| EPSRC Reference: |
EP/Z003407/1 |
| Title: |
Advanced Computational Methods for Imperfect/Uncertain Geometries |
| Principal Investigator: |
Cirak, Professor F |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
Engineering |
| Organisation: |
University of Cambridge |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 March 2025 |
Ends: |
29 February 2028 |
Value (£): |
373,189
|
| EPSRC Research Topic Classifications: |
| Numerical Analysis |
Structural Engineering |
|
| EPSRC Industrial Sector Classifications: |
|
| Related Grants: |
|
| Panel History: |
|
|
Summary on Grant Application Form |
|
The digital design and mechanical analysis of products and systems with uncertain or imperfect geometries present a significant challenge for current mathematical and computational modelling techniques. These imperfections can arise, for instance, from uncertain manufacturing conditions or wear and corrosion during operation and substantially degrade the performance of a product. This collaborative project between the University of Cambridge (UK) and Duke University (USA) aims to develop new computational and mathematical methods for products with random geometric imperfections by leveraging immersed boundary methods for simulation with probabilistic subdivision surfaces for geometry representation. The envisioned approach builds on the shifted boundary method developed by Duke University and the subdivision surfaces developed by the University of Cambridge. In addition to accelerating digital product development, the new techniques will be essential for future digital twins of products and systems, supporting their lifecycle from design, manufacturing and operation to maintenance. Currently, training digital twins in the presence of uncertain, complex geometries is laborious, slow and costly. The developed techniques will foster an ecosystem of computational methods that can efficiently interact with the meta-algorithms at the foundations of digital twins, including reduced-order modelling, machine learning, uncertainty quantification and optimisation.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.cam.ac.uk |