<?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="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/841CCE63-90F8-4BAA-B02B-70BFD1C5018A" ns1:id="841CCE63-90F8-4BAA-B02B-70BFD1C5018A"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/0983B0AB-AD9F-435F-945D-2AA816365712" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F6FD3838-6132-4A65-AE25-80F657FC212E" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F6FD3838-6132-4A65-AE25-80F657FC212E" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-06-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/E9118681-CC78-4314-83DB-E1401421D434" ns1:rel="FUND" ns1:start="2023-05-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10063410</ns2:identifier></ns2:identifiers><ns2:title>Teesside University and PSI Global Limited KTP 22_23 R5</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>Knowledge Transfer Partnership</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>To develop a Physics Informed Machine Learning and Computational Fluid Dynamics integrated model to accurately predict the full fluid flow of air/oil in separation products to optimise the efficiency and performance of compressor and vacuum pumps in the market place.</ns2:abstractText></ns2:project>