<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/980C938A-778D-4B2F-A3AF-98435C2406E9" ns1:id="980C938A-778D-4B2F-A3AF-98435C2406E9"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/59870C0D-9E40-4065-9D26-F2F27C5FEE99" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/E44D135E-F014-44D4-966F-305A69069183" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A7511831-607B-4196-A226-870292A6A98D" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/E44D135E-F014-44D4-966F-305A69069183" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2024-04-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/BE38A40C-93AF-4A80-9310-568E14AA3625" ns1:rel="FUND" ns1:start="2023-11-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10080391</ns2:identifier></ns2:identifiers><ns2:title>DATA - Drone Design using AI for Transport Applications</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>This 6-month feasibility study, a collaboration between Greenjets Ltd and the world leading Whittle Laboratory, University of Cambridge, aims to develop a scalable AI-driven design for customised e-propulsion engines in the transport sector, specifically for Advanced Aerial Mobility solutions, i.e. drones.

Customised drones offer optimised performance for various last-mile delivery use cases, reducing energy consumption and emissions compared to other vehicles.

Greenjets, as an early-revenue UK SME, faces challenges in the slow and expensive design process, hindering market penetration. To capitalise on the growing AAM market, Greenjets must achieve optimal bespoke designs within shorter timeframes.

The Whittle Laboratory, University of Cambridge has developed a novel methodology combining rapid testing and physical parameterisation to address this. Successful implementation of this methodology will enable AI-driven decision making, reducing design time and allowing Greenjets to scale production to meet diverse customer requirements efficiently.</ns2:abstractText></ns2:project>