Projects
Projects: Custom Search |
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| Reference Number | EP/Y034686/1 | |
| Title | SCALE:Industry empowerment to multiphase fluid dynamics simulations using Artificial Intelligence & statistical methods on modern hardware architectur | |
| Status | Started | |
| Energy Categories | Other Cross-Cutting Technologies or Research 30%; Not Energy Related 70%; |
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| Research Types | Training 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 10%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 30%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 10%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor M Gavaises Sch of Engineering and Mathematical Sci City University |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 October 2023 | |
| End Date | 30 September 2027 | |
| Duration | 48 months | |
| Total Grant Value | £521,352 | |
| Industrial Sectors | ||
| Region | London | |
| Programme | UKRI MSCA | |
| Investigators | Principal Investigator | Professor M Gavaises , Sch of Engineering and Mathematical Sci, City University (100.000%) |
| Industrial Collaborator | Project Contact , University of Hertfordshire (0.000%) Project Contact , McLaren Racing Ltd (0.000%) Project Contact , Lubrizol Ltd (0.000%) Project Contact , Precision Acoustics Ltd (0.000%) |
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| Web Site | ||
| Objectives | ||
| Abstract | Multi-phase, trans/supercritical and non-Newtonian fluid flows with heat and mass transfer are critical in enhancing the performance of energy production, propulsion and biomedical systems. Examples include: hydraulic turbomachines, ship propellers, CO2-neutral e-fuels and e-motor cooling systems, particleladen flows in inhalers and focused ultrasounds for drug delivery. What all these cases have in common is the high level of complexity which makes Direct Numerical Simulations impossible. State-of-the-art LES simulations rely on simplified assumptions but do not have yet the desired accuracy, while often require enormously expensive CPU resources.The aim of project (acronym 'SCALE') is to develop simulation methods and reduced-order models using physics-informed and data-driven Machine Learning and optimisation methods for such flow processes. These will be trained against 'ground-truth' databases that will be generated for the first time using both DNS and experimentally validated, industry-relevant LES and multi-fidelity RANS simulations. The new simulation tools will be applied for the first time to industrial problems and their ability to accelerate design times and improve accuracy will be jointly pursued and evaluated with the non-academic partners of SCALE. These are international corporations and market leaders in the aforementioned areas. Holistic training by experts from science and industry includes broad reviews on relevant scientific topics, modern high performance computing architectures suitable for performing such simulations, big data analytics as well as extensive support for mastering scientific tasks and transferring the knowledge acquired to industrial practice. SCALE will also deliver transferable soft skills training from a well-connected cohort of leaders with the ability to communicate across disciplines and within the general public. This coupling of research with industry makes SCALE a truly outstanding network for doctoral candidates to start their careers | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
