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[{"model": "core.projectfund", "pk": 23104, "fields": {"project": 285, "organisation": 2, "amount": 541230, "start_date": "2011-08-31", "end_date": "2016-02-29", "raw_data": 36574}}]
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[{"model": "core.projectfund", "pk": 15201, "fields": {"project": 285, "organisation": 2, "amount": 541230, "start_date": "2011-08-31", "end_date": "2016-02-29", "raw_data": 857}}]
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[{"model": "core.projectorganisation", "pk": 57932, "fields": {"project": 285, "organisation": 51, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 57931, "fields": {"project": 285, "organisation": 1377, "role": "LEAD_ORG"}}]
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[{"model": "core.projectperson", "pk": 35581, "fields": {"project": 285, "person": 248, "role": "PI_PER"}}]
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{"title": ["", "A New Integrated Approach to Measurements and Modelling of Combustion Generated Particulate Matter"], "description": ["", "\nParticulate Matter emissions are important for the health of both our planet and its population. Legislation on Particulate Matter (PM) is increasingly stringent and subject to the greatest change. However, Particulate Matter emissions are both the most difficult to measure and the most challenging to model. In simple terms, Particulate Matter is essentially soot (carbon) onto which species such as unburned hydrocarbons are adsorbed, and is intrinsic to all combustion processes.PM has been linked to global warming and serious epidemiological issues, and this has led to much regulation. Of greatest concern are the sub-micron particles that are invisible, yet it is these small particles that have the greatest deposition efficiency in the human respiratory system. While society is not yet able to replace combustion, technological developments can seek to minimise PM emissions.The complexity of measuring and modelling PM emissions means that modellers are dependent on published experimental data which can be old and incomplete. Furthermore, the modellers have no opportunity to specify the experiments. We have already collaborated on an informal basis, but without specific funding this has been very restricted. The integrated approach in this project will enable the modellers to specify the experiments, identify the most important measurements, and create a database that can be populated with the relevant experimental data. This will provide the modellers with immediate access to complete data.Oxford has a spark ignition engine with comprehensive optical access, and a range of burners: pre-mixed flat-flame (McKenna type), and co-flow (Santoro type, diffusion flame). Mass flow controllers allow us to vary the equivalence ratio of the core flow (pure fuel to the weak limit, with a choice of diluents) and the composition and flow of the annular flow (oxygen enriched or depleted air). The same fuels can be used in both the engine and burners, and in both cases the fuel composition can be controlled Measurement Capabilities - Oxford has a mix of proprietary and unique equipment for PM measurements. We can measure size distributions, mass loadings, composition, morphology and surface area. We also have a unique Differential Mobility Analyser that allows size segregation prior to PM characterisation. We have techniques for measuring temperature, as this has been identified by the modellers as of paramount importance. Most significantly, we will apply the novel technique - Laser Induced Grating Spectroscopy, LIGS temperature measurements of high accuracy and precision. In LIGS, the coherent, laser-like signal beam offers high discrimination against background scattering and luminosity to give a good signal-to-noise ratio in sooting flames. Potential exists for developing a transportable instrument for thermometry of flames in laboratory or technical combustion systems such as engines, gas turbines, incinerators etc.Computational modelling - Cambridge will create and improve computational models which describe combustion chemistry and soot particle formation. Combustion chemistry involves thousands of chemical reactions; the model must contain enough detail to describe the species which are important but must be concise enough to make numerical evaluation possible. Such models exist, but contain many parameters which need further refinement via experimental data. Modelling the formation and growth of soot particles is even more challenging. The chemical reactions between gas phase species and particle surfaces have to be combined with a population balance model to predict the particle mass and size. Eventually a detailed particle and chemistry model must be included in an engine model. These models are to be used to understand the mechanism of particle formation in an engine further and with this find operating modes which reduce particle formation and increase the efficiency of the engine.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| April 11, 2022, 1:46 a.m. |
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{"external_links": [809]}
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| April 11, 2022, 1:46 a.m. |
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[{"model": "core.project", "pk": 285, "fields": {"owner": null, "is_locked": false, "coped_id": "0193c654-7467-4763-965d-6fd2a6bb8ee2", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 842, "created": "2022-04-11T01:29:17.618Z", "modified": "2022-04-11T01:29:17.618Z", "external_links": []}}]
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