Ford 2011 6.7L Power Stroke® Diesel Engine Combustion System Development

Styron, Joshua; Baldwin, Brian; Fulton, Brien; Ives, D.; Ramanathan, S.

doi:10.4271/2011-01-0415

Cited by 49 publications

(24 citation statements)

References 2 publications

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“…However, further enhancements to injector nozzle technologies involving reduced injector sac volume for trapped fuel may be needed to address unburned hydrocarbon emissions [11] [18]. Similar analysis need to be done at other operating modes involving the drive cycle in order to come up with a duty cycle based "merit-function" for optimizing the combustion system hardware before engine testing [13]. Quantitatively matching the engine data to model predictions could still be a challenge and demands a continuous improvement process.…”

Section: Figure 17 Fsnox Vs Fshc Predictions As a Function Of Soi Swmentioning

confidence: 98%

“…The objective was to understand the effect of injector nozzle spray angle for these different bowl shapes and their impact on emissions and fuel consumption [11]. The compression ratio was held constant at 15.3 for the three bowls in order to understand the impact of spray-plume targeting area on the bowl and to come up with the best possible nozzle matching to meet the desired goals [13]. Figure 16 shows the predicted FSNOx vs FSDPM emissions [Mode 4] as a function of Main SOI sweeps for different bowl shapes with nozzle configurations at four different spray angles.…”

Section: Figure 14 Model Vs Experimental Data At Mode 8 -1700 Rpm 1mentioning

confidence: 99%

See 1 more Smart Citation

Thermodynamic Systems for Tier 2 Bin 2 Diesel Engines

Suresh¹,

Langenderfer²,

Arnett³

et al. 2013

SAE Int. J. Engines

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Light duty vehicle emission standards are getting more stringent than ever before as stipulated by US EPA Tier 2 Standards and LEV III regulations proposed by CARB. The research in this paper sponsored by US DoE is focused towards developing a Tier 2 Bin 2 Emissions compliant light duty pickup truck with class leading fuel economy targets of 22.4 mpg "City" / 34.3 mpg "Highway". Many advanced technologies comprising both engine and after-treatment systems are essential towards accomplishing this goal. The objective of this paper would be to discuss key engine technology enablers that will help in achieving the target emission levels and fuel economy. Several enabling technologies comprising air-handling, fuel system and base engine design requirements will be discussed in this paper highlighting both experimental and analytical evaluations. Mostly, this paper will focus on steady-state emissions and fuel consumption results that are tied to operating duty cycle with actual vehicle comparisons wherever applicable. GT-Power 1D cycle simulation analysis was done to narrow the turbocharger and air-handling architecture evaluation. The importance of turbocharger matching is critical to achieving both power density and emission goals. This paper highlights the challenges involved in selecting a single stage variable geometry turbocharger capable of delivering a 75hp/liter power density in addition to meeting light duty drive cycle emission requirements. The benefits of low pressure [LP] and high pressure [HP] EGR systems for this light-duty engine architecture will be explored in detail along with outlining the possible strategy of dual loop EGR to reduce overall fuel consumption. On the combustion system development, this paper will highlight the importance of compression ratio, variable swirl architecture, injector nozzle hardware and injection strategy optimization towards meeting the stringent emissions, fuel consumption and noise targets. Fundamental thermodynamic explanations relating the fuel consumption improvements back to closed cycle and open cycle work are also being presented. Also, the paper will discuss challenges involving unburned HC and CO emissions when operating in premixed/low temperature combustion like regimes involving higher EGR rates and early pilot injection timings. Potential solutions to mitigate such undesired emissions have been investigated both analytically and experimentally.

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Section: Figure 17 Fsnox Vs Fshc Predictions As a Function Of Soi Swmentioning

confidence: 98%

Section: Figure 14 Model Vs Experimental Data At Mode 8 -1700 Rpm 1mentioning

confidence: 99%

Thermodynamic Systems for Tier 2 Bin 2 Diesel Engines

Suresh¹,

Langenderfer²,

Arnett³

et al. 2013

SAE Int. J. Engines

View full text Add to dashboard Cite

show abstract

“…A 6.7L eight-cylinder heavy-duty diesel engine 13 from Ford Motor Company equipped with a variable geometry TC and HP-EGR is considered for electrification. For this, a REAT 5 system assisted by a DEMG is considered and a schematic of the plant is shown in Figure 1.…”

Section: Simulation Platform and Plant Modelmentioning

confidence: 99%

An assessment of performance trade-offs in diesel engines equipped with regenerative electrically assisted turbochargers

Song

Upadhyay

Xie

2018

International Journal of Engine Research

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The regenerative electrically assisted turbocharger offers performance benefits, such as reduced turbo-lag, over the conventional turbocharger. However, regenerative electrically assisted turbocharger introduces additional control degrees of freedom as well as new causalities in the air-path dynamics of boosted engines. This is because the electrical power applied to (or removed from) the turbocharger shaft disrupts the natural coupling between the engine exhaust, and the turbocharger operation found in conventionally boosted systems. The ideal performance objective of regenerative electrically assisted turbocharger is to achieve fast boost response with improved fuel economy and minimal impact on engine-out emissions. These performance criteria, as we show in this article, drive performance trade-offs that must be considered for optimal system performance. In this article, we investigate these trade-off relationships based on a high fidelity GT-SUITE engine model for a heavy-duty diesel engine. An optimal control law is used in conjunction with a control-oriented plant model. Results from load step tests and from the federal test cycle (FTP-75) indicate that using a properly designed optimal controller, it is possible to manage these trade-offs, and to simultaneously achieve benefits in boost response, FE as well as engine-out emissions.

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“…The proposed approaches were tested on multicomponent diesel fuel simulations on a 0.831, single-cylinder engine [37,38], A highly weighted typical U.S. Environmental Protection Agency city cycle operating point at 1500rpm engine speed and 3.8 bar indicated mean effective pressure (IMEP) was used as the refer ence simulation case. The engine was operated in lowtemperature combustion mode, featuring a single, early injection pulse and high exhaust gas recirculation (~55%).…”

Section: Engine Simulation Resultsmentioning

confidence: 99%

Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering

Perini

Krishnasamy

et al. 2014

Journal of Engineering for Gas Turbines and Power

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The need for more efficient and environmentally sustainable internal combustion engines is driving research towards the need to consider more realistic models for both fuel physics and chemistry. As far as compression ignition engines are concerned, phenome nological or lumped fuel models are unreliable to capture spray and combustion strategies outside o f their validation domains-typically, high-pressure injection and hightemperature combustion. Furthermore, the development of variable-reactivity combustion strategies also creates the need to model comprehensively different hydrocarbon families even in single fuel surrogates. From the computational point of view, challenges to achieving practical simulation times arise from the dimensions of the reaction mecha nism, which can be o f hundreds species even if hydrocarbon families are lumped into rep resentative compounds and, thus, modeled with nonelementary, skeletal reaction pathways. In this case, it is also impossible to pursue further mechanism reductions to lower dimensions, central processing unit (CPU) times for integrating chemical kinetics in internal combustion engine simulations ultimately scale with the number of cells in the grid and with the cube number of species in the reaction mechanism. In the present work, two approaches to reduce the demands of engine simulations with detailed chemistry are presented. The first one addresses the demands due to the solution of the chemistry ordi nary differential equation (ODE) system, and features the adoption of SpeedCHEM, a newly developed chemistry package that solves chemical kinetics using sparse analytical Jacobians. The second one aims to reduce the number of chemistry calculations by bin ning the computational fluid dynamics (CFD) cells of the engine grid into a subset of clusters, where chemistry is solved and then mapped back to the original domain. In par ticular, a high-dimensional representation of the chemical state space is adopted for keeping track of the different fuel components, and a newly developed bounding-boxconstrained k-means algorithm is used to subdivide the cells into reactively homogeneous clusters. The approaches have been tested on a number of simulations featuring multicomponent diesel fuel surrogates and different engine grids. The results show that signifi cant CPU time reductions, of about 1 order of magnitude, can be achieved without loss of accuracy in both engine performance and emissions predictions, prompting for their applicability to more refined or full-sized engine grids.

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Ford 2011 6.7L Power Stroke® Diesel Engine Combustion System Development

Cited by 49 publications

References 2 publications

Thermodynamic Systems for Tier 2 Bin 2 Diesel Engines

Thermodynamic Systems for Tier 2 Bin 2 Diesel Engines

An assessment of performance trade-offs in diesel engines equipped with regenerative electrically assisted turbochargers

Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering

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