Sensitivity analysis of fuel types and operational parameters on the particulate matter emissions from an aviation piston engine burning heavy fuels
•PM emissions from an aviation compression ignition engine were reported.•Effects of fuel type and engine parameters on PM emissions were quantified.•Accumulation mode PM obtained from SMPS fit AVL Opacimeter data.•RP3 and FT fuels exhibited lower PM emissions compared with diesel. Currently, genera...
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Veröffentlicht in: | Fuel (Guildford) 2017-08, Vol.202, p.520-528 |
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description | •PM emissions from an aviation compression ignition engine were reported.•Effects of fuel type and engine parameters on PM emissions were quantified.•Accumulation mode PM obtained from SMPS fit AVL Opacimeter data.•RP3 and FT fuels exhibited lower PM emissions compared with diesel.
Currently, general aviation aircrafts have growing demand for internal combustion engines burning heavy fuels (i.e. diesel or kerosene) due to the concerns on the safety, costs and availability of aviation gasoline (AVGAS). The application of heavy fuels requires the change of combustion mode from pre-mixed mode to diffusion mode, which will inevitably increase the particulate matter (PM) emissions as incomplete combustion products. In this work, the size-resolved number concentrations of the PM emissions emitted from an internal compression ignition engine burning diesel, RP3 and Fischer-Tropsch (FT) kerosene were studied by a Scanning Mobility Particle Sizer Spectrometer (SMPS). An opacimeter was utilized to measure the opacity of the soot emissions (linearly related to the soot mass), which was in consistent with the SMPS data. Results demonstrated that the FT fuel produced the lowest PM emissions due to absence of sulfur and aromatic contents. Diesel turned out to have the greatest ‘sooting’ tendency and produced more accumulation mode PM in number than FT fuel by a factor of four, and more PM in mass by approximately three times. Moreover, the effects of fuel types and engine operational parameters were quantified in a systematic manner by adopting the Response Surface Method (RSM) in Design of Experiments (DoE). According to the ANOVA (Analysis of Variance), the DoE derived model was statistically significant and demonstrated that the engine load was the dominant factor for soot generation, followed by injection pressure and fuel types. Relevant combustion parameters and their link with PM emissions were further discussed, illustrating that atomization process had great impact on the ignition delay and thus affected soot generation. |
doi_str_mv | 10.1016/j.fuel.2017.04.052 |
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Currently, general aviation aircrafts have growing demand for internal combustion engines burning heavy fuels (i.e. diesel or kerosene) due to the concerns on the safety, costs and availability of aviation gasoline (AVGAS). The application of heavy fuels requires the change of combustion mode from pre-mixed mode to diffusion mode, which will inevitably increase the particulate matter (PM) emissions as incomplete combustion products. In this work, the size-resolved number concentrations of the PM emissions emitted from an internal compression ignition engine burning diesel, RP3 and Fischer-Tropsch (FT) kerosene were studied by a Scanning Mobility Particle Sizer Spectrometer (SMPS). An opacimeter was utilized to measure the opacity of the soot emissions (linearly related to the soot mass), which was in consistent with the SMPS data. Results demonstrated that the FT fuel produced the lowest PM emissions due to absence of sulfur and aromatic contents. Diesel turned out to have the greatest ‘sooting’ tendency and produced more accumulation mode PM in number than FT fuel by a factor of four, and more PM in mass by approximately three times. Moreover, the effects of fuel types and engine operational parameters were quantified in a systematic manner by adopting the Response Surface Method (RSM) in Design of Experiments (DoE). According to the ANOVA (Analysis of Variance), the DoE derived model was statistically significant and demonstrated that the engine load was the dominant factor for soot generation, followed by injection pressure and fuel types. Relevant combustion parameters and their link with PM emissions were further discussed, illustrating that atomization process had great impact on the ignition delay and thus affected soot generation.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2017.04.052</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Atomizing ; Aviation piston engine ; Burning ; Combustion ; Combustion products ; Compression ; Data processing ; Design of Experiments ; Diesel ; Diesel engines ; Emission measurements ; Emissions ; Fuels ; Gasoline ; Ignition ; Internal combustion engines ; Kerosene ; Opacity ; Parameter sensitivity ; Particulate emissions ; Particulate matter ; Particulates ; Response Surface Method ; Response surface methodology ; RP3 ; Sensitivity analysis ; Soot ; Spontaneous combustion ; Statistical analysis ; Sulfur ; Variance analysis</subject><ispartof>Fuel (Guildford), 2017-08, Vol.202, p.520-528</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright Elsevier BV Aug 15, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-8df5404db359bb77754b62288988c3f3f7c811a1f90d1e26364723f0e5931e5b3</citedby><cites>FETCH-LOGICAL-c367t-8df5404db359bb77754b62288988c3f3f7c811a1f90d1e26364723f0e5931e5b3</cites><orcidid>0000-0002-4039-4222</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0016236117304647$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Chen, Longfei</creatorcontrib><creatorcontrib>Liang, Zhirong</creatorcontrib><creatorcontrib>Liu, Haoye</creatorcontrib><creatorcontrib>Ding, Shirun</creatorcontrib><creatorcontrib>Li, Yanfei</creatorcontrib><title>Sensitivity analysis of fuel types and operational parameters on the particulate matter emissions from an aviation piston engine burning heavy fuels</title><title>Fuel (Guildford)</title><description>•PM emissions from an aviation compression ignition engine were reported.•Effects of fuel type and engine parameters on PM emissions were quantified.•Accumulation mode PM obtained from SMPS fit AVL Opacimeter data.•RP3 and FT fuels exhibited lower PM emissions compared with diesel.
Currently, general aviation aircrafts have growing demand for internal combustion engines burning heavy fuels (i.e. diesel or kerosene) due to the concerns on the safety, costs and availability of aviation gasoline (AVGAS). The application of heavy fuels requires the change of combustion mode from pre-mixed mode to diffusion mode, which will inevitably increase the particulate matter (PM) emissions as incomplete combustion products. In this work, the size-resolved number concentrations of the PM emissions emitted from an internal compression ignition engine burning diesel, RP3 and Fischer-Tropsch (FT) kerosene were studied by a Scanning Mobility Particle Sizer Spectrometer (SMPS). An opacimeter was utilized to measure the opacity of the soot emissions (linearly related to the soot mass), which was in consistent with the SMPS data. Results demonstrated that the FT fuel produced the lowest PM emissions due to absence of sulfur and aromatic contents. Diesel turned out to have the greatest ‘sooting’ tendency and produced more accumulation mode PM in number than FT fuel by a factor of four, and more PM in mass by approximately three times. Moreover, the effects of fuel types and engine operational parameters were quantified in a systematic manner by adopting the Response Surface Method (RSM) in Design of Experiments (DoE). According to the ANOVA (Analysis of Variance), the DoE derived model was statistically significant and demonstrated that the engine load was the dominant factor for soot generation, followed by injection pressure and fuel types. Relevant combustion parameters and their link with PM emissions were further discussed, illustrating that atomization process had great impact on the ignition delay and thus affected soot generation.</description><subject>Atomizing</subject><subject>Aviation piston engine</subject><subject>Burning</subject><subject>Combustion</subject><subject>Combustion products</subject><subject>Compression</subject><subject>Data processing</subject><subject>Design of Experiments</subject><subject>Diesel</subject><subject>Diesel engines</subject><subject>Emission measurements</subject><subject>Emissions</subject><subject>Fuels</subject><subject>Gasoline</subject><subject>Ignition</subject><subject>Internal combustion engines</subject><subject>Kerosene</subject><subject>Opacity</subject><subject>Parameter sensitivity</subject><subject>Particulate emissions</subject><subject>Particulate matter</subject><subject>Particulates</subject><subject>Response Surface Method</subject><subject>Response surface methodology</subject><subject>RP3</subject><subject>Sensitivity analysis</subject><subject>Soot</subject><subject>Spontaneous combustion</subject><subject>Statistical analysis</subject><subject>Sulfur</subject><subject>Variance analysis</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEqXwA6wssU7wI44TiQ2qeEmVWABry0nGravmge1Uyn_wwTgta1YjzZx7Z-YidEtJSgnN73epGWGfMkJlSrKUCHaGFrSQPJFU8HO0IJFKGM_pJbryfkcIkYXIFujnAzpvgz3YMGHd6f3krce9wbMfDtMAPrYb3A_gdLB9JPCgnW4hgItgh8MW5k6w9bjXAXCrQxxhaK33kffYuL6NHlgf7NEBD9aHWKDb2A5wNbrOdhu8BX2Yjmv9Nboweu_h5q8u0dfz0-fqNVm_v7ytHtdJzXMZkqIxIiNZU3FRVpWUUmRVzlhRlEVRc8ONrAtKNTUlaSiwnOeZZNwQECWnICq-RHcn38H13yP4oHZ9vCauVIyQXJaCURYpdqJq13vvwKjB2Va7SVGi5vTVTs1nqzl9RTIV04-ih5MovgMHC0752kJXQ2Md1EE1vf1P_gud9JEf</recordid><startdate>20170815</startdate><enddate>20170815</enddate><creator>Chen, Longfei</creator><creator>Liang, Zhirong</creator><creator>Liu, Haoye</creator><creator>Ding, Shirun</creator><creator>Li, Yanfei</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-4039-4222</orcidid></search><sort><creationdate>20170815</creationdate><title>Sensitivity analysis of fuel types and operational parameters on the particulate matter emissions from an aviation piston engine burning heavy fuels</title><author>Chen, Longfei ; Liang, Zhirong ; Liu, Haoye ; Ding, Shirun ; Li, Yanfei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-8df5404db359bb77754b62288988c3f3f7c811a1f90d1e26364723f0e5931e5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Atomizing</topic><topic>Aviation piston engine</topic><topic>Burning</topic><topic>Combustion</topic><topic>Combustion products</topic><topic>Compression</topic><topic>Data processing</topic><topic>Design of Experiments</topic><topic>Diesel</topic><topic>Diesel engines</topic><topic>Emission measurements</topic><topic>Emissions</topic><topic>Fuels</topic><topic>Gasoline</topic><topic>Ignition</topic><topic>Internal combustion engines</topic><topic>Kerosene</topic><topic>Opacity</topic><topic>Parameter sensitivity</topic><topic>Particulate emissions</topic><topic>Particulate matter</topic><topic>Particulates</topic><topic>Response Surface Method</topic><topic>Response surface methodology</topic><topic>RP3</topic><topic>Sensitivity analysis</topic><topic>Soot</topic><topic>Spontaneous combustion</topic><topic>Statistical analysis</topic><topic>Sulfur</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Longfei</creatorcontrib><creatorcontrib>Liang, Zhirong</creatorcontrib><creatorcontrib>Liu, Haoye</creatorcontrib><creatorcontrib>Ding, Shirun</creatorcontrib><creatorcontrib>Li, Yanfei</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Longfei</au><au>Liang, Zhirong</au><au>Liu, Haoye</au><au>Ding, Shirun</au><au>Li, Yanfei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensitivity analysis of fuel types and operational parameters on the particulate matter emissions from an aviation piston engine burning heavy fuels</atitle><jtitle>Fuel (Guildford)</jtitle><date>2017-08-15</date><risdate>2017</risdate><volume>202</volume><spage>520</spage><epage>528</epage><pages>520-528</pages><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>•PM emissions from an aviation compression ignition engine were reported.•Effects of fuel type and engine parameters on PM emissions were quantified.•Accumulation mode PM obtained from SMPS fit AVL Opacimeter data.•RP3 and FT fuels exhibited lower PM emissions compared with diesel.
Currently, general aviation aircrafts have growing demand for internal combustion engines burning heavy fuels (i.e. diesel or kerosene) due to the concerns on the safety, costs and availability of aviation gasoline (AVGAS). The application of heavy fuels requires the change of combustion mode from pre-mixed mode to diffusion mode, which will inevitably increase the particulate matter (PM) emissions as incomplete combustion products. In this work, the size-resolved number concentrations of the PM emissions emitted from an internal compression ignition engine burning diesel, RP3 and Fischer-Tropsch (FT) kerosene were studied by a Scanning Mobility Particle Sizer Spectrometer (SMPS). An opacimeter was utilized to measure the opacity of the soot emissions (linearly related to the soot mass), which was in consistent with the SMPS data. Results demonstrated that the FT fuel produced the lowest PM emissions due to absence of sulfur and aromatic contents. Diesel turned out to have the greatest ‘sooting’ tendency and produced more accumulation mode PM in number than FT fuel by a factor of four, and more PM in mass by approximately three times. Moreover, the effects of fuel types and engine operational parameters were quantified in a systematic manner by adopting the Response Surface Method (RSM) in Design of Experiments (DoE). According to the ANOVA (Analysis of Variance), the DoE derived model was statistically significant and demonstrated that the engine load was the dominant factor for soot generation, followed by injection pressure and fuel types. Relevant combustion parameters and their link with PM emissions were further discussed, illustrating that atomization process had great impact on the ignition delay and thus affected soot generation.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2017.04.052</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4039-4222</orcidid></addata></record> |
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subjects | Atomizing Aviation piston engine Burning Combustion Combustion products Compression Data processing Design of Experiments Diesel Diesel engines Emission measurements Emissions Fuels Gasoline Ignition Internal combustion engines Kerosene Opacity Parameter sensitivity Particulate emissions Particulate matter Particulates Response Surface Method Response surface methodology RP3 Sensitivity analysis Soot Spontaneous combustion Statistical analysis Sulfur Variance analysis |
title | Sensitivity analysis of fuel types and operational parameters on the particulate matter emissions from an aviation piston engine burning heavy fuels |
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