Bodeker Scientific Limited Smart Ideas funded projects

Buildings contribute 30% of global energy consumption and 27% of emissions from the energy sector. To address these critical challenges, researchers are advancing Biomimetic Adaptive Building Façades (Bio-ABFs) as a promising solution to reduce energy use and emissions while improving building adaptability to climate changes.The research addresses two significant barriers: the lack of large-scale experimental data and the limitations of existing building simulation tools in evaluating dynamic systems. By integrating advanced 3D printing technologies with machine learning, the project aims to optimise real-time performance and improve the evaluation of Bio-ABFs under complex, real-world conditions. Additionally, the team is exploring 4D printing technologies using biomaterials inspired by plant adaptations to extreme climates. These biomaterials have the potential to provide passive, motor-free actuation through hierarchical microstructures, offering solutions to challenges related to durability and scalability in adaptive façade design.The dual-scale innovation approach involves:Motorised shading systems, optimised with AI and 3D printing, designed for full-scale architectural applications.Plant-inspired microstructures, fabricated using 4D printing, enabling material-driven shape morphing at smaller scales.The project is led by a multidisciplinary team, including experts from Bodeker Scientific, the University of Canterbury, and Scion, working alongside local partners such as Beca, WSP, and BRANZ. International collaborators, including BioMat and Buro Happold, contribute global expertise to the development of scalable shading prototypes.This initiative aligns with New Zealand’s net-zero energy goals, offering scalable solutions that integrate AI-driven design-to-fabrication pipelines with biomaterial-based Bio-ABFs. The outcomes aim to enhance building multifunctionality, improve energy efficiency, and significantly reduce emissions, setting a new standard for adaptive and sustainable building designs globally.

Bad weather can be far more than just an inconvenience. In NZ, where primary production contributes $22.5B to our economy, bad weather can incur significant economic, environmental and social costs. Mitigating the impacts of severe weather largely depends on the quality and reliability of weather forecasts.

The most highly damaging extreme weather events (e.g. hail or intense rainfall) often occur over small areas, driving a need for higher-spatial-resolution forecasts. By solving the mathematical equations describing atmospheric processes, numerical weather prediction (NWP) models predict how the weather will change - this computationally demanding task requires supercomputers. Increasing the model resolution, so that they resolve local weather events, adds large financial costs.

We will apply artificial intelligence methods to develop a new way of generating weather forecasts, producing high-resolution forecasts at a fraction of current costs. A neural network (NN) will be trained to learn how to generate weather at hyperlocal scales (several 100m) given data from a lower resolution NWP model. While the initial training may be computationally expensive, once trained, the NN can be applied to any NWP forecast to fill in the missing detail inside each grid-cell, at negligible cost. This cost reduction means that we can generate higher resolution forecasts than are currently available, and process many more forecasts to produce probabilistic risk assessments of rare but highly damaging events.

If successful, our fused-NN-NWP model will be incorporated into MetService’s NWP chain, delivering new hyperlocal weather forecasts, enhancing the ability of emergency managers to save lives and protect property, and industries to manage risks and minimise losses. The need for such forecasts will only increase as the frequency and severity of extreme weather events increase under climate change.