PPG is partnering with the US Department of Energy (DOE) Lawrence Berkeley National Laboratory (LBNL) for research aimed at developing energy-efficient coatings systems for the automotive industry.
The PPG project – ‘Modelling Coating Flow and Dynamics during Drying’ – was selected to receive funding through the DOE High Performance Computing for Energy Innovation (HPC4EI) programme.
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PPG scientists will collaborate with LBNL experts to model the complex physics that contribute to paint flow and levelling in a two-layer coatings system.
The insight gained will accelerate the introduction of new multi-layer coatings systems that can be co-cured in a single, lower-temperature bake, reducing paint line energy consumption for automotive OEMs by up to 30%.
“Beyond the energy savings achieved from fewer curing steps and faster process times, our research will provide a foundation for future models for water-based coatings and lighter-weight vehicle substrates,” said PPG development engineer, automotive OEM coatings, Xinyu Lu.
“PPG is at the forefront of coatings technologies that can help vehicle manufacturers significantly reduce their costs and environmental footprints.”
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By GlobalDataThe PPG initiative was one of 11 research projects selected in the latest HPC4EI award cycle. PPG has worked with the HPC4EI programme for many years to advance programmes that increase automotive manufacturing throughput and enable vehicle light-weighting.
In 2019, PPG also received a DOE grant to study the ageing characteristics of a new generation of structural adhesives needed to join high-strength steel, aluminium, magnesium and other substrates, which can help reduce vehicle mass and increase fuel economy.
The HPC4EI programme is funded by the DOE Office of Energy Efficiency and Renewables and the Fossil Energy Office. It uses DOE capabilities in high-performance computing to help improve manufacturing processes and further product and material development to reduce national energy consumption.
These high-performance computers use new mathematical breakthroughs to enable increased accuracy of engineering and science simulations and provide faster optimisation and enhanced data analytics.
