| Feb. 13, 2024, 4:19 p.m. |
Created
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[{"model": "core.projectfund", "pk": 61561, "fields": {"project": 9746, "organisation": 2, "amount": 329097, "start_date": "2007-07-02", "end_date": "2011-01-01", "raw_data": 175043}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 54411, "fields": {"project": 9746, "organisation": 2, "amount": 329097, "start_date": "2007-07-02", "end_date": "2011-01-01", "raw_data": 149261}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
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[{"model": "core.projectfund", "pk": 47203, "fields": {"project": 9746, "organisation": 2, "amount": 329097, "start_date": "2007-07-02", "end_date": "2011-01-01", "raw_data": 130249}}]
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| Dec. 5, 2023, 4:23 p.m. |
Created
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[{"model": "core.projectfund", "pk": 39954, "fields": {"project": 9746, "organisation": 2, "amount": 329097, "start_date": "2007-07-01", "end_date": "2011-01-01", "raw_data": 93983}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
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{"external_links": []}
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| Nov. 21, 2023, 4:36 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 32652, "fields": {"project": 9746, "organisation": 2, "amount": 329097, "start_date": "2007-07-01", "end_date": "2011-01-01", "raw_data": 53284}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 93520, "fields": {"project": 9746, "organisation": 11817, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 93519, "fields": {"project": 9746, "organisation": 10928, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58763, "fields": {"project": 9746, "person": 14450, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58762, "fields": {"project": 9746, "person": 14451, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:04 p.m. |
Updated
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{"title": ["", "Adaptive Cylinder Pressure Reconstruction for Production Engines"], "description": ["", "\nImproving the fuel efficiency of the IC engine is important to meet the growing demand for non-renewable energy, and to reduce the emission of carbon dioxide - a major contributor to global warming. Advanced feed-back control strategies offer an important way of improving engine efficiency for existing designs. But to fully exploit these control strategies, a cost effective, durable, and real-time method of measuring engine cylinder pressure is needed since existing sensors are far too expensive and are seriously undermined by long-term durability issues. The search for alternative means of cylinder pressure reconstruction for production engines has continued for two decades. This search is now of critical importance for both conventional and future HCCI engines. Two indirect pressure reconstruction methodologies have been proposed using either measured crank-shaft motion or measured engine-casing vibration. But although numerous methods have been suggested to exploit these two approaches not one single method has yet been fitted to a production engine. There are two reasons for this: i) the most promising method (recurrent neural network model) is still in need of a suitably tuned training methodology, and ii) fixed-parameter reconstruction models will not in general produce accurate pressure reconstruction on a different engine (even of the same type) owing to the effect of variability arising from normal differences in materials, manufacture, operating conditions, and component wear. A fully adaptive reconstruction technique is needed. This proposal aims to create a robust adaptive cylinder-pressure reconstruction methodology for production engines, and to test this methodology on real engine data. This is timely because preliminary studies point very favourably to the most suitable architecture for multi-cylinder pressure reconstruction, but as yet, it is not known how to train these models, even for application to single test engines. A detailed understanding of the stochastic parameter fitting problem is needed. Only then is it likely a suitable training strategy can be designed. More importantly, to address the needs of an adaptive system, a way has to be found to allow fixed-parameter systems become variable-parameter models. Three novel variable-parameter schemes are proposed and these will be appropriately tested. The big question however is how should such variable-parameter schemes be trained for adaptive reconstruction? This question will be addressed in the project.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:04 p.m. |
Added
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{"external_links": [39686]}
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| Nov. 20, 2023, 2:04 p.m. |
Created
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[{"model": "core.project", "pk": 9746, "fields": {"owner": null, "is_locked": false, "coped_id": "95540a1a-289a-4dda-860e-58e32fd05162", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 53267, "created": "2023-11-20T13:33:58.268Z", "modified": "2023-11-20T13:33:58.268Z", "external_links": []}}]
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