Xstream is an AI-based plugin in Grasshopper for engineers and architects designing modular research stations in extreme cold environments. The project focuses on developing a Graph Neural Networks system that learns from the geometric data of the modules and the climatic data of the sites to get instant analysis and evaluation of the Environmental and Design aspects of the user’s project. And it also provides Resilient Design for specific site conditions from the catalogue of modules.
This diagram highlights the key challenges in designing for extreme environments, categorized into two main areas: climate-specific and design-specific challenges. Climate-specific factors include natural disasters, high winds, precipitation, and solar radiation, which require specialized responses tailored to the local climate conditions. Design-specific considerations encompass energy efficiency, maintenance, environmental impact, and human comfort and safety, demanding innovative solutions to ensure resilience and sustainability in extreme conditions.
The “Problems in Designing Research Station” infographic outlines three major challenges: time-consuming processes, module definition, and climate-responsive aspects. It emphasizes the lengthy design phases involving multiple stakeholders, the necessity for modular design to enhance reusability and maintainability, and the importance of addressing extreme climatic conditions. The infographic also suggests overcoming these challenges through user-centric design, optimizing processes, and using digital tools for simulation and performance checks.
The “Features of XStream” diagram outlines five key attributes of the XStream model: modular design, energy efficiency, vision analysis, safety optimization, and climate adaptability. These features collectively enhance the design process, improve energy management, ensure safety, and account for environmental factors.
X stream catalogue of modules A number of modules aggregated to form a research station cluster- A representation for the same with the help of xstream catalogue.
The above diagram depicts the function of hydraulic ski-fitted legs for the research station set-up. Hydraulic ski-fitted legs comes with our predesigned set of catalogues.
The above illustration represents the research station setup with the use of stream catalogues of module. The user can also use their own set of catalogues with stream AI.
Xstream Datasets Collections
Xstream UI Demo Video – For both approach
Xstream catalogue – Research Module (Interior Render with Stable diffusion and Control.NET)
Xstream catalogue – Dinning Module (Interior Render with Stable diffusion and Control.NET)
Xstream catalogue – Command Module (Interior Render with Stable diffusion and Control.NET)
Xstream catalogue – Gathering Module (Interior Render with Stable diffusion and Control.NET)
