elk - Step 01 : Load .osm File > Step 02 : .osm File Data Output > Step 03 : Extract Polylines and Bake > Step 04 : Load .srtm topo file 7

ENVIRONMENTAL ANALYSIS

CHAPTER 02 Plugin : LADYBUG Use : Environmental Analysis Data Input Requirements : EPW format (Weather File) Source : Ladybug Tools

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2.1 ladybug : sunpath analysis

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

Month : January

Month : February

Month : February

Time : 12:00 hrs

Time : 12:00 hrs

Time : 17:00 hrs

Path of the sun for specified time of the day and month

Location of the sun for specified month along the red loop axis.

Location of the sun for specified time of the day in the specified month.

Legend : Time of the Year (Path) Time of the Month (Path) Location of the Sun

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2.1 ladybug : sunpath_Analysis PERIOD

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

Hourly Data : Dry Bulb Temperature

View of the Sunpath Analysis Period

Sun is denoted in different colours which represent temperature as per legend.

Analysis Period : Months: March to June (3-6) Day : 23 Hour : 9:00 to 14:00

North Side Elevation

South Side Elevation 10

Defined Period : By giving Input data of the exact time, day and month , Temperature, humidity, radiation, wind direction etc. can be extracted from the .epw file

Analysis Period : Annual Hour data of Temperature, humidity, radiation, wind direction etc. can be extracted from the .epw file

Steps to Run Ladybug - Step 01 : Fly LADYBUG > Step 02 : Download .epw weather file > Step 03 : Open and Import .epw weather file 11

2.2 ladybug : sHADE AND SHADOW ANALYSIS

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

Shade : Darkness created by the shadow

Location of the Sun on 19th February, 12:00 hrs

Legend : Shaded Area

Sunpath Component: The Shade and Shadow component uses the sun vectors from the sun path component in order to analyse shade and shadows of the buildings.

Shade and Shadow Component: It analyses the amount of shade as well as the shadow cast by the surrounding buildings considering the position of the sun at a given time.

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2.3 ladybug : SUNLIGHT HOURS ANALYSIS

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

NN 330 330

3030 Hours Hours

300 300

6060

10.00 10.00 9.00 9.00 8.00 8.00 7.00 7.00

WW

EE

6.00 6.00 5.00 5.00 4.00 4.00 3.00 3.00

240 240

120 120

2.00 2.00 1.00 1.00

210 210

150 150

0.00 0.00

SS

(PLAN) Number of hours the building would receive direct sunight.

Sun Location at 15:00 hrs on 21st January

(PERSPECTIVE) Number of hours the building would receive direct sunight.

LEGEND PARAMETER - The colour goes towards the brighter shade showing the effect of sunlight hours at the specified time of the day.

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Sunpath Analysis Component : Ladybug uses .epw file for collecting data. The sunpath component’s sun vector output is used by sunlight hour analysis component to generate data.

Sunlight Hours Component: It analyses the direct sunlight received by the geometry at the particular period given and gives an hourly colour legend output.

SUNLIGHT HOUR COMPONENT - The component uses data from the .epw file and the sun vectors from the sunpath analysis to produce sunlight hour analysis data. The input BREP Geometry is the buildings and the ground plane is the CONTEXT. 14

2.4 ladybug : SOLAR RADIATION ANALYSIS

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

Radiation Analysis on 8h day of April from 14:00 hrs to 15:00 hrs

Radiation Received by Buildings

Ground Plane : 0.07 to 0.71 kWh/m2 Radiation affects the ground plane when the context is the surrounding buildings. The shadow cast by the buildings influences the amount of radiation received by the ground plane.

North Side Elevation : 0.12 to 0.36 kWh/m2

South Side Elevation : 0.73 to 1.21 kWh/m2 15

Radiation on 15h day of June from 14:00 hrs to 15:00 hrs

Radiation on 15h day of August from 14:00 hrs to 15:00 hrs

Location of the Sun on 15th June from 14:00 hrs to 15 hrs

Location of the Sun on 15h day of August from 14:00 hrs to 15:00 hrs 16

Cumulative Sky Matrix Component: Calculates Sky Radiation for each hour of the year using Radiance’s gendaymtx function.

Select Sky Matrix Component: To select a specific Sky Matrix RADIATION ANALYSIS COMPONENT - The component uses data from the .epw file and the CUMULATIVE SKY MATRIX component in order to produce the data for radiation analysis. The context for analysis could be the buildings or the ground plane.

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2.5 ladybug : RADIATION ROSE N N 350 350 10 10 340 340 20 20 330 330 30 30 320 320 40 40 310 310 50 50

h/m2 kWh/m2

7 0.67

0 0.60

300 300

3 0.53

7 0.47

0 0.40

Location : London Latitude : 51.50 ON

290 290 280 280 W

W

kWh/m2 kWh/m2 0.49 0.49 0.44 0.44

60 60 70 70 80 80 E

E

0.34 0.34 0.29 0.29

260 260

100 100

0.24 0.24

7 0.27

250 250

110 110

0.20 0.20

3 0.13

7 0.07

0 0.00

240 240

120 120

230 230 130 130 220 220 140 140 210 210 150 150 200 200 160 160 190 190 170 170 S S

Diffuse Radiation 8 April 14:00 hrs - 8 April 15:00 hrs

0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00

NN

300 300

0.39 0.39

3 0.33

0 0.20

350 350 340 340 330 330 320 320 310 310

10 10

20 20

NN Longitude : 0.1278OW 350 10 1 350

kWh/m2 kWh/m2 30 30 40 40 50 50

0.60 0.60

60 60

0.53 0.53

290 290

70 70

280 280

80 80

WW

EE

260 260

100 100

250 250

110 110

240 240

120 120

230 230 220 220 210 210 200 200 190 190

SS

340 340 330 330 320 320 310 310

0.67 0.67

130 130 140 140 150 150 160 160 170 170

0.47 0.47 0.40 0.40

300 300 290 290 280 280 WW

0.33 0.33

260 260

0.27 0.27

250 250

0.20 0.20 0.13 0.13 0.07 0.07 0.00 0.00

240 240 230 230 220 220 210 210 200 200 190 190

Total Solar Radiation 8 April 14:00 hrs - 8 April 15:00 hrs

Ladybug Radiation Rose Component : It uses select Sky Matrix for the data. It generates a chart showing radiation (kwH/M2) data at a particular direction. The arrow shows the direction of the radiation and the length of the arrow shows the time period.

The colour denotes the intensity of the radiation.

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SS

17 1

2.6 ladybug : VIEW ROSE

72% of Blocked Views

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

28% of Blocked Views

47% of Blocked Views

Legend : Blocked View Visibility

View Rose Component : It uses the context as the buildings and shows which elements block the view and which views are clear.

View Point

View Point : The Plane is the point from which the view is visualized.

This can be moved using the evaluate surface component.

Radius: The radius of the view rose is the area that is too be considered for blocked views and visibility.

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m/s

2.7 ladybug : WIND ROSE m/s N NNW

10.60

NNE

NW

9.54

NE

N NNW

WNW

NW

WNW

8.48

NNE

ENE

NE

6.36

ENE

W

E

ESE

WSW ESE

WSW

SW

SE

SW

SE

SSW

SSW

SSE

5.30 4.24

E

W

7.42

SSE

3.18 2.12 1.06

S 0.00 Wind-Rose London Wea CtrLondon St James Wea Park_ENG_GBR 8 Wind-Rose Ctr St James Park_ENG_GBR 8 Diagram APR 14:00 - 8 DEC14:00 15:00Wind Hourly Data: Wind Speed APR - 8 Rose DEC 15:00 Hourly Data: Wind Speed (m/s) Calm for 0.02% of the time = 1 hours. Each closed (m/s) Calm for 0.02% of the time = 1 hours. Each closed polyline shows frequency of 0.1%. = 4 hours.

S

Location 10.60 : London Latitude : 51.50 ON 9.54: 0.1278OW Longitude

8.48 Wind Rose Component : 7.42 Ladybug Wind Rose uses the .epw file to extract the wind speed and direction.

6.36 This generates a pie chart showing 5.30 wind speed (m/s) data at a particular direction. In this case, WEST & SOUTH 4.24 WEST direction.

3.18

Wedges : The percentage of time the wind came from that particular direc2.12 tion.

1.06 the speed Colour : The colour denotes (m/s)

0.00

polyline shows frequency of 0.1%. = 4 hours.

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0.02

0.02

70 kJ/kg

70 kJ/kg

2.8 ladybug : PSYCHROMETRIC CHART 60 kJ/kg

kg

Location : London Latitude : 51.50 ON Longitude : 0.1278OW

60 kJ/kg

Comfort Polygons are different for different activities. 0.015 20% Cleaning : 35 kj/kg to 55kj/kg Dancing : 25 kj/kg to 45 kj/kg

20%

0.015

60%

40%

90% 80% 70%

60%

50%

40%

100 kJ/kg

0.01

0.025

90 kJ/kg

30% 80 kJ/kg

60 kJ/kg

20 kJ/kg

0.005

66.50

0.01

0 kJ/kg

119.70

93.10 79.80 0.005

66.50 53.20 39.90

0 kJ/kg

26.60

-10 kJ/kg

25 -20 -15 -20

133.00<=

106.40 10%

10 kJ/kg -10 kJ/kg

20

Hours

-10

-15 -5 30 0

35 5 10 -10

40

-5

15

20

25

0 30 4535

540

13.30

50 45

50

10

<=0.00

0.025

80 kJ/kg 0.02 70 kJ/kg

10%

0.005

40 kJ/kg 0.01 30 kJ/kg 10%

Operative Temperature

-15 25

66.50 53.20 39.90 26.60

-10 kJ/kg -20

119.70

79.80

0 kJ/kg

20

-10

-5

30 0

5

10 35

15

40

20

25

30

4535

40

106.40

66.50 133.00<=

93.10

0.005

10 kJ/kg

13.30

119.70

Hours

106.40

20 kJ/kg

26.60

133.00<=

79.80

0.015

20% 50 kJ/kg

39.90

Hours

93.10

60 kJ/kg

53.20

<=0.00 15

0.01 30%

106.40

79.80

10 kJ/kg

0.03 50%

90 kJ/kg

119.70

0.015

20%

30 kJ/kg

20 kJ/kg

90% 80% 70% 100 kJ/kg

93.10

50 kJ/kg

40 kJ/kg

110 kJ/kg

40 kJ/kg 133.00<=

0.02 30 kJ/kg

10%

70 kJ/kg

Activity: Dancing

Hours

Humidity Ratio

Activity: House Cleaning

0.03

Humidity Ratio

50 kJ/kg 110 kJ/kg

45 50

13.30 50

<=0.00

53.20 39.90 26.60 13.30 <=0.00

Operative Temperature

110 kJ/kg 90% 80% 70%

Psychrometric Chart: A psychrometric chart is a graphical representation of the psychrometric processes of air. Psychrometric processes include physical and thermodynamic properties such as dry bulb temperature, wet bulb temperature, humidity, enthalpy, and air density.

0.03 60%

50%

40%

100 kJ/kg

0.025

90 kJ/kg 30% 80 kJ/kg

0.02 70 kJ/kg

60 kJ/kg 20%

0.015

PSYCHROMETRIC CHART COMPONENT - The component uses data from the .epw file of the relative humidity, dry bulb temperature and the 50 kJ/kg

Hours

40 kJ/kg 0.01

133.00<=

barometric pressure to derive comfort polygons for the specified activity.

30 kJ/kg

93.10

20 kJ/kg 10 kJ/kg 0 kJ/kg -10 kJ/kg

119.70 106.40

10%

79.80 0.005

66.50 53.20 39.90 26.60

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2.9 ladybug : ANNUAL BAR CHARTS

Dry Bulb Temperature C - Hourly

Relative Humidity % - Hourly

Wind Speed m/s - Hourly

Relative Humidity Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Dry Bulb Temperature

Wind Speed Chart 22

Ladybug EPW file Data : It uses the .epw file for the data. Annual charts can be generated for Wind speed, Radiation, Dry bulb Temperature, Relative humidity, etc.

Ladybug 3d Chart : It uses the .epw file for the data. Required climate data should be given as input. This will generate a 3d chart for the annual climatic data.

A conditional statement can be mentioned, which will remove the data that does not fit the conditions.

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TERRAIN

DA1905_2019-20

CHAPTER 03 Plugin : SLOPE ANALYSIS Use : Slope Degree & Elevation Buildable and Non - Buildable Slopes

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3.1 SLOPE ELEVATION Highest Elevation Recessed Elevation River Bed

Elevated Land Area High Elevated Land Area Legend : Low points High Points

Lowest Point

Highest Point

Inference: The site has a 12 meter gradient throughout its surface.

The slope in towards the river on either side of it.

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3.2 SLOPE DEGREE Highest Degree Slope Value

Slope identifies the steepest downhill slope for a location on a surface.

The Evaluate Surface command takes an input surface raster and calculates an output raster containing the slope at each cell. The lower the slope value, the flatter the terrain; the higher the slope value, the steeper the terrain. The output slope raster can be calculated as percent slope or degree of slope.

Lowest Degree Slope Value Highest Degree

Lowest Degree

Inference: The site has slope degree of 0.18 to 0.77 units.

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NETWORKS

CHAPTER 04 Plugin : SPACE SYNTAX Use : Space Zoning & Networks

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4.1 SPACE syntax theory Representations of space Spatial elements are represented through their geometric forms and how people experience them. They can be geometrically derived (for example, point, axial line, segment, convex space and isovist) or functionally defined (for example, rooms in building).

Analysis of spatial relations Relationships between spatial elements result from their configuration. These relationships can be objectively analysed using various measures, included among which are integration(see figure A) and choice (see figure B ). These two measures reflect the two fundamental elements in human movement: firstly, the selection of a destination, and secondly, the selection of a route. One measures the ease of access (integration) and the other measures the passing flow (choice).

A. Space Syntax Integration Pattern of Greater London

Interpretive models Spatial models are developed to analyse, describe, explain and forecast different kinds of spatial and socio-economic phenomena. Practically,models are created to investigate empirical phenomena such as urban movement, urban crime, and centrality as a process as well as for general processes such as spatial intelligibility.

Reference : http://otp.spacesyntax.net/

A. Space Syntax Choice Pattern of Greater London 28

4.2 SPACE SYNTAX Classrooms Representations of space The Plugin requires data regarding : The list of spaces that it needs to create (populate rectangle with points) The areas of these spaces (gene pool) The areas that need to be connected to each other The Disko Graph Drawing Component and the Force Directed Drawing Component tries to establish a relationship based on the data provided and gives various iterations for zoning options based on the connections and areas as constraints to generate the drawing.

Administration Research Labs

Pantry Staffroom

Examination Room

Connections Between the Spaces

Disko Graph Drawing Output Iteration 29

4.3 INTEGRATION

Diagram explaining Integration Legend : The higher the integration value the more communal space is likely to be, the lower the value, the more private the space is likely to be.

Integration Pattern of an area in London

More Communal Space Semi-Public Space Private Space

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4.4 BETWEENESS / CHOICE

Diagram explaining Betweeness Legend : Indicates how often a node happens to be the shortest path between other spaces.

Betweeness Pattern of an area in London

Least Likely Used Moderately Used Most Likely Used

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4.5 CENTRALITY

Diagram explaining Centrality Control : A local dynamic measure describing what degree of choice does each space represent for its immidiate neighbors to move to.

Centrality Pattern of an area in London

Legend : Least Likely Moderately Most Likely

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AGENT BASED MODELLING

CHAPTER 05 Plugin : Physarealm | Quelea | Zebra Use : Agent Based Modelling (ABM) Theory Base : Slime Mould, Patterns Optimises to give the most efficient solution based on Crowd Behaviour Simulation , Shortest Path

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5.1 AGENT BASED MODELLING : PHYSAREALM

Food Source Emitter Path taken by Physarealm

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Slime Mould : A group of Japanese researchers has shown that P. polycephalum can find the shortest route connecting two food sources when placed in a maze with two oatmeal flakes. In a 2010 paper, P. pocephalum (Slime Mould) created a network similar to the existing Tokyo train system when oatmeal flakes were dispersed to represent towns on a map of the Tokyo area. Inspiration from this natural phenomenon has raised questions regarding how we can mimic and emulate it to find solutions to path planning. Architects and urban designers often face spatial planning problems, such as finding effective paths between points of interest.

Physarealm : Shortest Path Surface Environment : Boundary Condition, where, agents run on a uv space.

Birth Death Settings: Parameter for Birth and Death condition of Agents.

Physarealm Component: Agent Based Modelling

Population Interconnect: Connects pair of agents locations below a certain distance.

Population Trail: It gives the trails of each agent.

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5.2 AGENT BASED MODELLING : QUELEA Applications of Flocking : In Cologne, Germany, two biologists from the University of Leeds demonstrated a flock-like behavior in humans. The group of people exhibited a very similar behavioral pattern to that of a flock, where if 5% of the flock would change direction the others would follow suit.

When one person was designated as a predator and everyone else was to avoid him, the flock behaved very much like a school of fish.

Flocking has also been considered as a means of controlling the behavior of Unmanned Air Vehicles (UAVs).Flocking is a common technology in screensavers, and has found its use in animation. Flocking has been used in many films to generate crowds which move more realistically.

It has also been used for visualizing information and for optimization tasks.

Separation

Alignment

Cohesion

Rule Sets of Flocking Behavioural System

Quelea as Flocking Agents : Imitating the flocking of the quelea bird (always in groups).

Deconstruct Particle : Deconstucts a particle to expose its fields, i.e, Position, Velocity, Acceleration

Contain Force : Applies a force to keep agents away from the Environment Boundary

Construct Agent : Constructs the settings for an agent Box Emitter : Box environment for the agent

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EVOLUTIONARY ALGORITHM

CHAPTER 06 Plugin : GALAPAGOS Use : Single Objective Optimisation Theory Base : Evolutionary Algorithm Optimises to give the most efficient solution based on number constraints.

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6.0 Theory of evolutionary algorithm INDIVIDUAL A

INDIVIDUAL B

BREEDING

PHENOME

Gene Expression

GROWTH

Body Plan

BREEDING

Cross-over Recombination

MUTATIONS

Alterations in Gene Sequence

EMERGENCE OF POPULATION

Natural Selection

SURVIVING PHENOMES

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6.1 evolutionary algorithm - a

Galapagos Genetic Input : Galapagos can operate on anynumber of Slider objects. It requires certain sliders to be assigned to the Galapagos object for them to become part of the ‘Genome’. It is best to limit the slider range as much as possible, as this reduces the size of the search space.

Galapagos Fitness Input : Galapagos requires a single parameter which provides a fitness value. When Galapagos runs, it will attempt to maximize or minimize this fitness. Fitness parameters need not be number parameters. Any data type that can be converted into a number.

3D Optimisation

Optimisation Graph

Genomes

Iterations Genrated 39

6.2 evolutionary algorithm - b 3D Optimisation_ Bounding Box

Optimizing for Minimum Volume of the Bounding Box 3D Optimisation Using Bounding Box Optimization for Floor Space Index of a Building Number of Floors Site Curve to Evaluate Along

Number of Floors

Minimum Difference

Galapagos Evolutionary Solver Site Line to Evaluate

Maximum Floor Area : Minimum Number of Floors Given : Site Constraint

FAR Optimization 40

CHAPTER 07 Plugin : OCTOPUS Use : Multi Objective Optimisation Theory Base : Evolutionary Algorithm Optimises to give iterations based on number constraints.

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7.1 OCTOPUS - COURTYARD OPTIMISATION

Optimised Courtyard Iterations for Maximum Volume : Maximum Internal Courtyards : Maximum Central Courtyard Axis : Criterias Maximize Volume

Delaunay Mesh

Maximize Internal Courtyard Maximize Central Courtyard

Iterations

Results

Optimised Courtyard Iterations for Maximum Volume : Maximum Internal Courtyards : Maximum Central Courtyard 42

7.1 OCTOPUS - BUILDING THE ALGORITHM Offset Rectangle: Used to create smaller courtyards

Region Difference : Used to create the Central Courtyard

Octopus was originally made for Multi-Objective Evolutionary Optimization. It allows the search for many goals at once, producing a range of optimized trade-off solutions between the extremes of each goal. It is used and works similar to David Rutten’s Galapagos, but introduces the Pareto-Principle for Multiple Goals.

Courtyard Scale Factors : Create a gene pool with and randomize the courtyard values. This goes as criteria 01 for the octopus solver. i.e. Maximum Area of Courtyards.

G : Genome (Inputs for Octopus to solve) O : Multi-objective Optimisation Solving

Building Heights Factors : Create a gene pool with and randomize the building heights. This goes as criteria 02 for the octopus solver. i.e. Maximum Heights of Buildings, i.e Maximum Volume.

Obtain Number containers for the Maximum Possible Volume, Maximum Internal Courtyards and Maximum Central Courtyard. Use subtraction between the maximum values and changing values. Obtain these values in a number container.

Now, Octopus will optimise between the values and keep them the difference the lowest.

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7.2 OCTOPUS - VOLUME OPTIMISATION

Sculpting Volumes from a Geometry

Populating the Geometry with Points For creating Spheres.

Octopus optimises the sculpted volumes based on the numbers of the factor of scaling, the seed of the populate geometry and the radius of the spheres (gene pool). It uses the number container of the minimum volume to the sculpted, maximum radius and minimum changing volume.

Solid Difference to sculpt out the spheres from the mass.

Octopus 44

Anahita Ghatage F.Y. Digital Architecture , B.N.C.A | Semester 02 Tutors : Kaushik Sardesai | Vinita Wagh | Supriya Dhamale

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