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