Computational Design: Work Samples

WORK SAMPLES COMPUTATION | DATA | ROBOTICS AT H A R VA

R A N A D E

1.1 AUTOMATED MATERIAL ARTICULATION | 05-2020 | TEAM: DAVID FORRERO, ATHARVA RANADE Project additive manufactured processes by challenging architectural conventions of roof-wall-floor connections.

1.2 AUTOMATED MATERIAL ARTICULATION | 05-2020 | TEAM: DAVID FORRERO, ATHARVA RANADE The polyvalency is expressed in the spatial organization of the building while an automated grasshopper script encodes an ornate structural articulation.

Hyper-parameters

3D articulation with offsets Density: 300

1.3 AUTOMATED MATERIAL ARTICULATION | 05-2020 | TEAM: DAVID FORRERO, ATHARVA RANADE The script is tested on curves of different builds and densities. Offset values projects the curves out of the plane and creates a weaving pattern.

DATA ANALYSIS:

DATA VISUALIZATION:

DATA PREDICTION:

2.1 DATA ANALYSIS, PREDICTION + VISUALIZATION Project uses IoT + IEQ data to predict post occupancy electric meter and comfort levels.

Programs used: Colab, python, pandas, seaborn, sci-kit learn

Selecting a desired location: Blacksburg, VA

Selecting Day, Time and Month to determine the exact position of the sun

Running multiple simulations till desired daylighting is achieved.

3.1 FACADE INFORMED BY ALGORITHM The project uses a computation methodology to analyze and design a facade geometry. Ladybug, Honeybee and Voronoid scripts are used simultaneously.

Topological Optimization

Stress-Strain Analysis

4.1 TOPOLOGICAL OPTIMIZATION | 05-2020 | TEAM: PATRICK DANAHY, ANDREW BILLINGSLEY, ATHARVA RANADE Agent-based python script automates a robotic weaving pattern on a topological optimized mesh structure

4.2 AGENT BASED ROBOTIC WEAVING | 05-2020 | TEAM: PATRICK DANAHY, ANDREW BILLINGSLEY, ATHARVA RANADE The tool-path created with the agents is tested with a custom robotic-weaving tool. Robotic arms are simulated locally to check for collision and node-wrapping constraints.

Physical Model

Computation

Digital Model

5.1 MATERIAL TO DIGITAL COMPUTATION | 09-2018 | TEAM: EVAN MCIVER, ATHARVA RANADE A parametric modeling approach transforms a physical model into a digital model with varying parameters.

Placement of dowel

Height of dowel

1

3

Thickness of dowel 2

5.2 MATERIAL TO DIGITAL COMPUTATION | 09-2018 | TEAM: EVAN MCIVER, ATHARVA RANADE The physical model is fed into Image Trace to map out the location of each of the dowels and the nodes. A Grasshopper script is formulated using three constraints present in the physical model

Input Image

50 iterations

Style Image

150 iterations

6.1 AI GENERATED IMAGES Style-Transfer project using Pytorch and coded in Google-Colab notebook

300 iterations

7.1 BAROQUE UNDULATIONS LiDar scanned churches are transformed through mesh manipulation and objectified through undulation exterior closed topology